The need to delay retirement timing has been acknowledged in Western countries due to demographic ageing. The aim of the present study was to examine the buffering effects of job resources (decision authority, social support, work-time control, and rewards) on the association of exposures to physically demanding work tasks and physically hazardous work environment with non-disability retirement timing. Results from discrete-time event history analyses, in a sample of blue-collar workers (n = 1741; 2792 observations) from the nationwide longitudinal Swedish Longitudinal Occupational Survey of Health (SLOSH), supported that decision authority and social support may buffer the negative impact of heavy physical demands on working longer (continuing working vs retiring). Stratified analyses by gender showed that the buffering effect of decision authority remained statistically significant for men, while that of social support remained statistically significant for women. Moreover, an age effect was displayed, such that a buffering effect of social support on the association of heavy physical demands and high physical hazards with working longer were found among older men (≥64 years), but not younger (59-63 years). The findings suggest that heavy physical demands should be reduced, however, when not feasible physical demands should be accompanied by social support at work for delaying retirement.
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http://dx.doi.org/10.1016/j.ssmph.2023.101372 | DOI Listing |
JMIR Form Res
January 2025
Private Practice, Ballito, South Africa.
Background: Barriers to mental health assessment and intervention have been well documented within South Africa, in both urban and rural settings. Internationally, evidence has emerged for the effectiveness of technology and, specifically, app-based mental health tools and interventions to help overcome some of these barriers. However, research on digital interventions specific to the South African context and mental health is limited.
View Article and Find Full Text PDFJ Med Internet Res
January 2025
Department High-Tech Business and Entrepreneurship Section, Industrial Engineering and Business Information Systems, University of Twente, Enschede, Overijssel, Netherlands.
Health recommender systems (HRS) have the capability to improve human-centered care and prevention by personalizing content, such as health interventions or health information. HRS, an emerging and developing field, can play a unique role in the digital health field as they can offer relevant recommendations, not only based on what users themselves prefer and may be receptive to, but also using data about wider spheres of influence over human behavior, including peers, families, communities, and societies. We identify and discuss how HRS could play a unique role in decreasing health inequities.
View Article and Find Full Text PDFJ Med Internet Res
January 2025
School of Clinical Sciences, Monash University, Melbourne, Australia.
Background: eHealth interventions can favorably impact health outcomes and encourage health-promoting behaviors in children. More insight is needed from the perspective of children and their families regarding eHealth interventions, including features influencing program effectiveness.
Objective: This review aimed to explore families' experiences with family-focused web-based interventions for improving health.
Am J Drug Alcohol Abuse
January 2025
Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Palo Alto, CA, USA.
While social support benefits those in treatment for opioid use disorder, it is unclear how social support impacts patient outcomes. This study examines how support person attitudes toward buprenorphine and their communication about substance use are associated with the well-being of patients receiving buprenorphine treatment. We analyzed cross-sectional baseline data from 219 buprenorphine patients (40% female) and their support persons (72% female).
View Article and Find Full Text PDFJ Med Internet Res
January 2025
Department of Engineering Management and Systems Engineering, George Washington University, Washington, DC, United States.
Background: Large language model (LLM) artificial intelligence chatbots using generative language can offer smoking cessation information and advice. However, little is known about the reliability of the information provided to users.
Objective: This study aims to examine whether 3 ChatGPT chatbots-the World Health Organization's Sarah, BeFreeGPT, and BasicGPT-provide reliable information on how to quit smoking.
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