Evidence map›Paper›PMID 38573389›Full record

ArticleObesity surgery2024

Weight Loss with an AI-Powered Digital Platform for Lifestyle Intervention.

Sarfraz Khokhar, John Holden, Catherine Toomer, Angelo Del Parigi

Abstract read
PubMed Publisher
In one paragraph

Article in Obesity surgery, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed, 2 pooled it
–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

10 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Review
  4. Article
  5. Review
  6. Review
  7. Article
  8. A digital tool for self-reporting cardiovascular risk factors: The RADICAL study.International journal of cardiology. Cardiovascular risk and prevention · 2025
    Article
  9. Review
  10. 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.

Sarfraz KhokharDTx Research, Rasimo Systems, Raleigh, NC, 27601, USA. khokhar@rasimo.com.ORCID 0000-0003-1296-0474
John HoldenRockford-College of Medicine, University of Illinois, Rockford, IL, 6110, USA.
Catherine ToomerHealth Wellness and Weight Loss Centers, Aiken, SC, 29803, USA.
Angelo Del ParigiDTx Research, Rasimo Systems, Raleigh, NC, 27601, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundLifestyle intervention remains the cornerstone of weight loss programs in addition to pharmacological or surgical therapies. Artificial intelligence (AI) and other digital technologies can offer individualized approaches to lifestyle intervention to enable people with obesity to reach successful weight loss.

methodsSureMediks, a digital lifestyle intervention platform using AI, was tested by 391 participants (58% women) with a broad range of BMI (20-78 kg/m

resultsAll participants lost body weight (average 14%, range 4-22%). Almost all (98.7%) participants lost at least 5% of body weight, 75% lost at least 10%, 43% at least 15%, and 9% at least 20%, suggesting that this AI-powered lifestyle intervention was also effective in reducing the burden of obesity co-morbidities. Weight loss was partially positively correlated with female sex, accountability circle size, and participation in challenges, while it was negatively correlated with sub-goal reassignment. The latter three variables are specific features of the SureMediks weight loss program.

conclusionAn AI-assisted lifestyle intervention allowed people with different body sizes to lose 14% body weight on average, with 99% of them losing more than 5%, over 24 weeks. These results show that digital technologies and AI might provide a successful means to lose weight, before, during, and after pharmacological or surgical therapies.

Indexed as

Obesity, MorbidWeight Reduction ProgramsArtificial IntelligenceFemaleHumansLife StyleMaleObesityArtificial intelligenceDigital platformLifestyle interventionWeight loss

Identifiers

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.