ArticleAdvanced health care technologies2015
Smartphone applications to support weight loss: current perspectives.
Article in Advanced health care technologies, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT01814072 (Opt-IN), which is not on this map. Cited by 31 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Opt-IN: Optimization of Remotely Delivered Intensive Lifestyle Treatment for Obesity
Who cites it
31 citing papers in PubMed.
- Comparing Self-Monitoring Strategies for Weight Loss in a Smartphone App: Randomized Controlled Trial.JMIR mHealth and uHealth · 2019Trial
- Article
- Testing a Personalized Behavioral Weight Loss Approach Using Multifactor Prescriptions and Self-Experimentation: 12-Week mHealth Pilot Randomized Controlled Trial Results.Obesity science & practice · 2025Article
- 'The wisdom of crowds': a survey on the rating of nutritional values of meals in digital pictures.BMJ nutrition, prevention & health · 2025Article
- Results of a pilot sequential multiple assignment randomized trial using counseling to augment a digital weight loss program.Obesity science & practice · 2024Article
- The Efficacy of an mHealth App in Facilitating Weight Loss Among Japanese Fitness Center Members: Regression Analysis Study.JMIR formative research · 2023Article
- The Application of Digital Technologies and Artificial Intelligence in Healthcare: An Overview on Nutrition Assessment.Diseases (Basel, Switzerland) · 2023Article
- Article
- The EVO study protocol for a randomized controlled evaluation trial of an optimized weight management intervention.Contemporary clinical trials · 2022Article
- Development, feasibility and acceptability of a self-efficacy-enhancing smartphone application among pregnant women with gestational diabetes mellitus: single- arm pilot clinical trial.BMC pregnancy and childbirth · 2022Article
- Article
- Optimizing Adaptive Notifications in Mobile Health Interventions Systems: Reinforcement Learning from a Data-driven Behavioral Simulator.Journal of medical systems · 2021Article
- A randomized controlled trial of an innovative, user-friendly, interactive smartphone app-based lifestyle intervention for weight loss.Obesity science & practice · 2021Article
- Reinforcement Learning to Send Reminders at Right Moments in Smartphone Exercise Application: A Feasibility Study.International journal of environmental research and public health · 2021Article
- SMART: Study protocol for a sequential multiple assignment randomized controlled trial to optimize weight loss management.Contemporary clinical trials · 2019Article
- An expandable approach for design and personalization of digital, just-in-time adaptive interventions.Journal of the American Medical Informatics Association : JAMIA · 2019Article
- Popular Nutrition-Related Mobile Apps: An Agreement Assessment Against a UK Reference Method.JMIR mHealth and uHealth · 2019Article
- A Mixed Methods Study to Explore the Effects of Program Design Elements and Participant Characteristics on Parents' Engagement With an mHealth Program to Promote Healthy Infant Feeding: The Growing Healthy Program.Frontiers in endocrinology · 2019Article
- The Use of Mobile Health Applications to Improve Patient Experience: Cross-Sectional Study in Chinese Public Hospitals.JMIR mHealth and uHealth · 2018Article
- Describing the Process of Adopting Nutrition and Fitness Apps: Behavior Stage Model Approach.JMIR mHealth and uHealth · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Lower cost alternatives are needed for the traditional in-person behavioral weight loss programs to overcome challenges of lowering the worldwide prevalence of overweight and obesity. Smartphones have become ubiquitous and provide a unique platform to aid in the delivery of a behavioral weight loss program. The technological capabilities of a smartphone may address certain limitations of a traditional weight loss program, while also reducing the cost and burden on participants, interventionists, and health care providers. Awareness of the advantages smartphones offer for weight loss has led to the rapid development and proliferation of weight loss applications (apps). The built-in features and the mechanisms by which they work vary across apps. Although there are an extraordinary number of a weight loss apps available, most lack the same magnitude of evidence-based behavior change strategies typically used in traditional programs. As features develop and new capabilities are identified, we propose a conceptual model as a framework to guide the inclusion of features that can facilitate behavior change and lead to reductions in weight. Whereas the conventional wisdom about behavior change asserts that more is better (with respect to the number of behavior change techniques involved), this model suggests that less may be more because extra techniques may add burden and adversely impact engagement. Current evidence is promising and continues to emerge on the potential of smartphone use within weight loss programs; yet research is unable to keep up with the rapidly improving smartphone technology. Future studies are needed to refine the conceptual model's utility in the use of technology for weight loss, determine the effectiveness of intervention components utilizing smartphone technology, and identify novel and faster ways to evaluate the ever-changing technology.
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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.