Specific symptoms of the General Health Questionnaire (GHQ) in predicting persistence of psychological distress: Data from two prospective cohort studies

A cross-sectional study for the mental health status and sleep quality among college students in Macao during the COVID-19 pandemic

Objectives

The main focus of this study was to investigate the effect of the coronavirus disease (COVID-19) pandemic on the mental health condition and sleep quality of college students in Macao. In addition, the students’ behaviours during the pandemic, such as drinking alcohol, taking sleeping pills, and seeking psychological counselling were analyzed.

Method

A cross-sectional survey of mental health and sleep quality status, as well as the possible behavioral risk factors, was conducted among the college students of Macao in August, 2020 during the COVID-19 pandemic. An online self-report questionnaire survey method was applied to assess the general demographics and related lifestyle behaviors of students. The general mental health condition and sleep quality were evaluated through the General Health Questionnaire (GHQ-12) and Pittsburgh Sleep Quality Index (PSQI) questionnaires, respectively. The main statistical methods included the Chi-square test, Bonferroni correction, and Pearson correlation. Data analysis was performed using SPSS Version 24.0.

Results

A total of 980 students were investigated in the study, of which 977 completed the survey. During the COVID-19 pandemic period, overall college students in Macao were psychologically well adjusted and reported good quality of sleep. However, female students were in poorer psychological condition than males (P < 0.05). Moreover, the students over 20 years of age had poorer sleep quality than students aged less than or equal to 20 (P < 0.05). The significant differences were found among the students in different study majors for the mental health status and sleep quality (both P < 0.05), which were associated with certain behaviors, such as drinking alcohol, taking sleeping pills, and seeking for help in psychological counselling during the COVID-19 pandemic period.

Conclusions

Poor mental health status could be either the consequence or cause of sleep disturbance, which might further affected physical health. Therefore, regular assessment of mental health condition and sleep quality of college students is particularly necessary during public health emergencies, such as the COVID-19 pandemic, and appropriate intervention should be provided to the students.

Assessment of the Promoting Resilience in Stress Management Intervention for Adolescent and Young Adult Survivors of Cancer at 2 Years: Secondary Analysis of a Randomized Clinical Trial

IMPORTANCE

Adolescents and young adults (AYAs) with cancer have a high risk of poor psychosocial outcomes. The Promoting Resilience in Stress Management (PRISM) intervention is one of few psychosocial interventions targeting younger people with cancer that has demonstrated efficacy in a randomized clinical trial.

OBJECTIVE

To explore 2-year trajectories of patient-reported well-being among AYA cancer survivors. The hypothesis was that AYAs who initially responded to PRISM would report sustained positive changes.

DESIGN, SETTING, AND PARTICIPANTS

This secondary analysis of data from a single-center, parallel, phase 2 randomized clinical trial was conducted from January 2015 to October 2016. Eligible participants were English-speaking AYAs between ages 13 and 25 years with cancer treated at a single, quaternary children's hospital in the US. Participants were randomly assigned (1:1) to PRISM or usual care. Data were analyzed between March 2021 and June 2021.

INTERVENTION

PRISM is a brief, skills-based coaching program targeting 4 resilience resources (stress management, goal setting, cognitive reframing, and meaning making).

MAIN OUTCOMES AND MEASURES

Patient-reported cancer-related quality of life (QoL) (PedsQL scale), hope (Hope scale), resilience (Connor-Davidson Resilience scale), and psychological distress (Kessler-6 scale) were collected at baseline, 6, 12, and 24 months. Data from AYAs who remained alive at 24 months were analyzed. Improvement was defined as a positive change in instrument scores between baseline and 6 months (ie, the end of the PRISM program) and used linear mixed effects regression to assess changes over time.

RESULTS

A total of 57 AYAs were included in analysis at 24 months (62% of 92 randomized). Twenty-six participants (46%) were female; 36 (63%) were between ages 13 and 17 years; and 20 (35%) identified as part of a racial or ethnic minority group (4 [7%] Asian, 11 [19%] Hispanic or Latino, 16 [28%] mixed race or other). PRISM was associated with improved QoL, hope, resilience, and distress between baseline and 6 months, and these associations were sustained for QoL over 12 months (β = 7.4; 95% CI, 0.8 to 14; P = .03) and hope throughout the study period (12 months: β = 6.2; 95% CI, 2.7 to 9.6; P < .001; 24 months: β = 4.6; 95% CI, 1 to 8.3; P = .01). Of those whose scores improved between baseline and 6 months, PRISM participants more commonly reported sustained improvements in QoL (16 of 21 participants [76%] vs 8 of 17 [47%]), hope (14 of 24 participants [58%] vs 6 of 14 [43%]), resilience (9 of 18 participants [50%] vs 4 of 14 [29%]), and distress (14 of 19 participants [74%] vs 6 of 19 [32%]) at 24 months compared with usual care.

CONCLUSIONS AND RELEVANCE

These results suggest that PRISM was associated with sustained improvements in psychosocial outcomes as reported by adolescents and young adults. Those who responded to PRISM in the first 6 months reported continued well-being 2 years later.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT02340884.

The Frequent Stressor and Mental Health Monitoring-Paradigm: A Proposal for the Operationalization and Measurement of Resilience and the Identification of Resilience Processes in Longitudinal Observational Studies

Resilience has been defined as the maintenance or quick recovery of mental health during and after times of adversity. How to operationalize resilience and to determine the factors and processes that lead to good long-term mental health outcomes in stressor-exposed individuals is a matter of ongoing debate and of critical importance for the advancement of the field. One of the biggest challenges for implementing an outcome-based definition of resilience in longitudinal observational study designs lies in the fact that real-life adversity is usually unpredictable and that its substantial qualitative as well as temporal variability between subjects often precludes defining circumscribed time windows of inter-individually comparable stressor exposure relative to which the maintenance or recovery of mental health can be determined. To address this pertinent issue, we propose to frequently and regularly monitor stressor exposure (E) and mental health problems (P) throughout a study's observation period [Frequent Stressor and Mental Health Monitoring (FRESHMO)-paradigm]. On this basis, a subject's deviation at any single monitoring time point from the study sample's normative E-P relationship (the regression residual) can be used to calculate that subject's current mental health reactivity to stressor exposure ("stressor reactivity," SR). The SR score takes into account the individual extent of experienced adversity and is comparable between and within subjects. Individual SR time courses across monitoring time points reflect intra-individual temporal variability in SR, where periods of under-reactivity (negative SR score) are associated with accumulation of fewer mental health problems than is normal for the sample. If FRESHMO is accompanied by regular measurement of potential resilience factors, temporal changes in resilience factors can be used to predict SR time courses. An increase in a resilience factor measurement explaining a lagged decrease in SR can then be considered to index a process of adaptation to stressor exposure that promotes a resilient outcome (an allostatic resilience process). This design principle allows resilience research to move beyond merely determining baseline predictors of resilience outcomes, which cannot inform about how individuals successfully adjust and adapt when confronted with adversity. Hence, FRESHMO plus regular resilience factor monitoring incorporates a dynamic-systems perspective into resilience research.