Applied psychology. Health and well-being最新文献

Reactivity to physical activity (PA) measurement may result from the introduction of a measurement device, researcher observation, or both. Accessing data from prior to study enrollment afforded the rare opportunity to compare behavior during versus prior to participation. This study introduced researcher observation among adults who owned their own PA monitoring device, to test whether measurement reactivity can also be observed in experienced PA trackers, by comparing their data from before versus after the introduction of observation. In addition, the salience of researcher observation was manipulated to test for potential effects. Participants were 252 adults in the U.S. They completed two electronic surveys 14 days apart, in which they recorded steps per day as collected by their PA monitors over the previous 14 days. At the end of the first survey, they were randomized to view messages, which differed in emphasis on repeating entry of step data (i.e., “low” vs. “high” salience of researcher observation). Daily step counts did not change between 14-day reporting periods, though patterns differed by gender and starting level of PA. Patterns did not differ between experimental conditions. Overall, introducing researcher observation without introducing an unfamiliar measurement device results in no meaningful reactivity with respect to PA.

Negative emotions such as loneliness, depression, and anxiety (LDA) are prevalent and pose significant challenges to emotional well-being. Traditional methods of assessing LDA, reliant on questionnaires, often face limitations because of participants' inability or potential bias. This study introduces emoLDAnet, an artificial intelligence (AI)-driven psychological framework that leverages video-recorded conversations to detect negative emotions through the analysis of facial expressions and physiological signals. We recruited 50 participants to undergo questionnaires and interviews, with their responses recorded on video. The emoLDAnet employs a combination of deep learning (e.g., VGG11) and machine learning (e.g., decision trees [DTs]) to identify emotional states. The emoLDAnet incorporates the OCC–PAD–LDA psychological transformation model, enhancing the interpretability of AI decisions by translating facial expressions into psychologically meaningful data. Results indicate that emoLDAnet achieves high detection rates for loneliness, depression, and anxiety, with F1-scores exceeding 80% and Kendall's correlation coefficients above 0.5, demonstrating strong agreement with traditional scales. The study underscores the importance of the OCC–PAD–LDA model in improving screening accuracy and the significant impact of machine learning classifiers on the framework's performance. The emoLDAnet has the potential to support large-scale emotional well-being early screening and contribute to the advancement of mental health care.

How susceptible our daily affect is to fluctuations in physical health indicates how well we adapt to everyday health challenges. Adverse childhood experiences (ACEs) are known to have a lasting impact on everyday emotion regulation and adaptation across the lifespan, but less is known about whether and how such adversity is linked to the susceptibility of affect to everyday health challenges. This study therefore tested whether ACEs were associated with daily reports of positive and negative affect and examined weather they moderated emotional reactions to daily physical symptoms in adulthood and old age. We used data from the National Study of Daily Experiences 2 (NSDE-2) in which middle-aged and older adults (N = 2,022; M_age = 56 years; range: 33–84) reported symptoms and affect on eight consecutive evenings. Multi-level models indicated that cumulative ACEs as well as two individual childhood adversities (i.e. physical and sexual abuse by a parent) were independently associated with exacerbated increases in negative affect on days with more symptoms. Findings add to literature on the role of early adversity for the maintenance of everyday well-being and highlight the potential importance of such experiences for coping and adaption in the face of daily health challenges across adulthood and old age.

Frequently engaging in a positive health behaviour, like following a vegetarian or vegan (veg*n) diet, can bring benefits to both the individual and society. We investigated the roles of two psychological determinants of behaviour—instigation habits and self-regulation strategy use—in a cohort of individuals who were newly transitioning to a veg*n diet. In a longitudinal study over 6 months (7 waves including baseline), 222 individuals transitioning to a veg*n diet reported their monthly habit strength, craving frequency, self-regulation strategies and animal product consumption. Our results supported the benefits of having a healthy habit, as stronger habits predicted fewer cravings and lower consumption of animal products, in line with the person's target diet. However, in contrast to some theoretical accounts, having a strong instigation habit did not reduce the use of self-regulation strategies; people with strong habits used multiple strategies to maintain their diet, especially when they experienced frequent diet-inconsistent cravings. These findings challenge the idea that habits eliminate the need for self-regulation, and suggest that habits do not fully circumvent motivational challenges in the pursuit of complex health behaviours. Our results are consistent with recent suggestions that automatic and intentional processes act simultaneously during the enactment of complex health behaviours.

The high-level risk perception diffusion caused by public health emergencies seriously threatens public mental health and social stability. Much scholarly attention focused on the traditional epidemic models or simply combined content and social attributes, overlooking the differences in public individual characteristics. This paper proposes an S₁S₂EI_posI_neuI_negR model of risk perception diffusion by innovatively subdividing susceptible people and infectious people. Then, taking the Xi'an epidemic as an example (N = 105,417), this paper employs the sentiment analysis model of Word2Vec and Bi-LSTM to calculate the emotional value of microblog text to quantify public risk perception. Finally, numerical experiments are conducted to explore the effects of cross-evolution and emotional difference on risk perception diffusion under different scenarios. Findings reveal that a larger initial density of infectious people accelerates diffusion, with negative emotions playing a dominant role. In addition, the higher the risk perception level and the lower the heterogeneity, the greater the maximum impact and the final scale of diffusion. When the public health emergency deteriorates, the cross-evolution tends to shift to a high-risk perception. Otherwise, it tends to tilt to a low-risk perception. These findings provide critical insights for developing precise risk perception guidance strategies and enhancing public health governance capabilities.

Alcohol use is embedded within university culture. While the consequences of alcohol use on next-day physical health are well-known, less is known about the consequences to next-day emotional health. This study investigated the relationship between alcohol use and next-day mood and well-being using two daily diary studies with New Zealand university students. Participants completed a daily diary for 13 days (Study 1, n = 1114; 30.6% men, M_age = 19.6) or 7 days (Study 2, n = 212; 24.5% men, M_age = 19.4) where they reported previous night's alcohol consumption and sleep quality, along with today's mood and well-being. Night-time alcohol consumption was categorised into low-risk (women 1–4 drinks, men 1–5 drinks), medium-risk (women 5–9 drinks, men 6–11 drinks) and high-risk (women 10+ drinks, men 12+ drinks) according to New Zealand public health guidelines. Across both studies, medium- and especially high-risk drinking, but not low-risk drinking, was associated with lower next-day positive mood and well-being compared to nondrinking days. Poorer sleep quality partially accounted for the relationship of drinking with next-day mood and well-being in Study 1, but less so in Study 2. Findings suggest that interventions could promote lower risk drinking behaviours to benefit students' well-being.

Understanding the varied profiles of occupational well-being, their outcomes, and predictors is key to formulating effective strategies for enhancing teachers' occupational health and well-being. This study employed latent profile analysis (LPA) to identify distinct occupational well-being profiles and their outcomes among 366 Chinese teachers, and decision tree analysis to explore the factors predicting each profile. The results showed three occupational well-being profiles: burnout, engaged, and burnout-engaged. The “engaged” group exhibited normal ranges for depression and stress, along with mild anxiety. The “burnout” group demonstrated moderate depression and stress, coupled with severe anxiety. The “burnout-engaged” group was near the threshold of mild depression and moderate anxiety. The result of the decision tree model revealed that marital status, teaching experience, income, role as a class teacher, school type, and working hours significantly influenced these occupational well-being profiles. Specific combinations of variables were associated with each occupational well-being profile, offering a nuanced understanding of the risky and protective factors for teacher occupational well-being. By identifying distinct occupational well-being profiles among Chinese teachers and their outcomes, and elucidating the key predictors and their interrelations, this study provides insights into how to quickly screen for teachers in need of help at work, and how to design targeted interventions for different teachers.

This daily diary study drew on the allostatic load model to examine the predictive effect of COVID-19 stressful experiences (CSE) on somatic symptoms and anxious mood, as well as applying the biological sensitivity to context model to explore whether diurnal cortisol moderated the above associations. A total of 101 Chinese college students retrospectively reported CSE in October 2020, followed by 5-day diary reports on somatic symptoms and anxious mood in November 2020, with salivary cortisol collected on Days 2 to 4 to measure cortisol awakening response (CAR), diurnal cortisol slope (DCS), and daily cortisol output (area under the curve with respect to ground, AUC_g). Results of multilevel models showed that greater CSE predicted more somatic symptoms but not anxious mood, which was only observed at flatter CAR, flatter DCS, or low AUC_g. Furthermore, three-way interactions of CSE, CAR, and AUC_g significantly predicted both somatic symptoms and anxious mood. Specifically, greater CSE predicted more somatic symptoms at flatter CAR with low AUC_g, while predicting higher anxious mood at flatter CAR with high AUC_g. Our findings demonstrate the long-term consequences following the prior pandemic, especially highlighting the biological vulnerability related to the synergetic effects of diurnal cortisol rhythms and daily cortisol output.

This pre-registered study examines the longitudinal relationships between well-being, hair cortisol (a biomarker linked to poor health), and self-reported health. Accumulated cortisol output over three months was determined quarterly over the course of one year using hair samples. Well-being was assessed as affective well-being (via experience sampling), cognitive well-being (i.e., life satisfaction), and eudaimonic well-being (via the Ryff Scales of Psychological Well-Being). Self-reported health was measured using one item on the current state of health. The longitudinal analyses allowed for disentangling initial between-person differences from within-person changes and were based on a large panel study of working-age people (N = 726). The results indicate that hair cortisol levels were generally not associated with any of the examined well-being facets, regardless of the level of analysis. Further, deviations from well-being trait levels were not linked to subsequent within-person changes in hair cortisol (and vice versa), challenging the notion that cortisol output is a key physiological pathway through which well-being improves health. In contrast, self-reported health was positively correlated with affective, cognitive, and eudaimonic well-being at both the trait and within-person levels, whereas deviations from well-being trait levels were generally not associated with subsequent within-person changes in self-reported health, and vice versa.

Physical literacy is a multidimensional concept considered fundamental for lifelong participation in physical activity. Although theories on the relationship between physical literacy and human flourishing have been proposed, no comprehensive study of this relationship in adolescents has been found. This study aimed to predict human flourishing variables (physical activity, physical health, mental health, and academic performance) that correlate highly with adolescents' physical literacy. A sample of 1004 primary and middle school students was recruited and six machine learning algorithms (decision tree, random forest, AdaBoost, CatBoost, LightGBM, and k-nearest neighbours) were used. Random forest predicted physical activity, physical health, and mental health in the overall sample, with an accuracy of 53 percent, 86 per cent, and 91.7 percent, respectively; AdaBoost predicted academic performance with an accuracy of 98 per cent. Overall sample and sex subgroup predictions were generally consistent, with “sense of self and self-control” and “self-expression and communication with others” as the most significant variables. Family-type subgroup analysis results varied greatly, suggesting that one-child families should focus on “knowledge and understanding,” whereas multi-child families should focus on “self-expression and communication with others.” Awareness of adolescents' underlying characteristics may yield greater benefits when intervening through physical literacy.