Katrina F. Mateo, MPH

Study Overview

Objective. To assess how patients look for patient-obtained medication information (POMI) to prepare for a clinical appointment, whether they share those findings with their provider, and how health literacy and patient activation relate to a patient’s perception of the physician’s reaction to POMI.

Design. Cross-sectional survey-based study.

Setting and participants. The study took place over 1 week at 2 academic medical centers located in Las Vegas, Nevada, and Washington, DC. At a central waiting area at each facility, patients aged 18 and older waiting for their clinical appointment were invited to complete a survey, either on a computer tablet or with paper and pencil, before and after their appointment.

Measures and analysis. The pre-survey included demographic measures (age, gender, education, and ethnicity), the reason for the visit (routine care, sick visit, follow-up after survey, and follow-up after emergency room visit), and an item to assess self-report of perceived general health (from poor to excellent). Health literacy was assessed by a self-report measure that included subscales for the 3 dimensions of health literacy: functional, communicative, and critical health literacy [1]; together, these capture the ability of patients to retain health knowledge, gather and communicate health concepts, and apply health information. Patient activation was scored using the Patient Activation Measure (13 Likert-style items, total scale range 0–100); patient activation combines a patient’s self-reported knowledge, skill, and confidence for self-management of general health or a chronic condition [2]. Information seeking was measured by time spent (did not look for information, 1 hour, 2 hours, 3 hours, or more than 3 hours), and information channels used to look for POMI (eg, magazines/newspapers, internet website or search engine) were presented dichotomously (yes/no).

The post-survey first asked whether the participant shared information with their provider (yes/no). If the participant said yes, 4 items assessed their perception of the provider’s response, including amount of time spent discussing POMI, how seriously the provider considered the information, and overall reaction (scored as a mean, each item measured from 1–5, with 5 indicating the most positive reactions). For hypothesis testing, logistic regression models were used to test the effects of the independent variables. To explore the relationship between health literacy/patient activation and physician response, correlations were calculated.

Main results. Over 400 patients were asked to participate, and of these a total of 243 (60.75%) patients were eligible, consented, and completed surveys. Participants were predominantly white (57.6%), female (63%), had some college education or higher (80.2%), and had a clinical appointment for routine care (69.3%). The mean age was 47.04 years (SD, 15.78), the mean health status was 3.20 (SD, 0.94), and the mean Patient Activation Measure was 72.43 (SD, 16.00).

More than half of participants (58.26%) who responded to the item about information seeking indicated seeking POMI prior to their clinical appointment. Of these, the majority (88.7%) reported using the internet, particularly WebMD, as an information channel. Significant predictors of information seeking included age (P = 0.01, OR = 0.973), communicative health literacy (P = 0.01, or = 1.975), and critical health literacy (P = 0.05, OR = 1.518). Lower age, higher communicative health literacy, and higher critical health literacy increased the likelihood of the patient seeking POMI prior to the clinical appointment. Other assessed predictors were not significant, including gender, functional health literacy, patient activation, reason for visit, and reported health status.

58.2% of the 141 information-seeking patients talked to their health care provider about the information they found. However, no predictor variables included in a logistic regression analysis were significant, including age, gender, reason for visit, reported health status, functional health literacy, communicative health literacy, critical health literacy, and patient activation. For the research question (how do health literacy and patient activation relate to a patient’s perception of the physician’s reaction to POMI), the mean score on the 4-item measure was 4.08 (SD, 0.90), indicating a generally positive response; most reported the physician response was good or higher. Patient activation correlated positively with perceived physician response (r = 0.245, P = 0.03).

Conclusion. The lack of data to predict who will introduce POMI at the medical visit is disconcerting. Providers might consider directly asking or passively surveying what outside information sources the patient has engaged with, regardless of whether patient introduces the information or does not introduce it.

Commentary

Patient engagement plays an important role in health care [3]. Activated patients often have skills and confidence to engage in their health care and with their provider, which often contributes to better health outcomes and care experiences [2,4] as well as lower health care costs [5]. Health information is needed to make informed decisions, manage health, and practice healthy behaviors [6], and patients are increasingly taking an active role in seeking out medical or health information outside of the clinical encounter in order to make shared health decisions with their provider [7]. Indeed, one of the Healthy People 2020 goals is to “Use health communication strategies and health information technology  to improve population health outcomes and health care quality, and to achieve health equity” [8].

However, seeking POMI requires health literacy skills and supportive relationships, particularly when navigating the many channels and complexities of publicly available health information [8]. This is especially true on the internet, where there is often varying accuracy and clarity of information presented. According to 2011 data from the Pew Research Center [9], 74% of adults in the United States use the internet, and of those adults 80% have looked online for health information; 34% have read another person’s commentary or experience about health or medical issues on an online news group, website, or blog; 25% have watched an online video about health or medical issues; and 24% have consulted online reviews of particular drugs or medical treatments.

A general strength of this study was the cross-sectional design, which allowed for surveying patients around attitudes, motivations, and behaviors immediately before and after their clinical encounter. According to the authors, this study design was aimed to extend knowledge around information seeking and provider discussions that have occurred distally and relied on patient long-term recall. Additionally, this study surveyed a variety of patients (not limited to either primary or specialist appointments) at 2 different academic medical centers, and gave patients a choice to either take the survey on a computer tablet or traditional paper and pencil. Further, the authors assessed the reliability of scales used and included a number of predictor variables in the logistic regression models for hypothesis testing.

The authors acknowledged several limitations, including the use of convenience sampling and self-reported data with volunteer participants, which can result in self-selection bias and social desirability bias. As study participants were self-selecting, low health literacy patients may have been more likely to not volunteer to take the survey, which might explain the relatively high mean scores on the health literacy measures. Further, participants were mostly white, female, college-educated, health literate, and scheduled for a routine visit, which limits the generalizability of the study findings and the ability to identify significant predictors.

Regarding the study design, pre-/post-tests are usually used to measure the change in a situation, phenomenon, problem, or attitude. However, as the authors did not aim to measure any change during the clinical encounter itself, the use of only a post-test may have been more appropriate. The use of a pre-/post-test design may have increased the likelihood of patients both recalling POMI before the encounter and then sharing POMI with their provider. Also, in the post-survey, the authors only asked follow-up questions of patients that shared POMI with their provider. An open-response question could have been included to explore further why some patients chose not to introduce POMI during the clinical encounter. Lastly, the authors may have been able to reach more patients with lower health literacy if surveys were administered at public hospitals as opposed to academic medical centers. While some providers may perceive that patients in academic medical centers are more complex or may have limited access to care [10], patients at public hospitals and safety net hospitals tend to be of lower income and have limited or no insurance [11,12].

Applications For Clinical Practice

There are documented communication-enhancing techniques and strategies that providers and other health professionals use, particularly among patients with low health literacy [13]. Based on this study, the authors conclude that providers may try another strategy of directly asking or passively surveying any POMI, regardless of whether the patient initiates this conversation. Other research has acknowledged that recognition of health literacy status allows for the use of appropriate communication tools [14]. However, providers need to recognize barriers to health information seeking, particularly among minorities and underserved populations [15], as well as the potential for embarrassment that patients might experience as a result of revealing misunderstandings of health information or general reading difficulties [16]. This study highlights the need for further research to identify predictors of health information seeking and especially health information sharing by patients during the clinical encounter.

—Katrina F. Mateo, MPH

Study Overview

Objective. To assess how patients look for patient-obtained medication information (POMI) to prepare for a clinical appointment, whether they share those findings with their provider, and how health literacy and patient activation relate to a patient’s perception of the physician’s reaction to POMI.

Design. Cross-sectional survey-based study.

Setting and participants. The study took place over 1 week at 2 academic medical centers located in Las Vegas, Nevada, and Washington, DC. At a central waiting area at each facility, patients aged 18 and older waiting for their clinical appointment were invited to complete a survey, either on a computer tablet or with paper and pencil, before and after their appointment.

Measures and analysis. The pre-survey included demographic measures (age, gender, education, and ethnicity), the reason for the visit (routine care, sick visit, follow-up after survey, and follow-up after emergency room visit), and an item to assess self-report of perceived general health (from poor to excellent). Health literacy was assessed by a self-report measure that included subscales for the 3 dimensions of health literacy: functional, communicative, and critical health literacy [1]; together, these capture the ability of patients to retain health knowledge, gather and communicate health concepts, and apply health information. Patient activation was scored using the Patient Activation Measure (13 Likert-style items, total scale range 0–100); patient activation combines a patient’s self-reported knowledge, skill, and confidence for self-management of general health or a chronic condition [2]. Information seeking was measured by time spent (did not look for information, 1 hour, 2 hours, 3 hours, or more than 3 hours), and information channels used to look for POMI (eg, magazines/newspapers, internet website or search engine) were presented dichotomously (yes/no).

The post-survey first asked whether the participant shared information with their provider (yes/no). If the participant said yes, 4 items assessed their perception of the provider’s response, including amount of time spent discussing POMI, how seriously the provider considered the information, and overall reaction (scored as a mean, each item measured from 1–5, with 5 indicating the most positive reactions). For hypothesis testing, logistic regression models were used to test the effects of the independent variables. To explore the relationship between health literacy/patient activation and physician response, correlations were calculated.

Main results. Over 400 patients were asked to participate, and of these a total of 243 (60.75%) patients were eligible, consented, and completed surveys. Participants were predominantly white (57.6%), female (63%), had some college education or higher (80.2%), and had a clinical appointment for routine care (69.3%). The mean age was 47.04 years (SD, 15.78), the mean health status was 3.20 (SD, 0.94), and the mean Patient Activation Measure was 72.43 (SD, 16.00).

More than half of participants (58.26%) who responded to the item about information seeking indicated seeking POMI prior to their clinical appointment. Of these, the majority (88.7%) reported using the internet, particularly WebMD, as an information channel. Significant predictors of information seeking included age (P = 0.01, OR = 0.973), communicative health literacy (P = 0.01, or = 1.975), and critical health literacy (P = 0.05, OR = 1.518). Lower age, higher communicative health literacy, and higher critical health literacy increased the likelihood of the patient seeking POMI prior to the clinical appointment. Other assessed predictors were not significant, including gender, functional health literacy, patient activation, reason for visit, and reported health status.

58.2% of the 141 information-seeking patients talked to their health care provider about the information they found. However, no predictor variables included in a logistic regression analysis were significant, including age, gender, reason for visit, reported health status, functional health literacy, communicative health literacy, critical health literacy, and patient activation. For the research question (how do health literacy and patient activation relate to a patient’s perception of the physician’s reaction to POMI), the mean score on the 4-item measure was 4.08 (SD, 0.90), indicating a generally positive response; most reported the physician response was good or higher. Patient activation correlated positively with perceived physician response (r = 0.245, P = 0.03).

Conclusion. The lack of data to predict who will introduce POMI at the medical visit is disconcerting. Providers might consider directly asking or passively surveying what outside information sources the patient has engaged with, regardless of whether patient introduces the information or does not introduce it.

Commentary

Patient engagement plays an important role in health care [3]. Activated patients often have skills and confidence to engage in their health care and with their provider, which often contributes to better health outcomes and care experiences [2,4] as well as lower health care costs [5]. Health information is needed to make informed decisions, manage health, and practice healthy behaviors [6], and patients are increasingly taking an active role in seeking out medical or health information outside of the clinical encounter in order to make shared health decisions with their provider [7]. Indeed, one of the Healthy People 2020 goals is to “Use health communication strategies and health information technology  to improve population health outcomes and health care quality, and to achieve health equity” [8].

However, seeking POMI requires health literacy skills and supportive relationships, particularly when navigating the many channels and complexities of publicly available health information [8]. This is especially true on the internet, where there is often varying accuracy and clarity of information presented. According to 2011 data from the Pew Research Center [9], 74% of adults in the United States use the internet, and of those adults 80% have looked online for health information; 34% have read another person’s commentary or experience about health or medical issues on an online news group, website, or blog; 25% have watched an online video about health or medical issues; and 24% have consulted online reviews of particular drugs or medical treatments.

A general strength of this study was the cross-sectional design, which allowed for surveying patients around attitudes, motivations, and behaviors immediately before and after their clinical encounter. According to the authors, this study design was aimed to extend knowledge around information seeking and provider discussions that have occurred distally and relied on patient long-term recall. Additionally, this study surveyed a variety of patients (not limited to either primary or specialist appointments) at 2 different academic medical centers, and gave patients a choice to either take the survey on a computer tablet or traditional paper and pencil. Further, the authors assessed the reliability of scales used and included a number of predictor variables in the logistic regression models for hypothesis testing.

The authors acknowledged several limitations, including the use of convenience sampling and self-reported data with volunteer participants, which can result in self-selection bias and social desirability bias. As study participants were self-selecting, low health literacy patients may have been more likely to not volunteer to take the survey, which might explain the relatively high mean scores on the health literacy measures. Further, participants were mostly white, female, college-educated, health literate, and scheduled for a routine visit, which limits the generalizability of the study findings and the ability to identify significant predictors.

Regarding the study design, pre-/post-tests are usually used to measure the change in a situation, phenomenon, problem, or attitude. However, as the authors did not aim to measure any change during the clinical encounter itself, the use of only a post-test may have been more appropriate. The use of a pre-/post-test design may have increased the likelihood of patients both recalling POMI before the encounter and then sharing POMI with their provider. Also, in the post-survey, the authors only asked follow-up questions of patients that shared POMI with their provider. An open-response question could have been included to explore further why some patients chose not to introduce POMI during the clinical encounter. Lastly, the authors may have been able to reach more patients with lower health literacy if surveys were administered at public hospitals as opposed to academic medical centers. While some providers may perceive that patients in academic medical centers are more complex or may have limited access to care [10], patients at public hospitals and safety net hospitals tend to be of lower income and have limited or no insurance [11,12].

Applications For Clinical Practice

There are documented communication-enhancing techniques and strategies that providers and other health professionals use, particularly among patients with low health literacy [13]. Based on this study, the authors conclude that providers may try another strategy of directly asking or passively surveying any POMI, regardless of whether the patient initiates this conversation. Other research has acknowledged that recognition of health literacy status allows for the use of appropriate communication tools [14]. However, providers need to recognize barriers to health information seeking, particularly among minorities and underserved populations [15], as well as the potential for embarrassment that patients might experience as a result of revealing misunderstandings of health information or general reading difficulties [16]. This study highlights the need for further research to identify predictors of health information seeking and especially health information sharing by patients during the clinical encounter.

—Katrina F. Mateo, MPH

Study Overview

Objective. To assess how patients look for patient-obtained medication information (POMI) to prepare for a clinical appointment, whether they share those findings with their provider, and how health literacy and patient activation relate to a patient’s perception of the physician’s reaction to POMI.

Design. Cross-sectional survey-based study.

Setting and participants. The study took place over 1 week at 2 academic medical centers located in Las Vegas, Nevada, and Washington, DC. At a central waiting area at each facility, patients aged 18 and older waiting for their clinical appointment were invited to complete a survey, either on a computer tablet or with paper and pencil, before and after their appointment.

Measures and analysis. The pre-survey included demographic measures (age, gender, education, and ethnicity), the reason for the visit (routine care, sick visit, follow-up after survey, and follow-up after emergency room visit), and an item to assess self-report of perceived general health (from poor to excellent). Health literacy was assessed by a self-report measure that included subscales for the 3 dimensions of health literacy: functional, communicative, and critical health literacy [1]; together, these capture the ability of patients to retain health knowledge, gather and communicate health concepts, and apply health information. Patient activation was scored using the Patient Activation Measure (13 Likert-style items, total scale range 0–100); patient activation combines a patient’s self-reported knowledge, skill, and confidence for self-management of general health or a chronic condition [2]. Information seeking was measured by time spent (did not look for information, 1 hour, 2 hours, 3 hours, or more than 3 hours), and information channels used to look for POMI (eg, magazines/newspapers, internet website or search engine) were presented dichotomously (yes/no).

The post-survey first asked whether the participant shared information with their provider (yes/no). If the participant said yes, 4 items assessed their perception of the provider’s response, including amount of time spent discussing POMI, how seriously the provider considered the information, and overall reaction (scored as a mean, each item measured from 1–5, with 5 indicating the most positive reactions). For hypothesis testing, logistic regression models were used to test the effects of the independent variables. To explore the relationship between health literacy/patient activation and physician response, correlations were calculated.

Main results. Over 400 patients were asked to participate, and of these a total of 243 (60.75%) patients were eligible, consented, and completed surveys. Participants were predominantly white (57.6%), female (63%), had some college education or higher (80.2%), and had a clinical appointment for routine care (69.3%). The mean age was 47.04 years (SD, 15.78), the mean health status was 3.20 (SD, 0.94), and the mean Patient Activation Measure was 72.43 (SD, 16.00).

More than half of participants (58.26%) who responded to the item about information seeking indicated seeking POMI prior to their clinical appointment. Of these, the majority (88.7%) reported using the internet, particularly WebMD, as an information channel. Significant predictors of information seeking included age (P = 0.01, OR = 0.973), communicative health literacy (P = 0.01, or = 1.975), and critical health literacy (P = 0.05, OR = 1.518). Lower age, higher communicative health literacy, and higher critical health literacy increased the likelihood of the patient seeking POMI prior to the clinical appointment. Other assessed predictors were not significant, including gender, functional health literacy, patient activation, reason for visit, and reported health status.

58.2% of the 141 information-seeking patients talked to their health care provider about the information they found. However, no predictor variables included in a logistic regression analysis were significant, including age, gender, reason for visit, reported health status, functional health literacy, communicative health literacy, critical health literacy, and patient activation. For the research question (how do health literacy and patient activation relate to a patient’s perception of the physician’s reaction to POMI), the mean score on the 4-item measure was 4.08 (SD, 0.90), indicating a generally positive response; most reported the physician response was good or higher. Patient activation correlated positively with perceived physician response (r = 0.245, P = 0.03).

Conclusion. The lack of data to predict who will introduce POMI at the medical visit is disconcerting. Providers might consider directly asking or passively surveying what outside information sources the patient has engaged with, regardless of whether patient introduces the information or does not introduce it.

Commentary

Patient engagement plays an important role in health care [3]. Activated patients often have skills and confidence to engage in their health care and with their provider, which often contributes to better health outcomes and care experiences [2,4] as well as lower health care costs [5]. Health information is needed to make informed decisions, manage health, and practice healthy behaviors [6], and patients are increasingly taking an active role in seeking out medical or health information outside of the clinical encounter in order to make shared health decisions with their provider [7]. Indeed, one of the Healthy People 2020 goals is to “Use health communication strategies and health information technology  to improve population health outcomes and health care quality, and to achieve health equity” [8].

However, seeking POMI requires health literacy skills and supportive relationships, particularly when navigating the many channels and complexities of publicly available health information [8]. This is especially true on the internet, where there is often varying accuracy and clarity of information presented. According to 2011 data from the Pew Research Center [9], 74% of adults in the United States use the internet, and of those adults 80% have looked online for health information; 34% have read another person’s commentary or experience about health or medical issues on an online news group, website, or blog; 25% have watched an online video about health or medical issues; and 24% have consulted online reviews of particular drugs or medical treatments.

A general strength of this study was the cross-sectional design, which allowed for surveying patients around attitudes, motivations, and behaviors immediately before and after their clinical encounter. According to the authors, this study design was aimed to extend knowledge around information seeking and provider discussions that have occurred distally and relied on patient long-term recall. Additionally, this study surveyed a variety of patients (not limited to either primary or specialist appointments) at 2 different academic medical centers, and gave patients a choice to either take the survey on a computer tablet or traditional paper and pencil. Further, the authors assessed the reliability of scales used and included a number of predictor variables in the logistic regression models for hypothesis testing.

The authors acknowledged several limitations, including the use of convenience sampling and self-reported data with volunteer participants, which can result in self-selection bias and social desirability bias. As study participants were self-selecting, low health literacy patients may have been more likely to not volunteer to take the survey, which might explain the relatively high mean scores on the health literacy measures. Further, participants were mostly white, female, college-educated, health literate, and scheduled for a routine visit, which limits the generalizability of the study findings and the ability to identify significant predictors.

Regarding the study design, pre-/post-tests are usually used to measure the change in a situation, phenomenon, problem, or attitude. However, as the authors did not aim to measure any change during the clinical encounter itself, the use of only a post-test may have been more appropriate. The use of a pre-/post-test design may have increased the likelihood of patients both recalling POMI before the encounter and then sharing POMI with their provider. Also, in the post-survey, the authors only asked follow-up questions of patients that shared POMI with their provider. An open-response question could have been included to explore further why some patients chose not to introduce POMI during the clinical encounter. Lastly, the authors may have been able to reach more patients with lower health literacy if surveys were administered at public hospitals as opposed to academic medical centers. While some providers may perceive that patients in academic medical centers are more complex or may have limited access to care [10], patients at public hospitals and safety net hospitals tend to be of lower income and have limited or no insurance [11,12].

Applications For Clinical Practice

There are documented communication-enhancing techniques and strategies that providers and other health professionals use, particularly among patients with low health literacy [13]. Based on this study, the authors conclude that providers may try another strategy of directly asking or passively surveying any POMI, regardless of whether the patient initiates this conversation. Other research has acknowledged that recognition of health literacy status allows for the use of appropriate communication tools [14]. However, providers need to recognize barriers to health information seeking, particularly among minorities and underserved populations [15], as well as the potential for embarrassment that patients might experience as a result of revealing misunderstandings of health information or general reading difficulties [16]. This study highlights the need for further research to identify predictors of health information seeking and especially health information sharing by patients during the clinical encounter.

—Katrina F. Mateo, MPH

Study Overview

Objective. To determine the efficacy and cost-effectiveness of adding an evidence-based internet behavioral weight loss intervention alone or combined with optional group sessions to ShapeUp Rhode Island 2011 (SURI), a 3-month statewide wellness campaign.

Design. 3-arm randomized clinical trial.

Setting and participants. Study participants were recruited from the Rhode Island community via employers, media, and mass mailings at the time of SURI 2011 registration. Of the 3806 participants that joined the weight loss division, 1139 were willing to be contacted for research, and the first 431 were screened for study eligibility. Exclusion criteria were minimal: age < 18 years or > 70 years, body mass index (BMI) < 25 kg/m², pregnant, nursing, or plans to become pregnant, a serious medical condition (eg, cancer), unreliable internet access, non-English speaking, current or previous participation in our weight loss studies, and planned relocation. Those who reported a medical condition that could interfere with safe participation (eg, diabetes) obtained doctor’s consent to participate. Of those screened, 230 met inclusion criteria, completed orientation procedures, and were randomized using a 1:2:2 randomization scheme to the standard SURI program (S; n = 46); SURI plus internet behavioral weight loss intervention (SI; n = 90); or SURI plus internet behavioral weight loss intervention plus optional group sessions (SIG; n = 94). To avoid contamination, individuals on the same SURI team (see below) were randomized to the same intervention.

Intervention. Participants in the standard SURI program did not receive any behavioral weight loss treatment. SURI is a self-sustaining, annual community campaign designed to help Rhode Islanders lose weight and increase their physical activity through an online, team-based competition. Participants join in teams, enter the weight loss or physical activity division or both, and compete with other teams. Throughout the 3-month program, participants have access to a reporting SURI website where they submit their weekly weight and activity data and view their personal and team progress. They also receive paper logs to record weight and activity, a pedometer, access to newsletters and community workshops, and recognition for meeting goals.

Participants in the SI arm received the 3-month SURI program plus a 3-month internet behavioral weight loss intervention. Before SURI began, SI participants attended a 1-hour group meeting during which they received their weight loss goal (lose 1 to 2 pounds per week), calorie and fat gram goal (starting weight < 250 lbs: 1200–1500 kcal/day, 40–50 g of fat; starting weight ≥ 250 lbs: 1500–1800 kcal/day, 50–60 g of fat), and activity goal (gradually increase to 200 minutes of aerobic activity per week). During this session, participants were also taught self-monitoring skills and oriented to an internet behavioral weight loss intervention website developed by the authors. The intervention website included 12 weekly, 10- to 15-minute multimedia lessons based on the Diabetes Prevention Program and a self-monitoring platform where participants tracked their daily weight, calorie, and activity information. Participants received weekly automated feedback on their progress. The intervention website also included information on meal plans, prepackaged meals, and meal replacements.

Participants in the SIG arm received everything in SI and were additionally given the option to attend weekly group meetings at Miriam Hospital’s Weight Control and Diabetes Research Center during the 3 months. The 12 weekly, optional group sessions were led by masters-level staff with extensive training in behavioral weight loss. Sessions involved private weigh-ins and covered topics that supplemented the internet intervention (eg, recipe modification, portion control).

Main outcomes measures. The main outcome was weight loss at the end of the 3-month program. Participants completed measures (ie, weight, BMI) in person at baseline and 3 months (post-treatment), and at 6- and 12-month follow-up visits. Adherence measures included reported weight and physical activity on the SURI website (S, SI, and SIG), log ins, viewed lessons, and self-monitoring entries on the intervention website (SI, SIG), and number of groups meetings attended (SIG). To measure weight loss behaviors, the authors used the Weight Control Practices questionnaire to assess engagement in core weight loss strategies targeted in treatment, and the Paffenbarger questionnaire to assess weekly kcal expended in moderate to vigorous activity. The authors also assessed costs from the payer (labor, rent, intervention materials), participant (SURI registration fee, transportation, time spent on intervention), and societal perspective (sum of payer and participant costs) in order to calculate the cost per kg of weight lost in each study arm.

Results. Participants were predominantly female, non-Hispanic white, and had a mean BMI of 34.4 kg/m² (SE = 0.05). Groups differed only on education (P = 0.02), and attendance at post-treatment and 6- and 12-month follow-up were high (93%, 91%, and 86% respectively). The authors found that weight loss did not differ by educational attainment (P s > 0.57).

Overall, there was a significant group-by-time interaction for weight loss (P < 0.001). Percentage weight loss at 3 months differed among the 3 groups—S: 1.1% ± 0.9%; SI: 4.2% ± 0.6%; SIG: 6.1% ± 0.6% (P s ≤ 0.04). There was also an overall group effect for percentage of individuals achieving 5% weight loss (P < 0.001). SI and SIG had higher percentages of participants who achieved a 5% weight loss than the control (SI: 42%; SIG: 54%; S: 7%; P s < 0.001) but did not differ from one another (P = 0.01). Initial weight losses and percentage of participants who achieved a 5% weight loss were largely maintained through the no-treatment follow-up phase at 6-months, but the 3 groups no longer differed from one another at 12 months (S: 1.2% [SE =0.9]; SI: 2.2% [SE = 0.6]; SIG: 3.3% [SE = 0.6]; P s > 0.05).

All groups reported significant increases in physical activity over time (p < 0.001). More reporting of weight and physical activity data on the SURI website was associated with greater percentage weight loss (r = 0.25; P < 0.001). Number of log ins and lessons viewed on the intervention website were positively associated with percentage weight loss (r = 0.45; P ≤ 0.001; and r = 0.34; P ≤ 0.001 respectively). Greater attendance to group sessions was associated with better weight outcomes (r = 0.61; P ≤ 0.001). Younger age was associated with poorer adherence, including less reporting on the SURI website, viewing of lessons, and logging in to the weight loss website.

There was a significant group-by-time effect interaction for the use of behavioral weight loss strategies (P < 0.001), and increased use of these strategies was associated with greater percentage weight loss in all 3 groups post-treatment. At 12 months, however, there were no differences between groups in the use of these strategies (P s ≤ 0.07).

Cost per kg of weight loss was similar for S ($39) and SI ($35), but both were lower than SIG ($114).

Conclusion. Both intervention arms (SI and SIG) achieved more weight loss at 6 months than SURI alone. Although mean weight loss was greatest with optional group sessions (SIG), the addition of the behavioral intervention website alone (SI) was the most cost-effective method to enhance weight loss. Thus, adding a novel internet behavioral weight loss intervention to a statewide community health initiative may be a cost-effective approach to improving obesity treatment outcomes.

Commentary

Weight loss treatment is recommended for adults with a BMI of > 30 kg/m², as well as those with BMI < 25 kg/m² with weight-related comorbidities [1]. Intensive behavioral treatment should be the first line of intervention for overweight and obese individuals and can lead to 8% to 10% weight loss [2], particularly in initial months of treatment [3]. However, behavioral treatment is inherently challenging and time-consuming, and readily available to only a fraction of the intended population. Although weight losses achieved from intensive lifestyle interventions such as the Diabetes Prevention Program (DPP) [4] may be higher, innovative community weight loss programs that use a variety of weight loss strategies can provide opportunities to a wider population of overweight and obese individuals and at a lower cost [3].

This study built upon the authors’ previous work [5], which showed that SURI participants with behavioral weight loss strategies via email significantly improved 3-month weight losses. In this current study, they compared SURI alone to SURI with additional access to an internet behavioral weight loss website with or without optional group sessions. Since significant weight loss was not maintained at 12 months, this suggests that perhaps access to the behavioral weight loss website should have continued for longer and/or included a maintenance phase after the 3-month intervention. Weight loss often reaches its peak around 6 months, and weight regain occurs without effective maintenance therapy [6].

General strengths of the study included the use of a randomized, intention-to-treat design, dissemination of evidence-based weight loss strategies, objective outcomes measurement, adherence metrics, and strong retention of participants with clear accounting of all enrolled patients from recruitment through analysis. This study demonstrated significant weight loss in an intervention with minimal/optional health professional interaction. This intervention also placed responsibility on participants to self-monitor their diet and physical activity, participate in online lessons, and attend optional group sessions. The success of this community-based intervention suggests feasibility and scalability within a real-world setting. The authors also conducted cost-effectiveness analyses demonstrating that the SI program was more cost-effective than SIG.

However, there are weaknesses as well. In setting the sample size for each arm of this study, no justification was described for choosing a 1:2:2 randomization scheme. In randomized control trials, the allocation of participants into the different study arms is often balanced to equal numbers which maximizes statistical power [7]. However, the use of unequal randomization ratios among study arms can be beneficial and even necessary for various reasons including cost, availability of the intervention, overcoming intervention/treatment learning curves, and if a higher drop-out rate is anticipated. Providing a justification for unbalanced sample sizes would be helpful to future researchers looking to replicate the study. Additionally, participants were mostly non-Hispanic white and female, thus limiting generalizability. While representative of the broader Rhode Island population, findings based on this population this may not be applicable to vulnerable (ie, low literacy, resource-poor) or underrepresented populations (ie, minorities) [8].

Applications for Clinical Practice

An internet-based behavioral weight loss intervention, when added to a community weight management initiative, is cost-effective and can lead to short-term weight loss. Given that clinicians often lack time, training, and resources to adequately address obesity in the office [9,10], encouraging patients to enroll in similar programs may be an effective strategy to address such barriers. The study also highlights the need for maintenance interventions to help keep weight off. Findings should be replicated in more diverse communities.

—Katrina F. Mateo, MPH, and Melanie Jay, MD, MS

Study Overview

Objective. To determine the efficacy and cost-effectiveness of adding an evidence-based internet behavioral weight loss intervention alone or combined with optional group sessions to ShapeUp Rhode Island 2011 (SURI), a 3-month statewide wellness campaign.

Design. 3-arm randomized clinical trial.

Setting and participants. Study participants were recruited from the Rhode Island community via employers, media, and mass mailings at the time of SURI 2011 registration. Of the 3806 participants that joined the weight loss division, 1139 were willing to be contacted for research, and the first 431 were screened for study eligibility. Exclusion criteria were minimal: age < 18 years or > 70 years, body mass index (BMI) < 25 kg/m², pregnant, nursing, or plans to become pregnant, a serious medical condition (eg, cancer), unreliable internet access, non-English speaking, current or previous participation in our weight loss studies, and planned relocation. Those who reported a medical condition that could interfere with safe participation (eg, diabetes) obtained doctor’s consent to participate. Of those screened, 230 met inclusion criteria, completed orientation procedures, and were randomized using a 1:2:2 randomization scheme to the standard SURI program (S; n = 46); SURI plus internet behavioral weight loss intervention (SI; n = 90); or SURI plus internet behavioral weight loss intervention plus optional group sessions (SIG; n = 94). To avoid contamination, individuals on the same SURI team (see below) were randomized to the same intervention.

Intervention. Participants in the standard SURI program did not receive any behavioral weight loss treatment. SURI is a self-sustaining, annual community campaign designed to help Rhode Islanders lose weight and increase their physical activity through an online, team-based competition. Participants join in teams, enter the weight loss or physical activity division or both, and compete with other teams. Throughout the 3-month program, participants have access to a reporting SURI website where they submit their weekly weight and activity data and view their personal and team progress. They also receive paper logs to record weight and activity, a pedometer, access to newsletters and community workshops, and recognition for meeting goals.

Participants in the SI arm received the 3-month SURI program plus a 3-month internet behavioral weight loss intervention. Before SURI began, SI participants attended a 1-hour group meeting during which they received their weight loss goal (lose 1 to 2 pounds per week), calorie and fat gram goal (starting weight < 250 lbs: 1200–1500 kcal/day, 40–50 g of fat; starting weight ≥ 250 lbs: 1500–1800 kcal/day, 50–60 g of fat), and activity goal (gradually increase to 200 minutes of aerobic activity per week). During this session, participants were also taught self-monitoring skills and oriented to an internet behavioral weight loss intervention website developed by the authors. The intervention website included 12 weekly, 10- to 15-minute multimedia lessons based on the Diabetes Prevention Program and a self-monitoring platform where participants tracked their daily weight, calorie, and activity information. Participants received weekly automated feedback on their progress. The intervention website also included information on meal plans, prepackaged meals, and meal replacements.

Participants in the SIG arm received everything in SI and were additionally given the option to attend weekly group meetings at Miriam Hospital’s Weight Control and Diabetes Research Center during the 3 months. The 12 weekly, optional group sessions were led by masters-level staff with extensive training in behavioral weight loss. Sessions involved private weigh-ins and covered topics that supplemented the internet intervention (eg, recipe modification, portion control).

Main outcomes measures. The main outcome was weight loss at the end of the 3-month program. Participants completed measures (ie, weight, BMI) in person at baseline and 3 months (post-treatment), and at 6- and 12-month follow-up visits. Adherence measures included reported weight and physical activity on the SURI website (S, SI, and SIG), log ins, viewed lessons, and self-monitoring entries on the intervention website (SI, SIG), and number of groups meetings attended (SIG). To measure weight loss behaviors, the authors used the Weight Control Practices questionnaire to assess engagement in core weight loss strategies targeted in treatment, and the Paffenbarger questionnaire to assess weekly kcal expended in moderate to vigorous activity. The authors also assessed costs from the payer (labor, rent, intervention materials), participant (SURI registration fee, transportation, time spent on intervention), and societal perspective (sum of payer and participant costs) in order to calculate the cost per kg of weight lost in each study arm.

Results. Participants were predominantly female, non-Hispanic white, and had a mean BMI of 34.4 kg/m² (SE = 0.05). Groups differed only on education (P = 0.02), and attendance at post-treatment and 6- and 12-month follow-up were high (93%, 91%, and 86% respectively). The authors found that weight loss did not differ by educational attainment (P s > 0.57).

Overall, there was a significant group-by-time interaction for weight loss (P < 0.001). Percentage weight loss at 3 months differed among the 3 groups—S: 1.1% ± 0.9%; SI: 4.2% ± 0.6%; SIG: 6.1% ± 0.6% (P s ≤ 0.04). There was also an overall group effect for percentage of individuals achieving 5% weight loss (P < 0.001). SI and SIG had higher percentages of participants who achieved a 5% weight loss than the control (SI: 42%; SIG: 54%; S: 7%; P s < 0.001) but did not differ from one another (P = 0.01). Initial weight losses and percentage of participants who achieved a 5% weight loss were largely maintained through the no-treatment follow-up phase at 6-months, but the 3 groups no longer differed from one another at 12 months (S: 1.2% [SE =0.9]; SI: 2.2% [SE = 0.6]; SIG: 3.3% [SE = 0.6]; P s > 0.05).

All groups reported significant increases in physical activity over time (p < 0.001). More reporting of weight and physical activity data on the SURI website was associated with greater percentage weight loss (r = 0.25; P < 0.001). Number of log ins and lessons viewed on the intervention website were positively associated with percentage weight loss (r = 0.45; P ≤ 0.001; and r = 0.34; P ≤ 0.001 respectively). Greater attendance to group sessions was associated with better weight outcomes (r = 0.61; P ≤ 0.001). Younger age was associated with poorer adherence, including less reporting on the SURI website, viewing of lessons, and logging in to the weight loss website.

There was a significant group-by-time effect interaction for the use of behavioral weight loss strategies (P < 0.001), and increased use of these strategies was associated with greater percentage weight loss in all 3 groups post-treatment. At 12 months, however, there were no differences between groups in the use of these strategies (P s ≤ 0.07).

Cost per kg of weight loss was similar for S ($39) and SI ($35), but both were lower than SIG ($114).

Conclusion. Both intervention arms (SI and SIG) achieved more weight loss at 6 months than SURI alone. Although mean weight loss was greatest with optional group sessions (SIG), the addition of the behavioral intervention website alone (SI) was the most cost-effective method to enhance weight loss. Thus, adding a novel internet behavioral weight loss intervention to a statewide community health initiative may be a cost-effective approach to improving obesity treatment outcomes.

Commentary

Weight loss treatment is recommended for adults with a BMI of > 30 kg/m², as well as those with BMI < 25 kg/m² with weight-related comorbidities [1]. Intensive behavioral treatment should be the first line of intervention for overweight and obese individuals and can lead to 8% to 10% weight loss [2], particularly in initial months of treatment [3]. However, behavioral treatment is inherently challenging and time-consuming, and readily available to only a fraction of the intended population. Although weight losses achieved from intensive lifestyle interventions such as the Diabetes Prevention Program (DPP) [4] may be higher, innovative community weight loss programs that use a variety of weight loss strategies can provide opportunities to a wider population of overweight and obese individuals and at a lower cost [3].

This study built upon the authors’ previous work [5], which showed that SURI participants with behavioral weight loss strategies via email significantly improved 3-month weight losses. In this current study, they compared SURI alone to SURI with additional access to an internet behavioral weight loss website with or without optional group sessions. Since significant weight loss was not maintained at 12 months, this suggests that perhaps access to the behavioral weight loss website should have continued for longer and/or included a maintenance phase after the 3-month intervention. Weight loss often reaches its peak around 6 months, and weight regain occurs without effective maintenance therapy [6].

General strengths of the study included the use of a randomized, intention-to-treat design, dissemination of evidence-based weight loss strategies, objective outcomes measurement, adherence metrics, and strong retention of participants with clear accounting of all enrolled patients from recruitment through analysis. This study demonstrated significant weight loss in an intervention with minimal/optional health professional interaction. This intervention also placed responsibility on participants to self-monitor their diet and physical activity, participate in online lessons, and attend optional group sessions. The success of this community-based intervention suggests feasibility and scalability within a real-world setting. The authors also conducted cost-effectiveness analyses demonstrating that the SI program was more cost-effective than SIG.

However, there are weaknesses as well. In setting the sample size for each arm of this study, no justification was described for choosing a 1:2:2 randomization scheme. In randomized control trials, the allocation of participants into the different study arms is often balanced to equal numbers which maximizes statistical power [7]. However, the use of unequal randomization ratios among study arms can be beneficial and even necessary for various reasons including cost, availability of the intervention, overcoming intervention/treatment learning curves, and if a higher drop-out rate is anticipated. Providing a justification for unbalanced sample sizes would be helpful to future researchers looking to replicate the study. Additionally, participants were mostly non-Hispanic white and female, thus limiting generalizability. While representative of the broader Rhode Island population, findings based on this population this may not be applicable to vulnerable (ie, low literacy, resource-poor) or underrepresented populations (ie, minorities) [8].

Applications for Clinical Practice

An internet-based behavioral weight loss intervention, when added to a community weight management initiative, is cost-effective and can lead to short-term weight loss. Given that clinicians often lack time, training, and resources to adequately address obesity in the office [9,10], encouraging patients to enroll in similar programs may be an effective strategy to address such barriers. The study also highlights the need for maintenance interventions to help keep weight off. Findings should be replicated in more diverse communities.

—Katrina F. Mateo, MPH, and Melanie Jay, MD, MS

Study Overview

Objective. To determine the efficacy and cost-effectiveness of adding an evidence-based internet behavioral weight loss intervention alone or combined with optional group sessions to ShapeUp Rhode Island 2011 (SURI), a 3-month statewide wellness campaign.

Design. 3-arm randomized clinical trial.

Setting and participants. Study participants were recruited from the Rhode Island community via employers, media, and mass mailings at the time of SURI 2011 registration. Of the 3806 participants that joined the weight loss division, 1139 were willing to be contacted for research, and the first 431 were screened for study eligibility. Exclusion criteria were minimal: age < 18 years or > 70 years, body mass index (BMI) < 25 kg/m², pregnant, nursing, or plans to become pregnant, a serious medical condition (eg, cancer), unreliable internet access, non-English speaking, current or previous participation in our weight loss studies, and planned relocation. Those who reported a medical condition that could interfere with safe participation (eg, diabetes) obtained doctor’s consent to participate. Of those screened, 230 met inclusion criteria, completed orientation procedures, and were randomized using a 1:2:2 randomization scheme to the standard SURI program (S; n = 46); SURI plus internet behavioral weight loss intervention (SI; n = 90); or SURI plus internet behavioral weight loss intervention plus optional group sessions (SIG; n = 94). To avoid contamination, individuals on the same SURI team (see below) were randomized to the same intervention.

Intervention. Participants in the standard SURI program did not receive any behavioral weight loss treatment. SURI is a self-sustaining, annual community campaign designed to help Rhode Islanders lose weight and increase their physical activity through an online, team-based competition. Participants join in teams, enter the weight loss or physical activity division or both, and compete with other teams. Throughout the 3-month program, participants have access to a reporting SURI website where they submit their weekly weight and activity data and view their personal and team progress. They also receive paper logs to record weight and activity, a pedometer, access to newsletters and community workshops, and recognition for meeting goals.

Participants in the SI arm received the 3-month SURI program plus a 3-month internet behavioral weight loss intervention. Before SURI began, SI participants attended a 1-hour group meeting during which they received their weight loss goal (lose 1 to 2 pounds per week), calorie and fat gram goal (starting weight < 250 lbs: 1200–1500 kcal/day, 40–50 g of fat; starting weight ≥ 250 lbs: 1500–1800 kcal/day, 50–60 g of fat), and activity goal (gradually increase to 200 minutes of aerobic activity per week). During this session, participants were also taught self-monitoring skills and oriented to an internet behavioral weight loss intervention website developed by the authors. The intervention website included 12 weekly, 10- to 15-minute multimedia lessons based on the Diabetes Prevention Program and a self-monitoring platform where participants tracked their daily weight, calorie, and activity information. Participants received weekly automated feedback on their progress. The intervention website also included information on meal plans, prepackaged meals, and meal replacements.

Participants in the SIG arm received everything in SI and were additionally given the option to attend weekly group meetings at Miriam Hospital’s Weight Control and Diabetes Research Center during the 3 months. The 12 weekly, optional group sessions were led by masters-level staff with extensive training in behavioral weight loss. Sessions involved private weigh-ins and covered topics that supplemented the internet intervention (eg, recipe modification, portion control).

Main outcomes measures. The main outcome was weight loss at the end of the 3-month program. Participants completed measures (ie, weight, BMI) in person at baseline and 3 months (post-treatment), and at 6- and 12-month follow-up visits. Adherence measures included reported weight and physical activity on the SURI website (S, SI, and SIG), log ins, viewed lessons, and self-monitoring entries on the intervention website (SI, SIG), and number of groups meetings attended (SIG). To measure weight loss behaviors, the authors used the Weight Control Practices questionnaire to assess engagement in core weight loss strategies targeted in treatment, and the Paffenbarger questionnaire to assess weekly kcal expended in moderate to vigorous activity. The authors also assessed costs from the payer (labor, rent, intervention materials), participant (SURI registration fee, transportation, time spent on intervention), and societal perspective (sum of payer and participant costs) in order to calculate the cost per kg of weight lost in each study arm.

Results. Participants were predominantly female, non-Hispanic white, and had a mean BMI of 34.4 kg/m² (SE = 0.05). Groups differed only on education (P = 0.02), and attendance at post-treatment and 6- and 12-month follow-up were high (93%, 91%, and 86% respectively). The authors found that weight loss did not differ by educational attainment (P s > 0.57).

Overall, there was a significant group-by-time interaction for weight loss (P < 0.001). Percentage weight loss at 3 months differed among the 3 groups—S: 1.1% ± 0.9%; SI: 4.2% ± 0.6%; SIG: 6.1% ± 0.6% (P s ≤ 0.04). There was also an overall group effect for percentage of individuals achieving 5% weight loss (P < 0.001). SI and SIG had higher percentages of participants who achieved a 5% weight loss than the control (SI: 42%; SIG: 54%; S: 7%; P s < 0.001) but did not differ from one another (P = 0.01). Initial weight losses and percentage of participants who achieved a 5% weight loss were largely maintained through the no-treatment follow-up phase at 6-months, but the 3 groups no longer differed from one another at 12 months (S: 1.2% [SE =0.9]; SI: 2.2% [SE = 0.6]; SIG: 3.3% [SE = 0.6]; P s > 0.05).

All groups reported significant increases in physical activity over time (p < 0.001). More reporting of weight and physical activity data on the SURI website was associated with greater percentage weight loss (r = 0.25; P < 0.001). Number of log ins and lessons viewed on the intervention website were positively associated with percentage weight loss (r = 0.45; P ≤ 0.001; and r = 0.34; P ≤ 0.001 respectively). Greater attendance to group sessions was associated with better weight outcomes (r = 0.61; P ≤ 0.001). Younger age was associated with poorer adherence, including less reporting on the SURI website, viewing of lessons, and logging in to the weight loss website.

There was a significant group-by-time effect interaction for the use of behavioral weight loss strategies (P < 0.001), and increased use of these strategies was associated with greater percentage weight loss in all 3 groups post-treatment. At 12 months, however, there were no differences between groups in the use of these strategies (P s ≤ 0.07).

Cost per kg of weight loss was similar for S ($39) and SI ($35), but both were lower than SIG ($114).

Conclusion. Both intervention arms (SI and SIG) achieved more weight loss at 6 months than SURI alone. Although mean weight loss was greatest with optional group sessions (SIG), the addition of the behavioral intervention website alone (SI) was the most cost-effective method to enhance weight loss. Thus, adding a novel internet behavioral weight loss intervention to a statewide community health initiative may be a cost-effective approach to improving obesity treatment outcomes.

Commentary

Weight loss treatment is recommended for adults with a BMI of > 30 kg/m², as well as those with BMI < 25 kg/m² with weight-related comorbidities [1]. Intensive behavioral treatment should be the first line of intervention for overweight and obese individuals and can lead to 8% to 10% weight loss [2], particularly in initial months of treatment [3]. However, behavioral treatment is inherently challenging and time-consuming, and readily available to only a fraction of the intended population. Although weight losses achieved from intensive lifestyle interventions such as the Diabetes Prevention Program (DPP) [4] may be higher, innovative community weight loss programs that use a variety of weight loss strategies can provide opportunities to a wider population of overweight and obese individuals and at a lower cost [3].

This study built upon the authors’ previous work [5], which showed that SURI participants with behavioral weight loss strategies via email significantly improved 3-month weight losses. In this current study, they compared SURI alone to SURI with additional access to an internet behavioral weight loss website with or without optional group sessions. Since significant weight loss was not maintained at 12 months, this suggests that perhaps access to the behavioral weight loss website should have continued for longer and/or included a maintenance phase after the 3-month intervention. Weight loss often reaches its peak around 6 months, and weight regain occurs without effective maintenance therapy [6].

General strengths of the study included the use of a randomized, intention-to-treat design, dissemination of evidence-based weight loss strategies, objective outcomes measurement, adherence metrics, and strong retention of participants with clear accounting of all enrolled patients from recruitment through analysis. This study demonstrated significant weight loss in an intervention with minimal/optional health professional interaction. This intervention also placed responsibility on participants to self-monitor their diet and physical activity, participate in online lessons, and attend optional group sessions. The success of this community-based intervention suggests feasibility and scalability within a real-world setting. The authors also conducted cost-effectiveness analyses demonstrating that the SI program was more cost-effective than SIG.

However, there are weaknesses as well. In setting the sample size for each arm of this study, no justification was described for choosing a 1:2:2 randomization scheme. In randomized control trials, the allocation of participants into the different study arms is often balanced to equal numbers which maximizes statistical power [7]. However, the use of unequal randomization ratios among study arms can be beneficial and even necessary for various reasons including cost, availability of the intervention, overcoming intervention/treatment learning curves, and if a higher drop-out rate is anticipated. Providing a justification for unbalanced sample sizes would be helpful to future researchers looking to replicate the study. Additionally, participants were mostly non-Hispanic white and female, thus limiting generalizability. While representative of the broader Rhode Island population, findings based on this population this may not be applicable to vulnerable (ie, low literacy, resource-poor) or underrepresented populations (ie, minorities) [8].

Applications for Clinical Practice

An internet-based behavioral weight loss intervention, when added to a community weight management initiative, is cost-effective and can lead to short-term weight loss. Given that clinicians often lack time, training, and resources to adequately address obesity in the office [9,10], encouraging patients to enroll in similar programs may be an effective strategy to address such barriers. The study also highlights the need for maintenance interventions to help keep weight off. Findings should be replicated in more diverse communities.

—Katrina F. Mateo, MPH, and Melanie Jay, MD, MS