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

Identification of minimum thresholds for dietary self-monitoring to promote weight-loss maintenance

OBJECTIVE

Reduced schedules of dietary self-monitoring are typically recommended after the end of behavioral weight-loss programs; however, there exists little empirical evidence to guide these recommendations.

METHODS

We explored potential thresholds for dietary self-monitoring during a 9-month maintenance period following a 3-month weight-loss program in 74 adults with overweight or obesity (mean [SD] age = 50.7 [10.4] years, BMI = 31.2 [4.5] kg/m2) who were encouraged to self-monitor weight, dietary intake, and physical activity daily and report their adherence to self-monitoring each week via a study website.

RESULTS

Greater self-monitoring was correlated with less weight regain for thresholds of ≥3 days/week, with the largest benefit observed for thresholds of ≥5 to ≥6 days/week (all p < 0.05); significant weight gain was observed for thresholds of ≥1 to ≥2 days/week, whereas no change in weight was observed for thresholds of ≥3 to ≥4 days/week, and weight loss was observed with thresholds of ≥5 or more days/week.

CONCLUSIONS

Results demonstrate that self-monitoring at least 3 days/week may be beneficial for supporting long-term maintenance, although greater benefit (in relation to weight loss) may be realized at thresholds of 5 to 6 days/week. Future research should investigate whether individuals who were randomized to self-monitor at these different thresholds demonstrate differential patterns of weight-loss maintenance.

Understanding Heterogeneity in Individual Responses to Digital Lifestyle Intervention Through Self-Monitoring Adherence Trajectories in Adults With Overweight or Obesity: Secondary Analysis of a 6-Month Randomized Controlled Trial

BACKGROUND

Achieving clinically significant weight loss through lifestyle interventions for obesity management is challenging for most individuals. Improving intervention effectiveness involves early identification of intervention nonresponders and providing them with timely, tailored interventions. Early and frequent self-monitoring (SM) adherence predicts later weight loss success, making it a potential indicator for identifying nonresponders in the initial phase.

OBJECTIVE

This study aims to identify clinically meaningful participant subgroups based on longitudinal adherence to SM of diet, activity, and weight over 6 months as well as psychological predictors of participant subgroups from a self-determination theory (SDT) perspective.

METHODS

This was a secondary data analysis of a 6-month digital lifestyle intervention for adults with overweight or obesity. The participants were instructed to perform daily SM on 3 targets: diet, activity, and weight. Data from 50 participants (mean age: 53.0, SD 12.6 y) were analyzed. Group-based multitrajectory modeling was performed to identify subgroups with distinct trajectories of SM adherence across the 3 SM targets. Differences between subgroups were examined for changes in clinical outcomes (ie, body weight, hemoglobin A1c) and SDT constructs (ie, eating-related autonomous motivation and perceived competence for diet) over 6 months using linear mixed models.

RESULTS

Two distinct SM trajectory subgroups emerged: the Lower SM group (21/50, 42%), characterized by all-around low and rapidly declining SM, and the Higher SM group (29/50, 58%), characterized by moderate and declining diet and weight SM with high activity SM. Since week 2, participants in the Lower SM group exhibited significantly lower levels of diet (P=.003), activity (P=.002), and weight SM (P=.02) compared with the Higher SM group. In terms of clinical outcomes, the Higher SM group achieved a significant reduction in body weight (estimate: -6.06, SD 0.87 kg; P<.001) and hemoglobin A1c (estimate: -0.38, SD 0.11%; P=.02), whereas the Lower SM group exhibited no improvements. For SDT constructs, both groups maintained high levels of autonomous motivation for over 6 months. However, the Lower SM group experienced a significant decline in perceived competence (P=.005) compared with the Higher SM group, which maintained a high level of perceived competence throughout the intervention (P=.09).

CONCLUSIONS

The presence of the Lower SM group highlights the value of using longitudinal SM adherence trajectories as an intervention response indicator. Future adaptive trials should identify nonresponders within the initial 2 weeks based on their SM adherence and integrate intervention strategies to enhance perceived competence in diet to benefit nonresponders.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05071287; https://clinicaltrials.gov/study/NCT05071287.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.1016/j.cct.2022.106845.

The Effects of Providing a Connected Scale in an App-Based Digital Health Program: Cross-sectional Examination

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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.