ArticleDigital health
Trading pounds for points: Engagement and weight loss in a mobile health intervention.
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT02344836 (Refining and Pilot Testing Social Networks for Encouraging Healthy Behaviors), which is not on this map. Cited by 12 papers, 1 of them a synthesis that pooled it.
What it found
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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.
Refining and Pilot Testing Social Networks for Encouraging Healthy Behaviors: The Social Pounds Off Digitally (Social POD) Study
Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Mobile health (m-health) smartphone interventions for adolescents and adults with overweight or obesity.The Cochrane database of systematic reviews · 2024Pooled it
- Defining Adherence to Mobile Dietary Self-Monitoring and Assessing Tracking Over Time: Tracking at Least Two Eating Occasions per Day Is Best Marker of Adherence within Two Different Mobile Health Randomized Weight Loss Interventions.Journal of the Academy of Nutrition and Dietetics · 2019Trial
- App Engagement as a Predictor of Weight Loss in Blended-Care Interventions: Retrospective Observational Study Using Large-Scale Real-World Data.Journal of medical Internet research · 2024Observational
- Ethical and Information Governance Considerations for Promoting Digital Social Interventions in Primary Care.Journal of medical Internet research · 2023Article
- Challenges in Participant Engagement and Retention Using Mobile Health Apps: Literature Review.Journal of medical Internet research · 2022Review
- Mitigating Issues With/of/for True Personalization.Frontiers in artificial intelligence · 2022Article
- The potential of artificial intelligence in enhancing adult weight loss: a scoping review.Public health nutrition · 2021Article
- Influence of Personality on mHealth Use in Patients with Diabetes: Prospective Pilot Study.JMIR mHealth and uHealth · 2020Article
- Designing for engagement with self-monitoring: A user-centered approach with low-income, Latino adults with Type 2 Diabetes.International journal of medical informatics · 2019Article
- Enhancing digital health services: A machine learning approach to personalized exercise goal setting.Digital healthArticle
- Using social media for health research: Methodological and ethical considerations for recruitment and intervention delivery.Digital healthArticle
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThe objective of this study was to describe participant engagement and examine predictors of weight loss and points earned through the point-based incentive system of the Social Pounds Off Digitally (POD) app. MATERIALS AND
methodsOverweight and obese adults with Android smartphones/tablets (body mass index 25-49.9 kg/m
resultsA total of 4843 points were earned (mean = 202 ± 105 points/participant). Most participants earned all three prizes (62.5%), followed by two prizes (21%), no prizes (12.5%), and one prize (4%). Total points earned significantly predicted percent weight loss (
conclusionA mobile app yielded moderately high participant engagement, as demonstrated by points earned. Earning points was significantly associated with percent weight loss, and conscientiousness was significantly associated with total points earned. Future research should examine whether point systems impact health behavior and weight loss when rewards are discontinued. CLINICAL TRIAL REGISTRATION NUMBER: NCT02344836.
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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.