Evidence map›Paper›PMID 35848800›Full record

ReviewPsychiatry2022

Just-in-Time Adaptive Interventions for Suicide Prevention: Promise, Challenges, and Future Directions.

Daniel D L Coppersmith, Walter Dempsey, Evan M Kleiman, Kate H Bentley, Susan A Murphy, Matthew K Nock

Open access · greenAbstract readReview
In one paragraph

Review in Psychiatry, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 72 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
72citing papers in PubMed, 4 pooled it
18.0field-weighted citation impact, top 1% 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.

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

72 citing papers in PubMed, 4 syntheses or guidelines pooled it, 108 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Pooled it
  5. Trial
  6. Using smartphone surveys to predict next-week suicide attempts.Journal of psychopathology and clinical science · 2026
    Article
  7. Review
  8. Predicting Next-Day Passive Suicidal Ideation in At-Risk Youth.Suicide & life-threatening behavior · 2026
    Article
  9. Article
  10. Modeling the reciprocal dynamic between alcohol and suicide urges among college students: Ecological momentary assessment study.Psychology of addictive behaviors : journal of the Society of Psychologists in Addictive Behaviors · 2026
    Article
  11. Article
  12. Article
  13. Reducing the 'Silence Between Sessions': A Qualitative Study on Youth and Professionals' Perspectives on Digital Tools for Suicide Prevention.Health expectations : an international journal of public participation in health care and health policy · 2026
    Article
  14. Innovations to improve outcomes and uptake of psychotherapies for mental disorders: a state-of-the-art review.World psychiatry : official journal of the World Psychiatric Association (WPA) · 2026
    Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Article

12 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors at 2 institutions in 1 country.

Daniel D L CoppersmithORCID 0000-0002-9397-3228
Walter Dempsey
Kate H Bentley
Susan A Murphy
Matthew K Nock
Associated Press · USTwitter (United States) · US

Funding

Pilot and Mentoring CoreP50DA054039 · NIDA · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI LINDA M COLLINS, SUSAN A MURPHY · 2021 to 2026
$18.2M
TR&D3 - Rapid Translation of AI-powered Temporally Precise mHealth Interventions via Efficient and Embeddable Trustworthy Biomarker ImplementationsP41EB028242 · NIBIB · UNIVERSITY OF MEMPHIS · PI VIVEK SHETTY · 2020 to 2026
$9.5M
Novel Methods for Intensive Longitudinal Data in SMART Studies of Drug Abuse and HIVR01DA039901 · NIDA · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI ALMIRALL, DANIEL, NAHUM-SHANI, INBAL BILLIE · 2015 to 2024
$5.4M
Novel use of mHealth data to identify states of vulnerability and receptivity to JITAIs SupplementU01CA229437 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI NAHUM-SHANI, INBAL BILLIE, WETTER, DAVID W · 2018 to 2022
$2.8M
Digital Monitoring of Agitation for Short-Term Suicide Risk PredictionK23MH120436 · NIMH · MASSACHUSETTS GENERAL HOSPITAL · PI BENTLEY, KATE H. · 2019 to 2024
$1.0M
Personalized Digital Behavior Change Interventions to Promote Oral HealthUG3DE028723 · NIDCR · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI SHETTY, VIVEK · 2019 to 2019
$460k
NCI NIH HHS U01 CA229437NIBIB NIH HHS P41 EB028242NIDA NIH HHS P50 DA054039NIDA NIH HHS R01 DA039901NIDCR NIH HHS UG3 DE028723NIMH NIH HHS K23 MH120436
6 · The paper itself

Abstract

The suicide rate (currently 14 per 100,000) has barely changed in the United States over the past 100 years. There is a need for new ways of preventing suicide. Further, research has revealed that suicidal thoughts and behaviors and the factors that drive them are dynamic, heterogeneous, and interactive. Most existing interventions for suicidal thoughts and behaviors are infrequent, not accessible when most needed, and not systematically tailored to the person using their own data (e.g., from their own smartphone). Advances in technology offer an opportunity to develop new interventions that may better match the dynamic, heterogeneous, and interactive nature of suicidal thoughts and behaviors. Just-In-Time Adaptive Interventions (JITAIs), which use smartphones and wearables, are designed to provide the right type of support at the right time by adapting to changes in internal states and external contexts, offering a promising pathway toward more effective suicide prevention. In this review, we highlight the potential of JITAIs for suicide prevention, challenges ahead (e.g., measurement, ethics), and possible solutions to these challenges.

Indexed as

Suicide PreventionHumansSmartphoneSuicidal IdeationUnited States

Identifiers

PMID35848800
PMCPMC9643598
OpenAlexW4285730897

What OpenQuestion holds

Textmetadata
LicenceTDM
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.