Analysing self-regulatory behaviours in response to daily weighing: a think-aloud study with follow-up interviews.

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

8 citing papers in PubMed.

1. The effectiveness and safety of a mobile application-based self-regulation intervention to support weight loss among adults living with obesity: a large-scale pragmatic randomised controlled trial.BMC medicine · 2025
   Trial
2. Daily self-weighing compared with an active control causes greater negative affective lability in emerging adult women: A randomized trial.Applied psychology. Health and well-being · 2023
   Trial
3. Psychological and behavioral responses to daily weight gain during behavioral weight loss treatment.Journal of behavioral medicine · 2024
   Article
4. Barriers and Motivators to Weight Loss in People With Obesity.Cureus · 2023
   Review
5. Do monthly coaching calls influence proximal participant adherence in a behavioral weight loss program?Journal of behavioral medicine · 2023
   Article
6. Exploring women's thoughts on self-weighing during pregnancy: results of the Self-Weighing in Pregnancy: Experiences (SWIPE) study.BMC pregnancy and childbirth · 2021
   Article
7. Cognitive and behavioural strategies employed to overcome "lapses" and prevent "relapse" among weight-loss maintainers and regainers: A qualitative study.Clinical obesity · 2020
   Article
8. A deep learning-based early prediction framework for weight management using real-world lifelog data: GRU-ODE-Bayes model development and validation study.Digital health
   Article

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

Authors and funding

5 authors.

Funding

Abstract

PubMed holds no abstract for this paper.

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