Digital self-monitoring: Does adherence or association with outcomes differ by self-monitoring target?

Exploring factors of adherence to weight loss interventions in population with overweight/obesity: an umbrella review

Adherence is key for achieving the optimal benefits from a weight loss intervention. Despite the number of studies on factors that promote adherence, their findings suggest inconsistent and fragmented evidence. The aim of this study was to review the existing factors of adherence to weight loss interventions and to find factors that facilitate the design of effective intervention programs. Six databases were searched for this umbrella review; after the screening process, 21 studies were included. A total of 47 factors were identified in six groups as relevant for adherence: (i) sociodemographic (n = 7), (ii) physical activity (n = 2), (iii) dietary (n = 8), (iv) behavioral (n = 4), (v) pharmacological (n = 3), and (vi) multi-intervention (n = 23). In addition, a map of adherence factors was created. The main findings are that with respect to demographic factors, the development of personalized intervention strategies based on the characteristics of specific populations is encouraged. Moreover, self-monitoring has been shown to be effective in behavioral, dietary, and multi-interventions, while technology has shown potential in dietary, behavioral, and multi-interventions. In addition, multi-interventions are adherence-promoting strategies, although more evidence is required on adherence to pharmacological interventions. Overall, the factor map can be controlled and modified by researchers and practitioners to improve adherence to weight loss interventions.

Identifying Behavior Change Techniques in an Artificial Intelligence-Based Fitness App: A Content Analysis

In the field of artificial intelligence-based fitness apps, the effective integration of behavior change techniques (BCTs) is critical for promoting physical activity and improving health outcomes. However, the specific BCTs employed by apps and their impact on user engagement and behavior change are not explored sufficiently. This study investigates the Freeletics fitness app through a mixed-methods approach to evaluate the use of BCTs. In the quantitative analysis, fifteen unique BCTs were identified based on the Behavior Change Technique Taxonomy (V1). In the qualitative analysis, user reviews (n=400) were examined to understand perspectives on the app's effectiveness in promoting behavior change. Goal setting, action planning, self-monitoring of behavior, and social support were among the most prevalent BCTs identified in the Freeletics app, and their effectiveness in enhancing user engagement and promoting behavior change was also highlighted by user reviews. Among the areas of improvement identified in the study were the need for simplifying personalization options and addressing user concerns regarding the specificity of feedback. The study underscores the importance of integrating BCTs effectively within AI-based fitness apps to drive user engagement and facilitate behavior change. It contributes valuable insights into the design and implementation of BCTs in fitness apps and offers recommendations for developers, emphasizing the significance of goal setting, feedback mechanisms, self-monitoring, and social support. By understanding the impact of specific BCTs on user behavior and addressing user concerns, developers can create more effective fitness apps, ultimately promoting healthier lifestyles and positive behavior change.

The Role of Tele-Exercise for People with Type 2 Diabetes: A Scoping Review

BACKGROUND

Supervised exercise interventions tend to be more effective than unsupervised exercises or physical activity advice alone. However, people with type 2 diabetes may find it difficult to attend supervised exercise interventions due to several obstacles. Tele-exercise, or utilizing technology to deliver home-based exercise, might be a solution.

OBJECTIVE

This scoping review aimed to explore clinical trials investigating the impact of tele-exercise interventions in individuals with type 2 diabetes Methods: Four electronic databases were searched for the period up to January 2024 for clinical trials investigating the impact of tele-exercise on health-related outcomes in adults with type 2 diabetes.

RESULTS

Seven trials involving 460 individuals with type 2 diabetes met the inclusion criteria. In these trials, combined aerobic and resistance exercise programs were the main types delivered remotely. To deliver such programs, both synchronous (n = 4) and asynchronous (n = 3) delivery modes were adopted. Regardless of the delivery mode, all tele-exercise interventions led to improvements in various factors related to type 2 diabetes and its complications, including glycemic control, blood lipids, body composition, functional capacity, muscle strength, and quality of life. The improvements were also found to be as effective as those of supervised exercise.

CONCLUSIONS

Tele-exercise interventions seem to be feasible and as effective as supervised exercise interventions in terms of improving glycemic control, blood lipids, functional capacity, muscle strength, body composition, and quality of life for people with type 2 diabetes.

Why more successful? An analysis of participants' self-monitoring data in an online weight loss intervention

BACKGROUND

Self-monitoring is crucial for behavioral weight loss. However, few studies have examined the role of self-monitoring using mixed methods, which may hinder our understanding of its impact.

METHODS

This study examined self-monitoring data from 61 Chinese adults who participated in a 5-week online group intervention for weight loss. Participants reported their baseline Body Mass Index (BMI), weight loss motivation, and engaged in both daily quantitative self-monitoring (e.g., caloric intake, mood, sedentary behavior, etc.) and qualitative self-monitoring (e.g., daily log that summarizes the progress of weight loss). The timeliness of participants' daily self-monitoring data filling was assessed using a scoring rule. One-way repeated measurement ANOVA was employed to analyze the dynamics of each self-monitoring indicator. Correlation and regression analyses were used to reveal the relationship between baseline data, self-monitoring indicators, and weight change. Content analysis was utilized to analyze participants' qualitative self-monitoring data. Participants were categorized into three groups based on their weight loss outcomes, and a chi-square test was used to compare the frequency distribution between these groups.

RESULTS

After the intervention, participants achieved an average weight loss of 2.52 kg (SD = 1.36) and 3.99% (SD = 1.96%) of their initial weight. Daily caloric intake, weight loss satisfaction, frequency of daily log, and the speed of weight loss showed a downward trend, but daily sedentary time gradually increased. Moreover, regression analysis showed that baseline BMI, weight loss motivation, and timeliness of daily filling predicted final weight loss. Qualitative self-monitoring data analysis revealed four categories and nineteen subcategories. A significant difference in the frequency of qualitative data was observed, with the excellent group reporting a greater number of daily logs than expected in all categories and most subcategories, and the moderate and poor groups reporting less than expected in all categories and most subcategories.

CONCLUSION

The self-monitoring data in short-term online group intervention exhibited fluctuations. Participants with higher baseline BMI, higher levels of weight loss motivation, and timely self-monitoring achieved more weight loss. Participants who achieved greater weight loss reported a higher quantity of qualitative self-monitoring data. Practitioners should focus on enhancing dieters' weight loss motivation and promote adherence to self-monitoring practices.

Patterns and Predictors of Engagement With Digital Self-Monitoring During the Maintenance Phase of a Behavioral Weight Loss Program: Quantitative Study

BACKGROUND

Long-term self-monitoring (SM) of weight, diet, and exercise is commonly recommended by behavioral weight loss (BWL) treatments. However, sustained SM engagement is notoriously challenging; therefore, more must be learned about patterns of engagement with digital SM tools during weight loss maintenance (WLM). In addition, insight into characteristics that may influence SM engagement could inform tailored approaches for participants at risk for poor adherence.

OBJECTIVE

This study explored patterns of digital SM of weight, diet, and exercise during WLM (aim 1) and examined timing, patterns, and rates of disengagement and reengagement (aim 2). This study also assessed relationships between individual-level factors (weight-related information avoidance and weight bias internalization) and SM engagement (aim 3).

METHODS

Participants were 72 adults enrolled in a BWL program consisting of a 3-month period of weekly treatment designed to induce weight loss (phase I), followed by a 9-month period of less frequent contact to promote WLM (phase II). Participants were prescribed daily digital SM of weight, diet, and exercise. At baseline, self-report measures assessed weight-related information avoidance and weight bias internalization. SM adherence was objectively measured with the days per month that participants tracked weight, diet, and exercise. Repeated-measures ANOVA examined differences in adherence across SM targets. Multilevel modeling examined changes in adherence across phase II. Relationships between individual-level variables and SM adherence were assessed with Pearson correlations, 2-tailed independent samples t tests, and multilevel modeling.

RESULTS

During WLM, consistently high rates of SM (≥50% of the days in each month) were observed for 61% (44/72) of the participants for exercise, 40% (29/72) of the participants for weight, and 21% (15/72) of the participants for diet. Adherence for SM of exercise was higher than that for weight or diet (P<.001). Adherence decreased over time for all SM targets throughout phase II (P<.001), but SM of exercise dropped off later in WLM (mean 10.07, SD 2.83 months) than SM of weight (mean 7.92, SD 3.23 months) or diet (mean 7.58, SD 2.92 months; P<.001). Among participants with a period of low SM adherence (ie, <50% of the days in a month), only 33% (17/51 for weight, 19/57 for diet) to 46% (13/28 for exercise) subsequently had ≥1 months with high adherence. High weight-related information avoidance predicted a faster rate of decrease in dietary SM (P<.001). Participants with high weight bias internalization had the highest rates of weight SM (P=.03).

CONCLUSIONS

Participants in BWL programs have low adherence to the recommendation to sustain daily SM during WLM, particularly for SM of diet and weight. Weight-related information avoidance and weight bias internalization may be relevant indicators for SM engagement. Interventions may benefit from innovative strategies that target participants at key moments of risk for disengagement.

Collecting health-related research data using consumer-based wireless smart scales

BACKGROUND

Serious public-health concerns such as overweight and obesity are in many cases caused by excess intake of food combined with decreases in physical activity. Smart scales with wireless data transfer can, together with smart watches and trackers, observe changes in the population's health. They can present us with a picture of our metabolism, body health, and disease risks. Combining body composition data with physical activity measurements from devices such as smart watches could contribute to building a human digital twin.

OBJECTIVE

The objectives of this study were to (1) investigate the evolution of smart scales in the last decade, (2) map status and supported sensors of smart scales, (3) get an overview of how smart scales have been used in research, and (4) identify smart scales for current and future research.

METHOD

We searched for devices through web shops and smart scale tests/reviews, extracting data from the manufacturer's official website, user manuals when available, and data from web shops. We also searched scientific literature databases for smart scale usage in scientific papers.

RESULT

We identified 165 smart scales with a wireless connection from 72 different manufacturers, released between 2009 and end of 2021. Of these devices, 49 (28%) had been discontinued by end of 2021. We found that the use of major variables such as fat and muscle mass have been as good as constant over the years, and that minor variables such as visceral fat and protein mass have increased since 2015. The main contribution is a representative overview of consumer grade smart scales between 2009 and 2021.

CONCLUSION

The last six years have seen a distinct increase of these devices in the marketplace, measuring body composition with bone mass, muscle mass, fat mass, and water mass, in addition to weight. Still, the number of research projects featuring connected smart scales are few. One reason could be the lack of professionally accurate measurements, though trend analysis might be a more feasible usage scenario.

A Tailored Gender-Sensitive mHealth Weight Loss Intervention (I-GENDO): Development and Process Evaluation

BACKGROUND

Given the increase in the prevalence of overweight and obesity worldwide, the number of digital weight loss interventions has also risen. However, these interventions often lack theoretical background and data on long-term effectiveness. The consideration of individual and gender differences in weight-related psychological parameters might enhance the efficacy and sustainability of mobile-based weight loss interventions.

OBJECTIVE

This paper presented an introduction to and the process evaluation of a 12-week gender-sensitive mobile health (mHealth) weight loss intervention (I-GENDO) combining computer-based and self-tailoring features.

METHODS

Between August 2020 and August 2021, individuals with overweight (BMI 25.0-29.9 kg/m²), those with obesity class I (BMI 30.0-34.9 kg/m²), and those with obesity class II (BMI 35.0-39.9 kg/m²) were recruited to the I-GENDO project, a multicenter study in Germany. The mHealth intervention aimed at targeting individual psychological factors associated with the development and persistence of overweight and obesity (eg, emotional eating) using computer-based tailoring. Moreover, the intervention took a gender-sensitive approach by implementing self-tailoring of gender-targeted module versions. The computer-based assignment of the main modules, self-selection of gender-targeted module versions, and use patterns were evaluated while considering gender. Moreover, gender differences in the usability assessment were analyzed.

RESULTS

Data from the intervention arm of the study were processed. A total of 116 individuals with overweight and obesity (77/116, 66.4% women; age mean 47.28, SD 11.66 years; BMI mean 33.58, SD 3.79 kg/m2) were included in the analyses. Overall, the compliance (90/109, 82.6%) and satisfaction with the app (mean 86% approval) were high and comparable with those of other mobile weight loss interventions. The usability of the intervention was rated with 71% (5.0/7.0 points) satisfaction. More women obtained the main module that focused on emotion regulation skills. Most men and women selected women-targeted versions of the main modules. Women used the app more frequently and longer than men. However, women and men did not differ in the progress of use patterns throughout the intervention.

CONCLUSIONS

We developed a tailored gender-sensitive mHealth weight loss intervention. The usability of and engagement with the intervention were satisfactory, and the overall satisfaction with the intervention was also high. Gender differences must be considered in the evaluation of the effectiveness and sustainability of the intervention.

Remotely Conducted App-Based Intervention for Cardiovascular Disease and Diabetes Risk Awareness and Prevention: Single-Group Feasibility Trial

BACKGROUND

Cardiovascular disease and type 2 diabetes mellitus are two of the most prevalent chronic conditions worldwide. An unhealthy lifestyle greatly contributes to someone's risk of developing these conditions. Mobile health is an emerging technology that can help deliver health promotion interventions to the population, for example, in the form of health apps.

OBJECTIVE

The aim of this study was to test the feasibility of an app-based intervention for cardiovascular and diabetes risk awareness and prevention by measuring nonusage, dropout, adherence to app use, and usability of the app over 3 months.

METHODS

Participants were eligible if they were aged 45 years or older, resided in Australia, were free of cardiovascular disease and diabetes, were fluent in English, and owned a smartphone. In the beginning, participants received an email with instructions on how to install the app and a user guide. After 3 months, they received an email with an invitation to an end-of-study survey. The survey included questions about general smartphone use and the user version of the Mobile Application Rating Scale. We analyzed app-generated and survey data by using descriptive and inferential statistics as well as thematic analysis for open-text comments.

RESULTS

Recruitment took place between September and October 2021. Of the 46 participants who consented to the study, 20 (44%) never used the app and 15 (33%) dropped out. The median age of the app users at baseline was 62 (IQR 56-67) years. Adherence to app use, that is, using the app at least once a week over 3 months, was 17% (8/46) of the total sample and 31% (8/26) of all app users. The mean app quality rating on the user version of the Mobile Application Rating Scale was 3.5 (SD 0.6) of 5 points. The app scored the highest for the information section and the lowest for the engagement section of the scale.

CONCLUSIONS

Nonusage and dropouts were too high, and the adherence was too low to consider the intervention in its current form feasible. Potential barriers that we identified include the research team not actively engaging with participants early in the study to verify that all participants could install the app, the intervention did not involve direct contact with health care professionals, and the app did not have enough interactive features.

Self-Weighing Behaviors of Diverse Community-Dwelling Adults Motivated for a Lifestyle Change

We aimed to understand adults’ self-weighing behaviors and explore significant predictors of body mass index (BMI) accuracy based on self-reported height and weight in a diverse sample of community-dwelling adults. Methods: In this cross-sectional study, 531 adults participating in a physical activity program or a weight loss program were analyzed. Participants’ self-reported and objectively measured weight, height, weight scale ownership, self-weighing behaviors, and medical history were collected. Results: The mean age (standard deviation) was 50.0 (12.0) years with a range of 24 to 78 years. Out of 531 participants, 455 (85.7%) were women. The study population was diverse (58.9% non-White). In total, 409 (77.0%) participants had a weight scale at home, but only 222 (41.8%) weighed themselves at least once a week. The weight and BMI underestimation became much more significant as the participant’s weight increased (p ≤ 0.001). Employment status, high cholesterol, and low objectively measured weight were significant predictors of self-reported BMI accuracy after controlling for potential confounding factors (p < 0.05). Interestingly, ownership of a home weight scale and the frequency of self-weighing behavior were not significantly associated with the accuracy of self-reported BMI (p > 0.05). Conclusion: The accuracy of the participants’ BMI, based on self-reported height and weight, was significantly associated with employment status, high cholesterol, and low objectively measured weight, suggesting that BMI accuracy depends on multi factors.

The Effect of Tailored, Daily Smartphone Feedback to Lifestyle Self-Monitoring on Weight Loss at 12 Months: The SMARTER Randomized Clinical Trial (Preprint)

Background

Self-monitoring (SM) is the centerpiece of behavioral weight loss treatment, but the efficacy of smartphone-delivered SM feedback (FB) has not been tested in large, long-term, randomized trials.

Objective

The aim of this study was to establish the efficacy of providing remote FB to diet, physical activity (PA), and weight SM on improving weight loss outcomes when comparing the SM plus FB (SM+FB) condition to the SM-only condition in a 12-month randomized controlled trial. The study was a single-site, population-based trial that took place in southwestern Pennsylvania, USA, conducted between 2018 and 2021. Participants were smartphone users age ≥18 years, able to engage in moderate PA, with a mean BMI between 27 and 43 kg/m².

Methods

All participants received a 90-minute, one-to-one, in-person behavioral weight loss counseling session addressing behavioral strategies, establishing participants’ dietary and PA goals, and instructing on use of the PA tracker (Fitbit Charge 2), smart scale, and diet SM app. Only SM+FB participants had access to an investigator-developed smartphone app that read SM data, in which an algorithm selected tailored messages sent to the smartphone up to 3 times daily. The SM-only participants did not receive any tailored FB based on SM data. The primary outcome was percent weight change from baseline to 12 months. Secondary outcomes included engagement with digital tools (eg, monthly percentage of FB messages opened and monthly percentage of days adherent to the calorie goal).

Results

Participants (N=502) were on average 45.0 (SD 14.4) years old with a mean BMI of 33.7 (SD 4.0) kg/m². The sample was 79.5% female (n=399/502) and 82.5% White (n=414/502). At 12 months, retention was 78.5% (n=394/502) and similar by group (SM+FB: 202/251, 80.5%; SM: 192/251, 76.5%; P=.28). There was significant percent weight loss from baseline in both groups (SM+FB: –2.12%, 95% CI –3.04% to –1.21%, P<.001; SM: –2.39%, 95% CI –3.32% to –1.47%; P<.001), but no difference between the groups (–0.27%; 95% CI –1.57% to 1.03%; t =–0.41; P=.68). Similarly, 26.3% (66/251) of the SM+FB group and 29.1% (73/251) of the SM group achieved ≥5% weight loss (chi-square value=0.49; P=.49). A 1% increase in FB messages opened was associated with a 0.10 greater percent weight loss at 12 months (b=–0.10; 95% CI –0.13 to –0.07; t =–5.90; P<.001). A 1% increase in FB messages opened was associated with 0.12 greater percentage of days adherent to the calorie goal per month (b=0.12; 95% CI 0.07-0.17; F=22.19; P<.001).

Conclusions

There were no significant between-group differences in weight loss; however, the findings suggested that the use of commercially available digital SM tools with or without FB resulted in a clinically significant weight loss in over 25% of participants. Future studies need to test additional strategies that will promote greater engagement with digital tools.

Trial Registration

Clinicaltrials.gov NCT03367936; https://clinicaltrials.gov/ct2/show/NCT03367936

Beyond Calorie Tracking: A Pilot Trial of a Remotely Delivered Behavioral Weight Loss Intervention Using an Ad Libitum Plant-Based Diet (Preprint)

Background

Many traditional lifestyle interventions use calorie prescriptions, but most individuals have difficulty sustaining calorie tracking and thus weight loss. In contrast, whole food plant-based diets (WFPBDs) have previously shown significant weight loss without this issue. However, most WFPBD interventions are face-to-face and time-intensive, and do not leverage gold standard behavioral strategies for health behavior change.

Objective

This open pilot trial was the first to evaluate the feasibility of a fully featured, remotely delivered behavioral weight loss intervention using an ad libitum WFPBD.

Methods

Over 12 weeks, participants (N=15) with overweight or obesity received a newly designed program that integrated behavioral weight loss and a WFPBD prescription via weekly web-based modules and brief phone coaching calls. Assessments were performed at baseline, midtreatment (6 weeks), and after treatment (12 weeks).

Results

The intervention was rated as highly acceptable (mean 4.40 out of 5, SE 0.18), and attrition was low (6.7%). In all, intention-to-treat analyses revealed that 69% (10.4/15) of the participants lost 5% of their weight (mean –5.89, SE 0.68 kg). Predefined benchmarks for quality of life were met.

Conclusions

A pilot digital behavioral weight loss intervention with a non–energy-restricted WFPBD was feasible, and the mean acceptability was high. Minimal contact time (80-150 minutes of study interventionist time per participant over 12 weeks) led to clinically relevant weight loss and dietary adherence for most participants (10.4/15, 69% and 11.8/15, 79%, respectively), and quality of life improvements (reliable change indices >1.53). We hope that this work will serve as a springboard for future larger scale randomized controlled studies evaluating the efficacy of such programs for weight loss, dietary change, and quality of life.

Trial Registration

ClinicalTrials.gov NCT04892030; https://clinicaltrials.gov/ct2/show/NCT04892030

Consistency With and Disengagement From Self-monitoring of Weight, Dietary Intake, and Physical Activity in a Technology-Based Weight Loss Program: Exploratory Study

BACKGROUND

Digital self-monitoring tools offer promise to improve adherence to self-monitoring of weight and weight-related behaviors; however, less is known regarding the patterns of participant consistency and disengagement with these tools.

OBJECTIVE

This study characterizes the consistency of use and time to disengagement with digital self-monitoring tools during a 6-month weight loss intervention and investigates whether the provision of phone-based intervention improved self-monitoring adherence.

METHODS

Participants were 54 adults with overweight or obesity (mean age 49.6 years, SD 12.4 years; mean BMI 32.6 kg/m², SD 3.2 kg/m²) enrolled in a pilot trial assessing the impact of self-monitoring technology (Fitbit Zip, Aria scale, and smartphone app), with and without additional interventionist contact, on weight loss. All participants received weight loss education and were asked to self-monitor weight, dietary intake, and physical activity daily throughout the 6-month program. Consistency was defined as the number of weeks that participants adhered to self-monitoring recommendations (7 out of 7 days). Disengagement was defined as the first of 2 consecutive weeks that the 7-day self-monitoring adherence goal was not met. Wilcoxon signed-rank tests were used to examine differences in consistency and disengagement by behavioral targets. t tests (2-tailed) and Cox proportional hazards models were used to examine whether providing additional interventionist contact would lead to significant improvements in consistency and time to disengagement from self-monitoring tools, respectively. Linear regressions were used to examine associations between consistency, time to disengagement, and weight loss.

RESULTS

Participants consistently self-monitored physical activity for more weeks (mean 17.4 weeks, SD 8.5 weeks) than weight (mean 11.1 weeks, SD 8.5 weeks) or dietary intake (mean 10.8 weeks, SD 8.7 weeks; P<.05). Similarly, participants had a significantly longer time to disengagement from self-monitoring of physical activity (median 19.5 weeks) than weight (4 weeks) or dietary intake (10 weeks; P<.001). Participants randomized to receive additional interventionist contact had significantly greater consistency and longer time to disengagement for self-monitoring of dietary intake compared with participants who did not (P=.006); however, there were no statistically significant differences between groups for self-monitoring of weight or physical activity (P=.24 and P=.25, respectively). Greater consistency and longer time to disengagement were associated with greater weight loss for self-monitoring of weight and dietary intake (P<.001 and P=.004, respectively) but not for physical activity (P=.57).

CONCLUSIONS

Results demonstrated that self-monitoring adherence differed by behavioral target, with greater consistency and longer time to disengagement associated with lower-burden tools (ie, self-monitoring of physical activity). Consistent with supportive accountability theory, additional interventionist contact improved consistency and lengthened time to disengagement from self-monitoring of dietary intake. Given the observed associations between consistency, disengagement, and weight loss outcomes, it is important to identify additional methods of increasing consistency and engagement with digital self-monitoring tools.

Describing Transitions in Adherence to Physical Activity Self-monitoring and Goal Attainment in an Online Behavioral Weight Loss Program: Secondary Analysis of a Randomized Controlled Trial

Background

Standard behavioral weight loss interventions often set uniform physical activity (PA) goals and promote PA self-monitoring; however, adherence remains a challenge, and recommendations may not accommodate all individuals. Identifying patterns of PA goal attainment and self-monitoring behavior will offer a deeper understanding of how individuals adhere to different types of commonly prescribed PA recommendations (ie, minutes of moderate-to-vigorous physical activity [MVPA] and daily steps) and guide future recommendations for improved intervention effectiveness.

Objective

This study examined weekly patterns of adherence to step-based and minute-based PA goals and self-monitoring behavior during a 6-month online behavioral weight loss intervention.

Methods

Participants were prescribed weekly PA goals for steps (7000-10,000 steps/day) and minutes of MVPA (50-200 minutes/week) as part of a lifestyle program. Goals gradually increased during the initial 2 months, followed by 4 months of fixed goals. PA was self-reported daily on the study website. For each week, participants were categorized as adherent if they self-monitored their PA and met the program PA goal, suboptimally adherent if they self-monitored but did not meet the program goal, or nonadherent if they did not self-monitor. The probability of transitioning into a less adherent status was examined using multinomial logistic regression.

Results

Participants (N=212) were predominantly middle-aged females with obesity, and 67 (31.6%) self-identified as a racial/ethnic minority. Initially, 73 (34.4%) participants were categorized as adherent to step-based goals, with 110 [51.9%] suboptimally adherent and 29 [13.7%] nonadherent, and there was a high probability of either remaining suboptimally adherent from week to week or transitioning to a nonadherent status. However, 149 (70.3%) participants started out adherent to minute-based goals (34 [16%] suboptimally adherent and 29 [13.7%] nonadherent), with suboptimally adherent seen as the most variable status. During the graded goal phase, participants were more likely to transition to a less adherent status for minute-based goals (odds ratio [OR] 1.39, 95% CI 1.31-1.48) compared to step-based goals (OR 1.24, 95% CI 1.17-1.30); however, no differences were seen during the fixed goal phase (minute-based goals: OR 1.06, 95% CI 1.05-1.08; step-based goals: OR 1.07, 95% CI 1.05-1.08).

Conclusions

States of vulnerability to poor PA adherence can emerge rapidly and early in obesity treatment. There is a window of opportunity within the initial 2 months to bring more people toward adherent behavior, especially those who fail to meet the prescribed goals but engage in self-monitoring. Although this study describes the probability of adhering to step- and minute-based targets, it will be prudent to determine how individual characteristics and contextual states relate to these behavioral patterns, which can inform how best to adapt interventions.

Trial Registration

ClinicalTrials.gov NCT02688621; https://clinicaltrials.gov/ct2/show/NCT02688621

The Relationship Between Weight Loss Outcomes and Engagement in a Mobile Behavioral Change Intervention: Retrospective Analysis

Background

There is large variance in weight loss outcomes of digital behavior change interventions (DBCIs). It has been suggested that different patterns of engagement in the program could be responsible for this variance in outcomes. Previous studies have found that the amount of engagement on DBCIs, such as the number of meals logged or articles read, is positively associated with weight loss.

Objective

This retrospective study extends previous research by observing how important weight loss outcomes (high weight loss: 10% or greater body weight loss; moderate weight loss: between 5% to 10%; stable weight: 0 plus or minus 1%) are associated with engagement on a publicly available mobile DBCI (Noom) from 9 to 52 weeks.

Methods

Engagement and weight data for eligible participants (N=11,252) were extracted from the Noom database. Engagement measures included the number of articles read, meals logged, steps recorded, messages to coach, exercise logged, weigh-ins, and days with 1 meal logged per week. Weight was self-reported on the program. Multiple linear regressions examined how weight loss outcome (moderate and high vs stable) was associated with each engagement measure across 3 study time periods: 9-16 weeks, 17-32 weeks, and 33-52 weeks.

Results

At 9-16 weeks, among the 11,252 participants, 2594 (23.05%) had stable weight, 6440 (57.23%) had moderate weight loss, and 2218 (19.71%) had high weight loss. By 33-52 weeks, 525 (18.21%) had stable weight, 1214 (42.11%) had moderate weight loss, and 1144 (39.68%) had high weight loss. Regression results showed that moderate weight loss and high weight loss outcomes were associated with all engagement measures to a significantly greater degree than was stable weight (all P values <.001). These differences held across all time periods with the exception of exercise for the moderate weight loss category at 1 time period of 33-52 weeks. Exercise logging increased from 9 to 52 weeks regardless of the weight loss group.

Conclusions

Our results suggest that these clinically important weight loss outcomes are related to the number of articles read, meals logged, steps recorded, messages to coach, exercise logged, weigh-ins, and days with 1 meal logged per week both in the short-term and long-term (ie, 1 year) on Noom. This provides valuable data on engagement patterns over time on a self-directed mobile DBCI, can help inform how interventions tailor recommendations for engagement depending on how much weight individuals have lost, and raises important questions for future research on engagement in DBCIs.

A Pilot Randomized Controlled Trial of a Fitbit- and Facebook-Based Physical Activity Intervention for Young Adult Cancer Survivors

Purpose: Most young adult cancer survivors (YACS) do not meet physical activity (PA) guidelines. Although PA can improve health and quality of life (QOL), few randomized controlled trials (RCTs) of PA interventions for YACS exist. We conducted a pilot RCT to test feasibility of a PA intervention among YACS. Methods: We recruited 18-39-year-olds (≥1 and <5 years postcancer therapy) from Seattle Cancer Care Alliance. The 12-week intervention involved a wrist-worn PA-tracking device (Fitbit), a peer-based Facebook support group, step count goal setting, and a self-selected support "buddy." Controls received Fitbit only. Baseline assessments occurred before randomization; follow-up assessments occurred during intervention weeks 10-12. Feasibility criteria are listed below. Exploratory outcomes included PA, sedentary time (ST), QOL measures (e.g., fatigue), and self-determination theory (SDT) construct measures. Results: All feasibility criteria were met: We recruited 50 YACS, intervention participants wore the Fitbit on the majority of intervention days (82.9%), ≥75% of participants completed questionnaires at baseline (100%) and follow-up (93.9%). Exploratory analyses, adjusted for wave, accelerometer wear time, race, and income, showed significant group differences for change in ST (-52.4 vs. 2.5 minutes/day; p = 0.002) but no change in moderate-to-vigorous intensity PA (0.0 vs. -0.2 minutes/day; p = 0.40), comparing intervention participants to controls. The intervention (vs. control) group had a greater increase in fatigue interference (p = 0.03). No other significant differences in SDT or QOL measures were found. Conclusion: This Fitbit and Facebook-based PA intervention was feasible to YACS, with promising effects on reducing ST, and warrants a fully powered RCT. Clinical Trial Registration no.: NCT03233581.

Weight-Related Information Avoidance Prospectively Predicts Poorer Self-Monitoring and Engagement in a Behavioral Weight Loss Intervention

BACKGROUND

Self-monitoring is a key component of behavioral weight loss (BWL) interventions. Past research suggests that individuals may avoid self-monitoring in certain contexts (e.g., skipping self-weighing after higher-than-usual calorie intake). However, no studies have attempted to quantify individuals' inclination to avoid information about their weight control ("weight-related information avoidance"; WIA) or prospectively examined its implications for treatment engagement and outcomes in BWL programs.

PURPOSE

Characterize WIA using a validated questionnaire among adults enrolled in BWL treatment and examine whether WIA prospectively predicts self-monitoring adherence, session attendance, treatment discontinuation, or weight loss.

METHODS

Participants (N = 87; MBMI = 34.9 kg/m2, 83% female) completed a measure of WIA prior to starting a 12 week, group-based BWL intervention. Participants were given digital self-monitoring tools and instructed to self-monitor their food intake daily, physical activity daily, and body weight weekly (Weeks 1-10) and then daily (Weeks 11-12). Session attendance and treatment discontinuation were recorded. Weight was measured in-clinic pretreatment and posttreatment.

RESULTS

While mean WIA was low (M = 2.23, standard deviation [SD] = 0.95; potential scale range: 1-7), greater WIA predicted poorer attendance (r = -.23; p = .03) and poorer self-monitoring of physical activity (r = -.28; p = .009) and body weight (r = -.32; p = .003). WIA did not predict food monitoring (p = .08), treatment discontinuation (p = .09), or 12 week weight loss (p = .91).

CONCLUSIONS

Greater WIA, as assessed via a brief questionnaire, may place individuals at risk for poorer self-monitoring and treatment engagement during BWL. Further research on the implications of WIA in the context of weight management is warranted, including evaluation of correlates, moderators, and mechanisms of action of WIA.

CLINICAL TRIAL REGISTRATION

NCT03337139.

eHealth interventions targeting nutrition, physical activity, sedentary behavior, or obesity in adults: A scoping review of systematic reviews

A vast body of evidence regarding eHealth interventions for nutrition, physical activity, sedentary behavior, and obesity exists. This scoping review of systematic reviews aimed to evaluate the current level of evidence in this growing field. Seven electronic databases were searched for systematic reviews published until October 27, 2019. The systematic reviews must have included adult participants only and have evaluated eHealth behavioral interventions with the primary aim of changing nutrition, physical activity, and sedentary behavior or treating or preventing overweight and obesity. One hundred and six systematic reviews, published from 2006 to 2019, were included. Almost all (n = 98) reviews evaluated the efficacy of interventions. Over half (n = 61) included interventions focused on physical activity, followed by treatment of obesity (n = 28), nutrition (n = 22), prevention of obesity (n = 18), and sedentary behavior (n = 6). Many reviews (n = 46) evaluated one type of eHealth intervention only, while 60 included two or more types. Most reviews (n = 67) were rated as being of critically low methodological quality. This scoping review identified an increasing volume of systematic reviews evaluating eHealth interventions. It highlights several evidence gaps (e.g., evaluation of other outcomes, such as reach, engagement, or cost effectiveness), guiding future research efforts in this area.

Mobile health assisted self-monitoring is acceptable for supporting weight loss in rural men: a pragmatic randomized controlled feasibility trial

BACKGROUND

Addressing overweight and obesity among men at-risk for obesity-related diseases and disability in rural communities is a public health issue. Commercial smartphone applications (apps) that promote self-monitoring for weight loss are widely available. Evidence is lacking regarding what support is required to enhance user engagement with and effectiveness of those technologies. Pragmatically comparing these apps effectiveness, including rural men's desired forms of support when using them, can lead to greater weight loss intervention impact and reach. This study assessed the feasibility and acceptability of a mobile technology application applied differently across two groups for weight loss.

METHODS

In a two-armed, pragmatic pilot feasibility study, 80 overweight and obese men aged 40-69 were randomized using a 1:1 ratio to either an enhanced Mobile Technology Plus (MT+) intervention or a basic Mobile Technology (MT) intervention. The MT+ group had an enhanced smartphone app for self-monitoring (text messaging, discussion group, Wi-Fi scale) whereas the MT group received a basic app that allowed self-monitoring logging only. Assessments were collected at baseline, 3 and 6 months. App logs were analyzed to track engagement and adherence to self-monitoring. Acceptability was assessed via focus groups. Analysis included descriptive statistics and qualitative content analysis.

RESULTS

Of 80 men recruited, forty were allocated to each arm. All were included in the primary analysis. Recruitment ended after 10 months with a 97.5 and 92.5% (3 month, 6 month) retention rate. Over 90% of men reported via survey and focus groups that Lose-It app and smart scale (MT+) was an acceptable way to self-monitor weight, dietary intake and physical activity. Adherence to daily app self-monitoring of at least 800 dietary calories or more (reported respectively as MT+, MT) was positive with 73.4, 51.6% tracking at least 5 days a week. Adherence to tracking activity via recorded steps four or more days weekly was positive, 87.8, 64.6%. Men also adhered to self-weighing at least once weekly, 64, 46.3%. At 6 months, an observed mean weight loss was 7.03 kg (95% CI: 3.67, 10.39) for MT+ group and 4.14 kg (95% CI: 2.22, 6.06) for MT group, with 42.9 and 34.2% meeting ≥5% weight loss, respectively. No adverse events were reported.

CONCLUSIONS

This National Institutes of Health-funded pilot study using mobile technologies to support behavior change for weight loss was found to be feasible and acceptable among midlife and older rural men. The interventions demonstrated successful reductions in weight, noting differing adherence to lifestyle behaviors of eating, monitoring and activity between groups, with men in the MT+ having more favorable results. These findings will be used to inform the design of a larger scale, clinical trial.

TRIAL REGISTRATION

The trial was prospectively registered with ClinicalTrials NCT03329079 . 11/1/2017.

Patterns of self-monitoring technology use and weight loss in people with overweight or obesity

Mobile applications and paired devices allow individuals to self-monitor physical activity, dietary intake, and weight fluctuation concurrently. However, little is known regarding patterns of use of these self-monitoring technologies over time and their implications for weight loss. The objectives of this study were to identify distinct patterns of self-monitoring technology use and to investigate the associations between these patterns and weight change. We analyzed data from a 6-month weight loss intervention for school district employees with overweight or obesity (N = 225). We performed repeated measures latent profile analysis (RMLPA) to identify common patterns of self-monitoring technology use and used multiple linear regression to evaluate the relationship between self-monitoring technology use and weight change. RMLPA revealed four distinct profiles: minimal users (n = 65, 29% of sample), activity trackers (n = 124, 55%), dedicated all-around users (n = 25, 11%), and dedicated all-around users with exceptional food logging (n = 11, 5%). The dedicated all-around users with exceptional food logging lost the most weight (X2[1,225] = 5.27, p = .0217). Multiple linear regression revealed that, adjusting for covariates, only percentage of days of wireless weight scale use (B = -0.05, t(212) = -3.79, p < .001) was independently associated with weight loss. We identified distinct patterns in mHealth self-monitoring technology use for tracking weight loss behaviors. Self-monitoring of weight was most consistently linked to weight loss, while exceptional food logging characterized the group with the greatest weight loss. Weight loss interventions should promote self-monitoring of weight and consider encouraging food logging to individuals who have demonstrated consistent use of self-monitoring technologies.

Self-Monitoring via Digital Health in Weight Loss Interventions: A Systematic Review Among Adults with Overweight or Obesity

OBJECTIVE

Self-monitoring is a core component of behavioral obesity treatment, but it is unknown how digital health has been used for self-monitoring, what engagement rates are achieved in these interventions, and how self-monitoring and weight loss are related.

METHODS

This systematic review examined digital self-monitoring in behavioral weight loss interventions among adults with overweight or obesity. Six databases (PubMed, Embase, Scopus, PsycInfo, CINAHL, and ProQuest Dissertations & Theses) were searched for randomized controlled trials with interventions ≥ 12 weeks, weight outcomes ≥ 6 months, and outcomes on self-monitoring engagement and their relationship to weight loss.

RESULTS

Thirty-nine studies from 2009 to 2019 met inclusion criteria. Among the 67 interventions with digital self-monitoring, weight was tracked in 72% of them, diet in 81%, and physical activity in 82%. Websites were the most common self-monitoring modality, followed by mobile applications, wearables, electronic scales, and, finally, text messaging. Few interventions had digital self-monitoring engagement rates ≥ 75% of days. Rates were higher in digital- than in paper-based arms in 21 out of 34 comparisons and lower in just 2. Interventions with counseling had similar rates to standalone interventions. Greater digital self-monitoring was linked to weight loss in 74% of occurrences.

CONCLUSIONS

Self-monitoring via digital health is consistently associated with weight loss in behavioral obesity treatment.

Counselor Surveillance of Digital Self-Monitoring Data: A Pilot Randomized Controlled Trial

OBJECTIVE

This pilot study tested counselor access to participants' digital self-monitoring (SM) data as a means of improving long-term lifestyle modification (LM) outcomes.

METHODS

After 12 weeks of weight-loss treatment, participants (N = 77) were randomized to LM or LM+SHARE for weeks 13 to 52. All participants received monthly phone calls and weekly text messages from weeks 13 to 52 and were instructed to engage in daily digital SM of weight, eating, and exercise. In LM+SHARE, but not LM, counselors had access to SM device data. Assessments were conducted as weeks 0, 13, 26, and 52.

RESULTS

Retention, engagement, and treatment satisfaction were excellent. LM+SHARE participants, compared with LM, had more frequent SM of weight and eating. Weight loss continued at a similar rate in both conditions from weeks 13 to 26. From weeks 26 to 52, those in LM regained approximately 2 kg, whereas those in LM+SHARE maintained weight loss, a significant difference. Nonetheless, total weight loss did not significantly differ by condition. Engagement in dietary SM mediated the effect of condition on weight.

CONCLUSIONS

Counselor access to SM data is feasible and acceptable. Additional research is warranted to determine whether it can meaningfully improve outcomes.