Purpose: This study examined the relationship among social support, leisure time physical activity (LTPA), and mental health among people with cancer.
Design: Cross-sectional study.
Setting And Participants: Using the 2017 Health Information National Trends Survey, we extracted data of 504 respondents who had been diagnosed with any of the 22 types of cancer listed in the survey questionnaire.
Measures: As independent variables, we assessed 3 different types of support: emotional, informational, and tangible support. As mediating and outcome variables, we measured LTPA and mental health, respectively.
Analysis: Using AMOS version 22, a path analysis was conducted to measure model fit. A mediation test was then conducted using bootstrapping procedures.
Results: The hypothesized model provided an acceptable fit to the data. Specifically, emotional support ( = .15, p = .005), informational support ( = .13, p = .008), tangible support ( = .12, p = .010), and LTPA ( = .14, p = .001) were significantly associated with mental health. We revealed a significant mediating effect of LPTA on the relationship between emotional support and mental health (Estimate = .037, 95% CI = .001-.098, p < .05).
Conclusion: Social support and LTPA played a significant role in promoting mental health among people with cancer. In particular, the results confirmed that individuals with cancer who reported receiving emotional support tended to engage in LTPA and thus reported better mental health.
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http://dx.doi.org/10.1177/0890117120961321 | DOI Listing |
Biomed Phys Eng Express
January 2025
Brain Health Imaging Centre, Centre for Addiction and Mental Health, B68-250 College St, Toronto, Ontario, M5T 1R8, CANADA.
Objective: Arterial sampling for PET imaging often involves continuously measuring the radiotracer activity concentration in blood using an automatic blood sampling system (ABSS). We proposed and validated an external delay and dispersion correction procedure needed when a change in flow rate occurs during data acquisition. We also measured the external dispersion constant of [11C]CURB, [18F]FDG, [18F]FEPPA, and [18F]SynVesT-1.
View Article and Find Full Text PDFAustralas Psychiatry
January 2025
Consortium of Australian-Academic Psychiatrists for Independent Policy and Research Analysis (CAPIPRA), Canberra, ACT, Australia.
Objective: The Australian Institute of Health and Welfare publishes statistical indicator reports on the specialised mental health workforce. These include data for 2022-2023 on psychiatrists, mental health nurses, mental health occupational therapists, psychologists and mental health social workers. We provide a brief commentary on these reports, reflecting upon the implications of such changes for psychiatric practice and patient care.
View Article and Find Full Text PDFJ Med Internet Res
January 2025
Clinical Psychology and Psychotherapy, Department of Education and Psychology, Freie Universität Berlin, Berlin, Germany.
Background: Results on parental burden during the COVID-19 pandemic are predominantly available from nonrepresentative samples. Although sample selection can significantly influence results, the effects of sampling strategies have been largely underexplored.
Objective: This study aimed to investigate how sampling strategy may impact study results.
JMIR Ment Health
January 2025
Inspire, Belfast, United Kingdom.
Background: There is potential for digital mental health interventions to provide affordable, efficient, and scalable support to individuals. Digital interventions, including cognitive behavioral therapy, stress management, and mindfulness programs, have shown promise when applied in workplace settings.
Objective: The aim of this study is to conduct an umbrella review of systematic reviews in order to critically evaluate, synthesize, and summarize evidence of various digital mental health interventions available within a workplace setting.
J Med Internet Res
January 2025
School of Business, Innovation and Sustainability, Halmstad University, Halmstad, Sweden.
Background: Recent advancements in artificial intelligence (AI) have changed the care processes in mental health, particularly in decision-making support for health care professionals and individuals with mental health problems. AI systems provide support in several domains of mental health, including early detection, diagnostics, treatment, and self-care. The use of AI systems in care flows faces several challenges in relation to decision-making support, stemming from technology, end-user, and organizational perspectives with the AI disruption of care processes.
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