Evidence map›Paper›PMID 42558176›Full record

Articlenpj digital public health2026

The Digital Mental Health Study assessed feasibility of large-scale digital sensing for depression and anxiety.

Christopher S Douglas, Eliza Congdon, Crane Huang, Darsol Seok, Zachary D Cohen, Samir Akre, Veronica Tozzo, Feiyang Huang, Danielle Ramo-Larios, Raphe A Bernier and 7 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in npj digital public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

17 authors.

Christopher S Douglas *University of California, Los Angeles, Depression Grand Challenge, Los Angeles, CA USA.
Eliza Congdon *University of California, Los Angeles, Depression Grand Challenge, Los Angeles, CA USA.
Crane Huang *Apple Inc., Cupertino, CA USA.
Darsol Seok *University of California, Los Angeles, Depression Grand Challenge, Los Angeles, CA USA.
Zachary D CohenUniversity of California, Los Angeles, Depression Grand Challenge, Los Angeles, CA USA.
Samir AkreUniversity of California, Los Angeles, Depression Grand Challenge, Los Angeles, CA USA.
Veronica TozzoUniversity of California, Los Angeles, Depression Grand Challenge, Los Angeles, CA USA.
Feiyang HuangUniversity of California, Los Angeles, Depression Grand Challenge, Los Angeles, CA USA.
Danielle Ramo-LariosApple Inc., Cupertino, CA USA.
Raphe A BernierApple Inc., Cupertino, CA USA.
Jonathan FlintUniversity of California, Los Angeles, Depression Grand Challenge, Los Angeles, CA USA.
Arash NaeimUniversity of California, Los Angeles, Center for AI & SMART Health, Los Angeles, CA USA.
Brunilda BalliuUniversity of California, Los Angeles, Depression Grand Challenge, Los Angeles, CA USA.
Alex A T BuiUniversity of California, Los Angeles, Depression Grand Challenge, Los Angeles, CA USA.
Marian Stewart Bartlett *Apple Inc., Cupertino, CA USA.
Michelle G Craske *University of California, Los Angeles, Depression Grand Challenge, Los Angeles, CA USA.
Nelson B Freimer *University of California, Los Angeles, Depression Grand Challenge, Los Angeles, CA USA.

Funding

Washington University Psychiatry Residency Research Education ProgramR25MH112473 · NIMH · WASHINGTON UNIVERSITY · PI NURI B FARBER, Timothy Eric Spiegel · 2018 to 2026
$1.6M
NIMH NIH HHS R25 MH112473
6 · The paper itself

Abstract

Data passively obtained from smartphones and wearables can provide nearly continuous objective information that enables quantification of states and traits across broad physiological, behavioral, and emotional domains impacted in mental health conditions, including depression and anxiety. Widespread application of such digital phenotyping could transform the assessment of depression and anxiety in research and clinical care, but the field has lacked well-powered longitudinal studies demonstrating the utility of this approach. This paper describes the design and implementation of the Digital Mental Health Study (DMHS), which collected up to 12 months of sensor data from iPhone and Apple Watch in over 4000 consenting participants, a sample diverse by age, sex at birth, ethnicity, and depression symptom severity. To enable the use of these digital phenotypes to assay the complexity and heterogeneity of depression and anxiety, we designed a protocol of periodic self-report and interview-based scales optimized to assess elements of depression, anxiety, and perceived stress as broadly as possible while minimizing participant measurement burden. We report here the strategies used to recruit and enroll the DMHS sample, the process employed to develop study methods and protocols, and initial findings describing longitudinal symptom trajectories and demonstrating high participant engagement over 12 months.

Indexed as

Health carePsychology

Identifiers

PMID42558176
PMCPMC13437235

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.