Examining the Correlation Between Depression and Social Behavior on Smartphones Through Usage Metadata: Empirical Study

Abstract

Background 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. Objective The objective of this study was to explore depressed Chinese individuals’ social behavior in digital space through metadata collected via smartphones. Methods 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. Results The results found depressed users received less calls from contacts (all day: F1,116=3.995, P=.048, η2=0.033; afternoon: F1,116=5.278, P=.02, η2=0.044), and used social apps more frequently (all day: F1,116=6.801, P=.01, η2=0.055; evening: F1,116=6.902, P=.01, η2=0.056) than nondepressed ones. In the depressed group, females used Weibo more frequently than males (all day: F1,116=11.744, P=.001, η2=0.092; morning: F1,116=9.105, P=.003, η2=0.073; afternoon: F1,116=14.224, P<.001, η2=0.109; evening: F1,116=9.052, P=.003, η2=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). Conclusions 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.

A Mobile Sensing App to Monitor Youth Mental Health: Observational Pilot Study

“…In addition, we confirmed the potential discriminative validity of smartphone biomarkers observed among adults in a youth sample [ 51 ]. Although our results on the number of outgoing calls negatively correlating with internalizing symptoms are in line with previous studies in adults [ 13 , 15 , 18 ], differences in mobility patterns of youths with high anxiety symptoms as compared with high depression symptoms have not been described thus far [ 11 , 14 , 21 ]. However, these findings have to be interpreted considering the recent pandemic and the behavioral changes accompanying it [ 52 ].…”

“…Study participants with social anxiety and depression were also found to avoid public areas and engage in less leisure activities, choosing instead to spend more time at home after school [ 11 , 12 ]. Studies exploring phone use data have reported the number of outgoing calls and messages to negatively correlate with internalizing symptoms [ 13 , 15 , 18 ]. In addition, ambient light data, an indicator of quality of sleep, were found to be associated with internalizing symptoms [ 19 ].…”

Smartphone-Tracked Digital Markers of Momentary Subjective Stress in College Students: Idiographic Machine Learning Analysis

“…Extant evidence, however, suggests a complex and nuanced relation between smartphone use and mental health, with different types of app use showing different patterns of association. For example, smartphone use behaviors with particular theoretical relevance to stress are calling, mobile messaging, and using social media, but recent work on passively logged data found depression relates negatively to calling [21], whereas it relates positively to social media use [22]. Moreover, research suggests that relations between smartphone app use and mental health differ from person to person [12][13][14][15][16].…”

Acceptability of Personal Sensing Among People with Alcohol Use Disorder: Observational Study

“…More recently, mental health relevant research using personal sensing of raw data streams other than self-report is emerging. This includes methods to sense geolocation [14][15][16], cellular communications [16][17][18][19][20], sleep [20], and physiology [21,22], as examples.…”

“…Mental health research and applications with emerging, often more passively sensed, novel data streams like geolocation and cellular communications is still nascent. This research has predominately involved "proof-of-concept" studies that typically include only healthy controls or other convenience samples rather than people with psychiatric disorders [17][18][19]. It has also often used very small sample sizes and/or short monitoring periods [15,17,21,22].…”