ArticleContemporary clinical trials2019
SMART: Study protocol for a sequential multiple assignment randomized controlled trial to optimize weight loss management.
Article in Contemporary clinical trials, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 24 papers, 3 of them syntheses that pooled it.
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
SMART Weight Loss Management
Who cites it
24 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- The use of sequential multiple assignment randomized trials (SMARTs) in physical activity interventions: a systematic review.BMC medical research methodology · 2024Pooled it
- Mobile health (m-health) smartphone interventions for adolescents and adults with overweight or obesity.The Cochrane database of systematic reviews · 2024Pooled it
- Individualized interventions and precision health: Lessons learned from a systematic review and implications for analytics-driven geriatric research.Journal of the American Geriatrics Society · 2023Pooled it
- Depression and weight loss trajectories during an integrated behavioral intervention: Within-treatment analysis of the RAINBOW trial.PloS one · 2025Trial
- An Adaptive Behavioral Intervention for Weight Loss Management: A Randomized Clinical Trial.JAMA · 2024Trial
- Describing Transitions in Adherence to Physical Activity Self-monitoring and Goal Attainment in an Online Behavioral Weight Loss Program: Secondary Analysis of a Randomized Controlled Trial.Journal of medical Internet research · 2022Trial
- Optimal allocation to treatments in a sequential multiple assignment randomized trial.Statistical methods in medical research · 2021Trial
- A Factorial Experiment to Optimize Remotely Delivered Behavioral Treatment for Obesity: Results of the Opt-IN Study.Obesity (Silver Spring, Md.) · 2020Trial
- Weight management in obese cancer patients during curative active treatment: protocol for the CANOBESE feasibility study.Pilot and feasibility studies · 2026Article
- Just-in-Time Adaptive Interventions: Where Are We Now and What Is Next?Annual review of psychology · 2026Review
- Results of a pilot sequential multiple assignment randomized trial using counseling to augment a digital weight loss program.Obesity science & practice · 2024Article
- A machine-learned model for predicting weight loss success using weight change features early in treatment.NPJ digital medicine · 2024Article
- Design of experiments with sequential randomizations on multiple timescales: the hybrid experimental design.Behavior research methods · 2024Article
- An Explainable Artificial Intelligence Software Tool for Weight Management Experts (PRIMO): Mixed Methods Study.Journal of medical Internet research · 2023Article
- Optimizing adaptive stepped-care interventions to change adults' health behaviors: A systematic review.Journal of clinical and translational science · 2023Review
- The Development of a Novel mHealth Tool for Obstructive Sleep Apnea: Tracking Continuous Positive Airway Pressure Adherence as a Percentage of Time in Bed.Journal of medical Internet research · 2022Article
- Tailoring the Evidence for Exercise Oncology within Breast Cancer Care.Current oncology (Toronto, Ont.) · 2022Review
- The EVO study protocol for a randomized controlled evaluation trial of an optimized weight management intervention.Contemporary clinical trials · 2022Article
- MCMTC: A Pragmatic Framework for Selecting an Experimental Design to Inform the Development of Digital Interventions.Frontiers in digital health · 2022Article
- Digitally characterizing the dynamics of multiple health behavior change.Health psychology : official journal of the Division of Health Psychology, American Psychological Association · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
backgroundStepped care is a rational resource allocation approach to reduce population obesity. Evidence is lacking to guide decisions on use of low cost treatment components such as mobile health (mHealth) tools without compromising weight loss of those needing more expensive traditional treatment components (e.g., coaching, meal replacement). A sequential multiple assignment randomization trial (SMART) will be conducted to inform the development of an empirically based stepped care intervention that incorporates mHealth and traditional treatment components.
objectiveThe primary aim tests the non-inferiority of app alone, compared to app plus coaching, as first line obesity treatment, measured by weight change from baseline to 6 months. Secondary aims are to identify the best tactic to address early treatment non-response and the optimal treatment sequence for resource efficient weight loss. STUDY
designFour hundred participants, 18-60 years old with Body Mass Index between 27 and 45 kg/m SIGNIFICANCE: Results will inform construction of an obesity treatment algorithm that balances weight loss outcomes with resource consumption.
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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.