Abstract
Background
Information and communication technology (ICT) use can be a double-edged sword for mental health. This study aimed to examine whether different types of ICT use were diversely associated with mental health outcomes in hospital-quarantined patients during the COVID-19 pandemic, and to assess whether social support mediated these associations.
Methods
This cross-sectional study involved hospital-quarantined COVID-19 patients from 10 March to 28 April 2022 in Shenzhen, China. Patients reported their ICT use, including online entertainment, contacting family/friends, reading online news, working/studying, using social media platforms, and online shopping, as well as social support and mental health. To facilitate early detection and identify potential ICT measures for supporting quarantined patients’ mental health, we applied lower cutoff scores to classify mild-or-greater mental health symptoms as outcomes, including depression (Patient Health Questionnaire-9 score ≥ 5), anxiety (Generalized Anxiety Disorder Scale-7 ≥ 5), PTSD (PTSD Checklist-Civilian version score ≥ 38), somatization symptoms (Self-reported Somatic Symptom Scale-China score ≥ 30), and insomnia (Insomnia Severity Index score ≥ 8). The associations of ICT use with mental symptoms were estimated using adjusted prevalence ratios (aPRs) by Poisson regressions, adjusting for sex, age, education, marital status, monthly household income, and household registration. Sobel-Goodman Mediation tests were used to analyze the mediating effects of social support.
Results
Among the 217 patients (female 48.9%, 25–54 years 63.1%), over half had depression (56.3%), insomnia (54.5%), and anxiety (50.5%), followed by somatization symptoms (35.4%) and post-traumatic stress disorder (PTSD, 18.0%). The majority used ICT for entertainment (77.4%) and contacting family/friends (74.2%). Those using ICT for contacting family/friends, working/studying, and online shopping had less depression, anxiety, PTSD, or somatization symptoms (aPRs 0.52–0.78, all P ≤ 0.04). Social support mediated 24.5–42.1% of the associations of using ICT for contacting family/friends with depression, anxiety, somatization symptoms, and PTSD, and 43.4–52.4% of the associations of working/learning with them.
Conclusions
This study shows that the different ICT use varies in their associations with mental symptoms during quarantine by their motivation. Social support partially mediates the associations of contacting family/friends and working/studying with mental symptoms, emphasizing supportive digital environments to strengthen social connectedness and mental health for quarantined patients.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12889-026-27109-6.
Keywords: COVID-19 patients, Mental health, Information and communication technology, Hospital quarantine, Social support, Mediation
Introduction
Since December 2019, the coronavirus disease 2019 (COVID-19) pandemic has spread to more than 200 countries, infected over 65 million people, and caused a global health crisis [1]. Compared to the 1.8% hospitalization rate of seasonal influenza, more than half of newly crowned COVID-19 patients developed pneumonia and were hospitalized [2]. Hospital quarantine elicited psychopathologic responses such as social isolation, uncertainty, boredom, and reduced autonomy, which may trigger depression, anxiety, and even post-traumatic stress disorder (PTSD) [3, 4]. According to a cross-sectional survey, the prevalence of psychopathologic responses in China ranges from 24.4% to 31.6%, with a higher probability of occurrence among hospital-quarantined people [5]. Considering the possibility of future infectious diseases, attention to the mental health of quarantined COVID-19 patients remains essential.
Information and Communication Technologies (ICT), such as instant messaging (IM), social media websites, and video calls, enable people to communicate and share information in a convenient, interactive and cost-effective way [6]. In quarantine settings, ICT can serve as the substitute for restricted face-to-face interaction [7], help patients maintain daily routines, and facilitate access to services and resources [8]. ICT use was reported as a common coping strategy during COVID-19 restrictions [9]. For those living alone in long-term care facilities, ICT was found to be an essential resource for maintaining socio-emotional connections with their family members [10]. Also, Brooks et al. demonstrated that giving access to electronics can help isolated patients establish a two-way communication channel to reduce adverse physical and psychological effects of isolation [11]. However, little is known about what hospital-quarantined COVID-19 patients used ICT to do and how different ICT functions were related to their mental health.
ICT was particularly salient in quarantine contexts for individuals’ mental health, which can be conceptualized through an integrated framework. First, the Social Compensation Hypothesis posits that, when face-to-face interaction becomes limited, individuals increasingly rely on digital communication to compensate for social deficits, thereby preserving socio-emotional connection and reducing perceived isolation [12]. This aligns with the Compensation Internet Use Theory, which suggests that, to cope with negative affect and unmet social or emotional needs arising from adverse real-life circumstances, individuals use ICT as a coping mechanism [13]. However, due to the amount of compensation required to alleviate negative feelings, such motivation may sometimes cause adverse consequences such as addiction-like symptoms or even worse feelings [13]. Second, Self-Determination Theory emphasizes that satisfying basic psychological needs, including autonomy, competence, and relatedness, is essential for mental well‑being [14]. During quarantine, ICT use may support these needs by enabling individuals to stay informed, accomplish daily tasks, and sustain meaningful interpersonal connections despite physical restrictions. Finally, the Social Support Buffering Model explains why ICT use can serve as a protective buffering factor. Social support consists of emotional, informational, evaluative, and instrumental support [15]. By enhancing the amount and perceptions of these domains of social support, ICT helps buffer the detrimental mental health consequences of stressors such as social isolation and uncertainty.
Taken together, we hypothesize that certain types of ICT use during quarantine may serve as a compensatory and regulatory tool to the interrupted routine and physical isolation, foster social support and thus play a critical role in sustaining mental health. Therefore, this study aimed to: (1) examine whether different types of ICT use were differentially associated with mental health outcomes in hospital-quarantined patients during the COVID-19 pandemic, and (2) assess whether social support mediated the association between ICT use and mental health.
Methods
Study design and participants
Shenzhen is one of China’s high-tech centers and a pioneer in national policy implementation. From the first COVID-19 case discovered (10 January 2020) to the beginning of the first pandemic wave (October 2022) in Shenzhen, all patients who tested positive for COVID-19 were first hospitalized in the Shenzhen Third People's Hospital. Once their tests turned negative and symptoms became mild, they were transferred to the South China Hospital for a final 14-day hospital quarantine. This cross-sectional study was conducted at the South China Hospital in Shenzhen during March–April 2022. Targeted COVID-19 patients should meet the following inclusion criteria, (a) initially tested positive for COVID-19 and then turned negative with only mild symptoms after treatment; (b) in their final 14-day hospital quarantine; (c) having access to ICT devices such as smartphones, tablets, or laptops. On the first day of their final quarantine, they were invited through e-messages to voluntarily complete an online anonymous questionnaire using Questionnaire Star (www.wjx.cn), an online survey tool.
Prior to accessing any survey items, all participants were presented with an electronic informed consent statement. The statement informed participants that their decision to complete or decline the questionnaire bore no relationship to their medical care, treatment, or hospital quarantine arrangements, emphasized that participation was entirely voluntary, all their data would be anonymous and no identifiable information would be collected, and clarified that respondents could freely withdraw from the survey at any point without penalty. Only individuals who clicked the “Agree” button were able to proceed, and those who declined consent were automatically exited from the survey. To minimize duplicate responses, we implemented a one-time submission rule on the platform by restricting repeated entries from the same device and IP address. Additionally, the survey instructions explicitly informed respondents that the questionnaire could be completed only once. Details of the survey have been described before [7]. Totally 217 (29.8%) of the quarantined patients completed the questionnaires.
Measurements
Sociodemographic characteristics
Patients’ sociodemographic information was measured, including sex (female and male), age (< 25 years, 25–54 years, and ≥ 55 years), education levels (secondary or below, tertiary or above), monthly household income (< CNY 5,000, CNY 5,000–9,999, and ≥ CNY 10,000) (USD 1 = CNY 7.18), marital status (single, married or cohabitated, and widowed or divorced), and household registration (local and non-local).
ICT use
As shown in Additional File 1, patients’ ICT use was measured using the question “Over the past 7 days, what did you usually do with your mobile phone, tablet or laptop?” with multiple choices, including contacting family and friends, working or learning, playing video games, watching long videos (such as films, TV programs, or entertainment programs), watching short videos (such as Douyin, Kuaishou), reading online news, listening to music, reading e-books, using social media platforms (Microblog, Xiaohongshu), online shopping, and no use at all. Online entertainment was measured using a composite variable representing whether patients engaged in one or more of the following online entertainment (1) or not (0), including watching short videos (such as Douyin, Kuaishou), listening to music, watching long videos (such as films, TV programs, or entertainment programs), playing video games, and reading e-books, as well as 0 representing none of these ICT use. These activities were explicitly described as entertainment-oriented activities in the questionnaire and primarily engaged in for leisure and enjoyment rather than instrumental or informational purposes.
Social support
Social support was measured using the Social Support Rate Scale (SSRS), which has been widely accepted to be valid [16]. The SSRS score ranges from 0 to 66, with higher scores indicating better social support [17]. The Cronbach’s alpha of SSRS in this study is 0.700.
Mental health
This study examined 4 types of mental health problems, including depression, anxiety, PTSD, and somatization symptoms. Depression was measured using the Chinese version of the Patient Health Questionnaire (PHQ-9) [18], which consists of 9 questions with a 4-point Likert scale. The maximum score of PHQ-9 is 27, and a score ≥ 5 indicates mild-or-greater depression [19]. The Cronbach’s alpha of PHQ-9 in this study is 0.911. Anxiety was measured using the Chinese version of the Generalized Anxiety Disorder Scale (GAD-7) [20], consisting of 7 items with a 4-point Likert scale of 0–3, which has been proven its validation [21]. The maximum score on this scale is 21, and patients with GAD-7 scores ≥ 5 were considered to have mild-or-greater anxiety [22]. In this study, the Cronbach’s alpha of GAD-7 is 0.954. PTSD was measured using the Chinese version of the PTSD Checklist-Civilian version (PCL-C) [23], which can be used for PTSD screening, diagnosis, or symptom monitoring [24]. PCL-C is a validated tool with 17 items and a 5-point Likert scale of 1–5. The scores of PCL-C range from 17 to 85, and patients with scores ≥ 38 were considered to have mild-or-greater PTSD [25]. PCL-C showed excellent reliability with the Cronbach’s alpha of 0.957 in this study. Patients’ somatization symptoms were measured using the Chinese version of the Self-reported Somatic Symptom Scale-China (SSS-CN) [26], a 20-item scale with a 4-point Likert scale (from 1 = no to 4 = severe). The SSS-CN has a maximum score of 80, and patients with scores ≥ 30 were considered to have mild-or-greater somatization [27]. In this study, the Cronbach’s alpha of SSS-CN is 0.945.
Insomnia
The Insomnia Severity Index (ISI) was used to evaluate the perceived severity of insomnia, consisting of 7 items with 5-point Likert scales ranging from 0–4 [28]. Higher scores indicate more severe insomnia symptoms. Patients with ISI scores ≥ 8 were considered to have mild-or-greater insomnia. The ISI showed good reliability in this study with a Cronbach’s alpha of 0.940.
Statistical analysis
Categories and continuous variables were described using frequency (percentage) and mean ± standard deviation (SD), respectively. Adjusted prevalence ratios (aPRs) and 95% confidence intervals (CIs) were calculated using Poisson regression models to examine the associations between sociodemographic characteristics and different ICT use with mutual adjustment, as well as the associations between different ICT use and binary mental health outcomes (including depression, anxiety, PTSD, somatic symptoms, insomnia, and social support), adjusting for sex, age, education level, marital status, monthly household income, and household registration. In the Poisson regression models, we employed robust standard errors to enhance the reliability of estimates by providing more accurate CIs and hypothesis tests. To assess the robustness of the associations between ICT use and mental health, we also conducted sensitivity analyses using continuous mental health scores as the outcomes in multivariable linear regression models.
Sobel-Goodman Mediation tests were used to analyze the mediating effects of social support in the associations of different ICT use and mental health problems. The 95% bias-corrected bootstrap CIs of the total, indirect, and direct effects were calculated with 5,000 replications. Adjusted linear regressions with continuous outcomes were used to explore the mediating effects. A 2-sided P < 0.05 was considered statistically significant. STATA version 17.0 was used for all statistical analyses.
Results
Table 1 shows that, among the 217 respondents, the majority were male (51.2%), aged 25–54 years (63.1%), and those having secondary or below education level (73.7%), being married or cohabitated (58.5%), and local residents in Shenzhen (86.6%). Around one-third of them had a monthly household income of CNY ¥5,000–9,999 (30.4%) and ≥ CNY 10,000, respectively. During their hospital quarantine, most of them used ICT for entertainment (77.4%) and contacting family and friends (74.2%), followed by reading news online (44.2%), working/studying (29.5%), using social media platforms (21.7%), and online shopping (21.7%). Watching short videos was their primary entertainment (51.6%), followed by listening to music (42.4%) and watching long videos (41.5%). More than half of the respondents experienced depression (56.3%), insomnia (54.5%), and anxiety (50.5%), followed by somatization symptoms (35.4%) and PTSD (18.0%).
Table 1.
Sociodemographic characteristics of the study sample (N = 217)
| Sociodemographic characteristics | n (%)a |
|---|---|
| Sex | |
| Female | 106 (48.9) |
| Male | 111 (51.2) |
| Age, years | |
| < 25 | 44 (20.3) |
| 25–54 | 137 (63.1) |
| ≥ 55 | 36 (16.6) |
| Education levels | |
| Secondary or below | 160 (73.7) |
| Tertiary or above | 57 (26.3) |
| Monthly household income, CNYb | |
| < 5,000 | 77 (35.5) |
| 5,000–9,999 | 66 (30.4) |
| ≥ 10,000 | 74 (34.1) |
| Marital status | |
| Single | 75 (34.6) |
| Married or cohabitated | 127 (58.5) |
| Widowed or divorced | 15 (6.9) |
| Occupation | |
| Employed | 85 (39.2) |
| Housewife/retired | 46 (21.2) |
| Unemployed | 32 (14.8) |
| Students | 27 (12.4) |
| Others | 27 (12.4) |
| Household registration | |
| Local | 188 (86.6) |
| Non-local | 29 (13.7) |
| ICT use | |
| Online entertainmentc | 168 (77.4) |
| Contacting family/friends | 161 (74.2) |
| Watching short videos | 112 (51.6) |
| Reading news online | 96 (44.2) |
| Listening to music | 92 (42.4) |
| Watching long videos | 90 (41.5) |
| Working/studying | 64 (29.5) |
| Playing video games | 59 (27.2) |
| Reading e-books | 51 (23.5) |
| Using social media platforms | 47 (21.7) |
| Online shopping | 47 (21.7) |
| Mental health | |
| Depression | 116 (56.3) |
| Insomnia | 113 (54.5) |
| Anxiety | 104 (50.5) |
| Somatization symptoms | 73 (35.4) |
| PTSD | 37 (18.0) |
PTSD Post-traumatic stress disorder
aTotal percentages may not be 100.0% after rounding
bUSD 1 = CNY 7.18
cA composite variable represents having one or more of the following online entertainment, including watching short videos, listening to music, watching long videos, playing video games, and reading e-books
Table 2 shows that more females were more likely to use ICT for listening to music (aPR 1.35), watching long videos (1.73), using social media platforms (2.12), and online shopping (1.97) (all P ≤ 0.04). Compared with patients aged < 25 years, those aged 25–54 years were less likely to use ICT for working/studying (aPR 0.63), playing video games (0.62), and reading e-books (0.48) (all P ≤ 0.03), while those aged ≥ 55 years were less likely to use ICT for listening to music (0.42), playing video games (0.27), and using social media platforms (0.29) (all P ≤ 0.05). Patients with tertiary-or-above education levels were more likely to use ICT for watching long videos (aPR 1.64), working/studying (2.18), and using social media platforms (2.03) (all P ≤ 0.02). Compared with patients with lower monthly household income, those with income of CNY 5,000–9,999 were more likely to use ICT to contact family and friends (aPR 1.24), watch short videos (1.47), and listen to music (1.52) (P ≤ 0.04), and those with income ≥ CNY 10,000 were more likely to use ICT for reading news online (1.49), listening to music (1.82), working/studying (2.19), and online shopping (2.34) (all P ≤ 0.04). Compared with patients being single, those being married or cohabiting were less likely to use ICT for working/studying (aPR 0.57), and to play video games (0.52) (all P ≤ 0.04). Compared to local patients, non-local patients were less likely to use ICT to connect with family and friends (aPR 0.55), read news online (0.25), and for online entertainment (aPR 0.82), including listening to music (0.36), and watching long videos (0.28) (all P ≤ 0.001).
Table 2.
ICT use by sociodemographic characteristics, aPR (95% CI) (N = 217)
| Sociodemographic characteristics | Online entertainmentc | Contacting family/friends | Watching short videos | Reading news online | Listening to music | Watching long videos | Working/studying | Playing video games | Reading e-books | Using social media platforms | Online shopping |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Sex | |||||||||||
| Male | 1 (ref) | 1 (ref) | 1 (ref) | 1 (ref) | 1 (ref) | 1 (ref) | 1 (ref) | 1 (ref) | 1 (ref) | 1 (ref) | 1 (ref) |
| Female | 1.10 (0.96, 1.27) | 1.06 (0.91, 1.24) | 0.95 (0.73, 1.25) | 1.02 (0.58, 1.85) | 1.35 (1.00, 1.83)* | 1.73 (1.26, 2.36)*** | 0.83 (0.56, 1.23) | 0.66 (0.41, 1.06) | 1.20 (0.73, 1.96) | 2.12 (1.26, 3.58)** | 1.97 (1.14, 3.40)* |
| Age, years | |||||||||||
| < 25 | 1 (ref) | 1 (ref) | 1 (ref) | 1 (ref) | 1 (ref) | 1 (ref) | 1 (ref) | 1 (ref) | 1 (ref) | 1 (ref) | 1 (ref) |
| 25–54 | 0.98 (0.81, 1.17) | 0.93 (0.76, 1.14) | 1.03 (0.74, 1.44) | 1.16 (0.71, 1.91) | 0.69 (0.48, 1.02) | 1.04 (0.71, 1.54) | 0.63 (0.42, 0.96)* | 0.62 (0.40, 0.96)* | 0.48 (0.27, 0.86)** | 0.64 (0.37, 1.12) | 0.62 (0.29, 1.31) |
| ≥ 55 | 0.76 (0.54, 1.05) | 0.80 (0.58, 1.12) | 0.76 (0.45, 1.28) | 0.85 (0.45, 1.61) | 0.42 (0.22, 0.81)** | 0.58 (0.28, 1.21) | 0.70 (0.34, 1.41) | 0.27 (0.08, 0.92)* | 0.39 (0.15, 1.06) | 0.29 (0.09, 0.98)** | 0.44 (0.16, 1.18) |
| Education levels | |||||||||||
| Secondary or below | 1 (ref) | 1 (ref) | 1 (ref) | 1 (ref) | 1 (ref) | 1 (ref) | 1 (ref) | 1 (ref) | 1 (ref) | 1 (ref) | 1 (ref) |
| Tertiary or above | 1.02 (0.87, 1.20) | 1.11 (0.93, 1.33) | 0.77 (0.54, 1.10) | 1.15 (0.80, 1.65) | 1.20 (0.83, 1.73) | 1.64 (1.16, 2.32)** | 2.18 (1.38, 3.45)*** | 1.24 (0.78, 1.97) | 1.31 (0.71, 2.41) | 2.03 (1.12, 3.67)* | 1.21 (0.61, 2.42) |
| Monthly household income, CNYb | |||||||||||
| < 5,000 | 1 (ref) | 1 (ref) | 1 (ref) | 1 (ref) | 1 (ref) | 1 (ref) | 1 (ref) | 1 (ref) | 1 (ref) | 1 (ref) | 1 (ref) |
| 5,000–9,999 | 1.11 (0.94, 1.32) | 1.24 (1.02, 1.50)* | 1.47 (1.07, 2.01)* | 1.44 (0.96, 2.15) | 1.52 (1.02, 2.26)* | 0.93 (0.63, 1.38) | 1.44 (0.80, 2.61) | 0.68 (0.38, 1.23) | 0.99 (0.53, 1.85) | 1.60 (0.85, 3.00) | 0.96 (0.44, 2.07) |
| ≥ 10,000 | 1.06 (0.88, 1.28) | 1.12 (0.90, 1.39) | 1.12 (0.79, 1.59) | 1.49 (1.02, 2.19)* | 1.82 (1.22, 2.73)** | 1.14 (0.80, 1.63) | 2.19 (1.26, 3.80)** | 1.44 (0.93, 2.23) | 1.66 (0.94, 2.94) | 1.91 (0.98, 3.70) | 2.34 (1.28, 4.27)** |
| Marital status | |||||||||||
| Single | 1 (ref) | 1 (ref) | 1 (ref) | 1 (ref) | 1 (ref) | 1 (ref) | 1 (ref) | 1 (ref) | 1 (ref) | 1 (ref) | 1 (ref) |
| Married or cohabit | 0.84 (0.7, 1.01) | 1.00 (0.83, 1.21) | 0.80 (0.59, 1.19) | 1.32 (0.88, 1.98) | 0.77 (0.52, 1.15) | 0.87 (0.61, 1.24) | 0.57 (0.33, 0.97)* | 0.52 (0.30, 0.87)** | 0.78 (0.42, 1.45 | 0.76 (0.42, 1.39) | 1.26 (0.61, 2.61) |
| Divorce or widow | 0.63 (0.39, 1.01) | 0.86 (0.55, 1.37) | 0.50 (0.21, 1.19) | 1.46 (0.73, 2.91) | 1.18 (0.64, 2.15) | 0.71 (0.33, 1.51) | 0.93 (0.38, 2.27) | 0.23 (0.04, 1.45) | 0.57 (0.14, 2.38) | 1.03 (0.33, 3.16) | 1.07 (0.33, 3.46) |
| Household registration | |||||||||||
| Local | 1 (ref) | 1 (ref) | 1 (ref) | 1 (ref) | 1 (ref) | 1 (ref) | 1 (ref) | 1 (ref) | 1 (ref) | 1 (ref) | 1 (ref) |
| Non-local | 0.82 (0.71, 0.96)*** | 0.55 (0.40, 0.76)*** | 1.44 (0.60, 3.48) | 0.25 (0.14, 0.42)*** | 0.36 (0.24, 0.54)*** | 0.28 (0.18, 0.42)*** | 0.88 (0.55, 1.39) | 0.77 (0.40, 1.51) | 0.44 (0.17, 1.20) | 1.11 (0.63, 1.98) | 1.13 (0.52, 2.43) |
CI Confidence interval
aaPR, adjusted prevalence ratio, calculated using Poisson regressions with mutually adjustment
bUSD 1 = CNY 7.18
cA composite variable represents having one or more of the following online entertainment, including watching short videos, listening to music, watching long videos, playing video games, and reading e-books
*P < 0.05, ** P < 0.01, *** P < 0.001
Table 3 shows the associations of ICT use with mental health and insomnia. Those using ICT to contact family and friends were less likely to have depression (aPR 0.78), anxiety (0.71), PTSD (0.52), and somatization symptoms (0.69) (all P ≤ 0.04), but were more likely to have higher levels of social support (aPR 5.15, P < 0.001). Similarly, individuals who used ICT for working/studying were less likely to have depression (aPR 0.67), anxiety (0.65), and were more likely to have higher levels of social support (5.77) (all P ≤ 0.02). Those who used ICT for online shopping were less likely to have depression (aPR 0.69), anxiety (0.55), and insomnia (0.61) (all P ≤ 0.045). The sensitivity analyses using continuous mental health scores (Table S1) showed consistent results, which confirmed the robustness of our results using binary mental health scores.
Table 3.
The associations of different ICT use with social support, mental health problems, and insomnia, N = 217
| Different ICT use | Depression (n = 116, 56.3%) | Anxiety (n = 104, 50.5%) | PTSD (n = 37, 18.0%) | Somatization symptoms (n = 73, 35.4%) | Insomnia (n = 113, 54.9%) | Social support | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| n (%) | aPR (95% CI) | n (%) | aPR (95% CI) | n (%) | aPR (95% CI) | n (%) | aPR (95% CI) | n (%) | aPR (95% CI) | Mean ± SD | aβ (95% CI) | |
| Contacting family/friends | ||||||||||||
| No | 35 (66.0) | 1 (ref) | 33 (62.3) | 1 (ref) | 15 (28.3) | 1 (ref) | 24 (45.3) | 1 (ref) | 33 (62.3) | 1 (ref) | 33.36 ± 9.86 | 0 (ref) |
| Yes | 81 (52.9) | 0.78 (0.61, 0.99)* | 71 (46.4) | 0.71 (0.55, 0.92)** | 22 (14.4) | 0.85 (0.73, 0.99)* | 49 (32.0) | 0.69 (0.49, 0.98)* | 80 (52.3) | 0.85 (0.66, 1.09) | 38.43 ± 8.85 | 5.15 (2.46, 7.84)*** |
| Working/studying | ||||||||||||
| No | 89 (60.5) | 1 (ref) | 80 (54.4) | 1 (ref) | 32 (21.8) | 1 (ref) | 59 (40.1) | 1 (ref) | 83 (56.5) | 1 (ref) | 36.48 ± 9.12 | 0 (ref) |
| Yes | 27 (45.8) | 0.67 (0.48, 0.94)* | 24 (40.7) | 0.65 (0.45, 0.94)* | 5 (8.5) | 0.84 (0.72, 0.98)* | 14 (23.7) | 0.68 (0.40, 1.16) | 30 (50.9) | 0.91 (0.66, 1.26) | 38.64 ± 9.84 | 5.77 (2.90, 8.65)*** |
| Obtaining news and information | ||||||||||||
| No | 64 (54.7) | 1 (ref) | 56 (47.9) | 1 (ref) | 18 (15.4) | 1 (ref) | 38 (32.5) | 1 (ref) | 61 (52.1) | 1 (ref) | 36.14 ± 8.92 | 0 (ref) |
| Yes | 52 (58.4) | 1.04 (0.82, 1.32) | 48 (53.9) | 1.05 (0.80, 1.38) | 19 (21.4) | 1.00 (0.87, 1.15) | 35 (39.3) | 1.08 (0.76, 1.55) | 52 (58.4) | 1.10 (0.86, 1.41) | 38.35 ± 9.80 | 1.69 (−0.75, 4.13) |
| Scanning social media platforms | ||||||||||||
| No | 92 (55.4) | 1 (ref) | 82 (49.4) | 1 (ref) | 30 (18.1) | 1 (ref) | 58 (34.9) | 1 (ref) | 92 (55.4) | 1 (ref) | 36.69 ± 9.38 | 0 (ref) |
| Yes | 24 (60.0) | 1.07 (0.78, 1.47) | 22 (55.5) | 1.07 (0.76, 1.52) | 7 (17.5) | 0.92 (0.76, 1.11) | 15 (37.5) | 1.15 (0.73, 1.82) | 21 (52.5) | 0.99 (0.70, 1.39) | 38.68 ± 9.24 | 2.74 (−0.36, 5.85) |
| Online shopping | ||||||||||||
| No | 97 (59.2) | 1 (ref) | 89 (54.3) | 1 (ref) | 30 (18.3) | 1 (ref) | 59 (36.0) | 1 (ref) | 96 (58.5) | 1 (ref) | 36.45 ± 9.42 | 0 (ref) |
| Yes | 19 (45.2) | 0.69 (0.48, 0.99)* | 15 (35.7) | 0.55 (0.36, 0.84)** | 7 (16.7) | 0.84 (0.72, 1.00) | 14 (33.3) | 0.80 (0.50, 1.26) | 17 (40.5) | 0.61 (0.41, 0.90)* | 39.53 ± 8.85 | 2.65 (−0.34, 5.63) |
| Online entertainmenta | ||||||||||||
| No | 25 (52.1) | 1 (ref) | 22 (45.8) | 1 (ref) | 7 (14.6) | 1 (ref) | 13 (27.1) | 1 (ref) | 29 (60.4) | 1 (ref) | 36.06 ± 11.11 | 0 (ref) |
| Yes | 91 (57.6) | 1.08 (0.79, 1.49) | 82 (51.9) | 1.09 (0.76, 1.56) | 30 (19.0) | 1.04 (0.86, 1.25) | 60 (38.0) | 1.41 (0.84, 2.35) | 84 (53.2) | 0.87 (0.66, 1.14) | 37.43 ± 8.80 | 1.79 (−1.23, 4.81) |
CI Confidence interval, PTSD Post-traumatic stress disorder, aPR Adjusted prevalence ratio, calculated using Poisson regressions adjusted for sex, age, education level, marital status, monthly household income, household registration. aβ, adjusted β, calculated using multivariate linear regressions, adjusting for sex, age, education level, marital status, monthly household income, household registration
aA composite variable representing having one or more of online entertainments, including watching short videos, listening to music, watching long videos, playing video games, and reading e-books
*P < 0.05, **P < 0.01, ***P < 0.001
Mental health problems were dichotomized using lower cutoff scores to classify mild-or-greater mental health symptoms, including depression (PHQ-9 score ≥ 5), anxiety (GAD-7 ≥ 5), PTSD (PCL-C score ≥ 38), somatization symptoms (SSS-CN score ≥ 30), and insomnia (ISI score ≥ 8)
Table 4 shows the mediating effects of social support on the associations of different ICT use with mental health. Social support mediated 24.5–42.1% of the associations of using ICT to connect with family and friends with depression, anxiety, somatization symptoms, and PTSD, and 43.4–52.4% of the associations of working and learning with them.
Table 4.
The mediating effects of social support on the associations of different ICT use with depression, anxiety, somatization symptoms, and Post-Traumatic Stress Disorder symptoms
| Pathways | Total effects Adjust β (95% CI) | Direct effects Adjust β (95% CI) | Indirect effects Adjust β (95% CI) | Proportion of total effect being mediated (%) |
|---|---|---|---|---|
| Contacting family/friends → Social support → Depression | −0.413 (−0.761, −0.064)* | −0.239 (−0.575, 0.098) | −0.174 (−0.306, −0.043)** | 42.1 |
| Contacting family/friends → Social support → Anxiety | −0.441 (−0.788, −0.094)* | −0.273 (−0.607, 0.060) | −0.168 (−0.303, −0.032)* | 38.1 |
| Contacting family/friends → Social support → Somatization symptoms | −0.486 (−0.835, −0.136)** | −0.361 (−0.689, −0.033) | −0.125 (−0.231, −0.019)* | 25.7 |
| Contacting family/friends → Social support → PTSD | −0.265 (−0.497, −0.032)* | −0.20 (−0.430, 0.030) | −0.065 (−0.126, −0.004)* | 24.5 |
| Working/learning → Social support → Depression | −0.456 (−0.781, −0.130)** | −0.258 (−0.595, 0.080) | −0.198 (−0.333, −0.063)** | 43.4 |
| Working/learning → Social support → Anxiety | −0.378 (−0.655, −0.101)** | −0.180 (−0.474, 0.113) | −0.198 (−0.339, −0.057)** | 52.4 |
| Working/learning → Social support → Somatization symptoms | −0.336 (−0.638, 0.033)* | −0.181 (−0.497, 0.135) | −0.155 (−0.276, −0.033)* | 46.1 |
| Working and learning → Social support → PTSD | −0.180 (−0.368, 0.009) | −0.099 (−0.293, 0.950) | −0.081 (−0.150, −0.011)* | 45.0 |
CI Confidence interval, PTSD Post-traumatic stress disorder. Adjusted β (95% CI), adjusted for sex, age, education level, marital status, monthly household income, and household registration
*P < 0.05, **P < 0.01
In order to rigorously evaluate the mediation effect, a bootstrapping procedure employing 5000 bootstrap samples was employed to estimate the indirect effect and derive 95% confidence intervals, as adjusted for sex, age, education level, monthly household income, marital status, occupation, and household registration. The confidence interval encompassing zero signifies the absence of a statistically significant mediating (indirect) effect at a significance level of 5%
Discussion
This study is the first to show the distinct associations of different types of ICT use with mental health problems and insomnia in hospital-quarantined patients. Contacting family/friends was linked to less depression, anxiety, PTSD, somatization symptoms, and insomnia, while working/studying was associated with less depression, anxiety, and PTSD. Social support partially mediated these associations. However, obtaining news and information, scanning social media platforms, and online entertainment had no statistically significant associations.
Certain types of ICT use have the potential to protect mental health. Using ICT to contact family/friends can help foster a supportive social environment and maintain social connectedness through real-time interaction [29]. Interactive communication-oriented ICT activities, such as video calls and instant messages, were found to promote better mental health during the pandemic across diverse cultural contexts [7, 30, 31]. Meanwhile, engaging in online working/studying can also help alleviate stress from unfinished tasks and disrupted daily routines [7, 32], increase the sense of belonging and social support from the outside world, and ultimately reduce depression and anxiety during hospitalization [7, 33]. These patterns align with the Social Support Buffering Model, which proposes social support functions as a critical buffering resource that mitigates psychological distress [15]. Our findings were consistent with previous evidence, which showed that psychosocial resources, including perceived social support, can mitigate the adverse mental health effects of stressors during COVID-19 lockdowns [34–36].
In contrast, entertainment-oriented ICT use or passive information browsing may not activate these supportive processes. We found that social media, online entertainment, and obtaining news and information showed no statistically significant association with mental health. Such behaviors are more likely to be driven by gratification-seeking or compensatory motives, in which individuals use online activities to regulate negative emotions or escape stressful realities [37]. According to the Compensation Internet Use Theory, these patterns can increase the likelihood of excessive or problematic ICT use, which may undermine mental well-being [13]. Prior studies have suggested that passive ICT use, such as prolonged video viewing or excessive information seeking, may fail to enhance social connectedness and may even exacerbate psychological distress through mechanisms such as social comparison, information overload, or behavioral displacement [38–40]. Therefore, the psychological impact of ICT use is inherently dual-edged, depending on the functional purpose, interaction quality, and level of self-regulation involved. While active social interactions and target-driven ICT use can enhance social support and promote resilience. Passive, compensatory, or excessive engagement may heighten vulnerability to distress and maladaptive use patterns.
This study first reported the association of online shopping with less depression, anxiety, and insomnia in quarantined patients, which can also be explained by the Compensatory Internet Use Theory [13] and the Determination Theory [14]. Moderate shopping during quarantine may provide immediate pleasure and thus help alleviate negative feelings. The sense of choice and control involved in shopping can partially offset the reduced autonomy experienced during isolation, thereby improving emotional well-being. Also, looking forward to new items may enhance one’s sense of connection with the outside world. However, this intriguing finding may be influenced by residual confounding factors. For example, occupation, income level, previous mental conditions, and overall economic resources may simultaneously shape both the frequency of online shopping and mental health status. Moreover, the immediate sense of gratification or compensation derived from online shopping may relate to individuals’ emotion regulation styles [33], which are not considered in this study due to the limited length of the questionnaire.
We used social support as a mediator in our framework to reflect how different ICT use may actively generate or enhance supportive psychological resources, which then benefit mental health. If social support was treated as a moderator, it would imply that the level of social support simply strengthens or weakens the mental effects of ICT use, rather than being generated by ICT use itself. Active communication-oriented and goal-directed ICT use can strengthen feelings of social relatedness and autonomy, creating supportive ties that subsequently help relieve psychological distress. The partial mediation observed further underscores that this pathway varies across ICT types and mental symptoms. Notably, the mediating role of social support was even more pronounced for ICT use related to working or studying, with mediation proportions reaching up to 52.4%. This stronger mediation may reflect the dual role of work- and study-related ICT in maintaining both social integration and role continuity. According to the Role Theory and Self-Determination Theory, sustaining meaningful social roles and a sense of competence during stressful circumstances can enhance psychological well-being [32, 41]. ICT-facilitated engagement in working or learning may therefore reinforce more perceived social support by preserving individuals’ connections to organizational structures, colleagues, or academic communities, thereby amplifying its indirect effects on mental health.
ICT use also varied across sociodemographic characteristics, suggesting that different groups may mobilize digital tools to meet distinct needs during quarantine. Consistent with emerging evidence on sex differences in ICT preferences [42, 43], we also found that females were more likely to engage in socially oriented or affectively rich digital activities, such as listening to music, watching long-form video content, using social media platforms, and online shopping. During quarantine, women also appeared to exhibit heightened vulnerability to emotional distress [44], which may lead to greater reliance on ICT as a compensatory coping resource. Younger patients were more likely to use ICT for working, studying, and gaming, which may reflect ongoing academic or occupational demands as well as greater digital fluency [45]. In contrast, older patients may be less proficient or prefer using ICT, consistent with documented age-related disparities in digital access and skills [46]. Patients with lower incomes may use ICT more for socializing and entertainment purposes, whereas those with higher incomes may use ICT more for work-related and consumption-oriented activities. Non-local patients were less likely to use ICT for contacting family/friends, reading news and entertainment, which may be due to lack of digital literacy compared to local patients. These patterns may reflect both differential access to digital tools and variation in daily role demands across sociodemographic characteristics.
This study highlights the mental health symptoms of quarantined patients, with over half of them showing mild-or-greater depression, insomnia, and anxiety. Rather than using the cutoffs for clinical diagnoses of moderate-to-severe mental disorders [47], to facilitate early detection and identify potential ICT measures for supporting quarantined patients’ mental health, we applied lower cutoff scores to classify mild-or-greater mental health symptoms as outcomes, including depression (PHQ-9 score ≥ 5), anxiety (GAD-7 score ≥ 5), PTSD (PCL-C score ≥ 38), somatization symptoms (SSS-CN score ≥ 30), and insomnia (ISI score ≥ 8). Thus, in this study, the high estimates of mental problem prevalence reflect the burden of preclinical mental health symptoms rather than formally diagnosed mental disorders.
The present findings highlight the importance of effective ICT use for quarantined patients in clinical practice. Beyond merely providing access to ICT, quarantine care units should develop structured ICT support systems that actively promote adaptive digital engagement. Given that our evidence of how ICT use and social support may benefit mental health, units could implement integrated online platforms that combine communication tools, mental-health resources, and real-time technical assistance. Establishing stable channels of social connectedness, such as scheduled video calls with family, moderated peer-support groups, or clinician-guided digital check-ins, may reduce isolation and strengthen perceived social support. To minimize excessive or addictive ICT use and improve digital competence, facilities could introduce brief screening procedures to identify individuals at risk of maladaptive digital coping, alongside tailored guidance on balancing communication-oriented and entertainment-oriented ICT activities. Furthermore, allocating counseling, psycho-education, and onsite or remote technical support may help patients maintain adaptive ICT usage patterns, provide social support, and mitigate mental health risks during quarantine.
This study bears some limitations. First, the cross-sectional study design cannot establish causal relationships between ICT use and mental health outcomes. The sample size was relatively small, which may lead to some findings with no statistical significance. Self-reported questionnaires are susceptible to recall bias. Second, given the number of ICT activities tested across multiple outcomes, the analyses should be considered exploratory. As such, some observed associations may reflect chance findings. However, our findings align with previous evidence, supporting the associations between ICT communication and better mental health outcomes during the COVID-19 pandemic [7, 29, 30, 48, 49]. Third, the generalizability of this study may be constrained by cultural and quarantine policy variations. Further evidence from Western settings would help validate our findings and offer a more comprehensive understanding of using ICT in quarantined patients. Last, some potential confounding factors may not have been fully considered due to the limited length of the questionnaire. For instance, occupation could be related to both ICT use and mental health. Future studies should include occupation as a covariate to reduce confounding.
Conclusion
This study advances understanding of the associations between ICT use, social support, and mental health within quarantine-based healthcare settings. By giving evidence on potential psychological benefits and challenges of different ICT use, our findings highlight that interactive communication-oriented and goal-directed ICT use, in terms of contacting family/friends and studying/working, may benefit individuals’ mental wellbeing during isolation. The study adds to theoretical literature by showing that social support acts as a key mediator linking these ICT use to mental health, underscoring the need for supportive ICT environments in quarantine care, where digital communication, mental promotion resources, and technical support should be systematically integrated. Such frameworks can help transform ICT from a passive resource into an active medium for promoting psychological resilience. Although the cross-sectional study design and small sample size restrain causal interpretation of our findings, this study provides insights into designing ICT-supported interventions to enhance social connectedness and mental health in quarantine-based healthcare settings.
Supplementary Information
Acknowledgements
Not applicable.
Abbreviations
- ICT
Information and communication technology
- COVID-19
Coronavirus disease 2019
- aPR
Adjusted prevalence ratio
- PTSD
Post-traumatic stress disorder
- IM
Instant messaging
- SSRS
Social Support Rate Scale
- ISI
Insomnia Severity Index
- PHQ-9
Patient Health Questionnaire-9
- GAD-7
Generalized Anxiety Disorder Scale-7
- PCL-C
PTSD Checklist-Civilian version
- SSS-CN
Self-reported Somatic Symptom Scale-China
- SD
Standard deviation
- CI
Confidence interval
Authors’ contributions
RHN and CRW completed data cleaning and statistical analyses and wrote the first draft. ZYY, YNL, and SWL wrote the first draft. PFL and LJZ collected the data. TCL and WJG conceptualized and supervised the study, completed data cleaning and statistical analyses, and interpreted the results. All authors participated in the critical review and provided final approval for publication.
Funding
This work was supported by the Shenzhen Stability Support Program in Colleges and Universities of China [grant number 20220810202707001], the Project of Philosophy and Social Science of Guangdong Province [grant number GD23YSH10], the Start-up Fund for Scientific Research of Young Teachers in Shenzhen University [grant number 000002112408], and the Research Startup Fund from the Shenzhen Pengcheng Peacock Plan [no grant number].
Data availability
Data used in this study is not publicly available according to patients’ written consent. Data could be reasonably requested from the corresponding authors upon permission from South China Hospital of Shenzhen University.
Declarations
Ethics approval and consent to participate
The study protocol was approved by the Ethics Committee of the South China Hospital, Shenzhen University [No: 20220310001-A].
Consent for publication
Patients’ written informed consents for collecting and using data in academic studies and publications were obtained before answering the questionnaires.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Ruiheng Nong and Chenrui Wang and contributed equally to this work.
Contributor Information
Tie Cheng Liu, Email: l86t5c24@126.com.
Wei Jie Gong, Email: wjgong@szu.edu.cn.
References
- 1.Lau RI, Su Q, Lau ISF, Ching JYL, Wong MCS, Lau LHS, et al. A synbiotic preparation (SIM01) for post-acute COVID-19 syndrome in Hong Kong (RECOVERY): a randomised, double-blind, placebo-controlled trial. Lancet Infect Dis. 2024;24(3):256–65. 10.1016/S1473-3099(23)00685-0. [DOI] [PubMed] [Google Scholar]
- 2.Wu Z, McGoogan JM. Characteristics of and important lessons from the Coronavirus disease 2019 (COVID-19) outbreak in China: summary of a report of 72 314 cases from the Chinese center for disease control and prevention. JAMA. 2020;323(13):1239–42. 10.1001/jama.2020.2648. [DOI] [PubMed] [Google Scholar]
- 3.Wu T, Jia X, Shi H, Niu J, Yin X, Xie J, et al. Prevalence of mental health problems during the COVID-19 pandemic: a systematic review and meta-analysis. J Affect Disord. 2021;281:91–8. 10.1016/j.jad.2020.11.117. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Bao Y, Sun Y, Meng S, Shi J, Lu L. 2019-nCoV epidemic: address mental health care to empower society. Lancet. 2020;395(10224):e37–8. 10.1016/S0140-6736(20)30309-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Shi L, Lu Z-A, Que J-Y, Huang X-L, Liu L, Ran M-S, et al. Prevalence of and risk factors associated with mental health dymptoms among the general population in China during the Coronavirus disease 2019 pandemic. JAMA Netw Open. 2020;3(7):e2014053. 10.1001/jamanetworkopen.2020.14053. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Shen C, Wang MP, Chu JT, Wan A, Viswanath K, Chan SSC, et al. Sharing family life information through video calls and other information and communication technologies and the association with family well-being: population-based survey. JMIR Ment Health. 2017;4(4):e57. 10.2196/mental.8139. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Liu TC, Yu ZY, Lin SW, Nong RH, Lu YN, Wang CR, et al. ICT communication with family/friends and its associations with mental health and insomnia in COVID-19 patients during hospital quarantine: a cross-sectional study in Shenzhen, China. BMC Psychiatry. 2025;25(1):648. 10.1186/s12888-025-07079-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Yoo HJ, Lee H. Critical role of information and communication technology in nursing during the COVID-19 pandemic: a qualitative study. J Nurs Manag. 2022;30(8):3677–85. 10.1111/jonm.13880. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Sameer AS, Khan MA, Nissar S, Banday MZ. Assessment of mental health and various coping strategies among general population living under imposed COVID-lockdown across world: a cross-sectional study. Ethics Med Public Health. 2020;15:100571. 10.1016/j.jemep.2020.100571. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Beogo I, Sia D, Tchouaket Nguemeleu E, Zhao J, Gagnon M-P, Etowa J. Strengthening social capital to address isolation and loneliness in long-term care facilities during the COVID-19 pandemic: protocol for a systematic review of research on information and communication technologies. JMIR Res Protoc. 2022;11(3):e36269. 10.2196/36269. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Brooks SK, Webster RK, Smith LE, Woodland L, Wessely S, Greenberg N, et al. The psychological impact of quarantine and how to reduce it: rapid review of the evidence. Lancet. 2020;395(10227):912–20. 10.1016/S0140-6736(20)30460-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Valkenburg PM, Peter J. Who visits online dating sites? Exploring some characteristics of online daters. Cyberpsychol Behav. 2007;10(6):849–52. 10.1089/cpb.2007.9941. [DOI] [PubMed] [Google Scholar]
- 13.Kardefelt-Winther D. A conceptual and methodological critique of internet addiction research: towards a model of compensatory internet use. Comput Human Behav. 2014;31:351–4. 10.1016/j.chb.2013.10.059. [Google Scholar]
- 14.Deci EL, Ryan RM. The “what” and “why” of goal pursuits: human needs and the self-determination of behavior. Psychol Inq. 2000;11(4):227–68. 10.1207/S15327965PLI1104_01. [Google Scholar]
- 15.Cohen S, Wills TA. Stress, social support, and the buffering hypothesis. Psychol Bull. 1985;98(2):310. 10.1037/0033-2909.98.2.310. [PubMed] [Google Scholar]
- 16.Xiao SY. The theoretical basis and research application of the social support rating scale. J Clin Psychiatry. 1994;2:98–100. [Google Scholar]
- 17.Qi M, Zhou SJ, Guo ZC, Zhang LG, Min HJ, Li XM, et al. The effect of social support on mental health in Chinese adolescents during the outbreak of COVID-19. J Adolesc Health. 2020;67(4):514–8. 10.1016/j.jadohealth.2020.07.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Kroenke K, Spitzer RL, Williams JBW. The PHQ-9. J Gen Intern Med. 2001;16(9):606–13. 10.1046/j.1525-1497.2001.016009606.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Kocalevent R-D, Hinz A, Brähler E. Standardization of the depression screener Patient Health Questionnaire (PHQ-9) in the general population. Gen Hosp Psychiatry. 2013;35(5):551–5. 10.1016/j.genhosppsych.2013.04.006. [DOI] [PubMed] [Google Scholar]
- 20.Spitzer RL, Kroenke K, Williams JBW, Löwe B. A brief measure for assessing generalized anxiety disorder: the GAD-7. Arch Intern Med. 2006;166(10):1092–7. 10.1001/archinte.166.10.1092. [DOI] [PubMed] [Google Scholar]
- 21.Cao W, Fang Z, Hou G, Han M, Xu X, Dong J, et al. The psychological impact of the COVID-19 epidemic on college students in China. Psychiatr Res. 2020;287:112934. 10.1016/j.psychres.2020.112934. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Löwe B, Decker O, Müller S, Brähler E, Schellberg D, Herzog W, et al. Validation and standardization of the generalized anxiety disorder screener (GAD-7) in the general population. Med Care. 2008;46(3):266–74. 10.1097/MLR.0b013e318160d093. [DOI] [PubMed] [Google Scholar]
- 23.Ruggiero KJ, Del Ben K, Scotti JR, Rabalais AE. Psychometric properties of the PTSD checklist-civilian version. J Trauma Stress. 2003;16(5):495–502. 10.1023/a:1025714729117. [DOI] [PubMed] [Google Scholar]
- 24.Liu CH, Zhang E, Wong GTF, Hyun S, Hahm HC. Factors associated with depression, anxiety, and PTSD symptomatology during the COVID-19 pandemic: clinical implications for U.S. young adult mental health. Psychiatr Res. 2020;290:113172. 10.1016/j.psychres.2020.113172. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Mao J, Wang C, Teng C, Wang M, Zhou S, Zhao K, et al. Prevalence and associated factors of PTSD symptoms after the COVID-19 epidemic outbreak in an online survey in China: the age and gender differences matter. Neuropsychiatr Dis Treat. 2022;18:761–71. 10.2147/ndt.S351042. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Jiang M, Zhang W, Su X, Gao C, Chen B, Feng Z, et al. Identifying and measuring the severity of somatic symptom disorder using the self-reported somatic symptom scale-China (SSS-CN): a research protocol for a diagnostic study. BMJ Open. 2019;9(9):e024290. 10.1136/bmjopen-2018-024290. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Fu Y, Lin Q, Xiang Q, Wen X, Liu L. Comparison of SSS-CN and PHQ-15 in the evaluation of patients with suspected psychological disorders in cardiovascular medicine. Front Psychol. 2023;14:1027253. 10.3389/fpsyg.2023.1027253. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Morin CM, Belleville G, Bélanger L, Ivers H. The insomnia severity index: psychometric indicators to detect insomnia cases and evaluate treatment response. Sleep. 2011;34(5):601–8. 10.1093/sleep/34.5.601. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Gong WJ, Sit SMM, Wong BYM, Wu SYD, Lai AYK, Ho SY, et al. Associations of face-to-face and instant messaging family communication and their contents with family wellbeing and personal happiness amidst the COVID-19 pandemic. Front Psychiatry. 2022;13:780714. 10.3389/fpsyt.2022.780714. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Bonsaksen T, Thygesen H, Leung J, Ruffolo M, Schoultz M, Price D, Østertun Geirdal A: Video-based communication and its association with loneliness, mental health and quality of life among older people during the COVID-19 outbreak. Int J Environ Res Public Health 2021, 18(12). 10.3390/ijerph18126284. [DOI] [PMC free article] [PubMed]
- 31.Gong WJ, Wong BY, Ho SY, Lai AY, Zhao SZ, Wang MP, Lam TH: Family e-chat group use was associated with family wellbeing and personal happiness in Hong Kong adults amidst the COVID-19 pandemic. Int J Environ Res Public Health 2021, 18(17) 10.3390/ijerph18179139. [DOI] [PMC free article] [PubMed]
- 32.Talley AE, Kocum L, Schlegel RJ, Molix L, Bettencourt BA. Social roles, basic need satisfaction, and psychological health: the central role of competence. Pers Soc Psychol Bull. 2012;38(2):155–73. 10.1177/0146167211432762. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Lawrence LM, Ciorciari J, Kyrios M. Relationships that compulsive buying has with addiction, obsessive-compulsiveness, hoarding, and depression. Compr Psychiatry. 2014;55(5):1137–45. 10.1016/j.comppsych.2014.03.005. [DOI] [PubMed] [Google Scholar]
- 34.Gabbiadini A, Baldissarri C, Durante F, Valtorta RR, De Rosa M, Gallucci M: Together apart: The mitigating role of digital communication technologies on negative affect during the COVID-19 outbreak in Italy. Front Psychol 2020, 11 - 2020 10.3389/fpsyg.2020.554678. [DOI] [PMC free article] [PubMed]
- 35.Rossi R, Jannini TB, Socci V, Pacitti F, Lorenzo GD: Stressful life events and resilience during the COVID-19 lockdown measures in Italy: Association with mental health outcomes and age. Front Psychiatry 2021;12 - 2021 10.3389/fpsyt.2021.635832. [DOI] [PMC free article] [PubMed]
- 36.Liu C, Huang N, Ahmed F, Shahid M, Wang X, Guo J. The reverse buffering effects of social support on the relationships between stresses and mental health: a survey of Chinese adults during the COVID-19 lockdown. Eur J Psychotraumatol. 2021;12(1):1952777. 10.1080/20008198.2021.1952777. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Király O, Potenza MN, Stein DJ, King DL, Hodgins DC, Saunders JB, et al. Preventing problematic internet use during the COVID-19 pandemic: consensus guidance. Compr Psychiatry. 2020;100:152180. 10.1016/j.comppsych.2020.152180. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Matthes J, Karsay K, Schmuck D, Stevic A. “Too much to handle”: impact of mobile social networking sites on information overload, depressive symptoms, and well-being. Comput Human Behav. 2020;105:106217. [Google Scholar]
- 39.Utz S, Breuer J. The relationship between use of social network sites, online social support, and well-being: results from a six-wave longitudinal study. J Media Psychol. 2017;29(3):115–25. 10.1027/1864-1105/a000222. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40.Burnell K, George MJ, Vollet JW, Ehrenreich SE, Underwood MK. Passive social networking site use and well-being: the mediating roles of social comparison and the fear of missing out. Cyberpsychol J Psychosoc Res Cyberspace. 2019;13(3):5. 10.5817/CP2019-3-5. [Google Scholar]
- 41.Deci EL, Olafsen AH, Ryan RM. Self-determination theory in work organizations: the state of a science. Annu Rev Organ Psychol Organ Behav. 2017;4(1):19–43. 10.1146/annurev-orgpsych-032516-113108. [Google Scholar]
- 42.Qazi A, Hasan N, Abayomi-Alli O, Hardaker G, Scherer R, Sarker Y, et al. Gender differences in information and communication technology use & skills: a systematic review and meta-analysis. Educ Inf Technol. 2022;27(3):4225–58. 10.1007/s10639-021-10775-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43.Lim K, Meier EB. Different but similar: computer use patterns between young Korean males and females. Educ Technol Res Dev. 2011;59(4):575–92. 10.1007/s11423-011-9206-5. [Google Scholar]
- 44.Yuan K, Zheng YB, Wang YJ, Sun YK, Gong YM, Huang YT, et al. A systematic review and meta-analysis on prevalence of and risk factors associated with depression, anxiety and insomnia in infectious diseases, including COVID-19: a call to action. Mol Psychiatry. 2022;27(8):3214–22. 10.1038/s41380-022-01638-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 45.Outoukarte I, Ben Fares S, Itouni H, Rassou KK, Tahiri A. Distance learning in the wake of COVID-19 in Morocco. Heliyon. 2023;9(6):e16523. 10.1016/j.heliyon.2023.e16523. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 46.Litchfield I, Shukla D, Greenfield S. Impact of COVID-19 on the digital divide: a rapid review. BMJ Open. 2021;11(10):e053440. 10.1136/bmjopen-2021-053440. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 47.Pacitti F, Socci V, D’Aurizio G, Jannini TB, Rossi A, Siracusano A, et al. Obsessive-compulsive symptoms among the general population during the first COVID-19 epidemic wave in Italy. J Psychiatr Res. 2022;153:18–24. 10.1016/j.jpsychires.2022.06.043. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 48.Marciano L, Ostroumova M, Schulz PJ, Camerini AL. Digital media use and adolescents’ mental health during the COVID-19 pandemic: a systematic review and meta-analysis. Front Public Health. 2021;9:793868. 10.3389/fpubh.2021.793868. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 49.Li J. Digital technologies for mental health improvements in the COVID-19 pandemic: a scoping review. BMC Public Health. 2023;23(1):413. 10.1186/s12889-023-15302-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
Data used in this study is not publicly available according to patients’ written consent. Data could be reasonably requested from the corresponding authors upon permission from South China Hospital of Shenzhen University.