ArticleJMIR mHealth and uHealth2018
Usefulness of a Novel Mobile Diabetes Prevention Program Delivery Platform With Human Coaching: 65-Week Observational Follow-Up.
Article in JMIR mHealth and uHealth, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers, 4 of them syntheses that pooled it.
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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.
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Who cites it
41 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- eHealth Literacy and Type 2 Diabetes Prevention Among At-Risk Populations: Mechanistic Systematic Review Using Theory-Driven Thematic Analysis.Journal of medical Internet research · 2026Pooled it
- Effectiveness of Different Intervention Modes in Lifestyle Intervention for the Prevention of Type 2 Diabetes and the Reversion to Normoglycemia in Adults With Prediabetes: Systematic Review and Meta-Analysis of Randomized Controlled Trials.Journal of medical Internet research · 2025Pooled it
- Effectiveness of eHealth Nutritional Interventions for Middle-Aged and Older Adults: Systematic Review and Meta-analysis.Journal of medical Internet research · 2021Pooled it
- Human Coaching Methodologies for Automatic Electronic Coaching (eCoaching) as Behavioral Interventions With Information and Communication Technology: Systematic Review.Journal of medical Internet research · 2021Pooled it
- A novel smartphone app to change risk behaviors of women after gestational diabetes: A randomized controlled trial.PloS one · 2022Trial
- Mobile Delivery of the Diabetes Prevention Program in People With Prediabetes: Randomized Controlled Trial.JMIR mHealth and uHealth · 2020Trial
- Multidimensional Cognitive Behavioral Therapy for Obesity Applied by Psychologists Using a Digital Platform: Open-Label Randomized Controlled Trial.JMIR mHealth and uHealth · 2020Trial
- Community-engaged design of a virtual diabetes prevention program intervention with care coordination.BMC health services research · 2026Article
- 3. Prevention or Delay of Diabetes and Associated Comorbidities: Standards of Care in Diabetes-2026.Diabetes care · 2026Review
- The Application of Preventive Medicine in the Future Digital Health Era.Journal of medical Internet research · 2025Review
- Practical Awareness-Based Strategies for Eating (PASE): A Pilot and Feasibility Randomized Trial.Obesity science & practice · 2025Article
- 3. Prevention or Delay of Diabetes and Associated Comorbidities: Standards of Care in Diabetes-2025.Diabetes care · 2025Review
- Efficacy of a Multimodal Digital Behavior Change Intervention on Lifestyle Behavior, Cardiometabolic Biomarkers, and Medical Expenditure: Protocol for a Randomized Controlled Trial.JMIR research protocols · 2024Article
- Article
- 3. Prevention or Delay of Diabetes and Associated Comorbidities: Standards of Care in Diabetes-2024.Diabetes care · 2024Review
- Weight loss maintenance after a digital commercial behavior change program (Noom Weight): Observational cross-sectional survey study.Obesity science & practice · 2023Article
- Engagement with a nationally-implemented digital behaviour change intervention: Usage patterns over the 9-month duration of the National Health Service Digital Diabetes Prevention Programme.Internet interventions · 2023Article
- Adapting the Diabetes Prevention Program for Older Adults: Descriptive Study.JMIR formative research · 2023Article
- Cost-Utility of an Online Education Platform and Diabetes Personal Health Record: Analysis Over Ten Years.Journal of diabetes science and technology · 2023Article
- 3. Prevention or Delay of Type 2 Diabetes and Associated Comorbidities: Standards of Care in Diabetes-2023.Diabetes care · 2023Review
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIt is widely recognized that the prevalence of obesity and comorbidities including prediabetes and type 2 diabetes continue to increase worldwide. Results from a 24-week Diabetes Prevention Program (DPP) fully mobile pilot intervention were previously published showing promising evidence of the usefulness of DPP-based eHealth interventions on weight loss.
objectiveThis pilot study extends previous findings to evaluate weight loss results of core (up to week 16) and maintenance (postcore weeks) DPP interventions at 65 weeks from baseline.
methodsOriginally, 140 participants were invited and 43 overweight or obese adult participants with a diagnosis of prediabetes signed up to receive a 24-week virtual DPP with human coaching through a mobile platform. At 65 weeks, this pilot study evaluates weight loss and engagement in maintenance participants by means of repeated measures analysis of variances and backward multiple linear regression to examine predictors of weight loss. Last observation carried forward was used for endpoint measurements.
resultsAt 65 weeks, mean weight loss was 6.15% in starters who read 1 or more lessons per week on 4 or more core weeks, 7.36% in completers who read 9 or more lessons per week on core weeks, and 8.98% in maintenance completers who did any action in postcore weeks (all P<.001). Participants were highly engaged, with 80% (47/59) of the sample completing 9 lessons or more and 69% (32/47) of those completing the maintenance phase. In-app actions related to self-monitoring significantly predicted weight loss.
conclusionsIn comparison to eHealth programs, this pilot study shows that a fully mobile DPP can produce transformative weight loss. A fully mobile DPP intervention resulted in significant weight loss and high engagement during the maintenance phase, providing evidence for long-term potential as an alternative to in-person DPP by removing many of the barriers associated with in-person and other forms of virtual DPP.
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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.