Applied psychology. Health and well-being最新文献

Social–emotional skills are vital for individual development, yet research on which skills most effectively promote students' mental and physical health, particularly from a global perspective, remains limited. This study aims to address this gap by identifying the most important social–emotional skills using global data and machine learning approaches. Data from 61,585 students across nine countries, drawn from the OECD Social–Emotional Skills Survey, were analyzed (N_China = 7246, N_Finland = 5482, N_Colombia = 13,528, N_Canada = 7246, N_{Russia =}6434, N_Turkey = 5482, N_{South Korea} = 7246, N_Portugal=6434, and N_USA=6434). Six machine learning techniques—including Random Forest, Logistic Regression, AdaBoost, LightGBM, Artificial Neural Networks, and Support Vector Machines—were employed to identify critical social–emotional skills. The results indicated that the Random Forest algorithm performed best in the prediction models. After controlling for demographic variables, optimism, energy, and stress resistance were identified as the top three social–emotional skills contributing to both subjective well-being and physical health. Additionally, sociability and trust were found to be the fourth most important skills for well-being and physical health, respectively. These findings have significant implications for designing tailored interventions and training programs that enhance students' social–emotional skills and overall health.

Children with special educational needs (SEN) are a diverse group facing numerous challenges related to well-being and mental health. Understanding the predictors of well-being in this population requires the incorporation of diverse factors along with approaches that can uncover complexity in how these factors work together to influence well-being. We longitudinally predicted subjective well-being in a group of children with diverse special educational needs (N = 499; M = 8.4 ± 0.9 years). Thirty-two variables - ranging from demographics to various categories of life experiences - were used as predictors for both nonlinear machine learning and classical linear classifiers. Nonlinear machine learning classifiers exhibited much performance in predicting subjective well-being (F1 score = 0.72 to 0.84) compared to traditional linear classifiers. Overall, across all children, prior subjective well-being, numeracy, literacy skills, and interpersonal dimensions played important roles. However, clustering further identified four distinct clusters sharing important predictors: a ‘socializer’ cluster dominated by interpersonal functioning predictors, an ‘analyzer’ cluster emphasizing academic skills predictors, and two clusters with more diverse sets of important predictors. Our research highlights the multiple pathways toward well-being in children with SEN as uncovered by machine learning, with implications for understanding and supporting their well-being.

The significant prevalence of mental health problems among children and adolescents is a major concern worldwide. The current study evaluates the impact of a year-long, school-based intervention, Flourishing Students, on adolescents' salivary cortisol levels, depressive symptoms, psychological well-being, and hope. Utilizing a cluster randomized design with intervention and control classes, 72 students engaged in 32 well-being lessons during academic year 2016–2017, whereas 68 students followed their standard curriculum. Depressive symptoms and psychological well-being were assessed at three time points, while the stress hormone cortisol and hope were measured at two. The results showed a slight increase in cortisol levels from baseline to post-intervention during school hours in the control group, whereas no change occurred in the intervention group. Additionally, total cortisol levels at post-intervention were slightly lower among intervention-group students compared with control group students. Questionnaire data revealed significant interaction effects on psychological well-being and hope from baseline to post-intervention, and on depression and psychological well-being from baseline to the 5-month follow-up. The intervention group scored lower in depressive symptoms and higher in psychological well-being and hope compared with the control group. These findings underscore the essential role of comprehensive assessment methods in evaluating intervention efficacy in well-being programs.

The present research examines whether and how lay intuitive theory regarding the interconnectedness of the mind and body, which we term mind–body correspondence (MBC), predicts engagement in health behaviors. Specifically, we hypothesised that MBC would strengthen mind influences the body (MIB) and body influences the mind (BIM) beliefs, which would in turn increase mental and physical health behaviors, respectively. We tested associations among MBC (independent variable), MIB and BIM beliefs (proposed mediators), and mental and physical health behaviors (dependent variables) using correlational designs in Studies 1a to 1c. Results revealed positive associations between MBC and (1) MIB and BIM and (2) mental and physical health behaviors, as well as between MIB and mental health behaviors and between BIM and physical health behaviors. We then tested the full model in Study 2 with an experimental manipulation of MBC and in Study 3 with a two-wave longitudinal design. Both studies revealed significant indirect effects of MBC on mental health behaviors via MIB and on physical health behaviors via BIM. Together, our findings suggest that one can encourage mental health behaviors by emphasizing the mind-to-body connection and physical health behaviors by highlighting the body-to-mind connection.

In recent years, artificial intelligence (AI) chatbots have made significant strides in generating human-like conversations. With AI's expanding capabilities in mimicking human interactions, its affordability and accessibility underscore the potential of AI chatbots to facilitate negative emotional disclosure or venting. The study's primary objective is to highlight the potential benefits of AI-assisted venting by comparing its effectiveness to venting through a traditional journaling platform in reducing negative affect and increasing perceived social support. We conducted a pre-registered within-subject experiment involving 150 participants who completed both traditional venting and AI-assisted venting conditions with counterbalancing and a wash-out period of 1-week between the conditions. Results from the frequentist and Bayesian dependent samples t-test revealed that AI-assisted venting effectively reduced high and medium arousal negative affect such as anger, frustration and fear. However, participants in the AI-assisted venting condition did not experience a significant increase in perceived social support and perceived loneliness, suggesting that participants did not perceive the effective assistance from AI as social support. This study demonstrates the promising role of AI in improving individuals' emotional well-being, serving as a catalyst for a broader discussion on the evolving role of AI and its potential psychological implications.

Mindfulness instruction comprising both formal (FM) and informal (IM) mindfulness practice is increasingly offered to university students. FM involves sustaining attention on thoughts, emotions and bodily sensations through structured practices, while IM involves incorporating mindfulness into daily activities. However, recent evidence suggests that FM may pose challenges for students with recent non-suicidal self-injury (NSSI), whereas the flexibility and brevity inherent in IM may be better tolerated. This randomized controlled trial compared a FM induction, IM induction and control task among university students with (n = 103) and without (n = 123) past-year NSSI in terms of acceptability and pre-post state mindfulness, stress and well-being. Notably, results did not differ as a function of NSSI history. Two-way ANOVAs revealed that only IM was consistently preferred over the control task. Furthermore, three-way mixed ANOVAs revealed that—when assessed using brief Visual Analogue Scales—state well-being increased in all conditions, state mindfulness increased after both IM and FM, and state stress only decreased after IM. Notably, these differences by condition appeared to be of short duration as they were not found with lengthier measures. Results highlight the potential promise of IM and the importance of measurement selection when assessing the transient effects of mindfulness inductions in research.

Stress is a known contributor to psychosocial pathological conditions and reduction of well-being. The literature has highlighted the role of emotional schemas and psychological flexibility as psychological processes underlying this relationship in the short term. This two-wave longitudinal study analyses the mediating role of psychological flexibility and emotional schemas in the relationship between stress responses to COVID-19-related events and mental health 6 months later. Two hundred and seventy-six individuals were included in this study, completing measures of stress responses, emotional schemas, psychological flexibility and positive and negative mental health. Negative evaluation of emotions was a significant mediator in the longitudinal relationship between stress responses and negative mental health. Valued action and openness to experience mediate the longitudinal relationship between stress responses and positive mental health. This study underscores the importance of the different dimensions of psychological processes, suggesting that over time, more maladaptive psychological processes play a significant role in elucidating the relationship between stress responses and negative mental health. Similarly, it suggests that more adaptive processes may help explain the relationship between stress responses and positive mental health. This contributes to a better understanding of the stability of these processes, which is important for determining which intervention targets should be prioritised.

Sleep problems positively predict loneliness to a large extent, which would harm adolescent health. However, little is known about the underlying mechanism. This research investigates the potential mediating roles of rumination and resilience in the relation among adolescents and further examines the changing relationships. In Study 1, 1056 adolescents from two schools completed self-reported measures of sleep problems, loneliness, rumination, and resilience. We found that sleep problems were positively associated with loneliness, and that rumination and resilience mediated this relationship, both individually and sequentially. To examine the varying relationship between rumination, resilience, and loneliness, Study 2 adopted the cross-lagged design in a long-term longitudinal study of 131 students and found that rumination and resilience at T1 significantly predict loneliness at T2. To further directly explore the changing relationship between sleep problems and loneliness, we performed a short-term longitudinal study across 7 weeks of 242 adolescents in Study 3. The results replicated the findings of Study 2 and found sleep problems at T1 positively predicted loneliness at T2. These results underscore the significance of sleep problems in contributing to loneliness through mediating mechanisms and unveil the possible causal relationships, offering robust insights to alleviate adolescent loneliness.

The promotion of health and provision of care services for new recruits are issues of constant concern for military leaders and healthcare providers, as they are crucial to maintaining and operating military forces. The enhancement of military personnel's empowerment has been recognized as a core value in promoting health perception. However, the pathways between military personnel's sense of empowerment and health perception have not been thoroughly explored. The primary aim of this study is to examine the predictive power of different dimensions of empowerment (personal, interpersonal, and socio-political) on new recruits' health perception, and to further observe differences among subgroups, which will help us grasp the nuances of future health intervention measures. The research data were extracted from the “Military Career Development Study,” analyzing personal empowerment data from Wave 1 (W1) and perceived health data from Wave 2 (W2) (N = 2,232). In terms of analytical methods, five ML classifiers, including Decision Tree, Random Forest, Support Vector Machine, AdaBoost, and k-Nearest Neighbors (KNN) algorithms, were used for prediction in both the full sample and subsamples (gender and socioeconomic status). Results show that among the five ML classifiers, the Decision Tree performed best overall, achieving a prediction accuracy of 95.4%. The results by gender show that the ML models perform best for both males and females with the Decision Tree and Random Forest methods. For the Decision Tree, the accuracy rates were 94.9% for males and 95.1% for females; the F1 scores were 92.9% for males and 93.2% for females. For the Random Forest, the accuracy rates were 94.9% for males and 95.4% for females; the F1 scores were 92.7% for males and 93.2% for females. Regarding SES, the Decision Tree and Random Forest methods performed best. In the SES Low group, both methods achieved a prediction accuracy of 95.6% and an F1 score of 93.7%; in the SES high group, they achieved a prediction accuracy of 95.4% and an F1 score of 93.3%. However, the contribution of different dimensions of empowerment features varied significantly among subgroups. These findings can provide important information on the differences in health perception among military personnel.

Habits are cue-behavior associations learned through repetition that are assumed to be relatively stable. Thereby, unhealthy habits can pose a health risk due to facilitating relapse. In the absence of research on habit decay in daily life, we aimed to investigate how habit decreases over time when trying to degrade a habit and whether this differs by four health-risk behaviors (sedentary behavior, unhealthy snacking, alcohol consumption, and smoking). This 91-day intensive longitudinal study included four parallel non-randomized groups (one per behavior; N = 194). Habit strength was measured daily with the Self-Report Behavioral Automaticity Index (11,805 observations) and modelled over time with constant, linear, quadratic, cubic, asymptotic, and logistic models. Person-specific modelling revealed asymptotic and logistic models as the most common best-fitting models (54% of the sample). The time for habit decay to stabilize ranged from 1 to 65 days. Multilevel modelling indicated substantial between-person heterogeneity and suggested initial habit strength but not the decay process to vary by behavioral group. Findings suggest that habit decay when trying to degrade a habit typically follows a decelerating negative trend but that it is a highly idiosyncratic process. Recommendations include emphasizing the role of person-specific modelling and data visualization in habit research.