Katrina F. Mateo, PhD, MPH

Study Overview

Objective. To compare the effects of intermittent energy restriction (IER) with those of continuous energy restriction (CER) on blood pressure control and weight loss in overweight and obese patients with hypertension during a 6-month period.

Design. Randomized controlled trial.

Settings and participants. The trial was conducted at the Affiliated Hospital of Jiaxing University from June 1, 2020, to April 30, 2021. Chinese adults were recruited using advertisements and flyers posted in the hospital and local communities. Prior to participation in study activities, all participants gave informed consent prior to recruitment and were provided compensation in the form of a $38 voucher at 3 and 6 months for their time for participating in the study.

The main inclusion criteria were patients between the ages of 18 and 70 years, hypertension, and body mass index (BMI) ranging from 24 to 40 kg/m². The exclusion criteria were systolic blood pressure (SBP) ≥ 180 mmHg or diastolic blood pressure (DBP) ≥ 120 mmHg, type 1 or 2 diabetes with a history of severe hypoglycemic episodes, pregnancy or breastfeeding, usage of glucagon-like peptide 1 receptor agonists, weight loss > 5 kg within the past 3 months or previous weight loss surgery, and inability to adhere to the dietary protocol.

Of the 294 participants screened for eligibility, 205 were randomized in a 1:1 ratio to the IER group (n = 102) or the CER group (n = 103), stratified by sex and BMI (as overweight or obese). All participants were required to have a stable medication regimen and weight in the 3 months prior to enrollment and not to use weight-loss drugs or vitamin supplements for the duration of the study. Researchers and participants were not blinded to the study group assignment.

Interventions. Participants randomly assigned to the IER group followed a 5:2 eating pattern: a very-low-energy diet of 500-600 kcal for 2 days of the week along with their usual diet for the other 5 days. The 2 days of calorie restriction could be consecutive or nonconsecutive, with a minimum of 0.8 g supplemental protein per kg of body weight per day, in accordance with the 2016 Dietary Guidelines for Chinese Residents. The CER group was advised to consume 1000 kcal/day for women and 1200 kcal/day for men on a 7-day energy restriction. That is, they were prescribed a daily 25% restriction based on the general principles of a Mediterranean-type diet (30% fat, 45-50% carbohydrate, and 20-25% protein).

Both groups received dietary education from a qualified dietitian and were recommended to maintain their current daily activity levels throughout the trial. Written dietary information brochures with portion advice and sample meal plans were provided to improve compliance in each group. All participants received a digital cooking scale to weigh foods to ensure accuracy of intake and were required to keep a food diary while following the recommended recipe on 2 days/week during calorie restriction to help with adherence. No food was provided. All participants were followed up by regular outpatient visits to both cardiologists and dietitians once a month. Diet checklists, activity schedules, and weight were reviewed to assess compliance with dietary advice at each visit.

Of note, participants were encouraged to measure and record their BP twice daily, and if 2 consecutive BP readings were < 110/70 mmHg and/or accompanied by hypotensive episodes with symptoms (dizziness, nausea, headache, and fatigue), they were asked to contact the investigators directly. Antihypertensive medication changes were then made in consultation with cardiologists. In addition, a medication management protocol (ie, doses of antidiabetic medications, including insulin and sulfonylurea) was designed to avoid hypoglycemia. Medication could be reduced in the CER group based on the basal dose at the endocrinologist’s discretion. In the IER group, insulin and sulfonylureas were discontinued on calorie restriction days only, and long-acting insulin was discontinued the night before the IER day. Insulin was not to be resumed until a full day’s caloric intake was achieved.

Measures and analysis. The primary outcomes of this study were changes in BP and weight (measured using an automatic digital sphygmomanometer and an electronic scale), and the secondary outcomes were changes in body composition (assessed by dual-energy x-ray absorptiometry scanning), as well as glycosylated hemoglobin A_1c (HbA_1c) levels and blood lipids after 6 months. All outcome measures were recorded at baseline and at each monthly visit. Incidence rates of hypoglycemia were based on blood glucose (defined as blood glucose < 70 mg/dL) and/or symptomatic hypoglycemia (symptoms of sweating, paleness, dizziness, and confusion). Two cardiologists who were blind to the patients’ diet condition measured and recorded all pertinent clinical parameters and adjudicated serious adverse events.

Data were compared using independent-samples t-tests or the Mann–Whitney U test for continuous variables, and Pearson’s χ² test or Fisher’s exact test for categorial variables as appropriate. Repeated-measures ANOVA via a linear mixed model was employed to test the effects of diet, time, and their interaction. In subgroup analyses, differential effects of the intervention on the primary outcomes were evaluated with respect to patients’ level of education, domicile, and sex based on the statistical significance of the interaction term for the subgroup of interest in the multivariate model. Analyses were performed based on completers and on an intention-to-treat principle.

Main results. Among the 205 randomized participants, 118 were women and 87 were men; mean (SD) age was 50.5 (8.8) years; mean (SD) BMI was 28.7 (2.6); mean (SD) SBP was 143 (10) mmHg; and mean (SD) DBP was 91 (9) mmHg. At the end of the 6-month intervention, 173 (84.4%) completed the study (IER group: n = 88; CER group: n = 85). Both groups had similar dropout rates at 6 months (IER group: 14 participants [13.7%]; CER group: 18 participants [17.5%]; P = .83) and were well matched for baseline characteristics except for triglyceride levels.

In the completers analysis, both groups experienced significant reductions in weight (mean [SEM]), but there was no difference between treatment groups (−7.2 [0.6] kg in the IER group vs −7.1 [0.6] kg in the CER group; diet by time P = .72). Similarly, the change in SBP and DBP achieved was statistically significant over time, but there was also no difference between the dietary interventions (−8 [0.7] mmHg in the IER group vs −8 [0.6] mmHg in the CER group, diet by time P = .68; −6 [0.6] mmHg in the IER group vs −6 [0.5] mmHg in the CER group, diet by time P = .53]. Subgroup analyses of the association of the intervention with weight, SBP and DBP by sex, education, and domicile showed no significant between-group differences.

All measures of body composition decreased significantly at 6 months with both groups experiencing comparable reductions in total fat mass (−5.5 [0.6] kg in the IER group vs −4.8 [0.5] kg in the CER group, diet by time P = .08) and android fat mass (−1.1 [0.2] kg in the IER group vs −0.8 [0.2] kg in the CER group, diet by time P = .16). Of note, participants in the CER group lost significantly more total fat-free mass than did participants in the IER group (mean [SEM], −2.3 [0.2] kg vs −1.7 [0.2] kg; P = .03], and there was a trend toward a greater change in total fat mass in the IER group (P = .08). The secondary outcome of mean (SEM) HbA_1c (−0.2% [0.1%]) and blood lipid levels (triglyceride level, −1.0 [0.3] mmol/L; total cholesterol level, −0.9 [0.2] mmol/L; low-density lipoprotein cholesterol level, −0.9 [0.2 mmol/L; high-density lipoprotein cholesterol level, 0.7 [0.3] mmol/L] improved with weight loss (P < .05), with no differences between groups (diet by time P > .05).

The intention-to-treat analysis demonstrated that IER and CER are equally effective for weight loss and blood pressure control: both groups experienced significant reductions in weight, SBP, and DBP, but with no difference between treatment groups – mean (SEM) weight change with IER was −7.0 (0.6) kg vs −6.8 (0.6) kg with CER; the mean (SEM) SBP with IER was −7 (0.7) mmHg vs −7 (0.6) mmHg with CER; and the mean (SEM) DBP with IER was −6 (0.5) mmHg vs −5 (0.5) mmHg with CER, (diet by time P = .62, .39, and .41, respectively). There were favorable improvements in body composition, HbA_1c, and blood lipid levels, with no differences between groups.

Conclusion. A 2-day severe energy restriction with 5 days of habitual eating compared to 7 days of CER provides an acceptable alternative for BP control and weight loss in overweight and obese individuals with hypertension after 6 months. IER may offer a useful alternative strategy for this population, who find continuous weight-loss diets too difficult to maintain.

Commentary

Globally, obesity represents a major health challenge as it substantially increases the risk of diseases such as hypertension, type 2 diabetes, and coronary heart disease.¹ Lifestyle modifications, including weight loss and increased physical activity, are recommended in major guidelines as a first-step intervention in the treatment of hypertensive patients.² However, lifestyle and behavioral interventions aimed at reducing calorie intake through low-calorie dieting is challenging as it is dependent on individual motivation and adherence to a strict, continuous protocol. Further, CER strategies have limited effectiveness because complex and persistent hormonal, metabolic, and neurochemical adaptations defend against weight loss and promote weight regain.^3-4 IER has drawn attention in the popular media as an alternative to CER due to its feasibility and even potential for higher rates of compliance.⁵

This study adds to the literature as it is the first randomized controlled trial (to the knowledge of the authors at the time of publication) to explore 2 forms of energy restriction – CER and IER – and their impact on weight loss, BP, body composition, HbA_1c, and blood lipid levels in overweight and obese patients with high blood pressure. Results from this study showed that IER is as effective as, but not superior to, CER (in terms of the outcomes measures assessed). Specifically, findings highlighted that the 5:2 diet is an effective strategy and noninferior to that of daily calorie restriction for BP and weight control. In addition, both weight loss and BP reduction were greater in a subgroup of obese compared with overweight participants, which indicates that obese populations may benefit more from energy restriction. As the authors highlight, this study both aligns with and expands on current related literature.

This study has both strengths and limitations, especially with regard to the design and data analysis strategy. A key strength is the randomized controlled trial design which enables increased internal validity and decreases several sources of bias, including selection bias and confounding. In addition, it was also designed as a pragmatic trial, with the protocol reflecting efforts to replicate the real-world environment by not supplying meal replacements or food. Notably, only 9 patients could not comply with the protocol, indicating that acceptability of the diet protocol was high. However, as this was only a 6-month long study, further studies are needed to determine whether a 5:2 diet is sustainable (and effective) in the long-term compared with CER, which the authors highlight. The study was also adequately powered to detect clinically meaningful differences in weight loss and SBP, and appropriate analyses were performed on both the basis of completers and on an intention-to-treat principle. However, further studies are needed that are adequately powered to also detect clinically meaningful differences in the other measures, ie, body composition, HbA_1c, and blood lipid levels. Importantly, generalizability of findings from this study is limited as the study population comprises only Chinese adults, predominately middle-aged, overweight, and had mildly to moderately elevated SBP and DBP, and excluded diabetic patients. Thus, findings are not necessarily applicable to individuals with highly elevated blood pressure or poorly controlled diabetes.

Applications for Clinical Practice

Results of this study demonstrated that IER is an effective alternative diet strategy for weight loss and blood pressure control in overweight and obese patients with hypertension and is comparable to CER. This is relevant for clinical practice as IER may be easier to maintain in this population compared to continuous weight-loss diets. Importantly, both types of calorie restriction require clinical oversight as medication changes and periodic monitoring of hypotensive and hypoglycemic episodes are needed. Clinicians should consider what is feasible and sustainable for their patients when recommending intermittent energy restriction.

Financial disclosures: None.

Study Overview

Objective. To compare the effects of intermittent energy restriction (IER) with those of continuous energy restriction (CER) on blood pressure control and weight loss in overweight and obese patients with hypertension during a 6-month period.

Design. Randomized controlled trial.

Settings and participants. The trial was conducted at the Affiliated Hospital of Jiaxing University from June 1, 2020, to April 30, 2021. Chinese adults were recruited using advertisements and flyers posted in the hospital and local communities. Prior to participation in study activities, all participants gave informed consent prior to recruitment and were provided compensation in the form of a $38 voucher at 3 and 6 months for their time for participating in the study.

The main inclusion criteria were patients between the ages of 18 and 70 years, hypertension, and body mass index (BMI) ranging from 24 to 40 kg/m². The exclusion criteria were systolic blood pressure (SBP) ≥ 180 mmHg or diastolic blood pressure (DBP) ≥ 120 mmHg, type 1 or 2 diabetes with a history of severe hypoglycemic episodes, pregnancy or breastfeeding, usage of glucagon-like peptide 1 receptor agonists, weight loss > 5 kg within the past 3 months or previous weight loss surgery, and inability to adhere to the dietary protocol.

Of the 294 participants screened for eligibility, 205 were randomized in a 1:1 ratio to the IER group (n = 102) or the CER group (n = 103), stratified by sex and BMI (as overweight or obese). All participants were required to have a stable medication regimen and weight in the 3 months prior to enrollment and not to use weight-loss drugs or vitamin supplements for the duration of the study. Researchers and participants were not blinded to the study group assignment.

Interventions. Participants randomly assigned to the IER group followed a 5:2 eating pattern: a very-low-energy diet of 500-600 kcal for 2 days of the week along with their usual diet for the other 5 days. The 2 days of calorie restriction could be consecutive or nonconsecutive, with a minimum of 0.8 g supplemental protein per kg of body weight per day, in accordance with the 2016 Dietary Guidelines for Chinese Residents. The CER group was advised to consume 1000 kcal/day for women and 1200 kcal/day for men on a 7-day energy restriction. That is, they were prescribed a daily 25% restriction based on the general principles of a Mediterranean-type diet (30% fat, 45-50% carbohydrate, and 20-25% protein).

Both groups received dietary education from a qualified dietitian and were recommended to maintain their current daily activity levels throughout the trial. Written dietary information brochures with portion advice and sample meal plans were provided to improve compliance in each group. All participants received a digital cooking scale to weigh foods to ensure accuracy of intake and were required to keep a food diary while following the recommended recipe on 2 days/week during calorie restriction to help with adherence. No food was provided. All participants were followed up by regular outpatient visits to both cardiologists and dietitians once a month. Diet checklists, activity schedules, and weight were reviewed to assess compliance with dietary advice at each visit.

Of note, participants were encouraged to measure and record their BP twice daily, and if 2 consecutive BP readings were < 110/70 mmHg and/or accompanied by hypotensive episodes with symptoms (dizziness, nausea, headache, and fatigue), they were asked to contact the investigators directly. Antihypertensive medication changes were then made in consultation with cardiologists. In addition, a medication management protocol (ie, doses of antidiabetic medications, including insulin and sulfonylurea) was designed to avoid hypoglycemia. Medication could be reduced in the CER group based on the basal dose at the endocrinologist’s discretion. In the IER group, insulin and sulfonylureas were discontinued on calorie restriction days only, and long-acting insulin was discontinued the night before the IER day. Insulin was not to be resumed until a full day’s caloric intake was achieved.

Measures and analysis. The primary outcomes of this study were changes in BP and weight (measured using an automatic digital sphygmomanometer and an electronic scale), and the secondary outcomes were changes in body composition (assessed by dual-energy x-ray absorptiometry scanning), as well as glycosylated hemoglobin A_1c (HbA_1c) levels and blood lipids after 6 months. All outcome measures were recorded at baseline and at each monthly visit. Incidence rates of hypoglycemia were based on blood glucose (defined as blood glucose < 70 mg/dL) and/or symptomatic hypoglycemia (symptoms of sweating, paleness, dizziness, and confusion). Two cardiologists who were blind to the patients’ diet condition measured and recorded all pertinent clinical parameters and adjudicated serious adverse events.

Data were compared using independent-samples t-tests or the Mann–Whitney U test for continuous variables, and Pearson’s χ² test or Fisher’s exact test for categorial variables as appropriate. Repeated-measures ANOVA via a linear mixed model was employed to test the effects of diet, time, and their interaction. In subgroup analyses, differential effects of the intervention on the primary outcomes were evaluated with respect to patients’ level of education, domicile, and sex based on the statistical significance of the interaction term for the subgroup of interest in the multivariate model. Analyses were performed based on completers and on an intention-to-treat principle.

Main results. Among the 205 randomized participants, 118 were women and 87 were men; mean (SD) age was 50.5 (8.8) years; mean (SD) BMI was 28.7 (2.6); mean (SD) SBP was 143 (10) mmHg; and mean (SD) DBP was 91 (9) mmHg. At the end of the 6-month intervention, 173 (84.4%) completed the study (IER group: n = 88; CER group: n = 85). Both groups had similar dropout rates at 6 months (IER group: 14 participants [13.7%]; CER group: 18 participants [17.5%]; P = .83) and were well matched for baseline characteristics except for triglyceride levels.

In the completers analysis, both groups experienced significant reductions in weight (mean [SEM]), but there was no difference between treatment groups (−7.2 [0.6] kg in the IER group vs −7.1 [0.6] kg in the CER group; diet by time P = .72). Similarly, the change in SBP and DBP achieved was statistically significant over time, but there was also no difference between the dietary interventions (−8 [0.7] mmHg in the IER group vs −8 [0.6] mmHg in the CER group, diet by time P = .68; −6 [0.6] mmHg in the IER group vs −6 [0.5] mmHg in the CER group, diet by time P = .53]. Subgroup analyses of the association of the intervention with weight, SBP and DBP by sex, education, and domicile showed no significant between-group differences.

All measures of body composition decreased significantly at 6 months with both groups experiencing comparable reductions in total fat mass (−5.5 [0.6] kg in the IER group vs −4.8 [0.5] kg in the CER group, diet by time P = .08) and android fat mass (−1.1 [0.2] kg in the IER group vs −0.8 [0.2] kg in the CER group, diet by time P = .16). Of note, participants in the CER group lost significantly more total fat-free mass than did participants in the IER group (mean [SEM], −2.3 [0.2] kg vs −1.7 [0.2] kg; P = .03], and there was a trend toward a greater change in total fat mass in the IER group (P = .08). The secondary outcome of mean (SEM) HbA_1c (−0.2% [0.1%]) and blood lipid levels (triglyceride level, −1.0 [0.3] mmol/L; total cholesterol level, −0.9 [0.2] mmol/L; low-density lipoprotein cholesterol level, −0.9 [0.2 mmol/L; high-density lipoprotein cholesterol level, 0.7 [0.3] mmol/L] improved with weight loss (P < .05), with no differences between groups (diet by time P > .05).

The intention-to-treat analysis demonstrated that IER and CER are equally effective for weight loss and blood pressure control: both groups experienced significant reductions in weight, SBP, and DBP, but with no difference between treatment groups – mean (SEM) weight change with IER was −7.0 (0.6) kg vs −6.8 (0.6) kg with CER; the mean (SEM) SBP with IER was −7 (0.7) mmHg vs −7 (0.6) mmHg with CER; and the mean (SEM) DBP with IER was −6 (0.5) mmHg vs −5 (0.5) mmHg with CER, (diet by time P = .62, .39, and .41, respectively). There were favorable improvements in body composition, HbA_1c, and blood lipid levels, with no differences between groups.

Conclusion. A 2-day severe energy restriction with 5 days of habitual eating compared to 7 days of CER provides an acceptable alternative for BP control and weight loss in overweight and obese individuals with hypertension after 6 months. IER may offer a useful alternative strategy for this population, who find continuous weight-loss diets too difficult to maintain.

Commentary

Globally, obesity represents a major health challenge as it substantially increases the risk of diseases such as hypertension, type 2 diabetes, and coronary heart disease.¹ Lifestyle modifications, including weight loss and increased physical activity, are recommended in major guidelines as a first-step intervention in the treatment of hypertensive patients.² However, lifestyle and behavioral interventions aimed at reducing calorie intake through low-calorie dieting is challenging as it is dependent on individual motivation and adherence to a strict, continuous protocol. Further, CER strategies have limited effectiveness because complex and persistent hormonal, metabolic, and neurochemical adaptations defend against weight loss and promote weight regain.^3-4 IER has drawn attention in the popular media as an alternative to CER due to its feasibility and even potential for higher rates of compliance.⁵

This study adds to the literature as it is the first randomized controlled trial (to the knowledge of the authors at the time of publication) to explore 2 forms of energy restriction – CER and IER – and their impact on weight loss, BP, body composition, HbA_1c, and blood lipid levels in overweight and obese patients with high blood pressure. Results from this study showed that IER is as effective as, but not superior to, CER (in terms of the outcomes measures assessed). Specifically, findings highlighted that the 5:2 diet is an effective strategy and noninferior to that of daily calorie restriction for BP and weight control. In addition, both weight loss and BP reduction were greater in a subgroup of obese compared with overweight participants, which indicates that obese populations may benefit more from energy restriction. As the authors highlight, this study both aligns with and expands on current related literature.

This study has both strengths and limitations, especially with regard to the design and data analysis strategy. A key strength is the randomized controlled trial design which enables increased internal validity and decreases several sources of bias, including selection bias and confounding. In addition, it was also designed as a pragmatic trial, with the protocol reflecting efforts to replicate the real-world environment by not supplying meal replacements or food. Notably, only 9 patients could not comply with the protocol, indicating that acceptability of the diet protocol was high. However, as this was only a 6-month long study, further studies are needed to determine whether a 5:2 diet is sustainable (and effective) in the long-term compared with CER, which the authors highlight. The study was also adequately powered to detect clinically meaningful differences in weight loss and SBP, and appropriate analyses were performed on both the basis of completers and on an intention-to-treat principle. However, further studies are needed that are adequately powered to also detect clinically meaningful differences in the other measures, ie, body composition, HbA_1c, and blood lipid levels. Importantly, generalizability of findings from this study is limited as the study population comprises only Chinese adults, predominately middle-aged, overweight, and had mildly to moderately elevated SBP and DBP, and excluded diabetic patients. Thus, findings are not necessarily applicable to individuals with highly elevated blood pressure or poorly controlled diabetes.

Applications for Clinical Practice

Results of this study demonstrated that IER is an effective alternative diet strategy for weight loss and blood pressure control in overweight and obese patients with hypertension and is comparable to CER. This is relevant for clinical practice as IER may be easier to maintain in this population compared to continuous weight-loss diets. Importantly, both types of calorie restriction require clinical oversight as medication changes and periodic monitoring of hypotensive and hypoglycemic episodes are needed. Clinicians should consider what is feasible and sustainable for their patients when recommending intermittent energy restriction.

Financial disclosures: None.

Study Overview

Objective. To compare the effects of intermittent energy restriction (IER) with those of continuous energy restriction (CER) on blood pressure control and weight loss in overweight and obese patients with hypertension during a 6-month period.

Design. Randomized controlled trial.

Settings and participants. The trial was conducted at the Affiliated Hospital of Jiaxing University from June 1, 2020, to April 30, 2021. Chinese adults were recruited using advertisements and flyers posted in the hospital and local communities. Prior to participation in study activities, all participants gave informed consent prior to recruitment and were provided compensation in the form of a $38 voucher at 3 and 6 months for their time for participating in the study.

The main inclusion criteria were patients between the ages of 18 and 70 years, hypertension, and body mass index (BMI) ranging from 24 to 40 kg/m². The exclusion criteria were systolic blood pressure (SBP) ≥ 180 mmHg or diastolic blood pressure (DBP) ≥ 120 mmHg, type 1 or 2 diabetes with a history of severe hypoglycemic episodes, pregnancy or breastfeeding, usage of glucagon-like peptide 1 receptor agonists, weight loss > 5 kg within the past 3 months or previous weight loss surgery, and inability to adhere to the dietary protocol.

Of the 294 participants screened for eligibility, 205 were randomized in a 1:1 ratio to the IER group (n = 102) or the CER group (n = 103), stratified by sex and BMI (as overweight or obese). All participants were required to have a stable medication regimen and weight in the 3 months prior to enrollment and not to use weight-loss drugs or vitamin supplements for the duration of the study. Researchers and participants were not blinded to the study group assignment.

Interventions. Participants randomly assigned to the IER group followed a 5:2 eating pattern: a very-low-energy diet of 500-600 kcal for 2 days of the week along with their usual diet for the other 5 days. The 2 days of calorie restriction could be consecutive or nonconsecutive, with a minimum of 0.8 g supplemental protein per kg of body weight per day, in accordance with the 2016 Dietary Guidelines for Chinese Residents. The CER group was advised to consume 1000 kcal/day for women and 1200 kcal/day for men on a 7-day energy restriction. That is, they were prescribed a daily 25% restriction based on the general principles of a Mediterranean-type diet (30% fat, 45-50% carbohydrate, and 20-25% protein).

Both groups received dietary education from a qualified dietitian and were recommended to maintain their current daily activity levels throughout the trial. Written dietary information brochures with portion advice and sample meal plans were provided to improve compliance in each group. All participants received a digital cooking scale to weigh foods to ensure accuracy of intake and were required to keep a food diary while following the recommended recipe on 2 days/week during calorie restriction to help with adherence. No food was provided. All participants were followed up by regular outpatient visits to both cardiologists and dietitians once a month. Diet checklists, activity schedules, and weight were reviewed to assess compliance with dietary advice at each visit.

Of note, participants were encouraged to measure and record their BP twice daily, and if 2 consecutive BP readings were < 110/70 mmHg and/or accompanied by hypotensive episodes with symptoms (dizziness, nausea, headache, and fatigue), they were asked to contact the investigators directly. Antihypertensive medication changes were then made in consultation with cardiologists. In addition, a medication management protocol (ie, doses of antidiabetic medications, including insulin and sulfonylurea) was designed to avoid hypoglycemia. Medication could be reduced in the CER group based on the basal dose at the endocrinologist’s discretion. In the IER group, insulin and sulfonylureas were discontinued on calorie restriction days only, and long-acting insulin was discontinued the night before the IER day. Insulin was not to be resumed until a full day’s caloric intake was achieved.

Measures and analysis. The primary outcomes of this study were changes in BP and weight (measured using an automatic digital sphygmomanometer and an electronic scale), and the secondary outcomes were changes in body composition (assessed by dual-energy x-ray absorptiometry scanning), as well as glycosylated hemoglobin A_1c (HbA_1c) levels and blood lipids after 6 months. All outcome measures were recorded at baseline and at each monthly visit. Incidence rates of hypoglycemia were based on blood glucose (defined as blood glucose < 70 mg/dL) and/or symptomatic hypoglycemia (symptoms of sweating, paleness, dizziness, and confusion). Two cardiologists who were blind to the patients’ diet condition measured and recorded all pertinent clinical parameters and adjudicated serious adverse events.

Data were compared using independent-samples t-tests or the Mann–Whitney U test for continuous variables, and Pearson’s χ² test or Fisher’s exact test for categorial variables as appropriate. Repeated-measures ANOVA via a linear mixed model was employed to test the effects of diet, time, and their interaction. In subgroup analyses, differential effects of the intervention on the primary outcomes were evaluated with respect to patients’ level of education, domicile, and sex based on the statistical significance of the interaction term for the subgroup of interest in the multivariate model. Analyses were performed based on completers and on an intention-to-treat principle.

Main results. Among the 205 randomized participants, 118 were women and 87 were men; mean (SD) age was 50.5 (8.8) years; mean (SD) BMI was 28.7 (2.6); mean (SD) SBP was 143 (10) mmHg; and mean (SD) DBP was 91 (9) mmHg. At the end of the 6-month intervention, 173 (84.4%) completed the study (IER group: n = 88; CER group: n = 85). Both groups had similar dropout rates at 6 months (IER group: 14 participants [13.7%]; CER group: 18 participants [17.5%]; P = .83) and were well matched for baseline characteristics except for triglyceride levels.

In the completers analysis, both groups experienced significant reductions in weight (mean [SEM]), but there was no difference between treatment groups (−7.2 [0.6] kg in the IER group vs −7.1 [0.6] kg in the CER group; diet by time P = .72). Similarly, the change in SBP and DBP achieved was statistically significant over time, but there was also no difference between the dietary interventions (−8 [0.7] mmHg in the IER group vs −8 [0.6] mmHg in the CER group, diet by time P = .68; −6 [0.6] mmHg in the IER group vs −6 [0.5] mmHg in the CER group, diet by time P = .53]. Subgroup analyses of the association of the intervention with weight, SBP and DBP by sex, education, and domicile showed no significant between-group differences.

All measures of body composition decreased significantly at 6 months with both groups experiencing comparable reductions in total fat mass (−5.5 [0.6] kg in the IER group vs −4.8 [0.5] kg in the CER group, diet by time P = .08) and android fat mass (−1.1 [0.2] kg in the IER group vs −0.8 [0.2] kg in the CER group, diet by time P = .16). Of note, participants in the CER group lost significantly more total fat-free mass than did participants in the IER group (mean [SEM], −2.3 [0.2] kg vs −1.7 [0.2] kg; P = .03], and there was a trend toward a greater change in total fat mass in the IER group (P = .08). The secondary outcome of mean (SEM) HbA_1c (−0.2% [0.1%]) and blood lipid levels (triglyceride level, −1.0 [0.3] mmol/L; total cholesterol level, −0.9 [0.2] mmol/L; low-density lipoprotein cholesterol level, −0.9 [0.2 mmol/L; high-density lipoprotein cholesterol level, 0.7 [0.3] mmol/L] improved with weight loss (P < .05), with no differences between groups (diet by time P > .05).

The intention-to-treat analysis demonstrated that IER and CER are equally effective for weight loss and blood pressure control: both groups experienced significant reductions in weight, SBP, and DBP, but with no difference between treatment groups – mean (SEM) weight change with IER was −7.0 (0.6) kg vs −6.8 (0.6) kg with CER; the mean (SEM) SBP with IER was −7 (0.7) mmHg vs −7 (0.6) mmHg with CER; and the mean (SEM) DBP with IER was −6 (0.5) mmHg vs −5 (0.5) mmHg with CER, (diet by time P = .62, .39, and .41, respectively). There were favorable improvements in body composition, HbA_1c, and blood lipid levels, with no differences between groups.

Conclusion. A 2-day severe energy restriction with 5 days of habitual eating compared to 7 days of CER provides an acceptable alternative for BP control and weight loss in overweight and obese individuals with hypertension after 6 months. IER may offer a useful alternative strategy for this population, who find continuous weight-loss diets too difficult to maintain.

Commentary

Globally, obesity represents a major health challenge as it substantially increases the risk of diseases such as hypertension, type 2 diabetes, and coronary heart disease.¹ Lifestyle modifications, including weight loss and increased physical activity, are recommended in major guidelines as a first-step intervention in the treatment of hypertensive patients.² However, lifestyle and behavioral interventions aimed at reducing calorie intake through low-calorie dieting is challenging as it is dependent on individual motivation and adherence to a strict, continuous protocol. Further, CER strategies have limited effectiveness because complex and persistent hormonal, metabolic, and neurochemical adaptations defend against weight loss and promote weight regain.^3-4 IER has drawn attention in the popular media as an alternative to CER due to its feasibility and even potential for higher rates of compliance.⁵

This study adds to the literature as it is the first randomized controlled trial (to the knowledge of the authors at the time of publication) to explore 2 forms of energy restriction – CER and IER – and their impact on weight loss, BP, body composition, HbA_1c, and blood lipid levels in overweight and obese patients with high blood pressure. Results from this study showed that IER is as effective as, but not superior to, CER (in terms of the outcomes measures assessed). Specifically, findings highlighted that the 5:2 diet is an effective strategy and noninferior to that of daily calorie restriction for BP and weight control. In addition, both weight loss and BP reduction were greater in a subgroup of obese compared with overweight participants, which indicates that obese populations may benefit more from energy restriction. As the authors highlight, this study both aligns with and expands on current related literature.

This study has both strengths and limitations, especially with regard to the design and data analysis strategy. A key strength is the randomized controlled trial design which enables increased internal validity and decreases several sources of bias, including selection bias and confounding. In addition, it was also designed as a pragmatic trial, with the protocol reflecting efforts to replicate the real-world environment by not supplying meal replacements or food. Notably, only 9 patients could not comply with the protocol, indicating that acceptability of the diet protocol was high. However, as this was only a 6-month long study, further studies are needed to determine whether a 5:2 diet is sustainable (and effective) in the long-term compared with CER, which the authors highlight. The study was also adequately powered to detect clinically meaningful differences in weight loss and SBP, and appropriate analyses were performed on both the basis of completers and on an intention-to-treat principle. However, further studies are needed that are adequately powered to also detect clinically meaningful differences in the other measures, ie, body composition, HbA_1c, and blood lipid levels. Importantly, generalizability of findings from this study is limited as the study population comprises only Chinese adults, predominately middle-aged, overweight, and had mildly to moderately elevated SBP and DBP, and excluded diabetic patients. Thus, findings are not necessarily applicable to individuals with highly elevated blood pressure or poorly controlled diabetes.

Applications for Clinical Practice

Results of this study demonstrated that IER is an effective alternative diet strategy for weight loss and blood pressure control in overweight and obese patients with hypertension and is comparable to CER. This is relevant for clinical practice as IER may be easier to maintain in this population compared to continuous weight-loss diets. Importantly, both types of calorie restriction require clinical oversight as medication changes and periodic monitoring of hypotensive and hypoglycemic episodes are needed. Clinicians should consider what is feasible and sustainable for their patients when recommending intermittent energy restriction.

Financial disclosures: None.

Study Overview

Objective. To evaluate whether a digital intervention comprising self-monitoring of blood pressure (BP) with reminders and predetermined drug changes combined with lifestyle change support resulted in lower systolic BP in people receiving treatment for hypertension that was poorly controlled, and whether this approach was cost effective.

Design. Unmasked randomized controlled trial.

Settings and participants. Eligible participants were identified from clinical codes recorded in the electronic health records of 76 collaborating general practices from the National Institute for Health Research Clinical Research Network, a United Kingdom government agency. The practices sent invitation letters to eligible participants to come to the clinic to establish eligibility, take consent, and collect baseline data via online questionnaires.

Eligible participants were aged 18 years or older with treated hypertension, a mean baseline BP reading of more than 140/90 mm Hg and were taking no more than 3 antihypertensive drugs. Participants also needed to be willing to self-monitor and have access to the internet (with support from a family member if needed). Exclusions included BP greater than 180/110 mm Hg, atrial fibrillation, hypertension not managed by their general practitioner, chronic kidney disease stage 4-5, postural hypotension (> 20 mm Hg systolic drop), an acute cardiovascular event in the previous 3 months, terminal disease, or another condition which in the opinion of their general practitioner made participation inappropriate.

Of the 11 399 invitation letters sent out, 1389 (12%) potential participants responded positively and were screened for eligibility. Those who declined to take part could optionally give their reasons, and responses were gained from 2426 of 10 010 (24%). The mean age of those who gave a reason for declining was 73 years. The most commonly selected reasons for declining were not having access to the internet (982, 41%), not wanting to participate in a research trial (617, 25%) or an internet study (543, 22%), and not wanting to change drugs (535, 22%). Of the 1389 screened, 734 were ineligible, and 33 did not complete baseline measures and randomization. The remaining 622 people who were randomized in a 1:1 ratio to receive the HOME BP intervention (n = 305) or usual care (n = 317).

Intervention vs usual care. The HOME BP intervention for the self-management of high BP consisted of an integrated patient and health care practitioner online digital intervention, BP self-monitoring (using an Omron M3 monitor), health care practitioner directed and supervised titration of antihypertensive drugs, and user-selected lifestyle modifications. Participants were advised via automated email reminders to take 2 morning BP readings for 7 days each month and to enter online each second reading. Mean home BP was calculated, accompanied by feedback of BP results to both patients and professionals with optional evidence-based lifestyle advice (for healthy eating, physical activity, losing weight if appropriate, and salt and alcohol reduction) and motivational support through practice nurses or health care assistances (using the CARE approach – congratulate, ask, reassure, encourage).

Participants allocated to usual care were not provided with self-monitoring equipment or the HOME BP intervention but had online access to the information provided in a patient leaflet for hypertension. This information comprised definitions of hypertension, causes, and brief guidance on treatment, including lifestyle changes and drugs. These participants received routine hypertension care that typically consisted of clinic BP monitoring to titrate drugs, with appointments and drug changes made at the discretion of the general practitioner. Participants were not prevented from self-monitoring, but data on self-monitoring practices were collected at the end of the trial from patients and practitioners.

Measures and analysis. The primary outcome measure was the difference in systolic BP at 12-month follow-up between the intervention and usual care groups (adjusting for baseline BP, practice, BP target levels, and sex). Secondary outcomes included systolic and diastolic BP at 6 and 12 months, weight, modified patient enablement instrument, drug adherence, health-related quality of life, and side effects from the symptoms section of an adjusted illness perceptions questionnaire. At trial, registration participants and general practitioners were asked about their use of self-monitoring in the usual care group.

The primary analysis used general linear modelling to compare systolic BP in the intervention and usual care groups at follow-up, adjusting for baseline BP, practice (as a random effect to take into account clustering), BP target levels, and sex. Analyses were on an intention-to-treat basis and used multiple imputation for missing data. Sensitivity analyses used complete cases and a repeated measures technique. Secondary analyses used similar techniques to assess differences between groups. A within-trial economic analysis estimated cost per unit reduction in systolic BP by using similar adjustments and multiple imputation for missing values. Repeated bootstrapping was used to estimate the probability of the intervention being cost-effective at different levels of willingness to pay per unit reduction in BP.

Main results. The intervention and usual care groups did not differ significantly – participants had a mean age of 66 years and mean baseline clinical BP of 151.6/85.3 mm Hg and 151.7/86.4 mm Hg (usual care and intervention, respectively). Most participants were White British (94%), just more than half were men, and the time since diagnosis averaged around 11 years. The most deprived group (based on the English Index of Multiple Deprivation) accounted for 63/622 (10%), with the least deprived group accounting for 326/622 (52%).

After 1 year, data were available from 552 participants (88.6%) with imputation for the remaining 70 participants (11.4%). Mean BP dropped from 151.7/86.4 to 138.4/80.2 mm Hg in the intervention group and from 151.6/85.3 to 141.8/79.8 mm Hg in the usual care group, giving a mean difference in systolic BP of −3.4 mm Hg (95% CI −6.1 to −0.8 mm Hg) and a mean difference in diastolic BP of −0.5 mm Hg (−1.9 to 0.9 mm Hg). Exploratory subgroup analyses suggested that participants aged 67 years or older had a smaller effect size than those younger than 67. Similarly, while the effect sizes in the standard and diabetes target groups were similar, those older than 80 years with a higher target of 145/85 mm Hg showed little evidence of benefit. Results for other subgroups, including sex, baseline BP, deprivation, and history of self-monitoring, were similar between groups.

Engagement with the digital intervention was high, with 281/305 (92%) participants completing the 2 core training sessions, 268/305 (88%) completing a week of practice BP readings, and 243/305 (80%) completing at least 3 weeks of BP entries. Furthermore, 214/305 (70%) were still monitoring in the last 3 months of participation. However, less than 1/3 of participants chose to register on 1 of the optional lifestyle change modules. In the usual care group, a post-hoc analysis after 12 months showed that 112/234 (47%) patients reported monitoring their own BP at home at least once per month during the trial.

The difference in mean cost per patient was £38 (US $51.30, €41.9; 95% CI £27 to £47), which along with the decrease in systolic BP, gave an incremental cost per mm Hg BP reduction of £11 (£6 to £29). Bootstrapping analysis showed the intervention had high (90%) probability of being cost-effective at willingness to pay above £20 per unit reduction. The probabilities of being cost-effective for the intervention against usual care were 87%, 93%, and 97% at thresholds of £20, £30, and £50, respectively.

Conclusion. The HOME BP digital intervention for the management of hypertension by using self-monitored BP led to better control of systolic BP after 1 year than usual care, with low incremental costs. Implementation in primary care will require integration into clinical workflows and consideration of people who are digitally excluded.

Commentary

Elevated BP, also known as hypertension, is the most important, modifiable risk factor for cardiovascular disease and mortality.¹ Clinically significant effects and improvements in mortality can be achieved with relatively small reductions in BP levels. Long-established lifestyle modifications that effectively lower BP include weight loss, reduced sodium intake, increased physical activity, and limited alcohol intake. However, motivating patients to achieve lifestyle modifications is among the most difficult aspects of managing hypertension. Importantly, for individuals taking antihypertensive medication, lifestyle modification is recommended as adjunctive therapy to reduce BP. Given that target blood pressure levels are reached for less than half of adults, novel interventions are needed to improve BP control – in particular, individualized cognitive behavioral interventions are more likely to be effective than standardized, single-component interventions.

Guided self-management for hypertension as part of systematic, planned care offers the potential for improvements in adherence and in turn improved long-term patient outcomes.² Self-management can encompass a wide range of behaviors in addition to medication titration and monitoring of symptoms, such as individuals’ ability to manage physical, psychosocial and lifestyle behaviors related to their condition.³ Digital interventions leveraging apps, software, and/or technologies in particular have the potential to support people in self-management, allow for remote monitoring, and enable personalized and adaptive strategies for chronic disease management.^4-5 An example of a digital intervention in the context of guided self-management for hypertension can be a web-based program delivered by computer or phone that combines health information with decision support to help inform behavior change in patients and remote monitoring of patient status by health professionals. Well-designed digital interventions can effectively change patient health-related behaviors, improve patient knowledge and confidence for self-management of health, and lead to better health outcomes.^6-7

This study adds to the literature as a large, randomized controlled trial evaluating the effectiveness of a digital intervention in the field of hypertension and with follow-up for a year. The authors highlight that relatively few studies have been performed that combine self-monitoring with a digitally delivered cointervention, and none has shown a major effect in an adequately powered trial over a year. Results from this study showed that HOME BP, a digital intervention enabling self-management of hypertension, including self-monitoring, titration based on self-monitored BP, lifestyle advice, and behavioral support for patients and health care professionals, resulted in a worthwhile reduction of systolic BP. In addition, this reduction was achieved at modest cost based on the within trial cost effectiveness analysis.

There are many important strengths of this study, especially related to the design and analysis strategy, and some limitations. This study was designed as a randomized controlled trial with a 1 year follow-up period, although participants were unmasked to the group they were randomized to, which may have impacted their behaviors while in the study. As the authors state, the study was not only adequately powered to detect a difference in blood pressure, but also over-recruitment ensured such an effect was not missed. Recruiting from a large number of general practices ensured generalizability in terms of health care professionals. Importantly, while study participants mostly identified as predominantly White and tended to be of higher socioeconomic status, this is representative of the aged population in England and Wales. Nevertheless, generalizability of findings from this study is still limited to the demographic characteristics of the study population. Other strengths included inclusion of intention-to-treat analysis, multiple imputation for missing data, sensitivity analysis, as well as economic analysis and cost effectiveness analysis.

Of note, results from the study are only attributable to the digital interventions used in this study (digital web-based with limited mechanisms of behavior change and engagement built-in) and thus should not be generalized to all digital interventions for managing hypertension. Also, as the authors highlight, the relative importance of the different parts of the digital intervention were unable to be distinguished, although this type of analysis is important in multicomponent interventions to better understand the most effective mechanism impacting change in the primary outcome.

Applications for Clinical Practice

Results of this study demonstrated that among participants being treated with hypertension, those engaged with the HOME BP digital intervention (combining self-monitoring of blood pressure with guided self-management) had better control of systolic BP after 1 year compared to participants receiving usual care. While these findings have important implications in the management of hypertension in health care systems, its integration into clinical workflow, sustainability, long-term clinical effectiveness, and effectiveness among diverse populations is unclear. However, clinicians can still encourage and support the use of evidence-based digital tools for patient self-monitoring of BP and guided-management of lifestyle modifications to lower BP. Additionally, clinicians can proactively propose incorporating evidence-based digital interventions like HOME BP into routine clinical practice guidelines.

Financial disclosures: None.

Study Overview

Objective. To evaluate whether a digital intervention comprising self-monitoring of blood pressure (BP) with reminders and predetermined drug changes combined with lifestyle change support resulted in lower systolic BP in people receiving treatment for hypertension that was poorly controlled, and whether this approach was cost effective.

Design. Unmasked randomized controlled trial.

Settings and participants. Eligible participants were identified from clinical codes recorded in the electronic health records of 76 collaborating general practices from the National Institute for Health Research Clinical Research Network, a United Kingdom government agency. The practices sent invitation letters to eligible participants to come to the clinic to establish eligibility, take consent, and collect baseline data via online questionnaires.

Eligible participants were aged 18 years or older with treated hypertension, a mean baseline BP reading of more than 140/90 mm Hg and were taking no more than 3 antihypertensive drugs. Participants also needed to be willing to self-monitor and have access to the internet (with support from a family member if needed). Exclusions included BP greater than 180/110 mm Hg, atrial fibrillation, hypertension not managed by their general practitioner, chronic kidney disease stage 4-5, postural hypotension (> 20 mm Hg systolic drop), an acute cardiovascular event in the previous 3 months, terminal disease, or another condition which in the opinion of their general practitioner made participation inappropriate.

Of the 11 399 invitation letters sent out, 1389 (12%) potential participants responded positively and were screened for eligibility. Those who declined to take part could optionally give their reasons, and responses were gained from 2426 of 10 010 (24%). The mean age of those who gave a reason for declining was 73 years. The most commonly selected reasons for declining were not having access to the internet (982, 41%), not wanting to participate in a research trial (617, 25%) or an internet study (543, 22%), and not wanting to change drugs (535, 22%). Of the 1389 screened, 734 were ineligible, and 33 did not complete baseline measures and randomization. The remaining 622 people who were randomized in a 1:1 ratio to receive the HOME BP intervention (n = 305) or usual care (n = 317).

Intervention vs usual care. The HOME BP intervention for the self-management of high BP consisted of an integrated patient and health care practitioner online digital intervention, BP self-monitoring (using an Omron M3 monitor), health care practitioner directed and supervised titration of antihypertensive drugs, and user-selected lifestyle modifications. Participants were advised via automated email reminders to take 2 morning BP readings for 7 days each month and to enter online each second reading. Mean home BP was calculated, accompanied by feedback of BP results to both patients and professionals with optional evidence-based lifestyle advice (for healthy eating, physical activity, losing weight if appropriate, and salt and alcohol reduction) and motivational support through practice nurses or health care assistances (using the CARE approach – congratulate, ask, reassure, encourage).

Participants allocated to usual care were not provided with self-monitoring equipment or the HOME BP intervention but had online access to the information provided in a patient leaflet for hypertension. This information comprised definitions of hypertension, causes, and brief guidance on treatment, including lifestyle changes and drugs. These participants received routine hypertension care that typically consisted of clinic BP monitoring to titrate drugs, with appointments and drug changes made at the discretion of the general practitioner. Participants were not prevented from self-monitoring, but data on self-monitoring practices were collected at the end of the trial from patients and practitioners.

Measures and analysis. The primary outcome measure was the difference in systolic BP at 12-month follow-up between the intervention and usual care groups (adjusting for baseline BP, practice, BP target levels, and sex). Secondary outcomes included systolic and diastolic BP at 6 and 12 months, weight, modified patient enablement instrument, drug adherence, health-related quality of life, and side effects from the symptoms section of an adjusted illness perceptions questionnaire. At trial, registration participants and general practitioners were asked about their use of self-monitoring in the usual care group.

The primary analysis used general linear modelling to compare systolic BP in the intervention and usual care groups at follow-up, adjusting for baseline BP, practice (as a random effect to take into account clustering), BP target levels, and sex. Analyses were on an intention-to-treat basis and used multiple imputation for missing data. Sensitivity analyses used complete cases and a repeated measures technique. Secondary analyses used similar techniques to assess differences between groups. A within-trial economic analysis estimated cost per unit reduction in systolic BP by using similar adjustments and multiple imputation for missing values. Repeated bootstrapping was used to estimate the probability of the intervention being cost-effective at different levels of willingness to pay per unit reduction in BP.

Main results. The intervention and usual care groups did not differ significantly – participants had a mean age of 66 years and mean baseline clinical BP of 151.6/85.3 mm Hg and 151.7/86.4 mm Hg (usual care and intervention, respectively). Most participants were White British (94%), just more than half were men, and the time since diagnosis averaged around 11 years. The most deprived group (based on the English Index of Multiple Deprivation) accounted for 63/622 (10%), with the least deprived group accounting for 326/622 (52%).

After 1 year, data were available from 552 participants (88.6%) with imputation for the remaining 70 participants (11.4%). Mean BP dropped from 151.7/86.4 to 138.4/80.2 mm Hg in the intervention group and from 151.6/85.3 to 141.8/79.8 mm Hg in the usual care group, giving a mean difference in systolic BP of −3.4 mm Hg (95% CI −6.1 to −0.8 mm Hg) and a mean difference in diastolic BP of −0.5 mm Hg (−1.9 to 0.9 mm Hg). Exploratory subgroup analyses suggested that participants aged 67 years or older had a smaller effect size than those younger than 67. Similarly, while the effect sizes in the standard and diabetes target groups were similar, those older than 80 years with a higher target of 145/85 mm Hg showed little evidence of benefit. Results for other subgroups, including sex, baseline BP, deprivation, and history of self-monitoring, were similar between groups.

Engagement with the digital intervention was high, with 281/305 (92%) participants completing the 2 core training sessions, 268/305 (88%) completing a week of practice BP readings, and 243/305 (80%) completing at least 3 weeks of BP entries. Furthermore, 214/305 (70%) were still monitoring in the last 3 months of participation. However, less than 1/3 of participants chose to register on 1 of the optional lifestyle change modules. In the usual care group, a post-hoc analysis after 12 months showed that 112/234 (47%) patients reported monitoring their own BP at home at least once per month during the trial.

The difference in mean cost per patient was £38 (US $51.30, €41.9; 95% CI £27 to £47), which along with the decrease in systolic BP, gave an incremental cost per mm Hg BP reduction of £11 (£6 to £29). Bootstrapping analysis showed the intervention had high (90%) probability of being cost-effective at willingness to pay above £20 per unit reduction. The probabilities of being cost-effective for the intervention against usual care were 87%, 93%, and 97% at thresholds of £20, £30, and £50, respectively.

Conclusion. The HOME BP digital intervention for the management of hypertension by using self-monitored BP led to better control of systolic BP after 1 year than usual care, with low incremental costs. Implementation in primary care will require integration into clinical workflows and consideration of people who are digitally excluded.

Commentary

Elevated BP, also known as hypertension, is the most important, modifiable risk factor for cardiovascular disease and mortality.¹ Clinically significant effects and improvements in mortality can be achieved with relatively small reductions in BP levels. Long-established lifestyle modifications that effectively lower BP include weight loss, reduced sodium intake, increased physical activity, and limited alcohol intake. However, motivating patients to achieve lifestyle modifications is among the most difficult aspects of managing hypertension. Importantly, for individuals taking antihypertensive medication, lifestyle modification is recommended as adjunctive therapy to reduce BP. Given that target blood pressure levels are reached for less than half of adults, novel interventions are needed to improve BP control – in particular, individualized cognitive behavioral interventions are more likely to be effective than standardized, single-component interventions.

Guided self-management for hypertension as part of systematic, planned care offers the potential for improvements in adherence and in turn improved long-term patient outcomes.² Self-management can encompass a wide range of behaviors in addition to medication titration and monitoring of symptoms, such as individuals’ ability to manage physical, psychosocial and lifestyle behaviors related to their condition.³ Digital interventions leveraging apps, software, and/or technologies in particular have the potential to support people in self-management, allow for remote monitoring, and enable personalized and adaptive strategies for chronic disease management.^4-5 An example of a digital intervention in the context of guided self-management for hypertension can be a web-based program delivered by computer or phone that combines health information with decision support to help inform behavior change in patients and remote monitoring of patient status by health professionals. Well-designed digital interventions can effectively change patient health-related behaviors, improve patient knowledge and confidence for self-management of health, and lead to better health outcomes.^6-7

This study adds to the literature as a large, randomized controlled trial evaluating the effectiveness of a digital intervention in the field of hypertension and with follow-up for a year. The authors highlight that relatively few studies have been performed that combine self-monitoring with a digitally delivered cointervention, and none has shown a major effect in an adequately powered trial over a year. Results from this study showed that HOME BP, a digital intervention enabling self-management of hypertension, including self-monitoring, titration based on self-monitored BP, lifestyle advice, and behavioral support for patients and health care professionals, resulted in a worthwhile reduction of systolic BP. In addition, this reduction was achieved at modest cost based on the within trial cost effectiveness analysis.

There are many important strengths of this study, especially related to the design and analysis strategy, and some limitations. This study was designed as a randomized controlled trial with a 1 year follow-up period, although participants were unmasked to the group they were randomized to, which may have impacted their behaviors while in the study. As the authors state, the study was not only adequately powered to detect a difference in blood pressure, but also over-recruitment ensured such an effect was not missed. Recruiting from a large number of general practices ensured generalizability in terms of health care professionals. Importantly, while study participants mostly identified as predominantly White and tended to be of higher socioeconomic status, this is representative of the aged population in England and Wales. Nevertheless, generalizability of findings from this study is still limited to the demographic characteristics of the study population. Other strengths included inclusion of intention-to-treat analysis, multiple imputation for missing data, sensitivity analysis, as well as economic analysis and cost effectiveness analysis.

Of note, results from the study are only attributable to the digital interventions used in this study (digital web-based with limited mechanisms of behavior change and engagement built-in) and thus should not be generalized to all digital interventions for managing hypertension. Also, as the authors highlight, the relative importance of the different parts of the digital intervention were unable to be distinguished, although this type of analysis is important in multicomponent interventions to better understand the most effective mechanism impacting change in the primary outcome.

Applications for Clinical Practice

Results of this study demonstrated that among participants being treated with hypertension, those engaged with the HOME BP digital intervention (combining self-monitoring of blood pressure with guided self-management) had better control of systolic BP after 1 year compared to participants receiving usual care. While these findings have important implications in the management of hypertension in health care systems, its integration into clinical workflow, sustainability, long-term clinical effectiveness, and effectiveness among diverse populations is unclear. However, clinicians can still encourage and support the use of evidence-based digital tools for patient self-monitoring of BP and guided-management of lifestyle modifications to lower BP. Additionally, clinicians can proactively propose incorporating evidence-based digital interventions like HOME BP into routine clinical practice guidelines.

Financial disclosures: None.

Study Overview

Objective. To evaluate whether a digital intervention comprising self-monitoring of blood pressure (BP) with reminders and predetermined drug changes combined with lifestyle change support resulted in lower systolic BP in people receiving treatment for hypertension that was poorly controlled, and whether this approach was cost effective.

Design. Unmasked randomized controlled trial.

Settings and participants. Eligible participants were identified from clinical codes recorded in the electronic health records of 76 collaborating general practices from the National Institute for Health Research Clinical Research Network, a United Kingdom government agency. The practices sent invitation letters to eligible participants to come to the clinic to establish eligibility, take consent, and collect baseline data via online questionnaires.

Eligible participants were aged 18 years or older with treated hypertension, a mean baseline BP reading of more than 140/90 mm Hg and were taking no more than 3 antihypertensive drugs. Participants also needed to be willing to self-monitor and have access to the internet (with support from a family member if needed). Exclusions included BP greater than 180/110 mm Hg, atrial fibrillation, hypertension not managed by their general practitioner, chronic kidney disease stage 4-5, postural hypotension (> 20 mm Hg systolic drop), an acute cardiovascular event in the previous 3 months, terminal disease, or another condition which in the opinion of their general practitioner made participation inappropriate.

Of the 11 399 invitation letters sent out, 1389 (12%) potential participants responded positively and were screened for eligibility. Those who declined to take part could optionally give their reasons, and responses were gained from 2426 of 10 010 (24%). The mean age of those who gave a reason for declining was 73 years. The most commonly selected reasons for declining were not having access to the internet (982, 41%), not wanting to participate in a research trial (617, 25%) or an internet study (543, 22%), and not wanting to change drugs (535, 22%). Of the 1389 screened, 734 were ineligible, and 33 did not complete baseline measures and randomization. The remaining 622 people who were randomized in a 1:1 ratio to receive the HOME BP intervention (n = 305) or usual care (n = 317).

Intervention vs usual care. The HOME BP intervention for the self-management of high BP consisted of an integrated patient and health care practitioner online digital intervention, BP self-monitoring (using an Omron M3 monitor), health care practitioner directed and supervised titration of antihypertensive drugs, and user-selected lifestyle modifications. Participants were advised via automated email reminders to take 2 morning BP readings for 7 days each month and to enter online each second reading. Mean home BP was calculated, accompanied by feedback of BP results to both patients and professionals with optional evidence-based lifestyle advice (for healthy eating, physical activity, losing weight if appropriate, and salt and alcohol reduction) and motivational support through practice nurses or health care assistances (using the CARE approach – congratulate, ask, reassure, encourage).

Participants allocated to usual care were not provided with self-monitoring equipment or the HOME BP intervention but had online access to the information provided in a patient leaflet for hypertension. This information comprised definitions of hypertension, causes, and brief guidance on treatment, including lifestyle changes and drugs. These participants received routine hypertension care that typically consisted of clinic BP monitoring to titrate drugs, with appointments and drug changes made at the discretion of the general practitioner. Participants were not prevented from self-monitoring, but data on self-monitoring practices were collected at the end of the trial from patients and practitioners.

Measures and analysis. The primary outcome measure was the difference in systolic BP at 12-month follow-up between the intervention and usual care groups (adjusting for baseline BP, practice, BP target levels, and sex). Secondary outcomes included systolic and diastolic BP at 6 and 12 months, weight, modified patient enablement instrument, drug adherence, health-related quality of life, and side effects from the symptoms section of an adjusted illness perceptions questionnaire. At trial, registration participants and general practitioners were asked about their use of self-monitoring in the usual care group.

The primary analysis used general linear modelling to compare systolic BP in the intervention and usual care groups at follow-up, adjusting for baseline BP, practice (as a random effect to take into account clustering), BP target levels, and sex. Analyses were on an intention-to-treat basis and used multiple imputation for missing data. Sensitivity analyses used complete cases and a repeated measures technique. Secondary analyses used similar techniques to assess differences between groups. A within-trial economic analysis estimated cost per unit reduction in systolic BP by using similar adjustments and multiple imputation for missing values. Repeated bootstrapping was used to estimate the probability of the intervention being cost-effective at different levels of willingness to pay per unit reduction in BP.

Main results. The intervention and usual care groups did not differ significantly – participants had a mean age of 66 years and mean baseline clinical BP of 151.6/85.3 mm Hg and 151.7/86.4 mm Hg (usual care and intervention, respectively). Most participants were White British (94%), just more than half were men, and the time since diagnosis averaged around 11 years. The most deprived group (based on the English Index of Multiple Deprivation) accounted for 63/622 (10%), with the least deprived group accounting for 326/622 (52%).

After 1 year, data were available from 552 participants (88.6%) with imputation for the remaining 70 participants (11.4%). Mean BP dropped from 151.7/86.4 to 138.4/80.2 mm Hg in the intervention group and from 151.6/85.3 to 141.8/79.8 mm Hg in the usual care group, giving a mean difference in systolic BP of −3.4 mm Hg (95% CI −6.1 to −0.8 mm Hg) and a mean difference in diastolic BP of −0.5 mm Hg (−1.9 to 0.9 mm Hg). Exploratory subgroup analyses suggested that participants aged 67 years or older had a smaller effect size than those younger than 67. Similarly, while the effect sizes in the standard and diabetes target groups were similar, those older than 80 years with a higher target of 145/85 mm Hg showed little evidence of benefit. Results for other subgroups, including sex, baseline BP, deprivation, and history of self-monitoring, were similar between groups.

Engagement with the digital intervention was high, with 281/305 (92%) participants completing the 2 core training sessions, 268/305 (88%) completing a week of practice BP readings, and 243/305 (80%) completing at least 3 weeks of BP entries. Furthermore, 214/305 (70%) were still monitoring in the last 3 months of participation. However, less than 1/3 of participants chose to register on 1 of the optional lifestyle change modules. In the usual care group, a post-hoc analysis after 12 months showed that 112/234 (47%) patients reported monitoring their own BP at home at least once per month during the trial.

The difference in mean cost per patient was £38 (US $51.30, €41.9; 95% CI £27 to £47), which along with the decrease in systolic BP, gave an incremental cost per mm Hg BP reduction of £11 (£6 to £29). Bootstrapping analysis showed the intervention had high (90%) probability of being cost-effective at willingness to pay above £20 per unit reduction. The probabilities of being cost-effective for the intervention against usual care were 87%, 93%, and 97% at thresholds of £20, £30, and £50, respectively.

Conclusion. The HOME BP digital intervention for the management of hypertension by using self-monitored BP led to better control of systolic BP after 1 year than usual care, with low incremental costs. Implementation in primary care will require integration into clinical workflows and consideration of people who are digitally excluded.

Commentary

Elevated BP, also known as hypertension, is the most important, modifiable risk factor for cardiovascular disease and mortality.¹ Clinically significant effects and improvements in mortality can be achieved with relatively small reductions in BP levels. Long-established lifestyle modifications that effectively lower BP include weight loss, reduced sodium intake, increased physical activity, and limited alcohol intake. However, motivating patients to achieve lifestyle modifications is among the most difficult aspects of managing hypertension. Importantly, for individuals taking antihypertensive medication, lifestyle modification is recommended as adjunctive therapy to reduce BP. Given that target blood pressure levels are reached for less than half of adults, novel interventions are needed to improve BP control – in particular, individualized cognitive behavioral interventions are more likely to be effective than standardized, single-component interventions.

Guided self-management for hypertension as part of systematic, planned care offers the potential for improvements in adherence and in turn improved long-term patient outcomes.² Self-management can encompass a wide range of behaviors in addition to medication titration and monitoring of symptoms, such as individuals’ ability to manage physical, psychosocial and lifestyle behaviors related to their condition.³ Digital interventions leveraging apps, software, and/or technologies in particular have the potential to support people in self-management, allow for remote monitoring, and enable personalized and adaptive strategies for chronic disease management.^4-5 An example of a digital intervention in the context of guided self-management for hypertension can be a web-based program delivered by computer or phone that combines health information with decision support to help inform behavior change in patients and remote monitoring of patient status by health professionals. Well-designed digital interventions can effectively change patient health-related behaviors, improve patient knowledge and confidence for self-management of health, and lead to better health outcomes.^6-7

This study adds to the literature as a large, randomized controlled trial evaluating the effectiveness of a digital intervention in the field of hypertension and with follow-up for a year. The authors highlight that relatively few studies have been performed that combine self-monitoring with a digitally delivered cointervention, and none has shown a major effect in an adequately powered trial over a year. Results from this study showed that HOME BP, a digital intervention enabling self-management of hypertension, including self-monitoring, titration based on self-monitored BP, lifestyle advice, and behavioral support for patients and health care professionals, resulted in a worthwhile reduction of systolic BP. In addition, this reduction was achieved at modest cost based on the within trial cost effectiveness analysis.

There are many important strengths of this study, especially related to the design and analysis strategy, and some limitations. This study was designed as a randomized controlled trial with a 1 year follow-up period, although participants were unmasked to the group they were randomized to, which may have impacted their behaviors while in the study. As the authors state, the study was not only adequately powered to detect a difference in blood pressure, but also over-recruitment ensured such an effect was not missed. Recruiting from a large number of general practices ensured generalizability in terms of health care professionals. Importantly, while study participants mostly identified as predominantly White and tended to be of higher socioeconomic status, this is representative of the aged population in England and Wales. Nevertheless, generalizability of findings from this study is still limited to the demographic characteristics of the study population. Other strengths included inclusion of intention-to-treat analysis, multiple imputation for missing data, sensitivity analysis, as well as economic analysis and cost effectiveness analysis.

Of note, results from the study are only attributable to the digital interventions used in this study (digital web-based with limited mechanisms of behavior change and engagement built-in) and thus should not be generalized to all digital interventions for managing hypertension. Also, as the authors highlight, the relative importance of the different parts of the digital intervention were unable to be distinguished, although this type of analysis is important in multicomponent interventions to better understand the most effective mechanism impacting change in the primary outcome.

Applications for Clinical Practice

Results of this study demonstrated that among participants being treated with hypertension, those engaged with the HOME BP digital intervention (combining self-monitoring of blood pressure with guided self-management) had better control of systolic BP after 1 year compared to participants receiving usual care. While these findings have important implications in the management of hypertension in health care systems, its integration into clinical workflow, sustainability, long-term clinical effectiveness, and effectiveness among diverse populations is unclear. However, clinicians can still encourage and support the use of evidence-based digital tools for patient self-monitoring of BP and guided-management of lifestyle modifications to lower BP. Additionally, clinicians can proactively propose incorporating evidence-based digital interventions like HOME BP into routine clinical practice guidelines.

Financial disclosures: None.

Study Overview

Objective. To determine the effectiveness of a person-centered intervention (comprising personalized and cocreated treatment plans to promote physical activity) for individuals with chronic widespread pain when delivered with digital eHealth support compared with standard telephone follow-up.

Design. Single-blinded multicenter randomized controlled trial.

Settings and participants. Participants with chronic widespread pain (CWP) who had participated in a pain management program from 2010–16 at 5 primary health care rehabilitation centers in 5 cities or towns in the western part of Sweden were invited to join the study between March 2018 and April 2019 via letter providing information about the intervention. The letter was followed by a phone call 1-2 weeks later to screen for inclusion and exclusion criteria and interest in participating. Additional participants were invited to participate via a newspaper advertisement in 1 of the 5 cities.

Inclusion criteria were Swedish-speaking persons aged 20–65 years with CWP (defined as having pain in both sides of the body, pain above and below the waist, and axial pain for at least 3 months). Exclusion criteria included having other severe somatic or psychiatric disorders, dominating causes of pain other than CWP, or other severe disease interfering with the ability to be physically active, pregnancy, not having access to a smartphone or a computer, inability to speak or understand Swedish, ongoing physiotherapy treatment, and already exercising regularly. Of 716 people initially assessed for eligibility, 425 completed telephone screening, and 139 were randomized (using block randomization) to either the intervention arm (n = 69) or the active control arm (n = 70). Due to the nature of the intervention, it was not possible to blind the participants or the physiotherapist to group allocation. All participants provided written informed consent.

The 2 groups underwent the same first individual meeting with a physiotherapist to cocreate a health plan with physical activities, and, if needed, stress management, based on each participant’s individual preferences, obstacles, goals, and resources. The difference between the groups was the type of follow-up support. Participants in the intervention group had 1 follow-up meeting with the physiotherapist a week after the initial meeting (to review and adjust the health plan as needed) and thereafter were supported through a digital e-health platform (accessed via the participant’s smartphone or computer) during the 6-month follow-up period. Participants were encouraged to access the platform once a week to answer questions regarding their health, and the extent to which they had been able to manage their health plan during the previous week. In addition, the participant and physiotherapist could communicate via the platform as needed. Participants in the active control group had 1 follow-up phone call with the physiotherapist 1 month after the initial meeting (similarly to review and adjust the health plan as needed), and no further contact or support from the physiotherapist during the 6-month follow-up period.

Measures and analysis. The primary outcome measure was pain intensity during the previous week assessed with a 0–100 subscale from the Fibromyalgia Impact Questionnaire (FIQ-pain). Secondary outcome measures included overall health status (via FIQ-total with 10 subscales), global fatigue (via FIQ-fatigue subscale), multidimensional fatigue (via Multidimensional Fatigue Inventory, a 20-item questionnaire rated on a 1-5 Likert scale), clinical manifestations of stress (via Stress and Crisis Inventory, a 35-item questionnaire rated on a 0-4 Likert scale), self-efficacy (via General Self-Efficacy Scale, a 10-item questionnaire rated on a 1-4 Likert scale), health-related quality of life (via Short Form 36, specifically the Physical Component Summary composite score), leisure-time physical activity (via Leisure Time Physical Activity Instrument), and physical function (via 1-min chair-stand test). Additional demographic data on age, pain localization, pharmacological treatment, tobacco use, country of birth, level of education, family status, economic status, work status, sick-leave, and disability pension were collected via a questionnaire.

Between-group differences for changes in outcomes from baseline to 6-month follow-up were calculated using the Mann–Whitney U test for continuous data, and Pearson’s χ² or Fisher’s exact test for categorical data. Significance level was set at 5% with no adjustment for multiple comparisons. All analyses were made according to intention-to-treat by originally assigned group; missing cases were not included in the analysis.

Main results. Participants consisted of primarily middle-age, middle income, educated (> 12 years of education) females, with > 60% of participants working at least part-time (between-group differences in baseline data and demographic data not detailed in the article). A total of 29 participants were lost to follow-up. In the intervention group, lost-to-follow up participants were older, performed fewer hours of physical activity, and had lower mental fatigue at baseline, compared with those who were lost to follow-up in the active control group.

In between-group analyses, there were no significant differences in the primary outcome (pain intensity) from baseline to 6-month follow-up. The only significant difference in secondary outcomes was seen in global fatigue – the active control group improved significantly compared with the intervention group (P = .004).

In the intervention group, 87% of participants used the digital platform. Among these users, 35% contacted the physiotherapist (75% of these communications were health- or study-related issues, 25% were issues with the digital platform), 33% were contacted by the physiotherapist (96% of these communications were about the health plan and physical activity), and 32% never had any contact with the physiotherapist. There was a significant difference in the primary outcome (pain intensity) from baseline to 6-month follow-up between platform users and non-users (P = .03, mean change [SD] 3.8 [19.66] mm vs –20.5 [6.36] mm, respectively).

Conclusion. No significant differences were found between the groups after 6 months (except for a significant decrease in global fatigue in the active control group compared with the intervention group). Further development of interventions to support persons with CWP to maintain regular physical activity is needed.

Commentary

Chronic widespread pain is a disorder characterized by diffuse body pain persisting for at least 3 months.^1-2 It has been associated with lost work productivity, mental ill health, and reduced quality of life. The development of clinically effective and cost-effective pain management strategies for CWP is challenging given the syndrome complexity and heterogenous symptomology. Thus, multimodal, multidisciplinary management is widely advocated, often a combination of education and self-management, with integration of physical, non-pharmacological and pharmacological treatments.^1-3 Of note, physical exercise and cognitive behavioral therapy are 2 non-pharmacological treatments that hold some promise based on available evidence.

The pervasiveness of technology in nearly all aspects of daily life has corresponded with the development of implementation of a wide range of technology-based interventions for health purposes.⁴ Examples of electronic health or eHealth modalities include internet-based, telephone supported, interactive voice-response, videoconferencing, mobile apps, and virtual reality. While the use of technology in chronic pain management interventions has increased in recent years, the literature is still limited, heterogenous, and provides limited evidence on the efficacy of eHealth/digital interventions, let alone which specific modalities are most effective.^4-9

This study adds to the literature as a randomized controlled trial evaluating the effectiveness of a person-centered intervention for individuals with CWP delivered with digital eHealth support compared with standard telephone follow-up. Results showed no significant difference in the primary outcome of pain intensity and nearly all secondary outcomes between the intervention group (supported by the digital platform) and the active control group (supported by a follow-up phone call). Further, intervention participants who did not use the platform improved significantly more in pain intensity than those who used the platform.

While these results imply that digital support does not contribute to improvements in the outcomes measured, it is important these findings are interpreted with caution given the limitations of the study design as well as limitations with the intervention itself. Importantly, while this study was designed as a randomized controlled trial, the authors indicated that it was not possible to blind the participants or the physiotherapist to group allocation, which may have impacted their behaviors while in the study. In addition, as the authors note, an intervention aimed at increasing physical activity should ideally include an objective measure of activity and this was lacking in this study. The use of an actigraphy device for example would have provided objective, continuous data on movement and could have helped assess an important outcome measure – whether participants reached their physical activity goals or had increased their overall physical activity. In the analysis, there was no adjustment for multiple comparisons or use of imputation methods to handle missing values. Further, it was unclear whether differences in baseline data were evaluated and taken into consideration in between-group analyses. Lastly, results are only attributable to the eHealth mode used in this study (digital web-based with limited mechanisms of behavior change and engagement built-in) and thus should not be generalized to all digital/eHealth interventions persons with CWP.

Applications for Clinical Practice

While the results of this study failed to demonstrate significant differences between a physical activity-promoting intervention for persons with CWP with digital follow-up vs telephone follow-up, it remains important to consider person-centered principles when offering CWP management support. In this spirit, clinicians should consider a management approach that takes into account the individual’s knowledge, resources, and barriers, and also actively involves the patient in treatment planning to enhance the patient’s self-efficacy to manage their health. In addition, individual preference for a specific (or combination of) eHealth/digital modality should be considered and used to guide a comprehensive management plan, as well as used as a complementary modality to face-to-face care/support.

Study Overview

Objective. To determine the effectiveness of a person-centered intervention (comprising personalized and cocreated treatment plans to promote physical activity) for individuals with chronic widespread pain when delivered with digital eHealth support compared with standard telephone follow-up.

Design. Single-blinded multicenter randomized controlled trial.

Settings and participants. Participants with chronic widespread pain (CWP) who had participated in a pain management program from 2010–16 at 5 primary health care rehabilitation centers in 5 cities or towns in the western part of Sweden were invited to join the study between March 2018 and April 2019 via letter providing information about the intervention. The letter was followed by a phone call 1-2 weeks later to screen for inclusion and exclusion criteria and interest in participating. Additional participants were invited to participate via a newspaper advertisement in 1 of the 5 cities.

Inclusion criteria were Swedish-speaking persons aged 20–65 years with CWP (defined as having pain in both sides of the body, pain above and below the waist, and axial pain for at least 3 months). Exclusion criteria included having other severe somatic or psychiatric disorders, dominating causes of pain other than CWP, or other severe disease interfering with the ability to be physically active, pregnancy, not having access to a smartphone or a computer, inability to speak or understand Swedish, ongoing physiotherapy treatment, and already exercising regularly. Of 716 people initially assessed for eligibility, 425 completed telephone screening, and 139 were randomized (using block randomization) to either the intervention arm (n = 69) or the active control arm (n = 70). Due to the nature of the intervention, it was not possible to blind the participants or the physiotherapist to group allocation. All participants provided written informed consent.

The 2 groups underwent the same first individual meeting with a physiotherapist to cocreate a health plan with physical activities, and, if needed, stress management, based on each participant’s individual preferences, obstacles, goals, and resources. The difference between the groups was the type of follow-up support. Participants in the intervention group had 1 follow-up meeting with the physiotherapist a week after the initial meeting (to review and adjust the health plan as needed) and thereafter were supported through a digital e-health platform (accessed via the participant’s smartphone or computer) during the 6-month follow-up period. Participants were encouraged to access the platform once a week to answer questions regarding their health, and the extent to which they had been able to manage their health plan during the previous week. In addition, the participant and physiotherapist could communicate via the platform as needed. Participants in the active control group had 1 follow-up phone call with the physiotherapist 1 month after the initial meeting (similarly to review and adjust the health plan as needed), and no further contact or support from the physiotherapist during the 6-month follow-up period.

Measures and analysis. The primary outcome measure was pain intensity during the previous week assessed with a 0–100 subscale from the Fibromyalgia Impact Questionnaire (FIQ-pain). Secondary outcome measures included overall health status (via FIQ-total with 10 subscales), global fatigue (via FIQ-fatigue subscale), multidimensional fatigue (via Multidimensional Fatigue Inventory, a 20-item questionnaire rated on a 1-5 Likert scale), clinical manifestations of stress (via Stress and Crisis Inventory, a 35-item questionnaire rated on a 0-4 Likert scale), self-efficacy (via General Self-Efficacy Scale, a 10-item questionnaire rated on a 1-4 Likert scale), health-related quality of life (via Short Form 36, specifically the Physical Component Summary composite score), leisure-time physical activity (via Leisure Time Physical Activity Instrument), and physical function (via 1-min chair-stand test). Additional demographic data on age, pain localization, pharmacological treatment, tobacco use, country of birth, level of education, family status, economic status, work status, sick-leave, and disability pension were collected via a questionnaire.

Between-group differences for changes in outcomes from baseline to 6-month follow-up were calculated using the Mann–Whitney U test for continuous data, and Pearson’s χ² or Fisher’s exact test for categorical data. Significance level was set at 5% with no adjustment for multiple comparisons. All analyses were made according to intention-to-treat by originally assigned group; missing cases were not included in the analysis.

Main results. Participants consisted of primarily middle-age, middle income, educated (> 12 years of education) females, with > 60% of participants working at least part-time (between-group differences in baseline data and demographic data not detailed in the article). A total of 29 participants were lost to follow-up. In the intervention group, lost-to-follow up participants were older, performed fewer hours of physical activity, and had lower mental fatigue at baseline, compared with those who were lost to follow-up in the active control group.

In between-group analyses, there were no significant differences in the primary outcome (pain intensity) from baseline to 6-month follow-up. The only significant difference in secondary outcomes was seen in global fatigue – the active control group improved significantly compared with the intervention group (P = .004).

In the intervention group, 87% of participants used the digital platform. Among these users, 35% contacted the physiotherapist (75% of these communications were health- or study-related issues, 25% were issues with the digital platform), 33% were contacted by the physiotherapist (96% of these communications were about the health plan and physical activity), and 32% never had any contact with the physiotherapist. There was a significant difference in the primary outcome (pain intensity) from baseline to 6-month follow-up between platform users and non-users (P = .03, mean change [SD] 3.8 [19.66] mm vs –20.5 [6.36] mm, respectively).

Conclusion. No significant differences were found between the groups after 6 months (except for a significant decrease in global fatigue in the active control group compared with the intervention group). Further development of interventions to support persons with CWP to maintain regular physical activity is needed.

Commentary

Chronic widespread pain is a disorder characterized by diffuse body pain persisting for at least 3 months.^1-2 It has been associated with lost work productivity, mental ill health, and reduced quality of life. The development of clinically effective and cost-effective pain management strategies for CWP is challenging given the syndrome complexity and heterogenous symptomology. Thus, multimodal, multidisciplinary management is widely advocated, often a combination of education and self-management, with integration of physical, non-pharmacological and pharmacological treatments.^1-3 Of note, physical exercise and cognitive behavioral therapy are 2 non-pharmacological treatments that hold some promise based on available evidence.

The pervasiveness of technology in nearly all aspects of daily life has corresponded with the development of implementation of a wide range of technology-based interventions for health purposes.⁴ Examples of electronic health or eHealth modalities include internet-based, telephone supported, interactive voice-response, videoconferencing, mobile apps, and virtual reality. While the use of technology in chronic pain management interventions has increased in recent years, the literature is still limited, heterogenous, and provides limited evidence on the efficacy of eHealth/digital interventions, let alone which specific modalities are most effective.^4-9

This study adds to the literature as a randomized controlled trial evaluating the effectiveness of a person-centered intervention for individuals with CWP delivered with digital eHealth support compared with standard telephone follow-up. Results showed no significant difference in the primary outcome of pain intensity and nearly all secondary outcomes between the intervention group (supported by the digital platform) and the active control group (supported by a follow-up phone call). Further, intervention participants who did not use the platform improved significantly more in pain intensity than those who used the platform.

While these results imply that digital support does not contribute to improvements in the outcomes measured, it is important these findings are interpreted with caution given the limitations of the study design as well as limitations with the intervention itself. Importantly, while this study was designed as a randomized controlled trial, the authors indicated that it was not possible to blind the participants or the physiotherapist to group allocation, which may have impacted their behaviors while in the study. In addition, as the authors note, an intervention aimed at increasing physical activity should ideally include an objective measure of activity and this was lacking in this study. The use of an actigraphy device for example would have provided objective, continuous data on movement and could have helped assess an important outcome measure – whether participants reached their physical activity goals or had increased their overall physical activity. In the analysis, there was no adjustment for multiple comparisons or use of imputation methods to handle missing values. Further, it was unclear whether differences in baseline data were evaluated and taken into consideration in between-group analyses. Lastly, results are only attributable to the eHealth mode used in this study (digital web-based with limited mechanisms of behavior change and engagement built-in) and thus should not be generalized to all digital/eHealth interventions persons with CWP.

Applications for Clinical Practice

While the results of this study failed to demonstrate significant differences between a physical activity-promoting intervention for persons with CWP with digital follow-up vs telephone follow-up, it remains important to consider person-centered principles when offering CWP management support. In this spirit, clinicians should consider a management approach that takes into account the individual’s knowledge, resources, and barriers, and also actively involves the patient in treatment planning to enhance the patient’s self-efficacy to manage their health. In addition, individual preference for a specific (or combination of) eHealth/digital modality should be considered and used to guide a comprehensive management plan, as well as used as a complementary modality to face-to-face care/support.

Study Overview

Objective. To determine the effectiveness of a person-centered intervention (comprising personalized and cocreated treatment plans to promote physical activity) for individuals with chronic widespread pain when delivered with digital eHealth support compared with standard telephone follow-up.

Design. Single-blinded multicenter randomized controlled trial.

Settings and participants. Participants with chronic widespread pain (CWP) who had participated in a pain management program from 2010–16 at 5 primary health care rehabilitation centers in 5 cities or towns in the western part of Sweden were invited to join the study between March 2018 and April 2019 via letter providing information about the intervention. The letter was followed by a phone call 1-2 weeks later to screen for inclusion and exclusion criteria and interest in participating. Additional participants were invited to participate via a newspaper advertisement in 1 of the 5 cities.

Inclusion criteria were Swedish-speaking persons aged 20–65 years with CWP (defined as having pain in both sides of the body, pain above and below the waist, and axial pain for at least 3 months). Exclusion criteria included having other severe somatic or psychiatric disorders, dominating causes of pain other than CWP, or other severe disease interfering with the ability to be physically active, pregnancy, not having access to a smartphone or a computer, inability to speak or understand Swedish, ongoing physiotherapy treatment, and already exercising regularly. Of 716 people initially assessed for eligibility, 425 completed telephone screening, and 139 were randomized (using block randomization) to either the intervention arm (n = 69) or the active control arm (n = 70). Due to the nature of the intervention, it was not possible to blind the participants or the physiotherapist to group allocation. All participants provided written informed consent.

The 2 groups underwent the same first individual meeting with a physiotherapist to cocreate a health plan with physical activities, and, if needed, stress management, based on each participant’s individual preferences, obstacles, goals, and resources. The difference between the groups was the type of follow-up support. Participants in the intervention group had 1 follow-up meeting with the physiotherapist a week after the initial meeting (to review and adjust the health plan as needed) and thereafter were supported through a digital e-health platform (accessed via the participant’s smartphone or computer) during the 6-month follow-up period. Participants were encouraged to access the platform once a week to answer questions regarding their health, and the extent to which they had been able to manage their health plan during the previous week. In addition, the participant and physiotherapist could communicate via the platform as needed. Participants in the active control group had 1 follow-up phone call with the physiotherapist 1 month after the initial meeting (similarly to review and adjust the health plan as needed), and no further contact or support from the physiotherapist during the 6-month follow-up period.

Measures and analysis. The primary outcome measure was pain intensity during the previous week assessed with a 0–100 subscale from the Fibromyalgia Impact Questionnaire (FIQ-pain). Secondary outcome measures included overall health status (via FIQ-total with 10 subscales), global fatigue (via FIQ-fatigue subscale), multidimensional fatigue (via Multidimensional Fatigue Inventory, a 20-item questionnaire rated on a 1-5 Likert scale), clinical manifestations of stress (via Stress and Crisis Inventory, a 35-item questionnaire rated on a 0-4 Likert scale), self-efficacy (via General Self-Efficacy Scale, a 10-item questionnaire rated on a 1-4 Likert scale), health-related quality of life (via Short Form 36, specifically the Physical Component Summary composite score), leisure-time physical activity (via Leisure Time Physical Activity Instrument), and physical function (via 1-min chair-stand test). Additional demographic data on age, pain localization, pharmacological treatment, tobacco use, country of birth, level of education, family status, economic status, work status, sick-leave, and disability pension were collected via a questionnaire.

Between-group differences for changes in outcomes from baseline to 6-month follow-up were calculated using the Mann–Whitney U test for continuous data, and Pearson’s χ² or Fisher’s exact test for categorical data. Significance level was set at 5% with no adjustment for multiple comparisons. All analyses were made according to intention-to-treat by originally assigned group; missing cases were not included in the analysis.

Main results. Participants consisted of primarily middle-age, middle income, educated (> 12 years of education) females, with > 60% of participants working at least part-time (between-group differences in baseline data and demographic data not detailed in the article). A total of 29 participants were lost to follow-up. In the intervention group, lost-to-follow up participants were older, performed fewer hours of physical activity, and had lower mental fatigue at baseline, compared with those who were lost to follow-up in the active control group.

In between-group analyses, there were no significant differences in the primary outcome (pain intensity) from baseline to 6-month follow-up. The only significant difference in secondary outcomes was seen in global fatigue – the active control group improved significantly compared with the intervention group (P = .004).

In the intervention group, 87% of participants used the digital platform. Among these users, 35% contacted the physiotherapist (75% of these communications were health- or study-related issues, 25% were issues with the digital platform), 33% were contacted by the physiotherapist (96% of these communications were about the health plan and physical activity), and 32% never had any contact with the physiotherapist. There was a significant difference in the primary outcome (pain intensity) from baseline to 6-month follow-up between platform users and non-users (P = .03, mean change [SD] 3.8 [19.66] mm vs –20.5 [6.36] mm, respectively).

Conclusion. No significant differences were found between the groups after 6 months (except for a significant decrease in global fatigue in the active control group compared with the intervention group). Further development of interventions to support persons with CWP to maintain regular physical activity is needed.

Commentary

Chronic widespread pain is a disorder characterized by diffuse body pain persisting for at least 3 months.^1-2 It has been associated with lost work productivity, mental ill health, and reduced quality of life. The development of clinically effective and cost-effective pain management strategies for CWP is challenging given the syndrome complexity and heterogenous symptomology. Thus, multimodal, multidisciplinary management is widely advocated, often a combination of education and self-management, with integration of physical, non-pharmacological and pharmacological treatments.^1-3 Of note, physical exercise and cognitive behavioral therapy are 2 non-pharmacological treatments that hold some promise based on available evidence.

The pervasiveness of technology in nearly all aspects of daily life has corresponded with the development of implementation of a wide range of technology-based interventions for health purposes.⁴ Examples of electronic health or eHealth modalities include internet-based, telephone supported, interactive voice-response, videoconferencing, mobile apps, and virtual reality. While the use of technology in chronic pain management interventions has increased in recent years, the literature is still limited, heterogenous, and provides limited evidence on the efficacy of eHealth/digital interventions, let alone which specific modalities are most effective.^4-9

This study adds to the literature as a randomized controlled trial evaluating the effectiveness of a person-centered intervention for individuals with CWP delivered with digital eHealth support compared with standard telephone follow-up. Results showed no significant difference in the primary outcome of pain intensity and nearly all secondary outcomes between the intervention group (supported by the digital platform) and the active control group (supported by a follow-up phone call). Further, intervention participants who did not use the platform improved significantly more in pain intensity than those who used the platform.

While these results imply that digital support does not contribute to improvements in the outcomes measured, it is important these findings are interpreted with caution given the limitations of the study design as well as limitations with the intervention itself. Importantly, while this study was designed as a randomized controlled trial, the authors indicated that it was not possible to blind the participants or the physiotherapist to group allocation, which may have impacted their behaviors while in the study. In addition, as the authors note, an intervention aimed at increasing physical activity should ideally include an objective measure of activity and this was lacking in this study. The use of an actigraphy device for example would have provided objective, continuous data on movement and could have helped assess an important outcome measure – whether participants reached their physical activity goals or had increased their overall physical activity. In the analysis, there was no adjustment for multiple comparisons or use of imputation methods to handle missing values. Further, it was unclear whether differences in baseline data were evaluated and taken into consideration in between-group analyses. Lastly, results are only attributable to the eHealth mode used in this study (digital web-based with limited mechanisms of behavior change and engagement built-in) and thus should not be generalized to all digital/eHealth interventions persons with CWP.

Applications for Clinical Practice

While the results of this study failed to demonstrate significant differences between a physical activity-promoting intervention for persons with CWP with digital follow-up vs telephone follow-up, it remains important to consider person-centered principles when offering CWP management support. In this spirit, clinicians should consider a management approach that takes into account the individual’s knowledge, resources, and barriers, and also actively involves the patient in treatment planning to enhance the patient’s self-efficacy to manage their health. In addition, individual preference for a specific (or combination of) eHealth/digital modality should be considered and used to guide a comprehensive management plan, as well as used as a complementary modality to face-to-face care/support.

Study Overview

Objective. To determine the impact of providing genetically tailored and population-based lifestyle advice for weight management on key constructs of the Theory of Planned Behavior (TPB), a widely accepted theory used to help predict human lifestyle-related behaviors.

Design. Pragmatic, cluster, randomized controlled trial.

Settings and participants. This study took place at the East Elgin Family Health Team, a primary care clinic in Aylmer, Ontario, Canada. Recruitment occurred between April 2017 and September 2018, with staggered intervention cohorts occurring from May 2017 to September 2019. Participants enrolled in a weight management program at the clinic were invited to participate in the study if they met the following inclusion criteria: body mass index (BMI) ≥25 kg/m², >18 years of age, English-speaking, willing to undergo genetic testing, having access to a computer with internet at least 1 day per week, and not seeing another health care provider for weight loss advice outside of the study. Exclusion criteria included pregnancy and lactation. All participants provided written informed consent.

Interventions. At baseline, weight management program cohorts (average cohort size was 14 participants) were randomized (1:1) to receive either the standard population-based intervention (Group Lifestyle Balance, or GLB) or a modified GLB intervention, which included the provision of lifestyle genomics (LGx) information and advice (GLB+LGx). Both interventions aimed to assist participants with weight management and healthy lifestyle change, with particular focus on nutrition and physical activity (PA). Interventions were 12 months long, consisting of 23 group-based sessions and 3 one-on-one sessions with a registered dietitian after 3, 6, and 12 months (all sessions were face-to-face). To improve intervention adherence, participants were given reminder calls for their one-on-one appointments and for the start of their program. A sample size was calculated based on the primary outcome indicating that a total of 74 participants were needed (n = 37 per group) for this trial. By September 2019, this sample size was exceeded with 10 randomized groups (n = 140).

The 5 randomized standard GLB groups followed the established GLB program curriculum comprising population-based information and advice while focusing on following a calorie-controlled, moderate-fat (25% of calories) nutrition plan with at least 150 minutes of weekly moderate-intensity PA. Participants were also provided with a 1-page summary report of their nutrition and PA guidelines at the first group meeting outlining population-based targets, including acceptable macronutrient distribution ranges for protein, total fat, saturated fat, monounsaturated fat, polyunsaturated fat, sodium, calories, snacking, and PA.

The 5 randomized modified GLB+LGx groups followed a modified GLB program curriculum in which participants were given genetic-based information and advice, which differed from the advice given to the standard GLB group, while focusing on following a calorie-controlled nutrition plan. The nutrition and PA targets were personalized based on their individual genetic variation. For example, participants with the AA variant of FTO (rs9939609) were advised to engage in at least 30 to 60 minutes of PA daily 6 days per week, with muscle-strengthening activities at least 2 days per week, rather than receiving the standard population-based advice to aim for 150 minutes weekly of PA with at least 2 days per week of muscle-strengthening activity. Participants were also provided with a 1-page summary report of their nutrition and PA guidelines at the first group meeting, which outlined genetic-based information and advice related to protein, total fat, saturated fat, monounsaturated fat, polyunsaturated fat, sodium, calories, snacking, and PA.

Measures and analysis. Change in the TPB components (attitudes, subjective norms and perceived behavioral control) were measured via a TPB questionnaire at 5 time points: baseline (2-week run-in period), immediately after the first group session (where participants received a summary report of either population-based or genetic-based recommendations depending on group assignment), and after 3-, 6- and 12-month follow-ups. Attitudes, subjective norms, and perceived behavioral control were measured on a Likert scale from 1 through 7. Self-reported measures of actual behavioral control (including annual household income, perceptions about events arising in one’s day-to-day life that suddenly take up one’s free time, perceptions about the frequency of feeling ill or tired, and highest achieved level of education) were collected via survey questions and assessed on a Likert scale of 1 through 7. Stage of change was also measured, based on the Transtheoretical Model, using a Likert scale of 1 through 6.

Linear mixed models were used to conduct within- and between-group analyses using SPSS version 26.0, while controlling for measures of actual behavioral control. All analyses were intention-to-treat by originally assigned groups, with mean value imputation conducted for missing data. A Bonferroni correction for multiple testing was used. For all statistical analyses, the level of significance was set at P < 0.05 and trending towards significance at P = 0.05–0.06.

Main results. Participants consisted of primarily middle-age, middle-income, Caucasian females. Baseline attitudes towards the effectiveness of nutrition and PA for weight management were generally positive, and participants perceived that undergoing genetic testing would assist with weight management. Participants had overall neutral subjective norms related to friends and family consuming a healthy diet and engaging in PA, but perceived that their friends, family, and health care team (HCT) believed it was important for them to achieve their nutrition and PA recommendations. Participants overall also perceived that their HCT believed genetic testing could assist with weight management. Baseline measures of perceived behavioral control were overall neutral, with baseline stage of change between “motivation” and “action” (short-term; <3 months).

In within-group analyses, significant improvements (P < 0.05) in attitudes towards the effectiveness of nutrition and PA recommendations for weight management, subjective norms related to both friends and family consuming a healthy diet, and perceived behavioral control in changing PA/dietary intake and managing weight tended to be short-term in the GLB group and long-term for the GLB+LGx group. In all cases of between-group differences for changes in TPB components, the GLB group exhibited reductions in scores, whereas the GLB+LGx group exhibited increases or improvements. Between-group differences (short-term and long-term) in several measures of subjective norms were observed. For example, after 3 months, significant between-group differences were observed in changes in perception that friends believed LGx would help with weight management (P = 0.024). After 12 months, between-group differences trending towards significance were also observed in changes in perception that family members believed genetic testing would help with weight management (P = 0.05). Significant between-group differences and differences trending towards significance were also observed at 12 months for changes in perception that family believed it was important for the participant to achieve the PA recommendations (P = 0.049) and nutrition recommendations (P = 0.05). Between-group differences trending towards significance were also observed at 3 months in attitudes towards the effectiveness of LGx for weight management (P = 0.06). There were no significant between-group differences observed in changes in perceived behavioral control.

Conclusion. Results from this study support the hypothesis that the TPB can help provide a theoretical explanation for why genetically tailored lifestyle information and advice can lead to improvements in lifestyle behavior change.

Commentary

Because health behaviors are critical in areas such as prevention, treatment, and rehabilitation, it is important to describe and understand what drives these behaviors.¹ Theories are important tools in this effort as they aim to explain and predict health behavior and are used in the design and evaluation of interventions.¹ The TPB is one of the most widely accepted behavior change theories and posits that attitudes, subjective norms (or social pressures and behaviors), and perceived behavioral control are significant predictors of an individual’s intention to engage in behaviors.² TPB has been highlighted in the literature as a validated theory for predicting nutrition and PA intentions and resulting behaviors.^3,4

Motivating lifestyle behavior change in clinical practice can be challenging, but some studies have demonstrated how providing genetic information and advice (or lifestyle genomics) can help motivate changes in nutrition and PA among patients.^5-7 Because this has yet to be explained using the TPB, this study is an important contribution to the literature as it aimed to determine the impact of providing genetically tailored and population-based lifestyle advice for weight management on key constructs of the TPB. Briefly, results from within-group analyses in this study demonstrated that the provision of genetically tailored lifestyle information and advice (via the GLB+LGx intervention) tended to impact antecedents of behavior change, more so over the long-term, while population-based advice (via the standard GLB intervention) tended to impact antecedents of behavior change over the short-term (eg, attitudes towards dietary fat intake, perceptions that friends and family consume a healthy diet, and perceptions about the impact of genetic-based advice for weight management). In addition, between-group differences in subjective norms observed at 12 months suggested that social pressures and norms may be influencing long-term changes in lifestyle habits.

While key strengths of this study include its pragmatic cluster randomized controlled trial design, 12-month intervention duration, and intent-to-treat analyses, there are some study limitations, which are acknowledged by the authors. Generalizability is limited to the demographic characteristics of the study population (ie, middle-aged, middle-income, Caucasian females enrolled in a lifestyle change weight management program). Thus, replication of the study is needed in more diverse study populations and with health-related outcomes beyond weight management. In addition, as the authors indicate, future research should ensure the inclusion of theory-based questionnaires in genetic-based intervention studies assessing lifestyle behavior change to elucidate theory-based mechanisms of change.

Applications for Clinical Practice

Population-based research has consistently indicated that nutrition interventions typically impact short-term dietary changes. Confronting the challenge of long-term adherence to nutrition and PA recommendations requires an understanding of factors impacting long-term motivation and behavior change. With increased attention on and research into genetically tailored lifestyle advice (or lifestyle genomics), it is important for clinical practitioners to be familiar with the evidence supporting these approaches. In addition, this research highlights the need to consider individual factors (attitudes, subjective norms, and perceived behavioral control) that may predict successful change in lifestyle habits when providing nutrition and PA recommendations, whether population-based or genetically tailored.

—Katrina F. Mateo, PhD, MPH

Study Overview

Objective. To determine the impact of providing genetically tailored and population-based lifestyle advice for weight management on key constructs of the Theory of Planned Behavior (TPB), a widely accepted theory used to help predict human lifestyle-related behaviors.

Design. Pragmatic, cluster, randomized controlled trial.

Settings and participants. This study took place at the East Elgin Family Health Team, a primary care clinic in Aylmer, Ontario, Canada. Recruitment occurred between April 2017 and September 2018, with staggered intervention cohorts occurring from May 2017 to September 2019. Participants enrolled in a weight management program at the clinic were invited to participate in the study if they met the following inclusion criteria: body mass index (BMI) ≥25 kg/m², >18 years of age, English-speaking, willing to undergo genetic testing, having access to a computer with internet at least 1 day per week, and not seeing another health care provider for weight loss advice outside of the study. Exclusion criteria included pregnancy and lactation. All participants provided written informed consent.

Interventions. At baseline, weight management program cohorts (average cohort size was 14 participants) were randomized (1:1) to receive either the standard population-based intervention (Group Lifestyle Balance, or GLB) or a modified GLB intervention, which included the provision of lifestyle genomics (LGx) information and advice (GLB+LGx). Both interventions aimed to assist participants with weight management and healthy lifestyle change, with particular focus on nutrition and physical activity (PA). Interventions were 12 months long, consisting of 23 group-based sessions and 3 one-on-one sessions with a registered dietitian after 3, 6, and 12 months (all sessions were face-to-face). To improve intervention adherence, participants were given reminder calls for their one-on-one appointments and for the start of their program. A sample size was calculated based on the primary outcome indicating that a total of 74 participants were needed (n = 37 per group) for this trial. By September 2019, this sample size was exceeded with 10 randomized groups (n = 140).

The 5 randomized standard GLB groups followed the established GLB program curriculum comprising population-based information and advice while focusing on following a calorie-controlled, moderate-fat (25% of calories) nutrition plan with at least 150 minutes of weekly moderate-intensity PA. Participants were also provided with a 1-page summary report of their nutrition and PA guidelines at the first group meeting outlining population-based targets, including acceptable macronutrient distribution ranges for protein, total fat, saturated fat, monounsaturated fat, polyunsaturated fat, sodium, calories, snacking, and PA.

The 5 randomized modified GLB+LGx groups followed a modified GLB program curriculum in which participants were given genetic-based information and advice, which differed from the advice given to the standard GLB group, while focusing on following a calorie-controlled nutrition plan. The nutrition and PA targets were personalized based on their individual genetic variation. For example, participants with the AA variant of FTO (rs9939609) were advised to engage in at least 30 to 60 minutes of PA daily 6 days per week, with muscle-strengthening activities at least 2 days per week, rather than receiving the standard population-based advice to aim for 150 minutes weekly of PA with at least 2 days per week of muscle-strengthening activity. Participants were also provided with a 1-page summary report of their nutrition and PA guidelines at the first group meeting, which outlined genetic-based information and advice related to protein, total fat, saturated fat, monounsaturated fat, polyunsaturated fat, sodium, calories, snacking, and PA.

Measures and analysis. Change in the TPB components (attitudes, subjective norms and perceived behavioral control) were measured via a TPB questionnaire at 5 time points: baseline (2-week run-in period), immediately after the first group session (where participants received a summary report of either population-based or genetic-based recommendations depending on group assignment), and after 3-, 6- and 12-month follow-ups. Attitudes, subjective norms, and perceived behavioral control were measured on a Likert scale from 1 through 7. Self-reported measures of actual behavioral control (including annual household income, perceptions about events arising in one’s day-to-day life that suddenly take up one’s free time, perceptions about the frequency of feeling ill or tired, and highest achieved level of education) were collected via survey questions and assessed on a Likert scale of 1 through 7. Stage of change was also measured, based on the Transtheoretical Model, using a Likert scale of 1 through 6.

Linear mixed models were used to conduct within- and between-group analyses using SPSS version 26.0, while controlling for measures of actual behavioral control. All analyses were intention-to-treat by originally assigned groups, with mean value imputation conducted for missing data. A Bonferroni correction for multiple testing was used. For all statistical analyses, the level of significance was set at P < 0.05 and trending towards significance at P = 0.05–0.06.

Main results. Participants consisted of primarily middle-age, middle-income, Caucasian females. Baseline attitudes towards the effectiveness of nutrition and PA for weight management were generally positive, and participants perceived that undergoing genetic testing would assist with weight management. Participants had overall neutral subjective norms related to friends and family consuming a healthy diet and engaging in PA, but perceived that their friends, family, and health care team (HCT) believed it was important for them to achieve their nutrition and PA recommendations. Participants overall also perceived that their HCT believed genetic testing could assist with weight management. Baseline measures of perceived behavioral control were overall neutral, with baseline stage of change between “motivation” and “action” (short-term; <3 months).

In within-group analyses, significant improvements (P < 0.05) in attitudes towards the effectiveness of nutrition and PA recommendations for weight management, subjective norms related to both friends and family consuming a healthy diet, and perceived behavioral control in changing PA/dietary intake and managing weight tended to be short-term in the GLB group and long-term for the GLB+LGx group. In all cases of between-group differences for changes in TPB components, the GLB group exhibited reductions in scores, whereas the GLB+LGx group exhibited increases or improvements. Between-group differences (short-term and long-term) in several measures of subjective norms were observed. For example, after 3 months, significant between-group differences were observed in changes in perception that friends believed LGx would help with weight management (P = 0.024). After 12 months, between-group differences trending towards significance were also observed in changes in perception that family members believed genetic testing would help with weight management (P = 0.05). Significant between-group differences and differences trending towards significance were also observed at 12 months for changes in perception that family believed it was important for the participant to achieve the PA recommendations (P = 0.049) and nutrition recommendations (P = 0.05). Between-group differences trending towards significance were also observed at 3 months in attitudes towards the effectiveness of LGx for weight management (P = 0.06). There were no significant between-group differences observed in changes in perceived behavioral control.

Conclusion. Results from this study support the hypothesis that the TPB can help provide a theoretical explanation for why genetically tailored lifestyle information and advice can lead to improvements in lifestyle behavior change.

Commentary

Because health behaviors are critical in areas such as prevention, treatment, and rehabilitation, it is important to describe and understand what drives these behaviors.¹ Theories are important tools in this effort as they aim to explain and predict health behavior and are used in the design and evaluation of interventions.¹ The TPB is one of the most widely accepted behavior change theories and posits that attitudes, subjective norms (or social pressures and behaviors), and perceived behavioral control are significant predictors of an individual’s intention to engage in behaviors.² TPB has been highlighted in the literature as a validated theory for predicting nutrition and PA intentions and resulting behaviors.^3,4

Motivating lifestyle behavior change in clinical practice can be challenging, but some studies have demonstrated how providing genetic information and advice (or lifestyle genomics) can help motivate changes in nutrition and PA among patients.^5-7 Because this has yet to be explained using the TPB, this study is an important contribution to the literature as it aimed to determine the impact of providing genetically tailored and population-based lifestyle advice for weight management on key constructs of the TPB. Briefly, results from within-group analyses in this study demonstrated that the provision of genetically tailored lifestyle information and advice (via the GLB+LGx intervention) tended to impact antecedents of behavior change, more so over the long-term, while population-based advice (via the standard GLB intervention) tended to impact antecedents of behavior change over the short-term (eg, attitudes towards dietary fat intake, perceptions that friends and family consume a healthy diet, and perceptions about the impact of genetic-based advice for weight management). In addition, between-group differences in subjective norms observed at 12 months suggested that social pressures and norms may be influencing long-term changes in lifestyle habits.

While key strengths of this study include its pragmatic cluster randomized controlled trial design, 12-month intervention duration, and intent-to-treat analyses, there are some study limitations, which are acknowledged by the authors. Generalizability is limited to the demographic characteristics of the study population (ie, middle-aged, middle-income, Caucasian females enrolled in a lifestyle change weight management program). Thus, replication of the study is needed in more diverse study populations and with health-related outcomes beyond weight management. In addition, as the authors indicate, future research should ensure the inclusion of theory-based questionnaires in genetic-based intervention studies assessing lifestyle behavior change to elucidate theory-based mechanisms of change.

Applications for Clinical Practice

Population-based research has consistently indicated that nutrition interventions typically impact short-term dietary changes. Confronting the challenge of long-term adherence to nutrition and PA recommendations requires an understanding of factors impacting long-term motivation and behavior change. With increased attention on and research into genetically tailored lifestyle advice (or lifestyle genomics), it is important for clinical practitioners to be familiar with the evidence supporting these approaches. In addition, this research highlights the need to consider individual factors (attitudes, subjective norms, and perceived behavioral control) that may predict successful change in lifestyle habits when providing nutrition and PA recommendations, whether population-based or genetically tailored.

—Katrina F. Mateo, PhD, MPH

Study Overview

Objective. To determine the impact of providing genetically tailored and population-based lifestyle advice for weight management on key constructs of the Theory of Planned Behavior (TPB), a widely accepted theory used to help predict human lifestyle-related behaviors.

Design. Pragmatic, cluster, randomized controlled trial.

Settings and participants. This study took place at the East Elgin Family Health Team, a primary care clinic in Aylmer, Ontario, Canada. Recruitment occurred between April 2017 and September 2018, with staggered intervention cohorts occurring from May 2017 to September 2019. Participants enrolled in a weight management program at the clinic were invited to participate in the study if they met the following inclusion criteria: body mass index (BMI) ≥25 kg/m², >18 years of age, English-speaking, willing to undergo genetic testing, having access to a computer with internet at least 1 day per week, and not seeing another health care provider for weight loss advice outside of the study. Exclusion criteria included pregnancy and lactation. All participants provided written informed consent.

Interventions. At baseline, weight management program cohorts (average cohort size was 14 participants) were randomized (1:1) to receive either the standard population-based intervention (Group Lifestyle Balance, or GLB) or a modified GLB intervention, which included the provision of lifestyle genomics (LGx) information and advice (GLB+LGx). Both interventions aimed to assist participants with weight management and healthy lifestyle change, with particular focus on nutrition and physical activity (PA). Interventions were 12 months long, consisting of 23 group-based sessions and 3 one-on-one sessions with a registered dietitian after 3, 6, and 12 months (all sessions were face-to-face). To improve intervention adherence, participants were given reminder calls for their one-on-one appointments and for the start of their program. A sample size was calculated based on the primary outcome indicating that a total of 74 participants were needed (n = 37 per group) for this trial. By September 2019, this sample size was exceeded with 10 randomized groups (n = 140).

The 5 randomized standard GLB groups followed the established GLB program curriculum comprising population-based information and advice while focusing on following a calorie-controlled, moderate-fat (25% of calories) nutrition plan with at least 150 minutes of weekly moderate-intensity PA. Participants were also provided with a 1-page summary report of their nutrition and PA guidelines at the first group meeting outlining population-based targets, including acceptable macronutrient distribution ranges for protein, total fat, saturated fat, monounsaturated fat, polyunsaturated fat, sodium, calories, snacking, and PA.

The 5 randomized modified GLB+LGx groups followed a modified GLB program curriculum in which participants were given genetic-based information and advice, which differed from the advice given to the standard GLB group, while focusing on following a calorie-controlled nutrition plan. The nutrition and PA targets were personalized based on their individual genetic variation. For example, participants with the AA variant of FTO (rs9939609) were advised to engage in at least 30 to 60 minutes of PA daily 6 days per week, with muscle-strengthening activities at least 2 days per week, rather than receiving the standard population-based advice to aim for 150 minutes weekly of PA with at least 2 days per week of muscle-strengthening activity. Participants were also provided with a 1-page summary report of their nutrition and PA guidelines at the first group meeting, which outlined genetic-based information and advice related to protein, total fat, saturated fat, monounsaturated fat, polyunsaturated fat, sodium, calories, snacking, and PA.

Measures and analysis. Change in the TPB components (attitudes, subjective norms and perceived behavioral control) were measured via a TPB questionnaire at 5 time points: baseline (2-week run-in period), immediately after the first group session (where participants received a summary report of either population-based or genetic-based recommendations depending on group assignment), and after 3-, 6- and 12-month follow-ups. Attitudes, subjective norms, and perceived behavioral control were measured on a Likert scale from 1 through 7. Self-reported measures of actual behavioral control (including annual household income, perceptions about events arising in one’s day-to-day life that suddenly take up one’s free time, perceptions about the frequency of feeling ill or tired, and highest achieved level of education) were collected via survey questions and assessed on a Likert scale of 1 through 7. Stage of change was also measured, based on the Transtheoretical Model, using a Likert scale of 1 through 6.

Linear mixed models were used to conduct within- and between-group analyses using SPSS version 26.0, while controlling for measures of actual behavioral control. All analyses were intention-to-treat by originally assigned groups, with mean value imputation conducted for missing data. A Bonferroni correction for multiple testing was used. For all statistical analyses, the level of significance was set at P < 0.05 and trending towards significance at P = 0.05–0.06.

Main results. Participants consisted of primarily middle-age, middle-income, Caucasian females. Baseline attitudes towards the effectiveness of nutrition and PA for weight management were generally positive, and participants perceived that undergoing genetic testing would assist with weight management. Participants had overall neutral subjective norms related to friends and family consuming a healthy diet and engaging in PA, but perceived that their friends, family, and health care team (HCT) believed it was important for them to achieve their nutrition and PA recommendations. Participants overall also perceived that their HCT believed genetic testing could assist with weight management. Baseline measures of perceived behavioral control were overall neutral, with baseline stage of change between “motivation” and “action” (short-term; <3 months).

In within-group analyses, significant improvements (P < 0.05) in attitudes towards the effectiveness of nutrition and PA recommendations for weight management, subjective norms related to both friends and family consuming a healthy diet, and perceived behavioral control in changing PA/dietary intake and managing weight tended to be short-term in the GLB group and long-term for the GLB+LGx group. In all cases of between-group differences for changes in TPB components, the GLB group exhibited reductions in scores, whereas the GLB+LGx group exhibited increases or improvements. Between-group differences (short-term and long-term) in several measures of subjective norms were observed. For example, after 3 months, significant between-group differences were observed in changes in perception that friends believed LGx would help with weight management (P = 0.024). After 12 months, between-group differences trending towards significance were also observed in changes in perception that family members believed genetic testing would help with weight management (P = 0.05). Significant between-group differences and differences trending towards significance were also observed at 12 months for changes in perception that family believed it was important for the participant to achieve the PA recommendations (P = 0.049) and nutrition recommendations (P = 0.05). Between-group differences trending towards significance were also observed at 3 months in attitudes towards the effectiveness of LGx for weight management (P = 0.06). There were no significant between-group differences observed in changes in perceived behavioral control.

Conclusion. Results from this study support the hypothesis that the TPB can help provide a theoretical explanation for why genetically tailored lifestyle information and advice can lead to improvements in lifestyle behavior change.

Commentary

Because health behaviors are critical in areas such as prevention, treatment, and rehabilitation, it is important to describe and understand what drives these behaviors.¹ Theories are important tools in this effort as they aim to explain and predict health behavior and are used in the design and evaluation of interventions.¹ The TPB is one of the most widely accepted behavior change theories and posits that attitudes, subjective norms (or social pressures and behaviors), and perceived behavioral control are significant predictors of an individual’s intention to engage in behaviors.² TPB has been highlighted in the literature as a validated theory for predicting nutrition and PA intentions and resulting behaviors.^3,4

Motivating lifestyle behavior change in clinical practice can be challenging, but some studies have demonstrated how providing genetic information and advice (or lifestyle genomics) can help motivate changes in nutrition and PA among patients.^5-7 Because this has yet to be explained using the TPB, this study is an important contribution to the literature as it aimed to determine the impact of providing genetically tailored and population-based lifestyle advice for weight management on key constructs of the TPB. Briefly, results from within-group analyses in this study demonstrated that the provision of genetically tailored lifestyle information and advice (via the GLB+LGx intervention) tended to impact antecedents of behavior change, more so over the long-term, while population-based advice (via the standard GLB intervention) tended to impact antecedents of behavior change over the short-term (eg, attitudes towards dietary fat intake, perceptions that friends and family consume a healthy diet, and perceptions about the impact of genetic-based advice for weight management). In addition, between-group differences in subjective norms observed at 12 months suggested that social pressures and norms may be influencing long-term changes in lifestyle habits.

While key strengths of this study include its pragmatic cluster randomized controlled trial design, 12-month intervention duration, and intent-to-treat analyses, there are some study limitations, which are acknowledged by the authors. Generalizability is limited to the demographic characteristics of the study population (ie, middle-aged, middle-income, Caucasian females enrolled in a lifestyle change weight management program). Thus, replication of the study is needed in more diverse study populations and with health-related outcomes beyond weight management. In addition, as the authors indicate, future research should ensure the inclusion of theory-based questionnaires in genetic-based intervention studies assessing lifestyle behavior change to elucidate theory-based mechanisms of change.

Applications for Clinical Practice

Population-based research has consistently indicated that nutrition interventions typically impact short-term dietary changes. Confronting the challenge of long-term adherence to nutrition and PA recommendations requires an understanding of factors impacting long-term motivation and behavior change. With increased attention on and research into genetically tailored lifestyle advice (or lifestyle genomics), it is important for clinical practitioners to be familiar with the evidence supporting these approaches. In addition, this research highlights the need to consider individual factors (attitudes, subjective norms, and perceived behavioral control) that may predict successful change in lifestyle habits when providing nutrition and PA recommendations, whether population-based or genetically tailored.

—Katrina F. Mateo, PhD, MPH

Study Overview

Objective. To investigate the potential of Hospital Fit (a smartphone application with an accelerometer) to enhance physical activity levels and functional recovery following orthopedic surgery.

Design. Nonrandomized, quasi-experimental pilot study.

Settings and participants. Patients scheduled for an elective total knee arthroplasty (TKA) or total hip arthroplasty (THA) at the orthopedic ward of Maastricht University Medical Center in Maastricht, the Netherlands, were invited to participate. Patients scheduled for surgery between January 2017 and December 2018 were recruited for the control group at a rate of 1 patient per week (due to a limited number of accelerometers available). After development of Hospital Fit was completed in December 2018 (and sufficient accelerators had become available), patients scheduled for surgery between February 2019 and May 2019 were recruited for the intervention group. The ratio of patients included in the control and intervention group was set at 2:1, respectively.

At preoperative physiotherapy screenings (scheduled 6 weeks before surgery), patients received verbal and written information about the study. Patients were eligible if they met the following inclusion criteria: receiving physiotherapy after elective TKA or THA; able to walk independently 2 weeks prior to surgery, as scored on the Functional Ambulation Categories (FAC > 3); were expected to be discharged to their own home; were aged 18 years and older; and had a sufficient understanding of the Dutch language. Exclusion criteria were: the presence of contraindications to walking or wearing an accelerometer on the upper leg; admission to the intensive care unit; impaired cognition (delirium/dementia), as reported by the attending doctor; a life expectancy of less than 3 months; and previous participation in this study. Patients were contacted on the day of their surgery, and written informed consent was obtained prior to the initiation of any study activities.

Intervention. Once enrolled, all patients followed a standardized clinical care pathway for TKA or THA (see original article for additional details). Postoperative physiotherapy was administered to all participating patients, starting within 4 hours after surgery. The physiotherapy treatment was aimed at increasing physical activity levels and enhancing functional recovery. Control group patients only received physiotherapy (twice daily, 30 minutes per session) and had their physical activity levels monitored with an accelerometer, without receiving feedback, until functional recovery was achieved, as measured with the modified Iowa Level of Assistance Scale (mILAS). Intervention group patients used Hospital Fit in addition to physiotherapy. Hospital Fit consists of a smartphone-based app, connected to a MOX activity monitor via Bluetooth (device contains a tri-axial accelerometer sensor in a small waterproof housing attached to the upper leg). Hospital Fit enables objective activity monitoring, provides patients and their physiotherapists insights and real-time feedback on the number of minutes spent standing and walking per day, and offers a tailored exercise program supported by videos aimed at stimulating self-management.

Measures. The primary outcome measure was the time spent physically active (total number of minutes standing and walking) per day until discharge. Physical activity was monitored 24 hours a day; days with ≥ 20 hours of wear time were considered valid measurement days and were included in the analysis. After the last treatment session, the accelerometer was removed, and the raw tri-axial accelerometer data were uploaded and processed to classify minutes as “active” (standing and walking) or “sedentary” (lying and sitting). The secondary outcome measures were the achievement of functional recovery on postoperative day 1 (POD1). Functional recovery was assessed by the physiotherapist during each treatment session using the mILAS and was reported in the electronic health record. In the intervention group, it was also reported in the app. The achievement of functional recovery on POD1 was defined as having reached a total mILAS-score of 0 on or before POD1, using a dichotomized outcome (0 = mILAS = 0 > POD1; 1 = mILAS = 0 ≤ POD1).

The independent variables measured were: Hospital Fit use (control versus the intervention group), age, sex, body mass index (BMI), type of surgery (TKA or THA), and comorbidities assessed by the American Society of Anesthesiologists (ASA) classification (ASA class ≤ 2 versus ASA class = 3; a higher score indicates being less fit for surgery). The medical and demographic data measured were the type of walking aid used and length of stay, with the day of surgery being defined as day 1.

Analysis. Data analysis was performed according to the intention-to-treat principle. Missing values were not substituted; drop-outs were not replaced. Descriptive statistics were presented as means (SD) or as 95% confidence intervals (CI) for continuous variables. The median and interquartile ranges (IQR) were used to present non-normally distributed data. The frequencies and percentages were used to present categorical variables. A multiple linear regression analysis was performed to determine the association between the time spent physically active per day and Hospital Fit use, corrected for potential confounding factors (age, sex, BMI, ASA class, and type of surgery). A multiple logistic regression analysis was performed additionally to determine the association between the achievement of functional recovery on POD1 and Hospital Fit use, corrected for potential confounding factors. For all statistical analyses, the level of significance was set at P < 0.05. All statistical analyses were performed using SPSS (version 23.0.0.2; IBM Corporation, Armonk, NY).

Main results. Ninety-seven patients were recruited; after excluding 9 patients because of missing data, 88 were included for analysis, with 61 (69%) in the control group and 27 (31%) in the intervention group. A median (IQR) number of 1.00 (0) valid measurement days (≥ 20 hr wear time) was collected. Physical activity data for 84 patients (95%) was available on POD1 (n = 61 control group, n = 23 intervention group). On postoperative day 2 (POD2), the majority of patients were discharged (n = 61, 69%), and data for only 23 patients (26%) were available (n = 17 control, n = 6 intervention). From postoperative day 3 to day 7, data of valid measurement days were available for just 1 patient (intervention group). Due to the large reduction in valid measurement days from POD2 onward, data from these days were not included in the analysis.

Results of the multiple linear regression analysis showed that, corrected for age, patients who used Hospital Fit stood and walked an average of 28.43 minutes (95% CI, 5.55-51.32) more on POD1 than patients who did not use Hospital Fit. Also, the model showed that an increase in age led to a decrease in the number of minutes standing and walking on POD1. The results of the multiple logistic regression analysis also showed that, corrected for ASA class, the odds of achieving functional recovery on POD1 were 3.08 times higher (95% CI, 1.14-8.31) for patients who used Hospital Fit compared to patients who did not use Hospital Fit. Including ASA class in the model shows that a lower ASA class increased the odds ratio for a functional recovery on POD1.

Conclusion. A smartphone app combined with an accelerometer demonstrates the potential to enhance patients’ physical activity levels and functional recovery during hospitalization following joint replacement surgery.

Commentary

Although the beneficial effects of physical activity during hospitalization after surgery are well documented, patients continue to spend between 92% and 96% of their time lying or sitting.^1-3 Therefore, strategies aimed at increasing the amount of time spent standing and walking are needed. Postoperative physiotherapy aims to enhance physical activity levels and functional recovery of activities of daily living, which are essential to function independently at home.^4-7 Physiotherapists may be able to advise patients more effectively on their physical activity behavior if continuous physical activity monitoring with real-time feedback is implemented in standard care. Although mobile health (mHealth) tools are being used to monitor physical activity in support of outpatient physiotherapy within the orthopedic rehabilitation pathway,^8-10 there is currently no mHealth tool available that offers hospitalized patients and their physiotherapists essential strategies to enhance their physical activity levels and support their recovery process. In addition, because hospitalized patients frequently use walking aids and often have impaired gait, the algorithm of most available activity monitors is not validated for use in this population.

This study, therefore, is an important contribution to the literature, as it describes a preliminary evaluation of a novel mHealth tool—Hospital Fit—consisting of a smartphone application connected to an accelerometer whose algorithm has been validated to differentiate between lying/sitting and standing/walking among hospitalized patients. Briefly, results from this study showed an increase in the time spent standing and walking, as well as higher odds of functional recovery on POD1 from the introduction of Hospital Fit. While guidelines on the recommended amount of physical activity during hospitalization do not yet exist, an average improvement of 28 minutes (39%) standing and walking on POD1 can be considered a clinically relevant contribution to prevent the negative effects of inactivity.

This study has limitations, particularly related to the study design, which is acknowledged by the authors. The current study was a nonrandomized, quasi-experimental pilot study implemented at a single medical center, and therefore, the results have limited generalizability and more importantly, may not only be attributable to the introduction of Hospital Fit. In addition, as there was lag in patient recruitment where patients were initially selected for the control group over the course of 1 year, followed by selection of patients for the intervention group over 4 months (once Hospital Fit was developed), it is possible that awareness on the importance of physical activity during hospitalization increased among patients and health care professionals, which may have resulted in a bias in favor of the intervention group (and thus a potentially slight overestimation of results). Also, as individual functionalities of Hospital Fit were not investigated, relationships between each functionality and physical activity could not be established. As the authors indicated, future research is needed to determine the effectiveness of Hospital Fit (ie, a larger, cluster randomized controlled trial in a population of hospitalized patients with a longer length of stay). This study design would also enable investigation of the effect of individual functionalities of Hospital Fit on physical activity.

Applications for Clinical Practice

mHealth tools have the potential to increase patient awareness, support personalized care, and stimulate self-management. This study highlights the potential for a novel mHealth tool—Hospital Fit—to improve the amount of physical activity and shorten the time to functional recovery in hospitalized patients following orthopedic surgery. Further, mHealth tools like Hospital Fit may have a greater impact when the hospital stay of a patient permits the use of the tool for a longer period of time. More broadly, continuous objective monitoring through mHealth tools may provide patients and their physiotherapists enhanced and more detailed data to support and create more personalized recovery goals and related strategies.

Katrina F. Mateo, PhD, MPH

Study Overview

Objective. To investigate the potential of Hospital Fit (a smartphone application with an accelerometer) to enhance physical activity levels and functional recovery following orthopedic surgery.

Design. Nonrandomized, quasi-experimental pilot study.

Settings and participants. Patients scheduled for an elective total knee arthroplasty (TKA) or total hip arthroplasty (THA) at the orthopedic ward of Maastricht University Medical Center in Maastricht, the Netherlands, were invited to participate. Patients scheduled for surgery between January 2017 and December 2018 were recruited for the control group at a rate of 1 patient per week (due to a limited number of accelerometers available). After development of Hospital Fit was completed in December 2018 (and sufficient accelerators had become available), patients scheduled for surgery between February 2019 and May 2019 were recruited for the intervention group. The ratio of patients included in the control and intervention group was set at 2:1, respectively.

At preoperative physiotherapy screenings (scheduled 6 weeks before surgery), patients received verbal and written information about the study. Patients were eligible if they met the following inclusion criteria: receiving physiotherapy after elective TKA or THA; able to walk independently 2 weeks prior to surgery, as scored on the Functional Ambulation Categories (FAC > 3); were expected to be discharged to their own home; were aged 18 years and older; and had a sufficient understanding of the Dutch language. Exclusion criteria were: the presence of contraindications to walking or wearing an accelerometer on the upper leg; admission to the intensive care unit; impaired cognition (delirium/dementia), as reported by the attending doctor; a life expectancy of less than 3 months; and previous participation in this study. Patients were contacted on the day of their surgery, and written informed consent was obtained prior to the initiation of any study activities.

Intervention. Once enrolled, all patients followed a standardized clinical care pathway for TKA or THA (see original article for additional details). Postoperative physiotherapy was administered to all participating patients, starting within 4 hours after surgery. The physiotherapy treatment was aimed at increasing physical activity levels and enhancing functional recovery. Control group patients only received physiotherapy (twice daily, 30 minutes per session) and had their physical activity levels monitored with an accelerometer, without receiving feedback, until functional recovery was achieved, as measured with the modified Iowa Level of Assistance Scale (mILAS). Intervention group patients used Hospital Fit in addition to physiotherapy. Hospital Fit consists of a smartphone-based app, connected to a MOX activity monitor via Bluetooth (device contains a tri-axial accelerometer sensor in a small waterproof housing attached to the upper leg). Hospital Fit enables objective activity monitoring, provides patients and their physiotherapists insights and real-time feedback on the number of minutes spent standing and walking per day, and offers a tailored exercise program supported by videos aimed at stimulating self-management.

Measures. The primary outcome measure was the time spent physically active (total number of minutes standing and walking) per day until discharge. Physical activity was monitored 24 hours a day; days with ≥ 20 hours of wear time were considered valid measurement days and were included in the analysis. After the last treatment session, the accelerometer was removed, and the raw tri-axial accelerometer data were uploaded and processed to classify minutes as “active” (standing and walking) or “sedentary” (lying and sitting). The secondary outcome measures were the achievement of functional recovery on postoperative day 1 (POD1). Functional recovery was assessed by the physiotherapist during each treatment session using the mILAS and was reported in the electronic health record. In the intervention group, it was also reported in the app. The achievement of functional recovery on POD1 was defined as having reached a total mILAS-score of 0 on or before POD1, using a dichotomized outcome (0 = mILAS = 0 > POD1; 1 = mILAS = 0 ≤ POD1).

The independent variables measured were: Hospital Fit use (control versus the intervention group), age, sex, body mass index (BMI), type of surgery (TKA or THA), and comorbidities assessed by the American Society of Anesthesiologists (ASA) classification (ASA class ≤ 2 versus ASA class = 3; a higher score indicates being less fit for surgery). The medical and demographic data measured were the type of walking aid used and length of stay, with the day of surgery being defined as day 1.

Analysis. Data analysis was performed according to the intention-to-treat principle. Missing values were not substituted; drop-outs were not replaced. Descriptive statistics were presented as means (SD) or as 95% confidence intervals (CI) for continuous variables. The median and interquartile ranges (IQR) were used to present non-normally distributed data. The frequencies and percentages were used to present categorical variables. A multiple linear regression analysis was performed to determine the association between the time spent physically active per day and Hospital Fit use, corrected for potential confounding factors (age, sex, BMI, ASA class, and type of surgery). A multiple logistic regression analysis was performed additionally to determine the association between the achievement of functional recovery on POD1 and Hospital Fit use, corrected for potential confounding factors. For all statistical analyses, the level of significance was set at P < 0.05. All statistical analyses were performed using SPSS (version 23.0.0.2; IBM Corporation, Armonk, NY).

Main results. Ninety-seven patients were recruited; after excluding 9 patients because of missing data, 88 were included for analysis, with 61 (69%) in the control group and 27 (31%) in the intervention group. A median (IQR) number of 1.00 (0) valid measurement days (≥ 20 hr wear time) was collected. Physical activity data for 84 patients (95%) was available on POD1 (n = 61 control group, n = 23 intervention group). On postoperative day 2 (POD2), the majority of patients were discharged (n = 61, 69%), and data for only 23 patients (26%) were available (n = 17 control, n = 6 intervention). From postoperative day 3 to day 7, data of valid measurement days were available for just 1 patient (intervention group). Due to the large reduction in valid measurement days from POD2 onward, data from these days were not included in the analysis.

Results of the multiple linear regression analysis showed that, corrected for age, patients who used Hospital Fit stood and walked an average of 28.43 minutes (95% CI, 5.55-51.32) more on POD1 than patients who did not use Hospital Fit. Also, the model showed that an increase in age led to a decrease in the number of minutes standing and walking on POD1. The results of the multiple logistic regression analysis also showed that, corrected for ASA class, the odds of achieving functional recovery on POD1 were 3.08 times higher (95% CI, 1.14-8.31) for patients who used Hospital Fit compared to patients who did not use Hospital Fit. Including ASA class in the model shows that a lower ASA class increased the odds ratio for a functional recovery on POD1.

Conclusion. A smartphone app combined with an accelerometer demonstrates the potential to enhance patients’ physical activity levels and functional recovery during hospitalization following joint replacement surgery.

Commentary

Although the beneficial effects of physical activity during hospitalization after surgery are well documented, patients continue to spend between 92% and 96% of their time lying or sitting.^1-3 Therefore, strategies aimed at increasing the amount of time spent standing and walking are needed. Postoperative physiotherapy aims to enhance physical activity levels and functional recovery of activities of daily living, which are essential to function independently at home.^4-7 Physiotherapists may be able to advise patients more effectively on their physical activity behavior if continuous physical activity monitoring with real-time feedback is implemented in standard care. Although mobile health (mHealth) tools are being used to monitor physical activity in support of outpatient physiotherapy within the orthopedic rehabilitation pathway,^8-10 there is currently no mHealth tool available that offers hospitalized patients and their physiotherapists essential strategies to enhance their physical activity levels and support their recovery process. In addition, because hospitalized patients frequently use walking aids and often have impaired gait, the algorithm of most available activity monitors is not validated for use in this population.

This study, therefore, is an important contribution to the literature, as it describes a preliminary evaluation of a novel mHealth tool—Hospital Fit—consisting of a smartphone application connected to an accelerometer whose algorithm has been validated to differentiate between lying/sitting and standing/walking among hospitalized patients. Briefly, results from this study showed an increase in the time spent standing and walking, as well as higher odds of functional recovery on POD1 from the introduction of Hospital Fit. While guidelines on the recommended amount of physical activity during hospitalization do not yet exist, an average improvement of 28 minutes (39%) standing and walking on POD1 can be considered a clinically relevant contribution to prevent the negative effects of inactivity.

This study has limitations, particularly related to the study design, which is acknowledged by the authors. The current study was a nonrandomized, quasi-experimental pilot study implemented at a single medical center, and therefore, the results have limited generalizability and more importantly, may not only be attributable to the introduction of Hospital Fit. In addition, as there was lag in patient recruitment where patients were initially selected for the control group over the course of 1 year, followed by selection of patients for the intervention group over 4 months (once Hospital Fit was developed), it is possible that awareness on the importance of physical activity during hospitalization increased among patients and health care professionals, which may have resulted in a bias in favor of the intervention group (and thus a potentially slight overestimation of results). Also, as individual functionalities of Hospital Fit were not investigated, relationships between each functionality and physical activity could not be established. As the authors indicated, future research is needed to determine the effectiveness of Hospital Fit (ie, a larger, cluster randomized controlled trial in a population of hospitalized patients with a longer length of stay). This study design would also enable investigation of the effect of individual functionalities of Hospital Fit on physical activity.

Applications for Clinical Practice

mHealth tools have the potential to increase patient awareness, support personalized care, and stimulate self-management. This study highlights the potential for a novel mHealth tool—Hospital Fit—to improve the amount of physical activity and shorten the time to functional recovery in hospitalized patients following orthopedic surgery. Further, mHealth tools like Hospital Fit may have a greater impact when the hospital stay of a patient permits the use of the tool for a longer period of time. More broadly, continuous objective monitoring through mHealth tools may provide patients and their physiotherapists enhanced and more detailed data to support and create more personalized recovery goals and related strategies.

Katrina F. Mateo, PhD, MPH

Study Overview

Objective. To investigate the potential of Hospital Fit (a smartphone application with an accelerometer) to enhance physical activity levels and functional recovery following orthopedic surgery.

Design. Nonrandomized, quasi-experimental pilot study.

Settings and participants. Patients scheduled for an elective total knee arthroplasty (TKA) or total hip arthroplasty (THA) at the orthopedic ward of Maastricht University Medical Center in Maastricht, the Netherlands, were invited to participate. Patients scheduled for surgery between January 2017 and December 2018 were recruited for the control group at a rate of 1 patient per week (due to a limited number of accelerometers available). After development of Hospital Fit was completed in December 2018 (and sufficient accelerators had become available), patients scheduled for surgery between February 2019 and May 2019 were recruited for the intervention group. The ratio of patients included in the control and intervention group was set at 2:1, respectively.

At preoperative physiotherapy screenings (scheduled 6 weeks before surgery), patients received verbal and written information about the study. Patients were eligible if they met the following inclusion criteria: receiving physiotherapy after elective TKA or THA; able to walk independently 2 weeks prior to surgery, as scored on the Functional Ambulation Categories (FAC > 3); were expected to be discharged to their own home; were aged 18 years and older; and had a sufficient understanding of the Dutch language. Exclusion criteria were: the presence of contraindications to walking or wearing an accelerometer on the upper leg; admission to the intensive care unit; impaired cognition (delirium/dementia), as reported by the attending doctor; a life expectancy of less than 3 months; and previous participation in this study. Patients were contacted on the day of their surgery, and written informed consent was obtained prior to the initiation of any study activities.

Intervention. Once enrolled, all patients followed a standardized clinical care pathway for TKA or THA (see original article for additional details). Postoperative physiotherapy was administered to all participating patients, starting within 4 hours after surgery. The physiotherapy treatment was aimed at increasing physical activity levels and enhancing functional recovery. Control group patients only received physiotherapy (twice daily, 30 minutes per session) and had their physical activity levels monitored with an accelerometer, without receiving feedback, until functional recovery was achieved, as measured with the modified Iowa Level of Assistance Scale (mILAS). Intervention group patients used Hospital Fit in addition to physiotherapy. Hospital Fit consists of a smartphone-based app, connected to a MOX activity monitor via Bluetooth (device contains a tri-axial accelerometer sensor in a small waterproof housing attached to the upper leg). Hospital Fit enables objective activity monitoring, provides patients and their physiotherapists insights and real-time feedback on the number of minutes spent standing and walking per day, and offers a tailored exercise program supported by videos aimed at stimulating self-management.

Measures. The primary outcome measure was the time spent physically active (total number of minutes standing and walking) per day until discharge. Physical activity was monitored 24 hours a day; days with ≥ 20 hours of wear time were considered valid measurement days and were included in the analysis. After the last treatment session, the accelerometer was removed, and the raw tri-axial accelerometer data were uploaded and processed to classify minutes as “active” (standing and walking) or “sedentary” (lying and sitting). The secondary outcome measures were the achievement of functional recovery on postoperative day 1 (POD1). Functional recovery was assessed by the physiotherapist during each treatment session using the mILAS and was reported in the electronic health record. In the intervention group, it was also reported in the app. The achievement of functional recovery on POD1 was defined as having reached a total mILAS-score of 0 on or before POD1, using a dichotomized outcome (0 = mILAS = 0 > POD1; 1 = mILAS = 0 ≤ POD1).

The independent variables measured were: Hospital Fit use (control versus the intervention group), age, sex, body mass index (BMI), type of surgery (TKA or THA), and comorbidities assessed by the American Society of Anesthesiologists (ASA) classification (ASA class ≤ 2 versus ASA class = 3; a higher score indicates being less fit for surgery). The medical and demographic data measured were the type of walking aid used and length of stay, with the day of surgery being defined as day 1.

Analysis. Data analysis was performed according to the intention-to-treat principle. Missing values were not substituted; drop-outs were not replaced. Descriptive statistics were presented as means (SD) or as 95% confidence intervals (CI) for continuous variables. The median and interquartile ranges (IQR) were used to present non-normally distributed data. The frequencies and percentages were used to present categorical variables. A multiple linear regression analysis was performed to determine the association between the time spent physically active per day and Hospital Fit use, corrected for potential confounding factors (age, sex, BMI, ASA class, and type of surgery). A multiple logistic regression analysis was performed additionally to determine the association between the achievement of functional recovery on POD1 and Hospital Fit use, corrected for potential confounding factors. For all statistical analyses, the level of significance was set at P < 0.05. All statistical analyses were performed using SPSS (version 23.0.0.2; IBM Corporation, Armonk, NY).

Main results. Ninety-seven patients were recruited; after excluding 9 patients because of missing data, 88 were included for analysis, with 61 (69%) in the control group and 27 (31%) in the intervention group. A median (IQR) number of 1.00 (0) valid measurement days (≥ 20 hr wear time) was collected. Physical activity data for 84 patients (95%) was available on POD1 (n = 61 control group, n = 23 intervention group). On postoperative day 2 (POD2), the majority of patients were discharged (n = 61, 69%), and data for only 23 patients (26%) were available (n = 17 control, n = 6 intervention). From postoperative day 3 to day 7, data of valid measurement days were available for just 1 patient (intervention group). Due to the large reduction in valid measurement days from POD2 onward, data from these days were not included in the analysis.

Results of the multiple linear regression analysis showed that, corrected for age, patients who used Hospital Fit stood and walked an average of 28.43 minutes (95% CI, 5.55-51.32) more on POD1 than patients who did not use Hospital Fit. Also, the model showed that an increase in age led to a decrease in the number of minutes standing and walking on POD1. The results of the multiple logistic regression analysis also showed that, corrected for ASA class, the odds of achieving functional recovery on POD1 were 3.08 times higher (95% CI, 1.14-8.31) for patients who used Hospital Fit compared to patients who did not use Hospital Fit. Including ASA class in the model shows that a lower ASA class increased the odds ratio for a functional recovery on POD1.

Conclusion. A smartphone app combined with an accelerometer demonstrates the potential to enhance patients’ physical activity levels and functional recovery during hospitalization following joint replacement surgery.

Commentary

Although the beneficial effects of physical activity during hospitalization after surgery are well documented, patients continue to spend between 92% and 96% of their time lying or sitting.^1-3 Therefore, strategies aimed at increasing the amount of time spent standing and walking are needed. Postoperative physiotherapy aims to enhance physical activity levels and functional recovery of activities of daily living, which are essential to function independently at home.^4-7 Physiotherapists may be able to advise patients more effectively on their physical activity behavior if continuous physical activity monitoring with real-time feedback is implemented in standard care. Although mobile health (mHealth) tools are being used to monitor physical activity in support of outpatient physiotherapy within the orthopedic rehabilitation pathway,^8-10 there is currently no mHealth tool available that offers hospitalized patients and their physiotherapists essential strategies to enhance their physical activity levels and support their recovery process. In addition, because hospitalized patients frequently use walking aids and often have impaired gait, the algorithm of most available activity monitors is not validated for use in this population.

This study, therefore, is an important contribution to the literature, as it describes a preliminary evaluation of a novel mHealth tool—Hospital Fit—consisting of a smartphone application connected to an accelerometer whose algorithm has been validated to differentiate between lying/sitting and standing/walking among hospitalized patients. Briefly, results from this study showed an increase in the time spent standing and walking, as well as higher odds of functional recovery on POD1 from the introduction of Hospital Fit. While guidelines on the recommended amount of physical activity during hospitalization do not yet exist, an average improvement of 28 minutes (39%) standing and walking on POD1 can be considered a clinically relevant contribution to prevent the negative effects of inactivity.

This study has limitations, particularly related to the study design, which is acknowledged by the authors. The current study was a nonrandomized, quasi-experimental pilot study implemented at a single medical center, and therefore, the results have limited generalizability and more importantly, may not only be attributable to the introduction of Hospital Fit. In addition, as there was lag in patient recruitment where patients were initially selected for the control group over the course of 1 year, followed by selection of patients for the intervention group over 4 months (once Hospital Fit was developed), it is possible that awareness on the importance of physical activity during hospitalization increased among patients and health care professionals, which may have resulted in a bias in favor of the intervention group (and thus a potentially slight overestimation of results). Also, as individual functionalities of Hospital Fit were not investigated, relationships between each functionality and physical activity could not be established. As the authors indicated, future research is needed to determine the effectiveness of Hospital Fit (ie, a larger, cluster randomized controlled trial in a population of hospitalized patients with a longer length of stay). This study design would also enable investigation of the effect of individual functionalities of Hospital Fit on physical activity.

Applications for Clinical Practice

mHealth tools have the potential to increase patient awareness, support personalized care, and stimulate self-management. This study highlights the potential for a novel mHealth tool—Hospital Fit—to improve the amount of physical activity and shorten the time to functional recovery in hospitalized patients following orthopedic surgery. Further, mHealth tools like Hospital Fit may have a greater impact when the hospital stay of a patient permits the use of the tool for a longer period of time. More broadly, continuous objective monitoring through mHealth tools may provide patients and their physiotherapists enhanced and more detailed data to support and create more personalized recovery goals and related strategies.

Katrina F. Mateo, PhD, MPH

Study Overview

Objective. To compare the impact of conventional versus telemedicine follow-up of general surgery patients in outpatient clinics.

Design. Prospective randomized clinical trial.

Setting and participants. Participants were recruited from Hospital Germans Trias i Pujol, a tertiary care university hospital located in the outskirts of Barcelona (Catalonia, Spain). To be included in this study, participants had to have been treated in the general surgery department, have basic computer knowledge (ability to use e-mail or a social network), have a computer with webcam, and be 18 to 75 years of age, or they had to have a partner who met these criteria. Exclusion criteria included any disability making telemedicine follow-up impossible (eg, blindness, deafness, or mental disability; proctologic treatment; difficulty describing and/or showing complications in the surgical area; and clinical complications before discharge more severe than Clavien Dindo II), as well as withdrawal of consent. Patients who met the criteria and had just been discharged from the hospital were offered the opportunity to enroll by the surgeon in charge. Patients who agreed to participate provided informed consent and were assigned using a computerized block randomization list (allocation ratio 1:1).

Intervention. Time to visit was generally between 2 and 4 weeks after discharge (the interval to the follow-up visit was determined at the discretion of the treating surgeon, but always followed the usual schedule). To conduct the telemedicine follow-up through a video call, a medical cloud-based program fulfilling all European Union security and privacy policies was used. Four surgeons were assigned to perform the telemedicine visits and were trained on how to use the program before the study started. Visit format was the same in both groups: clinical and wound condition were assessed and pathology was discussed (the one difference was that physical exploration was not performed in the telemedicine group).

Main outcome measures. The primary outcome was the feasibility of telemedicine follow-up, and this was measured as the percentage of participants who completed follow-up in their corresponding group by the date scheduled at hospital discharge. Secondary outcomes included a comparison of clinical results and patient satisfaction. To assess the clinical results, extra visits to an outpatient clinic and/or the emergency department during the first 30 days after the follow-up visit were collected.

To evaluate patient satisfaction, a questionnaire was sent via email to the participants after the visit and, if they did not respond, a telephone survey was carried out (if there was no contact after 2 telephone calls, the participants was considered a missing value). The questionnaire was informed by the United Kingdom National Health Service outpatients questionnaire and the Telehealth Usability Questionnaire. It included 27 general questions asked of participants in both groups, plus 8 specific questions for participants in the conventional follow-up group and 14 specific questions for participants in the telemedicine group. To summarize all the included fields in the questionnaires (time to visit and visit length, comfort, tests and procedures performed before and during the visit, transport, waiting time, privacy, dealings with staff, platform usability, telemedicine, and satisfaction), participants were asked to provide a global satisfaction score on a scale from 1 to 5.

Analysis. To compare the groups in terms of proportion of outcomes, a chi-square test was used to analyze categorical variables. To compare medians between the groups, ordinal variables were analyzed using the Mann-Whitney U test. Statistical significance was set at P < 0.05.

Main results. Two-hundred patients were randomly allocated to 1 of the 2 groups, with 100 patients in each group. The groups did not differ significantly based on age (P = 0.836), gender (P = 0.393), or American Society of Anesthesiologists (ASA) score (P = 0.232). Time to visit did not differ significantly between the groups (P = 0.169), and while visits were generally shorter in the telemedicine group, the difference was not significant (P = 0.153). Diagnoses and treatments did not differ significantly between the groups (P = 0.853 and P = 0.461, respectively).

The primary outcome (follow-up feasibility) was achieved in 90% of the conventional follow-up group and in 74% of the telemedicine group (P = 0.003). Of the 10 patients in the conventional follow-up group who did not complete the follow-up, 8 did not attend the visit on the scheduled day and 2 were hospitalized for reasons not related to the study. In the telemedicine group, the 2 main reasons for failure to follow-up were technical difficulties (n = 10) and requests by patients to attend a conventional visit after being allocated to the telemedicine group (n = 10). Among the remaining 6 patients in the telemedicine group who did not attend a visit, 3 visited the outpatient clinic because of a known surgical wound infection before the visit, 2 did not respond to the video call and could not be contacted by other means, and 1 had other face-to-face visits scheduled in different departments of the hospital the same day as the telemedicine appointment.

There were no statistically significant differences in the clinical results of the 164 patients meeting the primary endpoint (P = 0.832). Twelve of the 90 (13.3%) patients in the conventional group attended extra visits after the follow-up, while 9 of the 74 patients (12.1%) in the telemedicine group (P = 0.823) attended extra visits after follow-up. The median global patient satisfaction score was 5 in both the conventional group (range, 2-5) and the telemedicine group (range, 1-5), with no statistically significant differences (P = 0.099). When patients in the telemedicine group were asked if they would accept the use of telemedicine as part of their medical treatment on an ongoing basis, they rated the proposition with a median score of 5 (range, 1-5).

Conclusion. Telemedicine is a feasible and acceptable complementary service to facilitate postoperative management in selected general surgery patients. This option produces good satisfaction rates and maintains clinical outcomes.

Commentary

In recent years, telemedicine has gained increased popularity in both medicine and surgery, affording surgeons greater opportunities for patient care, mentoring, collaboration, and teaching, without the limits of geographic boundaries. Telemedicine can be broadly described as a health care service utilizing telecommunication technologies for the purpose of communicating with and diagnosing and treating patients remotely.^1-4 To date, literature on telemedicine in surgical care has been limited.

In their systematic review, published in 2018, Asiri et al identified 24 studies published between 1998 and 2018, which included 3 randomized controlled trials, 3 pilot studies, 4 retrospective studies, and 14 prospective observational studies. In these studies, telemedicine protocols were used for preoperative assessment, diagnostic purposes, or consultation with another surgical department (10 studies); postoperative wound assessment (9 studies); and follow-up in place of conventional clinic visits (5 studies).³ In a 2017 systematic review of telemedicine for post-discharge surgical care, Gunter et al identified 21 studies, which included 3 randomized controlled trials, 6 pilot or feasibility studies, 4 retrospective record reviews, 2 case series, and 6 surveys.⁴ In these studies, telemedicine protocols were used for scheduled follow-up (10 studies), routine and ongoing monitoring (5 studies), or management of issues that arose after surgery (2 studies). These 2 reviews found telemedicine to be feasible, useful, and acceptable for postoperative evaluation and follow-up among both providers and patients.

Additional benefits noted in these studies included savings in patient travel, time, and cost. Perspectives on savings to the health system were mixed—while clinic time slots may open as a result of follow-up visits being done via telemedicine (resulting in potential improvements in access to surgical services and decreased wait times), there are still significant direct costs for purchasing necessary equipment and for educating and training providers on the use of the equipment. Other published reviews have discussed in greater detail the application, benefits, limitations, and barriers to telemedicine and provided insight from the perspectives of patients, providers, and health care systems.^1,2

Because studies on the use of telemedicine are limited, particularly in general surgery, and few of these studies have used a randomized clinical trial design, the present study is an important contribution to the literature. The authors found a significant difference between groups in terms of percentage of completed follow-up visits—90% of conventional follow-up group participants completed their visit versus 74% of telemedicine group participants. However, these differences were primarily attributed to technical difficulties experienced by telemedicine group participants, as well requests to have a conventional follow-up visit. In addition, telemedicine capabilities were limited to video calls via computers and webcams, and it is likely that successful completion of the follow-up visit would have been higher in the telemedicine group had the use of video calls via tablets or smartphones been an option. Perhaps more important, no significant differences were found in clinical outcomes (extra visits within 30 days after the follow-up visit) or patient satisfaction.

A key strength of this study is the use of a randomized clinical trial design to evaluate telemedicine as an alternative method for conducting patient visits following general surgery. Inclusion and exclusion criteria did not impose strict limitations on potential participants. Also, the authors evaluated differences in time to visit, length of visit, clinical results, and patient satisfaction between groups, in addition to the primary measure of completion of the follow-up visit.

This study has important limitations that should be noted as well, particularly related to the study design, some of which are acknowledged by the authors. Because this study was implemented in only 1 hospital, specifically, a tertiary care university hospital on the outskirts of an urban European city, the generalizability of the findings is limited. Also, the likelihood of selection bias is high, as enrollment was not offered to all patients who were discharged from the hospital and met inclusion criteria (limited by patient workload). The comparison of clinical results was limited, as the selected measure focused only on extra visits to an outpatient clinic and/or the emergency department during the first 30 days after the follow-up visit. This chosen measure does not account for less severe clinical results that did not require an additional visit, and does not represent a nuanced comparison of specific clinical indicators. In addition, this measure does not account for clinical complications that may have occurred beyond the 30-day period. Recall bias also was likely, given that the patient satisfaction questionnaire was delivered via email to patients at a later time after the follow-up visit, instead of being administered immediately after the visit. Last, group differences at baseline were assessed based only on age, gender, and ASA score, which does not preclude potential differences related to other factors, such as race/ethnicity, household income, comorbidities, insurance, and zip code. Future research with a similar objective would benefit from a randomized clinical trial design that recruits a wider diversity of patients across different clinic settings and incorporates more nuanced measures of primary and secondary outcomes.

Applications for Clinical Practice

With the ongoing COVID-19 pandemic, the integration of telemedicine capabilities into hospital systems is becoming more widespread and is proceeding at an accelerated pace. This study provides evidence that telemedicine is a feasible and acceptable complementary service to facilitate postoperative management in selected general surgery patients. Assuming that the needed technology and appropriate program training are available, telemedicine should be offered to patients, especially to maximize savings in terms of travel, time, and cost. However, the option for conventional (in-person) follow-up should remain, particularly in cases where there may be barriers to successful follow-up visits via telemedicine, including limited digital literacy, lack of access to necessary equipment, language/communication barriers, complex follow-up treatment, and difficulties in describing or showing complications in the surgical area.

–Katrina F. Mateo, PhD, MPH

Study Overview

Objective. To compare the impact of conventional versus telemedicine follow-up of general surgery patients in outpatient clinics.

Design. Prospective randomized clinical trial.

Setting and participants. Participants were recruited from Hospital Germans Trias i Pujol, a tertiary care university hospital located in the outskirts of Barcelona (Catalonia, Spain). To be included in this study, participants had to have been treated in the general surgery department, have basic computer knowledge (ability to use e-mail or a social network), have a computer with webcam, and be 18 to 75 years of age, or they had to have a partner who met these criteria. Exclusion criteria included any disability making telemedicine follow-up impossible (eg, blindness, deafness, or mental disability; proctologic treatment; difficulty describing and/or showing complications in the surgical area; and clinical complications before discharge more severe than Clavien Dindo II), as well as withdrawal of consent. Patients who met the criteria and had just been discharged from the hospital were offered the opportunity to enroll by the surgeon in charge. Patients who agreed to participate provided informed consent and were assigned using a computerized block randomization list (allocation ratio 1:1).

Intervention. Time to visit was generally between 2 and 4 weeks after discharge (the interval to the follow-up visit was determined at the discretion of the treating surgeon, but always followed the usual schedule). To conduct the telemedicine follow-up through a video call, a medical cloud-based program fulfilling all European Union security and privacy policies was used. Four surgeons were assigned to perform the telemedicine visits and were trained on how to use the program before the study started. Visit format was the same in both groups: clinical and wound condition were assessed and pathology was discussed (the one difference was that physical exploration was not performed in the telemedicine group).

Main outcome measures. The primary outcome was the feasibility of telemedicine follow-up, and this was measured as the percentage of participants who completed follow-up in their corresponding group by the date scheduled at hospital discharge. Secondary outcomes included a comparison of clinical results and patient satisfaction. To assess the clinical results, extra visits to an outpatient clinic and/or the emergency department during the first 30 days after the follow-up visit were collected.

To evaluate patient satisfaction, a questionnaire was sent via email to the participants after the visit and, if they did not respond, a telephone survey was carried out (if there was no contact after 2 telephone calls, the participants was considered a missing value). The questionnaire was informed by the United Kingdom National Health Service outpatients questionnaire and the Telehealth Usability Questionnaire. It included 27 general questions asked of participants in both groups, plus 8 specific questions for participants in the conventional follow-up group and 14 specific questions for participants in the telemedicine group. To summarize all the included fields in the questionnaires (time to visit and visit length, comfort, tests and procedures performed before and during the visit, transport, waiting time, privacy, dealings with staff, platform usability, telemedicine, and satisfaction), participants were asked to provide a global satisfaction score on a scale from 1 to 5.

Analysis. To compare the groups in terms of proportion of outcomes, a chi-square test was used to analyze categorical variables. To compare medians between the groups, ordinal variables were analyzed using the Mann-Whitney U test. Statistical significance was set at P < 0.05.

Main results. Two-hundred patients were randomly allocated to 1 of the 2 groups, with 100 patients in each group. The groups did not differ significantly based on age (P = 0.836), gender (P = 0.393), or American Society of Anesthesiologists (ASA) score (P = 0.232). Time to visit did not differ significantly between the groups (P = 0.169), and while visits were generally shorter in the telemedicine group, the difference was not significant (P = 0.153). Diagnoses and treatments did not differ significantly between the groups (P = 0.853 and P = 0.461, respectively).

The primary outcome (follow-up feasibility) was achieved in 90% of the conventional follow-up group and in 74% of the telemedicine group (P = 0.003). Of the 10 patients in the conventional follow-up group who did not complete the follow-up, 8 did not attend the visit on the scheduled day and 2 were hospitalized for reasons not related to the study. In the telemedicine group, the 2 main reasons for failure to follow-up were technical difficulties (n = 10) and requests by patients to attend a conventional visit after being allocated to the telemedicine group (n = 10). Among the remaining 6 patients in the telemedicine group who did not attend a visit, 3 visited the outpatient clinic because of a known surgical wound infection before the visit, 2 did not respond to the video call and could not be contacted by other means, and 1 had other face-to-face visits scheduled in different departments of the hospital the same day as the telemedicine appointment.

There were no statistically significant differences in the clinical results of the 164 patients meeting the primary endpoint (P = 0.832). Twelve of the 90 (13.3%) patients in the conventional group attended extra visits after the follow-up, while 9 of the 74 patients (12.1%) in the telemedicine group (P = 0.823) attended extra visits after follow-up. The median global patient satisfaction score was 5 in both the conventional group (range, 2-5) and the telemedicine group (range, 1-5), with no statistically significant differences (P = 0.099). When patients in the telemedicine group were asked if they would accept the use of telemedicine as part of their medical treatment on an ongoing basis, they rated the proposition with a median score of 5 (range, 1-5).

Conclusion. Telemedicine is a feasible and acceptable complementary service to facilitate postoperative management in selected general surgery patients. This option produces good satisfaction rates and maintains clinical outcomes.

Commentary

In recent years, telemedicine has gained increased popularity in both medicine and surgery, affording surgeons greater opportunities for patient care, mentoring, collaboration, and teaching, without the limits of geographic boundaries. Telemedicine can be broadly described as a health care service utilizing telecommunication technologies for the purpose of communicating with and diagnosing and treating patients remotely.^1-4 To date, literature on telemedicine in surgical care has been limited.

In their systematic review, published in 2018, Asiri et al identified 24 studies published between 1998 and 2018, which included 3 randomized controlled trials, 3 pilot studies, 4 retrospective studies, and 14 prospective observational studies. In these studies, telemedicine protocols were used for preoperative assessment, diagnostic purposes, or consultation with another surgical department (10 studies); postoperative wound assessment (9 studies); and follow-up in place of conventional clinic visits (5 studies).³ In a 2017 systematic review of telemedicine for post-discharge surgical care, Gunter et al identified 21 studies, which included 3 randomized controlled trials, 6 pilot or feasibility studies, 4 retrospective record reviews, 2 case series, and 6 surveys.⁴ In these studies, telemedicine protocols were used for scheduled follow-up (10 studies), routine and ongoing monitoring (5 studies), or management of issues that arose after surgery (2 studies). These 2 reviews found telemedicine to be feasible, useful, and acceptable for postoperative evaluation and follow-up among both providers and patients.

Additional benefits noted in these studies included savings in patient travel, time, and cost. Perspectives on savings to the health system were mixed—while clinic time slots may open as a result of follow-up visits being done via telemedicine (resulting in potential improvements in access to surgical services and decreased wait times), there are still significant direct costs for purchasing necessary equipment and for educating and training providers on the use of the equipment. Other published reviews have discussed in greater detail the application, benefits, limitations, and barriers to telemedicine and provided insight from the perspectives of patients, providers, and health care systems.^1,2

Because studies on the use of telemedicine are limited, particularly in general surgery, and few of these studies have used a randomized clinical trial design, the present study is an important contribution to the literature. The authors found a significant difference between groups in terms of percentage of completed follow-up visits—90% of conventional follow-up group participants completed their visit versus 74% of telemedicine group participants. However, these differences were primarily attributed to technical difficulties experienced by telemedicine group participants, as well requests to have a conventional follow-up visit. In addition, telemedicine capabilities were limited to video calls via computers and webcams, and it is likely that successful completion of the follow-up visit would have been higher in the telemedicine group had the use of video calls via tablets or smartphones been an option. Perhaps more important, no significant differences were found in clinical outcomes (extra visits within 30 days after the follow-up visit) or patient satisfaction.

A key strength of this study is the use of a randomized clinical trial design to evaluate telemedicine as an alternative method for conducting patient visits following general surgery. Inclusion and exclusion criteria did not impose strict limitations on potential participants. Also, the authors evaluated differences in time to visit, length of visit, clinical results, and patient satisfaction between groups, in addition to the primary measure of completion of the follow-up visit.

This study has important limitations that should be noted as well, particularly related to the study design, some of which are acknowledged by the authors. Because this study was implemented in only 1 hospital, specifically, a tertiary care university hospital on the outskirts of an urban European city, the generalizability of the findings is limited. Also, the likelihood of selection bias is high, as enrollment was not offered to all patients who were discharged from the hospital and met inclusion criteria (limited by patient workload). The comparison of clinical results was limited, as the selected measure focused only on extra visits to an outpatient clinic and/or the emergency department during the first 30 days after the follow-up visit. This chosen measure does not account for less severe clinical results that did not require an additional visit, and does not represent a nuanced comparison of specific clinical indicators. In addition, this measure does not account for clinical complications that may have occurred beyond the 30-day period. Recall bias also was likely, given that the patient satisfaction questionnaire was delivered via email to patients at a later time after the follow-up visit, instead of being administered immediately after the visit. Last, group differences at baseline were assessed based only on age, gender, and ASA score, which does not preclude potential differences related to other factors, such as race/ethnicity, household income, comorbidities, insurance, and zip code. Future research with a similar objective would benefit from a randomized clinical trial design that recruits a wider diversity of patients across different clinic settings and incorporates more nuanced measures of primary and secondary outcomes.

Applications for Clinical Practice

With the ongoing COVID-19 pandemic, the integration of telemedicine capabilities into hospital systems is becoming more widespread and is proceeding at an accelerated pace. This study provides evidence that telemedicine is a feasible and acceptable complementary service to facilitate postoperative management in selected general surgery patients. Assuming that the needed technology and appropriate program training are available, telemedicine should be offered to patients, especially to maximize savings in terms of travel, time, and cost. However, the option for conventional (in-person) follow-up should remain, particularly in cases where there may be barriers to successful follow-up visits via telemedicine, including limited digital literacy, lack of access to necessary equipment, language/communication barriers, complex follow-up treatment, and difficulties in describing or showing complications in the surgical area.

–Katrina F. Mateo, PhD, MPH

Study Overview

Objective. To compare the impact of conventional versus telemedicine follow-up of general surgery patients in outpatient clinics.

Design. Prospective randomized clinical trial.

Setting and participants. Participants were recruited from Hospital Germans Trias i Pujol, a tertiary care university hospital located in the outskirts of Barcelona (Catalonia, Spain). To be included in this study, participants had to have been treated in the general surgery department, have basic computer knowledge (ability to use e-mail or a social network), have a computer with webcam, and be 18 to 75 years of age, or they had to have a partner who met these criteria. Exclusion criteria included any disability making telemedicine follow-up impossible (eg, blindness, deafness, or mental disability; proctologic treatment; difficulty describing and/or showing complications in the surgical area; and clinical complications before discharge more severe than Clavien Dindo II), as well as withdrawal of consent. Patients who met the criteria and had just been discharged from the hospital were offered the opportunity to enroll by the surgeon in charge. Patients who agreed to participate provided informed consent and were assigned using a computerized block randomization list (allocation ratio 1:1).

Intervention. Time to visit was generally between 2 and 4 weeks after discharge (the interval to the follow-up visit was determined at the discretion of the treating surgeon, but always followed the usual schedule). To conduct the telemedicine follow-up through a video call, a medical cloud-based program fulfilling all European Union security and privacy policies was used. Four surgeons were assigned to perform the telemedicine visits and were trained on how to use the program before the study started. Visit format was the same in both groups: clinical and wound condition were assessed and pathology was discussed (the one difference was that physical exploration was not performed in the telemedicine group).

Main outcome measures. The primary outcome was the feasibility of telemedicine follow-up, and this was measured as the percentage of participants who completed follow-up in their corresponding group by the date scheduled at hospital discharge. Secondary outcomes included a comparison of clinical results and patient satisfaction. To assess the clinical results, extra visits to an outpatient clinic and/or the emergency department during the first 30 days after the follow-up visit were collected.

To evaluate patient satisfaction, a questionnaire was sent via email to the participants after the visit and, if they did not respond, a telephone survey was carried out (if there was no contact after 2 telephone calls, the participants was considered a missing value). The questionnaire was informed by the United Kingdom National Health Service outpatients questionnaire and the Telehealth Usability Questionnaire. It included 27 general questions asked of participants in both groups, plus 8 specific questions for participants in the conventional follow-up group and 14 specific questions for participants in the telemedicine group. To summarize all the included fields in the questionnaires (time to visit and visit length, comfort, tests and procedures performed before and during the visit, transport, waiting time, privacy, dealings with staff, platform usability, telemedicine, and satisfaction), participants were asked to provide a global satisfaction score on a scale from 1 to 5.

Analysis. To compare the groups in terms of proportion of outcomes, a chi-square test was used to analyze categorical variables. To compare medians between the groups, ordinal variables were analyzed using the Mann-Whitney U test. Statistical significance was set at P < 0.05.

Main results. Two-hundred patients were randomly allocated to 1 of the 2 groups, with 100 patients in each group. The groups did not differ significantly based on age (P = 0.836), gender (P = 0.393), or American Society of Anesthesiologists (ASA) score (P = 0.232). Time to visit did not differ significantly between the groups (P = 0.169), and while visits were generally shorter in the telemedicine group, the difference was not significant (P = 0.153). Diagnoses and treatments did not differ significantly between the groups (P = 0.853 and P = 0.461, respectively).

The primary outcome (follow-up feasibility) was achieved in 90% of the conventional follow-up group and in 74% of the telemedicine group (P = 0.003). Of the 10 patients in the conventional follow-up group who did not complete the follow-up, 8 did not attend the visit on the scheduled day and 2 were hospitalized for reasons not related to the study. In the telemedicine group, the 2 main reasons for failure to follow-up were technical difficulties (n = 10) and requests by patients to attend a conventional visit after being allocated to the telemedicine group (n = 10). Among the remaining 6 patients in the telemedicine group who did not attend a visit, 3 visited the outpatient clinic because of a known surgical wound infection before the visit, 2 did not respond to the video call and could not be contacted by other means, and 1 had other face-to-face visits scheduled in different departments of the hospital the same day as the telemedicine appointment.

There were no statistically significant differences in the clinical results of the 164 patients meeting the primary endpoint (P = 0.832). Twelve of the 90 (13.3%) patients in the conventional group attended extra visits after the follow-up, while 9 of the 74 patients (12.1%) in the telemedicine group (P = 0.823) attended extra visits after follow-up. The median global patient satisfaction score was 5 in both the conventional group (range, 2-5) and the telemedicine group (range, 1-5), with no statistically significant differences (P = 0.099). When patients in the telemedicine group were asked if they would accept the use of telemedicine as part of their medical treatment on an ongoing basis, they rated the proposition with a median score of 5 (range, 1-5).

Conclusion. Telemedicine is a feasible and acceptable complementary service to facilitate postoperative management in selected general surgery patients. This option produces good satisfaction rates and maintains clinical outcomes.

Commentary

In recent years, telemedicine has gained increased popularity in both medicine and surgery, affording surgeons greater opportunities for patient care, mentoring, collaboration, and teaching, without the limits of geographic boundaries. Telemedicine can be broadly described as a health care service utilizing telecommunication technologies for the purpose of communicating with and diagnosing and treating patients remotely.^1-4 To date, literature on telemedicine in surgical care has been limited.

In their systematic review, published in 2018, Asiri et al identified 24 studies published between 1998 and 2018, which included 3 randomized controlled trials, 3 pilot studies, 4 retrospective studies, and 14 prospective observational studies. In these studies, telemedicine protocols were used for preoperative assessment, diagnostic purposes, or consultation with another surgical department (10 studies); postoperative wound assessment (9 studies); and follow-up in place of conventional clinic visits (5 studies).³ In a 2017 systematic review of telemedicine for post-discharge surgical care, Gunter et al identified 21 studies, which included 3 randomized controlled trials, 6 pilot or feasibility studies, 4 retrospective record reviews, 2 case series, and 6 surveys.⁴ In these studies, telemedicine protocols were used for scheduled follow-up (10 studies), routine and ongoing monitoring (5 studies), or management of issues that arose after surgery (2 studies). These 2 reviews found telemedicine to be feasible, useful, and acceptable for postoperative evaluation and follow-up among both providers and patients.

Additional benefits noted in these studies included savings in patient travel, time, and cost. Perspectives on savings to the health system were mixed—while clinic time slots may open as a result of follow-up visits being done via telemedicine (resulting in potential improvements in access to surgical services and decreased wait times), there are still significant direct costs for purchasing necessary equipment and for educating and training providers on the use of the equipment. Other published reviews have discussed in greater detail the application, benefits, limitations, and barriers to telemedicine and provided insight from the perspectives of patients, providers, and health care systems.^1,2

Because studies on the use of telemedicine are limited, particularly in general surgery, and few of these studies have used a randomized clinical trial design, the present study is an important contribution to the literature. The authors found a significant difference between groups in terms of percentage of completed follow-up visits—90% of conventional follow-up group participants completed their visit versus 74% of telemedicine group participants. However, these differences were primarily attributed to technical difficulties experienced by telemedicine group participants, as well requests to have a conventional follow-up visit. In addition, telemedicine capabilities were limited to video calls via computers and webcams, and it is likely that successful completion of the follow-up visit would have been higher in the telemedicine group had the use of video calls via tablets or smartphones been an option. Perhaps more important, no significant differences were found in clinical outcomes (extra visits within 30 days after the follow-up visit) or patient satisfaction.

A key strength of this study is the use of a randomized clinical trial design to evaluate telemedicine as an alternative method for conducting patient visits following general surgery. Inclusion and exclusion criteria did not impose strict limitations on potential participants. Also, the authors evaluated differences in time to visit, length of visit, clinical results, and patient satisfaction between groups, in addition to the primary measure of completion of the follow-up visit.

This study has important limitations that should be noted as well, particularly related to the study design, some of which are acknowledged by the authors. Because this study was implemented in only 1 hospital, specifically, a tertiary care university hospital on the outskirts of an urban European city, the generalizability of the findings is limited. Also, the likelihood of selection bias is high, as enrollment was not offered to all patients who were discharged from the hospital and met inclusion criteria (limited by patient workload). The comparison of clinical results was limited, as the selected measure focused only on extra visits to an outpatient clinic and/or the emergency department during the first 30 days after the follow-up visit. This chosen measure does not account for less severe clinical results that did not require an additional visit, and does not represent a nuanced comparison of specific clinical indicators. In addition, this measure does not account for clinical complications that may have occurred beyond the 30-day period. Recall bias also was likely, given that the patient satisfaction questionnaire was delivered via email to patients at a later time after the follow-up visit, instead of being administered immediately after the visit. Last, group differences at baseline were assessed based only on age, gender, and ASA score, which does not preclude potential differences related to other factors, such as race/ethnicity, household income, comorbidities, insurance, and zip code. Future research with a similar objective would benefit from a randomized clinical trial design that recruits a wider diversity of patients across different clinic settings and incorporates more nuanced measures of primary and secondary outcomes.

Applications for Clinical Practice

With the ongoing COVID-19 pandemic, the integration of telemedicine capabilities into hospital systems is becoming more widespread and is proceeding at an accelerated pace. This study provides evidence that telemedicine is a feasible and acceptable complementary service to facilitate postoperative management in selected general surgery patients. Assuming that the needed technology and appropriate program training are available, telemedicine should be offered to patients, especially to maximize savings in terms of travel, time, and cost. However, the option for conventional (in-person) follow-up should remain, particularly in cases where there may be barriers to successful follow-up visits via telemedicine, including limited digital literacy, lack of access to necessary equipment, language/communication barriers, complex follow-up treatment, and difficulties in describing or showing complications in the surgical area.

–Katrina F. Mateo, PhD, MPH