Evidence map›Paper›PMID 41171275›Full record

Trial reportJAMA network open2025

Brief Digital Interventions for Psychological Distress: An AI-Enhanced Response-Adaptive Randomized Clinical Trial.

Jill Newby, Sunil Gupta, Leonard Hoon, WuYi Zheng, Alexis E Whitton, Kit Huckvale, Eileen Stech, Andrew Mackinnon, Manisha Senadeera, Artur Shvetcov and 19 more

Abstract readRandomized Controlled Trial
PubMed Full text at PMC Publisher
In one paragraph

Trial report in JAMA network open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 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

5 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Trial
  3. Trial
  4. Article
  5. 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

29 authors.

Jill NewbyBlack Dog Institute, University of New South Wales Sydney, New South Wales, Sydney, Australia.
Sunil GuptaApplied Artificial Intelligence Initiative, Deakin University, Victoria, Melbourne, Australia.
Leonard HoonApplied Artificial Intelligence Initiative, Deakin University, Victoria, Melbourne, Australia.
WuYi ZhengBlack Dog Institute, University of New South Wales Sydney, New South Wales, Sydney, Australia.
Alexis E WhittonBlack Dog Institute, University of New South Wales Sydney, New South Wales, Sydney, Australia.
Kit HuckvaleBlack Dog Institute, University of New South Wales Sydney, New South Wales, Sydney, Australia.
Eileen StechBlack Dog Institute, University of New South Wales Sydney, New South Wales, Sydney, Australia.
Andrew MackinnonBlack Dog Institute, University of New South Wales Sydney, New South Wales, Sydney, Australia.
Manisha SenadeeraApplied Artificial Intelligence Initiative, Deakin University, Victoria, Melbourne, Australia.
Artur ShvetcovBlack Dog Institute, University of New South Wales Sydney, New South Wales, Sydney, Australia.
Julian BerkApplied Artificial Intelligence Initiative, Deakin University, Victoria, Melbourne, Australia.
Aimy SladeBlack Dog Institute, University of New South Wales Sydney, New South Wales, Sydney, Australia.
Michael J SpoelmaBlack Dog Institute, University of New South Wales Sydney, New South Wales, Sydney, Australia.
Jin HanBlack Dog Institute, University of New South Wales Sydney, New South Wales, Sydney, Australia.
Joanne R BeamesBlack Dog Institute, University of New South Wales Sydney, New South Wales, Sydney, Australia.
Rena LogothetisApplied Artificial Intelligence Initiative, Deakin University, Victoria, Melbourne, Australia.
Omar DabashBlack Dog Institute, University of New South Wales Sydney, New South Wales, Sydney, Australia.
Stefanus KurniawanApplied Artificial Intelligence Initiative, Deakin University, Victoria, Melbourne, Australia.
Rajesh VasaApplied Artificial Intelligence Initiative, Deakin University, Victoria, Melbourne, Australia.
Kon MouzakisApplied Artificial Intelligence Initiative, Deakin University, Victoria, Melbourne, Australia.
Stuart CameronApplied Artificial Intelligence Initiative, Deakin University, Victoria, Melbourne, Australia.
Akash AgarwalApplied Artificial Intelligence Initiative, Deakin University, Victoria, Melbourne, Australia.
Joshua AsburyApplied Artificial Intelligence Initiative, Deakin University, Victoria, Melbourne, Australia.
Joost Funke KupperApplied Artificial Intelligence Initiative, Deakin University, Victoria, Melbourne, Australia.
Aliza Werner-SeidlerBlack Dog Institute, University of New South Wales Sydney, New South Wales, Sydney, Australia.
Simon RosenbaumDiscipline of Psychiatry and Mental Health, University of New South Wales Sydney, New South Wales, Sydney, Australia.
Henry CutlerCentre for the Health Economy, Macquarie University, New South Wales, Sydney, Australia.
Svetha VenkateshApplied Artificial Intelligence Initiative, Deakin University, Victoria, Melbourne, Australia.
Helen ChristensenBlack Dog Institute, University of New South Wales Sydney, New South Wales, Sydney, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Importance: A large proportion of college students report experiencing psychological distress. Smartphone app-based interventions may alleviate distress, but their effectiveness across severity levels is unclear. Artificial intelligence (AI)-enhanced response-adaptive randomized clinical trials may offer an efficient method to evaluate competing interventions. Objective: To compare the effectiveness of 3 brief, 2-week self-guided smartphone application-based interventions (physical activity, mindfulness, sleep hygiene) or an active control (ecological momentary assessment [EMA]) for reducing psychological distress among college students with mild, moderate, or severe distress. Design, Setting, and Participants: This population-based AI-enhanced response-adaptive randomized clinical trial included 1282 participants with distress scores of 20 or more on the 10-item Kessler Psychological Distress Scale in 12 minitrials from November 9, 2021, with final follow-up on February 17, 2023. Participants' distress was categorized as mild, moderate, or severe based on their normalized 21-item Depression Anxiety Stress Scale (DASS-21) scores at screening. Interventions: After a 2-week onboarding period of daily EMA, participants were assigned by a contextual multi-armed bandit algorithm to 1 of 4 two-week self-guided app interventions: physical activity, mindfulness, sleep hygiene, or a control that continued EMA. Main Outcomes and Measures: The primary outcome was change in psychological distress (DASS-21 total score) from week 2 (before intervention) to week 4 (after intervention). The primary end point was after the intervention (4 weeks). Secondary outcomes included DASS-21 subscale scores, self-reported physical activity, mindfulness, sleep quality, and app engagement, usability, and satisfaction. Analysis was performed on an intention-to-treat basis. Results: A total of 1282 individuals (mean [SD] age, 23.5 [5.2] years; 950 women [74.1%]) participated: physical activity (n = 305), mindfulness (n = 453), sleep hygiene (n = 431), or a control that continued EMA (n = 93). Among 349 participants with severe distress, physical activity (n = 79) and mindfulness (n = 180) were significantly more effective than the EMA control (n = 29) in reducing DASS-21 total scores (physical activity vs control: standardized mean difference [SMD], 0.62 [95% CI, 0.23-1.02]; mindfulness vs control: SMD, 0.53 [95% CI, 0.19-0.87]) and sleep hygiene (n = 61) (physical activity vs sleep hygiene: SMD, 0.50 [95% CI, 0.16-0.84]; mindfulness vs sleep hygiene: SMD, 0.41 [95% CI, 0.13-0.69]). Among 494 participants with mild distress, physical activity (n = 161) and sleep hygiene (n = 224) were significantly more effective than control (n = 37) in reducing DASS-21 total scores (physical activity vs control: SMD, 0.58 [95% CI, 0.30-0.86]; sleep hygiene vs control: SMD, 0.47 [95% CI, 0.20-0.73]). No significant group differences were observed among participants with moderate distress (n = 439). Conclusions and Relevance: In this AI-enhanced response-adaptive randomized clinical trial among college students, physical activity and mindfulness were most effective for severe distress, while physical activity and sleep hygiene were most effective for mild distress. These findings can guide personalized mental health interventions for college students. This trial improved efficiency by minimizing control group allocation but had reduced power to detect significant group differences. Trial Registration: http://anzctr.org.au Identifier: ACTRN12621001223820.

Indexed as

Artificial IntelligenceMobile ApplicationsPsychological DistressStress, PsychologicalAdolescentAdultEcological Momentary AssessmentExerciseFemaleHumansMaleMindfulnessSleep HygieneStudentsYoung Adult

Identifiers

PMID41171275
PMCPMC12579342

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.