Mind the gap: Development and validation of an evolutionary mismatched lifestyle scale and its impact on health and wellbeing.

Identifying an integrative framework that could appropriately delineate underlying mechanisms and individual risk/protective factors for human health has remained elusive. Evolutionary mismatch theory provides a comprehensive, integrative model for understanding the underlying causes and mechanisms of a wide range of modern health and well-being problems, ranging from obesity to depression. Despite growing interest regarding its importance though, no psychometrically-sound measure of evolutionary mismatch yet exists to facilitate research and intervention. To construct such a scale, aimed at gauging individual differences in the extent to which people's modern lifestyles are mismatched with ancestral conditions, we conducted four studies (a pilot study, followed by 3 main studies, with a final sample of 1901 participants across the main studies). Results from exploratory and confirmatory factor analyses have produced a 36-item evolutionary mismatched lifestyle scale (EMLS) with 7 subdomains of mismatched behaviours (e.g., diet, physical activity, relationships, social media use) that is psychometrically sound. Further, the EMLS is associated with physical, mental and subjective health. We explore the potential of the EMLS as a tool for examining interpersonal and cultural variations in health and wellbeing, while also discussing the limitations of the scale and future directions in relation to further psychometric examinations.

Loneliness, perceived social support, and their changes predict medical adherence over 12 months among patients with coronary heart disease.

OBJECTIVES: This study investigated whether changes in loneliness and perceived social support predicted medical adherence in patients with coronary heart disease (CHD) over 12 months. Moreover, short-term and long-term buffering effects of social support on the association between loneliness and medical adherence were systematically examined. DESIGN: A three-wave longitudinal study. METHODS: Participants were 255 CHD patients with a mean age of 63 years. Medical adherence, loneliness, and perceived social support were assessed at baseline, 3 months, and 12 months. Hierarchical regression analyses were conducted to examine the influences of loneliness and social support as well as their changes on medical adherence over 12 months. Moderation analyses were performed to test buffering effects of baseline social support and its changes against loneliness and its changes, respectively. RESULTS: Changes in loneliness significantly predicted medical adherence at 12 months (ß = -.23, p = .001) but not at 3 months (ß = -.10, p = .142). Changes in social support predicted medical adherence at both 3 (ß = .23, p = .002) and 12 months (ß = .26, p = .001). Social support concurrently buffered the adverse impact of loneliness on medical adherence (B = .29, SE = .12, p = .020) at baseline but did not at 3 or 12 months (Bs = -.21 to .40, SEs = .12 to .30, ps = .177 to .847). CONCLUSIONS: Findings highlight the importance of monitoring loneliness and perceived social support continuously over time for CHD patients to promote medical adherence.

A pilot randomised controlled trial exploring the feasibility and efficacy of a human-AI sleep coaching model for improving sleep among university students.

Objective: Sleep quality is a crucial concern, particularly among youth. The integration of health coaching with question-answering (QA) systems presents the potential to foster behavioural changes and enhance health outcomes. This study proposes a novel human-AI sleep coaching model, combining health coaching by peers and a QA system, and assesses its feasibility and efficacy in improving university students' sleep quality. Methods: In a four-week unblinded pilot randomised controlled trial, 59 university students (mean age: 21.9; 64% males) were randomly assigned to the intervention (health coaching and QA system; n = 30) or the control conditions (QA system; n = 29). Outcomes included efficacy of the intervention on sleep quality (Pittsburgh Sleep Quality Index; PSQI), objective and self-reported sleep measures (obtained from Fitbit and sleep diaries) and feasibility of the study procedures and the intervention. Results: Analysis revealed no significant differences in sleep quality (PSQI) between intervention and control groups (adjusted mean difference = -0.51, 95% CI: [-1.55-0.77], p = 0.40). The intervention group demonstrated significant improvements in Fitbit measures of total sleep time (adjusted mean difference = 32.5, 95% CI: [5.9-59.1], p = 0.02) and time in bed (adjusted mean difference = 32.3, 95% CI: [2.7-61.9], p = 0.03) compared to the control group, although other sleep measures were insignificant. Adherence was high, with the majority of the intervention group attending all health coaching sessions. Most participants completed baseline and post-intervention self-report measures, all diary entries, and consistently wore Fitbits during sleep. Conclusions: The proposed model showed improvements in specific sleep measures for university students and the feasibility of the study procedures and intervention. Future research may extend the intervention period to see substantive sleep quality improvements.