Evidence map›Paper›PMID 36030226›Full record

ArticleScientific data2022

Real-world behavioral dataset from two fully remote smartphone-based randomized clinical trials for depression.

Abhishek Pratap, Ava Homiar, Luke Waninger, Calvin Herd, Christine Suver, Joshua Volponi, Joaquin A Anguera, Pat Areán

Abstract readDataset
In one paragraph

Article in Scientific data, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

  1. Trial
  2. Trial
  3. Trial
  4. Review
  5. Observational
  6. Article
  7. Article
  8. Article
  9. Article
  10. Digital and precision clinical trials: innovations for testing mental health medications, devices, and psychosocial treatments.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2024
    Review
  11. Article
  12. 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.

Abhishek PratapKrembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, Canada. abhishek.vit@gmail.com.ORCID 0000-0002-5289-6932
Ava HomiarKrembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, Canada.ORCID 0000-0001-5772-2475
Luke WaningerDepartment of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, USA.
Calvin HerdKrembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, Canada.
Christine SuverSage Bionetworks, Seattle, WA, USA.ORCID 0000-0002-2986-385X
Joshua VolponiDepartment of Neurology, University of California San Francisco, San Francisco, WA, USA.
Joaquin A AngueraDepartment of Neurology, University of California San Francisco, San Francisco, WA, USA.
Pat AreánDepartment of Psychiatry & Behavioral Sciences, University of Washington, Seattle, WA, USA.

Funding

Treating Depression in Low-Income Elderly with CIND/AMCIK24MH074717 · NIMH · UNIVERSITY OF WASHINGTON · PI AREAN, PATRICIA A. · 2005 to 2016
$1.7M
Can Metal Health Apps Work in the Real World? A Feasibility Pilot StudyR34MH100466 · NIMH · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI ANGUERA, JOAQUIN A, GAZZALEY, ADAM H · 2013 to 2015
$818k
NIMH NIH HHS K24 MH074717NIMH NIH HHS R34 MH100466U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH) R34MH100466
6 · The paper itself

Abstract

Most people with mental health disorders cannot receive timely and evidence-based care despite billions of dollars spent by healthcare systems. Researchers have been exploring using digital health technologies to measure behavior in real-world settings with mixed results. There is a need to create accessible and computable digital mental health datasets to advance inclusive and transparently validated research for creating robust real-world digital biomarkers of mental health. Here we share and describe one of the largest and most diverse real-world behavior datasets from over two thousand individuals across the US. The data were generated as part of the two NIMH-funded randomized clinical trials conducted to assess the effectiveness of delivering mental health care continuously remotely. The longitudinal dataset consists of self-assessment of mood, depression, anxiety, and passively gathered phone-based behavioral data streams in real-world settings. This dataset will provide a timely and long-term data resource to evaluate analytical approaches for developing digital behavioral markers and understand the effectiveness of mental health care delivered continuously and remotely.

Indexed as

DepressionSmartphoneAnxietyHumansMental HealthRandomized Controlled Trials as Topic

Identifiers

PMID36030226
PMCPMC9420101

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.