ArticleObesity science & practice2023
Examining paradoxical session attendance and weight loss relationships in a clinic based lifestyle modification intervention.
Article in Obesity science & practice, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Adherence to Behavioral Weight Management: A Scoping Review of Definitions, Measurement, and Components.Obesity reviews : an official journal of the International Association for the Study of Obesity · 2026Article
- Examining paradoxical session attendance and weight loss relationships in a clinic based lifestyle modification intervention.Obesity science & practice · 2023Article
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
Objective: Evaluations of lifestyle modification interventions (LMIs), modeled after the Diabetes Prevention Program, have repeatedly shown a dose-response relationship between session attendance and weight loss. Despite this, not all participants had "average" weight loss experiences. Nearly one-third of LMI participants experienced unexpected, paradoxical outcomes (i.e., high attendance with little weight loss, and low attendance with clinically significant weight loss). Paradoxical weight-loss outcomes were characterized based on session attendance among participants in a group-based LMI in a real-world healthcare setting. This group-based LMI was delivered over 1 year to participants with the possibility of attending up to 25 sessions total. Methods: LMI participants identified in 2010-2017 from electronic health records were characterized as having low (<75%) or high (≥75%) session attendance. Weight-loss outcomes were defined as expected (≥5%, high-attendance; <5%, low-attendance) or paradoxical (≥5%, low-attendance; <5%, high-attendance). Paradoxical-outcome-associated characteristics were identified using logistic regression. Results: Among 1813 LMI participants, 1498 (82.6%) had low and 315 (17.4%) high session attendance; 555 (30.6%) had paradoxical outcomes, comprising 415 (74.8%) responders (≥5% weight-loss) and 140 (25.2%) non-responders (<5% weight-loss). Among participants with high session attendance, paradoxical non-responders were more likely to be female (odds ratio [OR]: 2.76; 95% confidence interval [CI]: 1.32, 5.77) and have type 2 diabetes (OR: 3.32; 95% CI: 1.01, 10.95). Among low-attendance participants, paradoxical responders were more likely to be non-Hispanic White and less likely to be non-Hispanic Black (OR: 0.35; 95% CI: 0.18, 0.69), non-Hispanic Asian (OR: 0.40; 95% CI: 0.22, 0.73), or Hispanic (OR: 0.53; 95% CI: 0.35, 0.80). Conclusions: In a healthcare setting, nearly one-third of LMI participants experienced paradoxical outcomes. More research is needed to understand the facilitators and barriers to weight loss above and beyond session attendance.
Indexed as
Identifiers
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.