Evidence map›Paper›PMID 34184991›Full record

Trial reportJournal of medical Internet research2021

Machine Learning Analysis to Identify Digital Behavioral Phenotypes for Engagement and Health Outcome Efficacy of an mHealth Intervention for Obesity: Randomized Controlled Trial.

Meelim Kim, Jaeyeong Yang, Woo-Young Ahn, Hyung Jin Choi

Registry-linked trialOpen access · goldAbstract readRandomized Controlled Trial
PubMed Full text at PMC Publisher
In one paragraph

Trial report in Journal of medical Internet research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03465306 (Developing and Validating Clinical Efficacy for Obesity Digital CBT Model), which is not on this map. Cited by 24 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed, 3 pooled it
4.5field-weighted citation impact, top 5% of its field
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.

NCT03465306 nacompletednot on this map

Developing and Validating Clinical Efficacy for Obesity Digital CBT Model

TypeinterventionalSponsorSeoul National University HospitalRan2017 to 2018Enrolled70ConditionsSearch MeSHArmsCognitive Behavioral Therapy, Self-guided
3 · Its place in the literature

Who cites it

24 citing papers in PubMed, 3 syntheses or guidelines pooled it, 43 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Trial
  5. Trial
  6. Observational
  7. Article
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  9. Review
  10. Review
  11. [Application of Digital Nutrition Technologies in Adult Weight Management].Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition · 2025
    Article
  12. Observational
  13. Article
  14. Review
  15. Article
  16. Review
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  18. Article
  19. Digital therapeutics in the clinic.Bioengineering & translational medicine · 2023
    Review
  20. Article
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 at 1 institution in 1 country.

Meelim Kim *Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, Republic of Korea.ORCID 0000-0002-0507-1727
Jaeyeong Yang *Department of Psychology, Seoul National University, Seoul, Republic of Korea.ORCID 0000-0001-8037-590X
Woo-Young AhnDepartment of Psychology, Seoul National University, Seoul, Republic of Korea.ORCID 0000-0002-5900-8432
Hyung Jin ChoiDepartment of Biomedical Sciences, Seoul National University College of Medicine, Seoul, Republic of Korea.ORCID 0000-0003-0593-6978
Seoul National University · KR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe digital health care community has been urged to enhance engagement and clinical outcomes by analyzing multidimensional digital phenotypes.

objectiveThis study aims to use a machine learning approach to investigate the performance of multivariate phenotypes in predicting the engagement rate and health outcomes of digital cognitive behavioral therapy.

methodsWe leveraged both conventional phenotypes assessed by validated psychological questionnaires and multidimensional digital phenotypes within time-series data from a mobile app of 45 participants undergoing digital cognitive behavioral therapy for 8 weeks. We conducted a machine learning analysis to discriminate the important characteristics.

resultsA higher engagement rate was associated with higher weight loss at 8 weeks (r=-0.59; P<.001) and 24 weeks (r=-0.52; P=.001). Applying the machine learning approach, lower self-esteem on the conventional phenotype and higher in-app motivational measures on digital phenotypes commonly accounted for both engagement and health outcomes. In addition, 16 types of digital phenotypes (ie, lower intake of high-calorie food and evening snacks and higher interaction frequency with mentors) predicted engagement rates (mean R

conclusionsOur findings successfully demonstrated how multiple psychological constructs, such as emotional, cognitive, behavioral, and motivational phenotypes, elucidate the mechanisms and clinical efficacy of a digital intervention using the machine learning method. Accordingly, our study designed an interpretable digital phenotype model, including multiple aspects of motivation before and during the intervention, predicting both engagement and clinical efficacy. This line of research may shed light on the development of advanced prevention and personalized digital therapeutics.

trial registrationClinicalTrials.gov NCT03465306; https://clinicaltrials.gov/ct2/show/NCT03465306.

Indexed as

ObesityTelemedicineHumansMachine LearningOutcome Assessment, Health CarePhenotypeclinical efficacydigital phenotypein-app engagementmachine learning analysismobile phone

Identifiers

PMID34184991
PMCPMC8277339
OpenAlexW3162309386

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.