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: Oct 16, 2025
Open Peer Review Period: Oct 28, 2025 - Dec 23, 2025
(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 Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn (a note "no longer under consideration" will appear above).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Warning: This is an author submission that is not peer-reviewed or edited.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.

Autonomous Digital Lifestyle Intervention for Obesity: A Comparative Study of Software-Generated vs. Provider-Delivered Body Composition Outcomes

  • Ioanna Pagani; 
  • Maria Syed; 
  • Cynthia Ages; 
  • Michael Bell; 
  • Yannis Raftopoulos

ABSTRACT

Background:

Software-based interventions have emerged as effective tools for managing chronic diseases. Lifestyle intervention (LI) apps can promote weight loss, but many depend on human coaches or healthcare providers, thus introducing higher costs and variability in feedback. Most existing apps rely on calorie-counting approaches and lack structured, individualized guidance, leaving users to determine how to translate general recommendations into daily nutrition and exercise practices. Moreover, many do not provide rapid, adaptive feedback to adjust meal or activity plans in real time.

Objective:

To compare the RightBMI App, an autonomous, protein-focused digital lifestyle intervention, with provider-delivered lifestyle intervention (LI) for weight loss and body composition changes in bariatric surgery candidates.

Methods:

A prospective randomized trial (April 2024 - April 2025, Holyoke Medical Center) enrolled 160 patients (App: n=80; no-App: n=80). RightBMI provided personalized nutrition and exercise plans. Outcomes (% total body weight loss [%TBWL], % fat mass loss [%FML], % visceral fat loss [%VFL], % muscle mass loss [%MML]) were assessed over 24 weeks using Generalized Estimating Equations (GEE) with multiple imputation, adjusting for age, BMI, and sex (no-App as reference).

Results:

GEE models showed significant time effects for %TBWL (week 8: coefficient 2.91, p<0.001; week 24: 18.12, p<0.001) and %FML (week 8: 5.30, p<0.001; week 24: 33.65, p<0.001) in the no-App group. The App group had greater %TBWL at weeks 12 (2.64%, p<0.001) and 16 (2.47%, p=0.027), and %FML at weeks 12 (2.94%, p=0.028) and 16 (4.20%, p=0.003). At week 24, outcomes were comparable: App group (%TBWL: 20.01%, %FML: 36.81%, %VFL: 31.99%, %MML: -0.09%) vs. no-App (%TBWL: 19.97%, %FML: 35.74%, %VFL: 32.97%, %MML: 0.70%; all p>0.89). Both groups maintained stable %MML with %TBWL ~20% and %FML >32%. User satisfaction was high (8.76/10).

Conclusions:

The RightBMI App outperformed provider-led LI at mid-study, achieving comparable 24-week weight loss (~20%) and muscle preservation. With 90% bariatric surgery clearance (vs. 71.25% no-App) and low dropout (3.75% App, 1.25% no-App), RightBMI is a scalable, effective tool for obesity management.


 Citation

Please cite as:

Pagani I, Syed M, Ages C, Bell M, Raftopoulos Y

Autonomous Digital Lifestyle Intervention for Obesity: A Comparative Study of Software-Generated vs. Provider-Delivered Body Composition Outcomes

JMIR Preprints. 16/10/2025:86010

DOI: 10.2196/preprints.86010

URL: https://preprints.jmir.org/preprint/86010

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.