Which Smartphone-Based Sensing Features Matter in Depression Prediction? Results from an observation study. (Preprint)

Author:

Terhorst YannikORCID,Messner Eva-Maria,Asare Kennedy Opoku,Montag ChristianORCID,Kannen Christopher,Baumeister HaraldORCID

Abstract

BACKGROUND

Unobtrusively collected objective sensor data from everyday devices like smartphones provide a novel paradigm to infer mental health symptoms. This process called smart sensing allows a fine-grained assessment of various features (e.g., time spent at home based on the GPS sensor). Based on its prevalence and impact depression is a promising target for smart sensing. However, currently it is unclear which sensor- based features should be used in depression prediction and if they hold an incremental benefit over established fine-grained assessments like Ecological Momentary Assessment (EMA).

OBJECTIVE

Hence, the present study investigated various features based on the smartphone screen, app usage, and call sensor alongside EMA to infer to depression severity. Bivariate, cluster-wise, and cluster-combined analysis were conducted to determine the incremental benefit of smart sensing features among each other and over EMA in parsimonious regression models for depression severity.

METHODS

In this exploratory observation study participants were recruited from the general population. Participants needed to be 18 years of age, provide written informed consent, and own an Android-based smartphone. Sensor data and EMA were collected via the INSIGHTS app. Depression severity was assessed by the PHQ-8 questionnaire. Missing data was handled by multiple imputations. Correlation analyses were conducted for bivariate associations, and stepwise linear regression analyses to find the best prediction models for depression severity. Models were compared by adjusted R2. All analyses were pooled across the imputed data sets according to Rubin’s rule.

RESULTS

A total of N=107 participants were included in the study. Age ranged from 18 to 56 years (M=22.81, SD=7.32) and 78% of the participants identified themselves as female. Depression severity was subclinical on average (M=5.82, SD=4.44, PHQ-8 ≥10: 18.7%). Small to medium correlations were found for depression severity and EMA (e.g., valence: r = -.55, 05%-CI: -.67 to -.41) and small correlations with sensing features (e.g., screen duration: r = .37, 95%-CI: .20 to .53). EMA features could explain 35.38% (95%-CI: 20.73% to 49.64%) of variance and sensing features adj. R2 = 20.45% (95%-CI: 7.81% to 35.59%). The best regression model contained EMA and sensing features (R2 = 45.15%, 95%-CI: 30.39% to 58.53%).

CONCLUSIONS

Our findings underline the potential of smart sensing and EMA to infer to depression both as isolated paradigms and especially when combined. While these could become important parts in clinical decision support systems for depression diagnostics and treatment in future, confirmatory studies are highly needed before an application in routine care. Furthermore, privacy, ethical, and acceptance issues need to be addressed.

Publisher

JMIR Publications Inc.

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3