Maintenance Notice

Due to necessary scheduled maintenance, the JMIR Publications website will be unavailable from Wednesday, July 01, 2020 at 8:00 PM to 10:00 PM EST. We apologize in advance for any inconvenience this may cause you.

Who will be affected?

Currently submitted to: JMIR mHealth and uHealth

Date Submitted: May 12, 2026
Open Peer Review Period: May 12, 2026 - Jul 7, 2026
(closed for review but you can still tweet)

NOTE: This is an unreviewed Preprint

Warning: This is a unreviewed preprint (What is a preprint?). Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn (a note "no longer under consideration" will appear above).

Peer review me: Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period (in this case, a "Peer Review Me" button to sign up as reviewer is displayed above). All preprints currently open for review are listed here. Outside of the formal open peer-review period we encourage you to tweet about the preprint.

Citation: Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author).

Final version: If our system detects a final peer-reviewed "version of record" (VoR) published in any journal, a link to that VoR will appear below. Readers are then encourage to cite the VoR instead of this preprint.

Settings: If you are the author, you can login and change the preprint display settings, but the preprint URL/DOI is supposed to be stable and citable, so it should not be removed once posted.

Submit: To post your own preprint, simply submit to any JMIR journal, and choose the appropriate settings to expose your submitted version as preprint.

Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.

The impact of digital health interventions on eating behavior traits in adults with overweight or obesity: A systematic review and meta-analysis

  • Xiuqin Feng; 
  • yan he; 
  • SiYi Dong; 
  • Yanjie Liu; 
  • XinRui Cao; 
  • WanYa Pan; 
  • WenHao Tian; 
  • Yuan Zhao

ABSTRACT

Background:

Digital health interventions offer a scalable approach to modern weight management but their specific efficacy in modifying psychological eating behavior traits remains underexplored. Synthesizing this evidence is critical for optimizing future digital therapeutics.

Objective:

To evaluate the impact of digital health interventions on specific eating behavior traits among adults with overweight or obesity compared to standard care and to determine the influence of intervention duration and theoretical frameworks on these outcomes.

Methods:

A comprehensive literature search was conducted across six major electronic databases to identify relevant randomized controlled trials. Eligible studies included adults with elevated body mass indices and measured psychological constructs of eating behavior. Standardized mean differences were calculated using a random effects model and evidence certainty was evaluated utilizing the GRADE framework.

Results:

Fifteen unique trials comprising 1518 participants met the inclusion criteria. The pooled synthesis demonstrated that digital interventions yielded robust reductions in both emotional eating and uncontrolled binge eating supported by a moderate certainty of evidence. Subgroup analysis indicated that sustained digital engagement exceeding eight weeks is essential to achieve definitive improvements in emotional eating. The overall impact on cognitive restraint was highly variable. However this statistical inconsistency was systematically driven by underlying therapeutic frameworks where traditional cognitive behavioral therapies significantly increased restrictive behaviors while mindfulness and acceptance based approaches distinctly reduced them.

Conclusions:

Digital health platforms effectively mitigate maladaptive eating patterns particularly when user engagement is sustained beyond eight weeks. The theoretical divergence observed in cognitive restraint outcomes highlights the necessity for a precision medicine approach in digital behavioral care. Clinicians should evaluate dominant eating phenotypes prior to prescribing digital tools to ensure therapeutic algorithms match individual psychological profiles. Clinical Trial: https://www.crd.york.ac.uk/PROSPERO/view/CRD420261285265


 Citation

Please cite as:

Feng X, he y, Dong S, Liu Y, Cao X, Pan W, Tian W, Zhao Y

The impact of digital health interventions on eating behavior traits in adults with overweight or obesity: A systematic review and meta-analysis

JMIR Preprints. 12/05/2026:101112

DOI: 10.2196/preprints.101112

URL: https://preprints.jmir.org/preprint/101112

Download PDF


Request queued. Please wait while the file is being generated. It may take some time.

© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.