Evidence map›Paper›PMID 40277515›Full record

SynthesisBiosensors2025

Fusing Wearable Biosensors with Artificial Intelligence for Mental Health Monitoring: A Systematic Review.

Ali Kargarandehkordi, Shizhe Li, Kaiying Lin, Kristina T Phillips, Roberto M Benzo, Peter Washington

Abstract readSystematic Review
In one paragraph

Synthesis in Biosensors, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
20citing 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

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

  1. Pooled it
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  9. Sensors Fusion in Digital Healthcare Applications.Sensors (Basel, Switzerland) · 2026
    Article
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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

6 authors.

Ali KargarandehkordiInformation and Computer Sciences, University of Hawaii at Manoa, Honolulu, HI 96822, USA.ORCID 0000-0002-2714-9476
Shizhe LiDepartment of Statistics, Stanford University, Stanford, CA 94305, USA.ORCID 0009-0003-1726-8871
Kaiying LinInformation and Computer Sciences, University of Hawaii at Manoa, Honolulu, HI 96822, USA.
Kristina T PhillipsCenter for Integrated Health Care Research, Kaiser Permanente Hawaii, Honolulu, HI 96817, USA.
Roberto M BenzoDivision of Cancer Prevention and Control, Department of Internal Medicine, College of Medicine, The Ohio State University Comprehensive Cancer Center, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA.
Peter WashingtonInformation and Computer Sciences, University of Hawaii at Manoa, Honolulu, HI 96822, USA.ORCID 0000-0003-3276-4411

Funding

Tracking and Evaluation CoreU54GM138062 · NIGMS · UNIVERSITY OF HAWAII AT MANOA · PI KAHOLOKULA, JOSEPH KEAWE'AIMOKU · 2021 to 2025
$15.5M
Hawai'i Community Foundation MedRes_2023_00002689NIGMS NIH HHS U54 GM138062NIGMS NIH HHS U54GM138062NIH HHS 1U54GM138062-02A1U.S. National Science Foundation 2406251
6 · The paper itself

Abstract

The development of digital instruments for mental health monitoring using biosensor data from wearable devices can enable remote, longitudinal, and objective quantitative benchmarks. To survey developments and trends in this field, we conducted a systematic review of artificial intelligence (AI) models using data from wearable biosensors to predict mental health conditions and symptoms. Following PRISMA guidelines, we identified 48 studies using a variety of wearable and smartphone biosensors including heart rate, heart rate variability (HRV), electrodermal activity/galvanic skin response (EDA/GSR), and digital proxies for biosignals such as accelerometry, location, audio, and usage metadata. We observed several technical and methodological challenges across studies in this field, including lack of ecological validity, data heterogeneity, small sample sizes, and battery drainage issues. We outline several corresponding opportunities for advancement in the field of AI-driven biosensing for mental health.

Indexed as

Artificial IntelligenceBiosensing TechniquesMental HealthWearable Electronic DevicesHeart RateHumansMonitoring, Physiologicanxietydepressionmachine learningmental healthstressstudywearable

Identifiers

PMID40277515
PMCPMC12025234

What OpenQuestion holds

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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.