Trial reportObesity (Silver Spring, Md.)2022
A cluster-randomized controlled trial of automated internet weight-loss programs in primary care: Role of automated provider feedback.
Trial report in Obesity (Silver Spring, Md.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 2 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Automated lifestyle interventions and weight loss: a systematic review of randomized controlled trials.International journal of obesity (2005) · 2025Pooled it
- Evaluation of the Aspects of Digital Interventions That Successfully Support Weight Loss: Systematic Review With Component Network Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Considering depression as a risk factor for early disengagement in a 12-month eHealth weight loss intervention.Journal of health psychology · 2026Trial
- Motivation, accountability, and outcomes in a randomized controlled trial of internet weight-loss programs in primary care.Journal of health psychology · 2025Trial
- Measuring the influence of depressive symptoms on engagement, adherence, and weight loss in an eHealth intervention.PLOS digital health · 2025Article
- Who is Focusing on Women's Health: A Bibliometric Analysis of Global Research Trends on Overweight in Female Patients with Gynecologic or Breast Cancer.International journal of women's health · 2024Article
- Impact of a digital employer-based weight loss program on individuals age 65 or older.Frontiers in aging · 2024Article
- Personal goal setting eHealth component associated with improved weight loss at 6 months: A mixed methods secondary analysis.Digital healthArticle
- Examining latent trajectories of participant engagement in a 12-month eHealth weight management intervention.Digital healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
objectiveDespite the high prevalence of obesity and associated health risks in the United States adult population, few primary care providers (PCPs) have time and training to provide weight-management counseling to their patients. This study aims to compare the effect of referral to a comprehensive automated digital weight-loss program, with or without provider email feedback, with usual care on weight loss in patients with overweight or obesity.
methodsA total of 550 adults (mean [SD], 51.4 [11.2] years, BMI = 35.1 [5.5] kg/m
resultsWeight changes (mean [SE]) at 12 months were -0.92 (0.46), -3.68 (0.46), and -3.58 (0.48) kg in the EUC, IWL, and IWL + PCP groups, respectively. Outcomes were significantly different in EUC versus IWL and EUC versus IWL + PCP (p < 0.001), but not in IWL versus IWL + PCP.
conclusionsReferral by PCPs to an automated weight-loss program holds promise for patients with obesity. Future research should explore ways to further promote accountability and adherence.
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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.