ArticleJMIR formative research2023
Automated Messaging Delivered Alongside Behavioral Treatment for Weight Loss: Qualitative Study.
Article in JMIR formative research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05231824 (Using Artificial Intelligence to Optimize Delivery of Weight Loss Treatment), which is not on this map. Cited by 1 paper.
What it found
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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.
Using Artificial Intelligence to Optimize Delivery of Weight Loss Treatment
Who cites it
1 citing paper in PubMed, 1 citations in OpenAlex.
- Comparing Large Language Model AI and Human-Generated Coaching Messages for Behavioral Weight Loss.Journal of technology in behavioral science · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
backgroundMobile health interventions for weight loss frequently use automated messaging. However, this intervention modality appears to have limited weight loss efficacy. Furthermore, data on users' subjective experiences while receiving automated messaging-based interventions for weight loss are scarce, especially for more advanced messaging systems providing users with individually tailored, data-informed feedback.
objectiveThe purpose of this study was to characterize the experiences of individuals with overweight or obesity who received automated messages for 6-12 months as part of a behavioral weight loss trial.
methodsParticipants (n=40) provided Likert-scale ratings of messaging acceptability and completed a structured qualitative interview (n=39) focused on their experiences with the messaging system and generating suggestions for improvement. Interview data were analyzed using thematic analysis.
resultsParticipants found the messages most useful for summarizing goal progress and least useful for suggesting new behavioral strategies. Overall message acceptability was moderate (2.67 out of 5). From the interviews, 2 meta-themes emerged. Participants indicated that although the messages provided useful reminders of intervention goals and skills, they did not adequately capture their lived experiences while losing weight.
conclusionsMany participants found the automated messages insufficiently tailored to their personal weight loss experiences. Future studies should explore alternative methods for message tailoring (eg, allowing for a higher degree of participant input and interactivity) that may boost treatment engagement and efficacy.
trial registrationClinicalTrials.gov NCT05231824; https://clinicaltrials.gov/study/NCT05231824.
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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.