Evidence map›Paper›PMID 34309569›Full record

SynthesisJournal of medical Internet research2021

Just-in-Time Adaptive Mechanisms of Popular Mobile Apps for Individuals With Depression: Systematic App Search and Literature Review.

Gisbert W Teepe, Ashish Da Fonseca, Birgit Kleim, Nicholas C Jacobson, Alicia Salamanca Sanabria, Lorainne Tudor Car, Elgar Fleisch, Tobias Kowatsch

Registry-linked trialOpen access · goldAbstract readSystematic Review
In one paragraph

Synthesis 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 NCT06783907 (Technological-based Personalized Care Intervention for Supporting Older People With Diabetes Mellitus), which is not on this map. Cited by 40 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
40citing papers in PubMed, 4 pooled it
5.2field-weighted citation impact, top 4% 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.

NCT06783907 nacompletednot on this mapstarted 2023, after this paper: background citation

Technological-based Personalized Care Intervention for Supporting Older People With Diabetes Mellitus

TypeinterventionalSponsorHong Kong Metropolitan UniversityRan2023 to 2025Enrolled60ConditionsDiabetes MellitusArmsNon-invasive blood glucose monitoring, Traditional blood glucose monitoring
3 · Its place in the literature

Who cites it

40 citing papers in PubMed, 4 syntheses or guidelines pooled it, 55 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Pooled it
  5. Trial
  6. Review
  7. Article
  8. Review
  9. Article
  10. Review
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Review
  18. Article
  19. Article
  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

8 authors at 5 institutions in 4 countries.

Gisbert W TeepeCentre for Digital Health Interventions, Department of Management, Technology, and Economics, ETH Zurich, Zurich, Switzerland.ORCID 0000-0002-2264-9797
Ashish Da FonsecaCentre for Digital Health Interventions, Institute of Technology Management, University of St. Gallen, St. Gallen, Switzerland.ORCID 0000-0001-7732-0625
Birgit KleimExperimental Psychopathology and Psychotherapy, Department of Psychology, University of Zurich, Zurich, Switzerland.ORCID 0000-0001-9114-2917
Nicholas C JacobsonCenter for Technology and Behavioral Health, Departments of Biomedical Data Science and Psychiatry, Geisel School of Medicine, Dartmouth College, Hanover, NH, United States.ORCID 0000-0002-8832-4741
Alicia Salamanca SanabriaFuture Health Technologies, Singapore-ETH Centre, Campus for Research Excellence And Technological Enterprise, Singapore, Singapore.ORCID 0000-0002-2756-5592
Lorainne Tudor CarLee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore.ORCID 0000-0001-8414-7664
Elgar FleischCentre for Digital Health Interventions, Department of Management, Technology, and Economics, ETH Zurich, Zurich, Switzerland.ORCID 0000-0002-4842-1117
Tobias KowatschCentre for Digital Health Interventions, Department of Management, Technology, and Economics, ETH Zurich, Zurich, Switzerland.ORCID 0000-0001-5939-4145
University of St.Gallen · CHDartmouth College · USETH Zurich · CHNanyang Technological University · SGUniversity of Zurich · CH

Funding

Treatment Development & Evaluation CoreP30DA029926 · NIDA · DARTMOUTH COLLEGE · PI Lisa A. Marsch · 2011 to 2026
$21.5M
Personalized Deep Learning Models of Rapid Changes in Major Depressive Disorder Symptoms using Passive Sensor Data from Smartphones and Wearable DevicesR01MH123482 · NIMH · DARTMOUTH COLLEGE · PI JACOBSON, NICHOLAS CHARLES · 2020 to 2024
$2.6M
NIDA NIH HHS P30 DA029926NIMH NIH HHS R01 MH123482
6 · The paper itself

Abstract

backgroundThe number of smartphone apps that focus on the prevention, diagnosis, and treatment of depression is increasing. A promising approach to increase the effectiveness of the apps while reducing the individual's burden is the use of just-in-time adaptive intervention (JITAI) mechanisms. JITAIs are designed to improve the effectiveness of the intervention and reduce the burden on the person using the intervention by providing the right type of support at the right time. The right type of support and the right time are determined by measuring the state of vulnerability and the state of receptivity, respectively.

objectiveThe aim of this study is to systematically assess the use of JITAI mechanisms in popular apps for individuals with depression.

methodsWe systematically searched for apps addressing depression in the Apple App Store and Google Play Store, as well as in curated lists from the Anxiety and Depression Association of America, the United Kingdom National Health Service, and the American Psychological Association in August 2020. The relevant apps were ranked according to the number of reviews (Apple App Store) or downloads (Google Play Store). For each app, 2 authors separately reviewed all publications concerning the app found within scientific databases (PubMed, Cochrane Register of Controlled Trials, PsycINFO, Google Scholar, IEEE Xplore, Web of Science, ACM Portal, and Science Direct), publications cited on the app's website, information on the app's website, and the app itself. All types of measurements (eg, open questions, closed questions, and device analytics) found in the apps were recorded and reviewed.

resultsNone of the 28 reviewed apps used JITAI mechanisms to tailor content to situations, states, or individuals. Of the 28 apps, 3 (11%) did not use any measurements, 20 (71%) exclusively used self-reports that were insufficient to leverage the full potential of the JITAIs, and the 5 (18%) apps using self-reports and passive measurements used them as progress or task indicators only. Although 34% (23/68) of the reviewed publications investigated the effectiveness of the apps and 21% (14/68) investigated their efficacy, no publication mentioned or evaluated JITAI mechanisms.

conclusionsPromising JITAI mechanisms have not yet been translated into mainstream depression apps. Although the wide range of passive measurements available from smartphones were rarely used, self-reported outcomes were used by 71% (20/28) of the apps. However, in both cases, the measured outcomes were not used to tailor content and timing along a state of vulnerability or receptivity. Owing to this lack of tailoring to individual, state, or situation, we argue that the apps cannot be considered JITAIs. The lack of publications investigating whether JITAI mechanisms lead to an increase in the effectiveness or efficacy of the apps highlights the need for further research, especially in real-world apps.

Indexed as

Mobile ApplicationsAnxiety DisordersDepressionHumansSmartphoneState Medicinedepressiondigital mental healtheffectivenessjust-in-time adaptive interventionsmobile phonesmartphone applications

Identifiers

PMID34309569
PMCPMC8512178
OpenAlexW3201871456

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.