Evidence map›Paper›PMID 35030442›Full record

ArticleBehaviour research and therapy2022

Digital biomarkers of anxiety disorder symptom changes: Personalized deep learning models using smartphone sensors accurately predict anxiety symptoms from ecological momentary assessments.

Nicholas C Jacobson, Sukanya Bhattacharya

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Behaviour research and therapy, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers, 6 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
43citing papers in PubMed, 6 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

43 citing papers in PubMed, 6 syntheses or guidelines pooled it.

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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

2 authors.

Nicholas C JacobsonCenter for Technology and Behavioral Health, Departments of Biomedical Data Science and Psychiatry, Geisel School of Medicine, Dartmouth College, 46 Centerra Parkway; Suite 300, Office # 333S, Lebanon, NH, 03766, USA. Electronic address: Nicholas.C.Jacobson@dartmouth.edu.
Sukanya BhattacharyaDartmouth College, 46 Centerra Parkway; Suite 300, Office # 333S, Lebanon, NH, 03766, USA.

Funding

Treatment Development & Evaluation CoreP30DA029926 · NIDA · DARTMOUTH COLLEGE · PI Lisa A. Marsch · 2011 to 2026
$21.5M
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
NIDA NIH HHS P30 DA029926NIMH NIH HHS R01 MH123482
6 · The paper itself

Abstract

Smartphones are capable of passively capturing persons' social interactions, movement patterns, physiological activation, and physical environment. Nevertheless, little research has examined whether momentary anxiety symptoms can be accurately assessed using these methodologies. In this research, we utilize smartphone sensors and personalized deep learning models to predict future anxiety symptoms among a sample reporting clinical anxiety disorder symptoms. Participants (N = 32) with generalized anxiety disorder and/or social anxiety disorder (based on self-report) installed a smartphone application and completed ecological momentary assessment symptoms assessing their anxiety and avoidance symptoms hourly for the course of one week (T = 2007 assessments). During the same period, the smartphone app collected information about physiological activation (heart rate and heart rate variability), exposure to light, social contact, and GPS location. GPS locations were coded to reveal the type of location and the weather information. Personalized deep learning models using the smartphone sensor data were capable of predicting the majority of total variation in anxiety symptoms (R

Indexed as

Deep LearningSmartphoneAnxietyAnxiety DisordersBiomarkersEcological Momentary AssessmentHumansBiomarkersAnxiety disordersDigital phenotypingEcological momentary assessmentGeneralized anxiety disorderPassive sensingSocial anxiety disorder

Identifiers

PMID35030442
PMCPMC8858490

What OpenQuestion holds

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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.