Evidence map›Paper›PMID 41415603›Full record

ArticleIEEE transactions on affective computing

Datasets of Smartphone Modalities for Depression Assessment: A Scoping Review.

M L Tlachac, Michael V Heinz, Anastasia C Bryan, Arielle LaPreay, Geri Louise Dimas, Tingting Zhao, Nicholas C Jacobson, Samuel S Ogden

Abstract read
In one paragraph

Article in IEEE transactions on affective computing. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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.

M L TlachacDepartment of Information Systems and Analytics, Bryant University, Smithfield, RI 02911 USA.
Michael V HeinzDepartment of Psychiatry, Dartmouth-Hitchcock Medical Center, Lebanon, NH 03766 USA.
Anastasia C BryanCenter for Technology and Behavioral Health, Dartmouth College, Lebanon, NH 03766 USA.
Arielle LaPreayDepartment of Information Systems and Analytics, Bryant University, Smithfield, RI 02911 USA.
Geri Louise DimasDepartment of Information Systems and Analytics, Bryant University, Smithfield, RI 02911 USA.
Tingting ZhaoCollege of Business, University of Rhode Island, Kingston, RI 02881 USA.
Nicholas C JacobsonDepartments of Biomedical Data Science and Psychiatry, Dartmouth College, Lebanon, NH 03766 USA.
Samuel S OgdenSchool of Computing and Design at California State University Monterey Bay, Seaside, CA 93955 USA.

Funding

Personalized Deep Learning Models of Rapid Changes in Major Depressive Disorder Symptoms using Passive Sensor Data from Smartphones and Wearable DevicesR01MH123482 · NIMH · DARTMOUTH COLLEGE · PI JACOBSON, NICHOLAS CHARLES · 2020 to 2024
$2.6M
Training Program for Quantitative Population Sciences in CancerT32CA134286 · NCI · DARTMOUTH COLLEGE · PI KARAGAS, MARGARET RITA, TOSTESON, TOR D · 2020 to 2024
$1.5M
NCI NIH HHS T32 CA134286NIMH NIH HHS R01 MH123482
6 · The paper itself

Abstract

As mobile sensing research for depression assessment is rapidly expanding, it is vital to uncover fundamental patterns and overarching limitations in the datasets available for such research, especially as many analyses are conducted on the same datasets. As such, we conduct a scoping review by identifying 80 datasets through the start of 2024 that contain smartphone modalities and depression labels. These datasets originated from 72 manuscripts and approximately 60 research groups. The most collected smartphone modalities included location and/or activity for 68.75%, communication logs for 47.5%, phone use for 41.25%, vocal utterances for 30%, and WiFi and/or Bluetooth connectivity for 28.75% of the datasets. The PHQ-8 and PHQ-9 (Patient Health Questionnaire) were the most popular screening instruments, administered for 53.75% of datasets. Of the 80 datasets, 31.25% recruited from student populations, 22.5% from patient populations, and 7.5% recruited crowdsourced workers. Excluding the nine datasets that did not report demographics, 73% reported a majority of women participants. Given the importance of datasets on analysis outcomes, this scoping review is an invaluable resource to inform the state of science and guide future mobile health research.

Indexed as

Data CollectionDigital BiomarkerDigital HealthDigital PhenotypeMobile Health

Identifiers

PMID41415603
PMCPMC12710863

What OpenQuestion holds

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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.