Trial reportJMIR mHealth and uHealth2021
Assessing the Contribution of Self-Monitoring Through a Commercial Weight Loss App: Mediation and Predictive Modeling Study.
Trial report in JMIR mHealth and uHealth, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT02063178 (Dissemination of the Look Ahead Weight Management Treatment in the Military), which is not on this map. Cited by 11 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Dissemination of the Look Ahead Weight Management Treatment in the Military
Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it, 13 citations in OpenAlex.
- Randomized Controlled Trials to Treat Obesity in Military Populations: A Systematic Review and Meta-Analysis.Nutrients · 2023Pooled it
- Added Technology for Weight Management in Cardiac Rehabilitation: Biopsychosocial Factors and Rehabilitees' Talk in a Mixed Method Study.Journal of medical Internet research · 2026Trial
- An Exploratory Analysis of Patterns of Weight Change in a Post-Cessation Weight Management Trial.Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco · 2026Trial
- The effect of an app-based dietary education on dietary intake and cardiometabolic risk markers in people with type 2 diabetes: results from a randomized controlled trial.Nutrition journal · 2025Trial
- Perspectives on the Form, Magnitude, Certainty, Target, and Frequency of Financial Incentives in a Weight Loss Program.American journal of health promotion : AJHP · 2022Trial
- Assessing the Contribution of Self-Monitoring Through a Commercial Weight Loss App: Mediation and Predictive Modeling Study.JMIR mHealth and uHealth · 2021Trial
- Impacts of Behavioral Compliance on Weight-Loss and Metabolic Profile in a Smartphone App-Based Lifestyle Intervention or Plus Dietitian Supporting: A Randomized and Controlled Trial Among Chinese.Phenomics (Cham, Switzerland) · 2025Article
- Digital applications for diet monitoring, planning, and precision nutrition for citizens and professionals: a state of the art.Nutrition reviews · 2025Review
- Can Digital Technologies Be Useful for Weight Loss in Individuals with Overweight or Obesity? A Systematic Review.Healthcare (Basel, Switzerland) · 2024Review
- An Explainable Artificial Intelligence Software Tool for Weight Management Experts (PRIMO): Mixed Methods Study.Journal of medical Internet research · 2023Article
- Expert opinions on reducing dietary self-monitoring burden and maintaining efficacy in weight loss programs: A Delphi study.Obesity science & practice · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 1 institution in 1 country.
Funding
Abstract
backgroundElectronic self-monitoring technology has the potential to provide unique insights into important behaviors for inducing weight loss.
objectiveThe aim of this study is to investigate the effects of electronic self-monitoring behavior (using the commercial Lose It! app) and weight loss interventions (with differing amounts of counselor feedback and support) on 4- and 12-month weight loss.
methodsIn this secondary analysis of the Fit Blue study, we compared the results of two interventions of a randomized controlled trial. Counselor-initiated participants received consistent support from the interventionists, and self-paced participants received assistance upon request. The participants (N=191), who were active duty military personnel, were encouraged to self-monitor their diet and exercise with the Lose It! app or website. We examined the associations between intervention assignment and self-monitoring behaviors. We conducted a mediation analysis of the intervention assignment for weight loss through multiple mediators-app use (calculated from the first principal component [PC] of electronically collected variables), number of weigh-ins, and 4-month weight change. We used linear regression to predict weight loss at 4 and 12 months, and the accuracy was measured using cross-validation.
resultsOn average, the counselor-initiated-treatment participants used the app more frequently than the self-paced-treatment participants. The first PC represented app use frequencies, the second represented calories recorded, and the third represented reported exercise frequency and exercise caloric expenditure. We found that 4-month weight loss was partially mediated through app use (ie, the first PC; 60.3%) and the number of weigh-ins (55.8%). However, the 12-month weight loss was almost fully mediated by 4-month weight loss (94.8%). Linear regression using app data from the first 8 weeks, the number of self-weigh-ins at 8 weeks, and baseline data explained approximately 30% of the variance in 4-month weight loss. App use frequency (first PC; P=.001), self-monitored caloric intake (second PC; P=.001), and the frequency of self-weighing at 8 weeks (P=.008) were important predictors of 4-month weight loss. Predictions for 12-month weight with the same variables produced an R
conclusionsWe found that app use and daily reported caloric intake had a substantial impact on weight loss prediction at 4 months. Our analysis did not find evidence of an association between participant self-monitoring exercise information and weight loss. As 12-month weight loss was completely mediated by 4-month weight loss, intervention targets should focus on promoting early and frequent dietary intake self-monitoring and self-weighing to promote early weight loss, which leads to long-term success.
trial registrationClinicalTrials.gov NCT02063178; https://clinicaltrials.gov/ct2/show/NCT02063178.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.