Evidence map›Paper›PMID 39538385›Full record

ArticleObesity reviews : an official journal of the International Association for the Study of Obesity2025

Recommender systems use in weight management mHealth interventions: A scoping review.

Bianca A de Castro, Sara M Levens, Margaret Sullivan, George Shaw

Abstract readScoping Review
In one paragraph

Article in Obesity reviews : an official journal of the International Association for the Study of Obesity, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Recommender systems use in weight management mHealth interventions: A scoping review.Obesity reviews : an official journal of the International Association for the Study of Obesity · 2025
    Article
  5. Review
4 · The record

Corrections and comments

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

5 · Who and what money

Authors and funding

4 authors.

Bianca A de CastroDepartment of Epidemiology and Community Health, University of North Carolina at Charlotte, Charlotte, North Carolina, USA.
Sara M LevensDepartment of Psychological Science, University of North Carolina at Charlotte, Charlotte, North Carolina, USA.
Margaret SullivanThe School of Information at Florida State University, Tallahassee, Florida, USA.
George ShawDepartment of Health Management and Policy, School of Data Science, University of North Carolina at Charlotte, Charlotte, North Carolina, USA.ORCID 0000-0001-5508-3139

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The use of recommender systems in mobile health apps for weight control has grown, but user app uptake and engagement remain limited. The objective of our scoping review was to explore the influence of recommender systems on mHealth app user engagement, identify the theoretical frameworks that have been applied on digital health interventions designed for weight management, and examine the key aspects that support tailoring user engagement through recommender systems. Based on existing literature, we identified 13 articles on recommender systems for weight management. Themes emerged, including theoretical underpinnings, authors' domain knowledge, user motivation, and design. Most studies used constructs from the social cognitive theory. We found inconsistencies between authors' domain knowledge and the intervention's content, with few professionals from the health and psychology fields. Only 46% of articles measured user engagement, whereas gamification and tailored messages were common app features. Despite some positive weight change results, more attention is needed toward implementing behavior theory and other strategies to promote app user engagement. Future studies should more accurately measure user motivation and identify the best features and behavioral constructs to increase app user interaction. Larger studies with a more diverse population are needed to generalize findings and evaluate weight loss maintenance and physical activity habits among users of recommender system.

Indexed as

Mobile ApplicationsObesityTelemedicineWeight Reduction ProgramsHumansMotivationmobile apprecommender systemuser engagementweight management

Identifiers

PMID39538385
PMCPMC11791383

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.