CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, China.
School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, China.
JMIR Mhealth Uhealth. 2021 Jan 6;9(1):e19046. doi: 10.2196/19046.
As smartphone has been widely used, understanding how depression correlates with social behavior on smartphones can be beneficial for early diagnosis of depression. An enormous amount of research relied on self-report questionnaires, which is not objective. Only recently the increased availability of rich data about human behavior in digital space has provided new perspectives for the investigation of individual differences.
The objective of this study was to explore depressed Chinese individuals' social behavior in digital space through metadata collected via smartphones.
A total of 120 participants were recruited to carry a smartphone with a metadata collection app (MobileSens). At the end of metadata collection, they were instructed to complete the Center for Epidemiological Studies-Depression Scale (CES-D). We then separated participants into nondepressed and depressed groups based on their scores on CES-D. From the metadata of smartphone usage, we extracted 44 features, including traditional social behaviors such as making calls and sending SMS text messages, and the usage of social apps (eg, WeChat and Sina Weibo, 2 popular social apps in China). The 2-way ANOVA (nondepressed vs depressed × male vs female) and multiple logistic regression analysis were conducted to investigate differences in social behaviors on smartphones among users.
The results found depressed users received less calls from contacts (all day: F=3.995, P=.048, η=0.033; afternoon: F=5.278, P=.02, η=0.044), and used social apps more frequently (all day: F=6.801, P=.01, η=0.055; evening: F=6.902, P=.01, η=0.056) than nondepressed ones. In the depressed group, females used Weibo more frequently than males (all day: F=11.744, P=.001, η=0.092; morning: F=9.105, P=.003, η=0.073; afternoon: F=14.224, P<.001, η=0.109; evening: F=9.052, P=.003, η=0.072). Moreover, usage of social apps in the evening emerged as a predictor of depressive symptoms for all participants (odds ratio [OR] 1.007, 95% CI 1.001-1.013; P=.02) and male (OR 1.013, 95% CI 1.003-1.022; P=.01), and usage of Weibo in the morning emerged as a predictor for female (OR 1.183, 95% CI 1.015-1.378; P=.03).
This paper finds that there exists a certain correlation between depression and social behavior on smartphones. The result may be useful to improve social interaction for depressed individuals in the daily lives and may be insightful for early diagnosis of depression.
随着智能手机的广泛应用，了解抑郁与智能手机上的社交行为之间的关系，有助于对抑郁症进行早期诊断。大量研究依赖于自我报告问卷，这并不客观。直到最近，人们可以获取关于数字空间中人类行为的丰富数据，这为个体差异的研究提供了新的视角。
本研究旨在通过智能手机采集的元数据，探究抑郁的中国个体在数字空间中的社交行为。
共招募了 120 名参与者，他们携带一部装有元数据采集应用程序（MobileSens）的智能手机。在元数据采集结束时，他们被要求完成《流行病学研究中心抑郁量表》（Center for Epidemiological Studies-Depression Scale，CES-D）。然后，我们根据 CES-D 得分将参与者分为非抑郁组和抑郁组。我们从智能手机使用的元数据中提取了 44 个特征，包括打电话和发送短信等传统社交行为，以及使用微信和新浪微博等社交应用程序（中国两个流行的社交应用程序）。我们采用双向方差分析（非抑郁 vs 抑郁×男性 vs 女性）和多元逻辑回归分析，来探究用户在智能手机上的社交行为差异。
结果发现，与非抑郁者相比，抑郁者接到联系人电话的次数更少（全天：F=3.995，P=.048，η=0.033；下午：F=5.278，P=.02，η=0.044），使用社交应用程序的频率更高（全天：F=6.801，P=.01，η=0.055；晚上：F=6.902，P=.01，η=0.056）。在抑郁组中，女性比男性更频繁地使用微博（全天：F=11.744，P=.001，η=0.092；上午：F=9.105，P=.003，η=0.073；下午：F=14.224，P<.001，η=0.109；晚上：F=9.052，P=.003，η=0.072）。此外，对于所有参与者而言，晚上使用社交应用程序（优势比[odds ratio，OR] 1.007，95%置信区间[confidence interval，CI] 1.001-1.013；P=.02）和男性（OR 1.013，95% CI 1.003-1.022；P=.01）是抑郁症状的预测因素，而对于女性而言，早上使用微博（OR 1.183，95% CI 1.015-1.378；P=.03）是抑郁症状的预测因素。
本文发现抑郁与智能手机上的社交行为之间存在一定的相关性。研究结果可能有助于改善日常生活中抑郁个体的社交互动，对抑郁症的早期诊断具有一定的启示意义。
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