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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
1. Brown CJ, Roth DL, Allman RM. Validation of use of wireless monitors to measure levels of mobility during hospitalization. J Rehabil Res Dev. 2008;45:551-558.
2. Pedersen MM, Bodilsen AC, Petersen J, et al. Twenty-four-hour mobility during acute hospitalization in older medical patients. J Gerontol Ser A Biol Sci Med Sci. 2013;68:331–337.
3. Evensen S, Sletvold O, Lydersen S, Taraldsen K. Physical activity among hospitalized older adults – an observational study. BMC Geriatr. 2017;17:110.
4. Engdal M, Foss OA, Taraldsen K, et al. Daily physical activity in total hip arthroplasty patients undergoing different surgical approaches: a cohort study. Am J Phys Med Rehabil. 2017;96:473-478.
5. Hoogeboom TJ, Dronkers JJ, Hulzebos EH, van Meeteren NL. Merits of exercise therapy before and after major surgery. Curr Opin Anaesthesiol. 2014;27:161-166.
6. Hoogeboom TJ, van Meeteren NL, Schank K, et al. Risk factors for delayed inpatient functional recovery after total knee arthroplasty. Biomed Res Int. 2015:2015:167643.
7. Lenssen AF, Crijns YH, Waltje EM, et al. Efficiency of immediate postoperative inpatient physical therapy following total knee arthroplasty: an RCT. BMC Musculoskelet Disord. 2006;7:71.
8. Ramkumar PN, Haeberle HS, Ramanathan D, et al. Remote patient monitoring using mobile health for total knee arthroplasty: validation of a wearable and machine learning-based surveillance platform. J Arthroplast. 2019;34:2253-2259.
9. Ramkumar PN, Haeberle HS, Bloomfield MR, et al. Artificial Intelligence and arthroplasty at a single institution: Real-world applications of machine learning to big data, value-based care, mobile health, and remote patient monitoring. J Arthroplast. 2019;34:2204-2209.
10. Correia FD, Nogueira A, Magalhães I, et al, et al. Medium-term outcomes of digital versus conventional home-based rehabilitation after total knee arthroplasty: prospective, parallel-group feasibility study. JMIR Rehabil Assist Technol. 2019;6:e13111.
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
1. Brown CJ, Roth DL, Allman RM. Validation of use of wireless monitors to measure levels of mobility during hospitalization. J Rehabil Res Dev. 2008;45:551-558.
2. Pedersen MM, Bodilsen AC, Petersen J, et al. Twenty-four-hour mobility during acute hospitalization in older medical patients. J Gerontol Ser A Biol Sci Med Sci. 2013;68:331–337.
3. Evensen S, Sletvold O, Lydersen S, Taraldsen K. Physical activity among hospitalized older adults – an observational study. BMC Geriatr. 2017;17:110.
4. Engdal M, Foss OA, Taraldsen K, et al. Daily physical activity in total hip arthroplasty patients undergoing different surgical approaches: a cohort study. Am J Phys Med Rehabil. 2017;96:473-478.
5. Hoogeboom TJ, Dronkers JJ, Hulzebos EH, van Meeteren NL. Merits of exercise therapy before and after major surgery. Curr Opin Anaesthesiol. 2014;27:161-166.
6. Hoogeboom TJ, van Meeteren NL, Schank K, et al. Risk factors for delayed inpatient functional recovery after total knee arthroplasty. Biomed Res Int. 2015:2015:167643.
7. Lenssen AF, Crijns YH, Waltje EM, et al. Efficiency of immediate postoperative inpatient physical therapy following total knee arthroplasty: an RCT. BMC Musculoskelet Disord. 2006;7:71.
8. Ramkumar PN, Haeberle HS, Ramanathan D, et al. Remote patient monitoring using mobile health for total knee arthroplasty: validation of a wearable and machine learning-based surveillance platform. J Arthroplast. 2019;34:2253-2259.
9. Ramkumar PN, Haeberle HS, Bloomfield MR, et al. Artificial Intelligence and arthroplasty at a single institution: Real-world applications of machine learning to big data, value-based care, mobile health, and remote patient monitoring. J Arthroplast. 2019;34:2204-2209.
10. Correia FD, Nogueira A, Magalhães I, et al, et al. Medium-term outcomes of digital versus conventional home-based rehabilitation after total knee arthroplasty: prospective, parallel-group feasibility study. JMIR Rehabil Assist Technol. 2019;6:e13111.
1. Brown CJ, Roth DL, Allman RM. Validation of use of wireless monitors to measure levels of mobility during hospitalization. J Rehabil Res Dev. 2008;45:551-558.
2. Pedersen MM, Bodilsen AC, Petersen J, et al. Twenty-four-hour mobility during acute hospitalization in older medical patients. J Gerontol Ser A Biol Sci Med Sci. 2013;68:331–337.
3. Evensen S, Sletvold O, Lydersen S, Taraldsen K. Physical activity among hospitalized older adults – an observational study. BMC Geriatr. 2017;17:110.
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