Distinguishing the Effect of Time Spent at Home during COVID-19 Pandemic on the Mental Health of Urban and Suburban College Students Using Cell Phone Geolocation

Abstract

The aim of this study was to assess the correlation of depression and anxiety with time spent at home among students at two universities—one urban and the other suburban—during the COVID-19 pandemic. Methods: Geolocation data from the smartphones of 124 participants were collected between February 2021 and May 2021. The level of depression was estimated by the PHQ-9 and PHQ-2 screening tools, and anxiety scores were estimated by the GAD-2 and GAD-7 screening tools. Results: 51% of participants in the PHQ-9 surveys indicated mild to severe depression. Participants spent on average 75% of their time at home during COVID. Time spent at home had a positive correlation with the mental health of urban students but a negative correlation with suburban students. The relation between the time at home with mental health was stronger among female participants than among male participants. Correlations between female depression, anxiety, and time at home were significant. Conclusions: Lockdown and distance learning contributed to the high levels of depression in university students. This research highlights the importance of time spent at home for mental health being during the pandemic and the importance of distinguishing between urban and suburban settings when formulating public health recommendations. Quality of time spent at home versus time spent outside differentiated the mental well-being of students located in different environments. Staying at home may be recommended for students without access to safe outdoor places as it is associated with lower levels of depression.

A Systematic Review of Location Data for Depression Prediction

“…Of the 31 studies, 21 (68%) [14,27,[35][36][37][38][39][40][41][42][43][44][45][46][47][48][49][50][51][52][53] were conducted after 2019.…”

“…The largest number of location data studies was conducted in the United States (20, 65%) [10,19,20,22,27,[30][31][32][33][34][35]37,39,41,44,[46][47][48]50,51] followed by six (19%) [29,36,45,49,52,53] in Europe and five (16%) [14,38,40,42,43] in other regions.…”

“…Of the 31 studies, 10 (32%) [10,20,22,27,31,32,35,37,39,51] were conducted with college students, 8 (26%) [14,38,[40][41][42]47,52,53] targeted the group diagnosed with depression, and 7 (23%) [19,29,30,37,43,45,46] worked with the normal group. Three studies (10%) [36,49,50] involved both depressed and normal groups.…”

“…In terms of sample size, 15 studies (48%) [10,20,22,[31][32][33]35,36,41,43,45,[47][48][49][50] included more than 30 to less than 100 participants, followed by 7 studies (23%) [14,29,[37][38][39][40]51] between 100 and 200 participants, and 8 studies (26%) [27,30,34,42,44,46,52,53] with more than 200 participants. In terms of duration, 11 studies (35%) [10,14,20,[32][33][34]37,38,43,48,50] were conducted between 8 and 12 weeks, and 8 studies (26%) [35,39,42,44,47,[51][52][53] in more than 12 weeks. Five studies (16%) …”

Semi-supervised Graph Instance Transformer for Mental Health Inference

“…Dong et al [48], for example, observe that during COVID-19, people become more sedentary and spend more time at home due to infection risk reduction and public intervention. Therefore, the collected data can come from varying domains, causing domain shift problems that downgrade the generalizability of previously trained models [49]. KD enables the apprentice model to adapt to the newly collected data and overcome knowledge forgetting.…”