Applied psychology. Health and well-being最新文献

Study Objectives: The coding of semistructured interview transcripts is a critical step for thematic analysis of qualitative data. However, the coding process is often labor-intensive and time-consuming. The emergence of generative artificial intelligence (GenAI) presents new opportunities to enhance the efficiency of qualitative coding. This study proposed a computational pipeline using GenAI to automatically extract themes from interview transcripts. Methods: Using transcripts from interviews conducted with maternity care providers in South Carolina, we leveraged ChatGPT for inductive coding to generate codes from interview transcripts without a predetermined coding scheme. Structured prompts were designed to instruct ChatGPT to generate and summarize codes. The performance of GenAI was evaluated by comparing the AI-generated codes with those generated manually. Results: GenAI demonstrated promise in detecting and summarizing codes from interview transcripts. ChatGPT exhibited an overall accuracy exceeding 80% in inductive coding. More impressively, GenAI reduced the time required for coding by 81%. Discussion: GenAI models are capable of efficiently processing language datasets and performing multi-level semantic identification. However, challenges such as inaccuracy, systematic biases, and privacy concerns must be acknowledged and addressed. Future research should focus on refining these models to enhance reliability and address inherent limitations associated with their application in qualitative research.

Inadequate physical activity poses significant health threats to young people. Adolescents are at a developmental stage characterized by heightened impulsivity, which may limit the explanatory power of rational behavior theories in this population. This study developed an integrated model to explain adolescents' physical activity while examining the mechanism of peer influence from a dual-process perspective. The hypothetical model integrated the Theory of Planned Behavior with constructs from social, reactive, and habit theories and was tested using a two-wave prospective correlational survey design. Participants (n = 638) completed a baseline questionnaire assessing all psychological constructs. One week later, participants reported their time spent in physical activity over the past week in a follow-up questionnaire. Structural equation modeling was used to evaluate model fit and predictive utility. The model explained 14.6%, 7.1%, and 18.4% of the variances in averaged overall, weekday, and weekend physical activity levels, respectively. The latent variable of automaticity, representing impulsive processing, significantly explained adolescents' physical activity, while the reflective pathway through intention was not statistically significant. In addition, physical activity was statistically significantly explained by prototype favorability and prototype similarity via the impulsive pathway. Combining strategies to modify peer influence and impulsive processing is suggested for promoting adolescents' physical activity.

This study aimed to investigate the associations among life satisfaction, depressive symptoms, and common comorbid disorders during the COVID-19 pandemic. This combined cross-sectional and longitudinal study pooled national-level data from the 2021 Health and Retirement Study Perspectives on the Pandemic in the United States and the Big Data Drove Mental Health study in China. A total of 2,036 participants were included in this cross-sectional study conducted from July 2020 to August 2022. In this longitudinal prospective study, 4,503 participants were eligible for participation from January 2018 to June 2022. Life satisfaction was measured using five items from the Satisfaction with Life Scale. In a cross-sectional study, life satisfaction was found to be negatively associated with depressive symptoms, and depressive symptoms were comorbid with loneliness, anxiety, and dependency. The causal association between life satisfaction, depressive symptoms, and depressive symptoms comorbid with disorders was confirmed in a longitudinal prospective study. Low levels of life satisfaction increased the prevalence of comorbid disorders by 42.9% during the COVID-19 pandemic. Life satisfaction has played an important role during the COVID-19 pandemic and can be an effective strategy for depressive symptoms and depressive symptoms comorbid with disorder interventions among older adults.

Studies show that foods perceived as healthier are often also seen as more sustainable, suggesting a ‘healthy = sustainable’ heuristic. However, the direction of this effect remains unclear. This study aims to investigate (1) whether perceived healthiness influences perceived sustainability or whether the reverse effect occurs and (2) whether inter-individual differences, such as health interest, moderate these effects.

In an online study, participants (n = 712) were shown pictures of foods with high or low healthiness indices and asked to rate their sustainability. Conversely, they were also shown pictures of foods with high or low sustainability indices and asked to rate their healthiness. Additionally, participants' interest in health and sustainability and their belief in a relationship between the healthiness and sustainability of foods were measured. Exploratory analyses included label credibility as a control variable.

Foods with a higher healthiness index were perceived as more sustainable (effect size: δ = 0.39; [CI: 0.36; 0.41]). Similarly, higher sustainability indices led to higher healthiness ratings (effect size: δ = 0.22; [CI: 0.20; 0.24]). Moderating effects of interests were small and dependent on whether label credibility was accounted for.

The results support the assumption of a ‘healthy = sustainable’ heuristic, indicating that the effect is bidirectional. The implications for food labelling are discussed.

Despite growing evidence linking urban landscapes to human affect, there remains limited understanding of which environmental aspects of landscapes contribute to positive affect and how they exert their influence. Moreover, the role and significance of urban nightscapes have been largely underexplored. To investigate the affective connections between nighttime environments and individuals, a psychometric tool—the Nightscape Affect Index (NAI)—was designed, developed, and validated. The NAI was systematically assessed for its psychometric properties, including reliability, factor structure, and subsequent retest validation. Comprising three subscales—nightscape attraction, adaptation, and attachment—the NAI demonstrated strong internal consistency and excellent reliability. The scale also provided robust evidence for convergent, discriminant, and structural validity. We discuss how the NAI can be applied in both research and practice to enhance our understanding of the relationship between urban nightscapes and public affective responses, as well as its potential in evaluating the effectiveness of interventions and strategies to reduce light pollution.

Why do some people give a thank-you gift, while others do not? What are the predictors of thank-you gift-giving? We conducted three studies to examine the relative predictive values of indebtedness, gratitude, fondness, and respect in gift-giving. In Study 1 (N = 120), participants read two scenarios that depict two high school teachers and imagined how much gratitude, indebtedness, fondness, and respect they would feel toward each teacher and whether they would give a thank-you gift upon graduation. A multiple regression analysis showed that indebtedness was the only predictor of a thank-you gift. In Study 2 (N = 327, pre-registered), we directly replicated the results of Study 1. In Study 3 (N = 561, pre-registered), we asked participants to identify up to three high school teachers who wrote them recommendation letters and to state whether they gave them a thank-you gift. Replicating Studies 1 and 2, a multilevel analysis showed that indebtedness uniquely predicted whom participants gave a thank-you gift. A thank-you gift seems to be best predicted by the emotion of indebtedness.

Savoring and balanced time perspective are two time-based constructs linked to subjective well-being (including life satisfaction, and positive and negative affect). However, their interaction and joint impact on subjective well-being remain underexplored. This study investigates the relationships among three variables across two studies. In Study 1 (N = 395), a cross-sectional design was employed to investigate both direct and indirect links between savoring, balanced time perspective, and subjective well-being, emphasizing the mediating role of balanced time perspective. The results revealed direct relationships between variables, with a balanced time perspective mediating the influence of savoring on life satisfaction and positive affect, but not on negative affect. Study 2 (N = 127) employed a 14-day savoring intervention to evaluate its effects on three variables. The intervention group showed significant improvements in savoring, balanced time perspective, life satisfaction, and positive affect, along with reductions in negative affect. Mediation analysis revealed that the savoring intervention indirectly enhanced life satisfaction through a balanced time perspective, while directly influencing positive and negative affect. These findings highlight the interconnected roles of savoring and balanced time perspective in enhancing subjective well-being, offering a temporal framework to better understand and improve subjective well-being.

This study aimed to assess the effectiveness of a newly developed Emotion-Focused Training for Helping Professions in addressing levels of compassion fatigue, compassion satisfaction and self-criticism, both in the short and long term. A randomized controlled trial was conducted, with 667 participants recruited and randomly assigned to either the experimental active or control passive group ending up with 370 participants at follow-up measurement. The experimental group underwent a 14-day asynchronous online training program delivered via email, while the control group did not engage in any specific task except for the pre-, post- and follow-up assessments. The results from the linear mixed effects model analyses indicated significant group-by-time interactions for The Forms of Self-Criticizing/Attacking & Self-Reassuring Scale, and for The Compassion Satisfaction and Compassion Fatigue Scale. Specifically, participants in the Emotion-Focused Training for Helping Professions group exhibited a notable reduction in self-criticism and a notable increase in self-compassion and compassion satisfaction over time. The participants' scores of compassion fatigue decreased in both intervention as well as control groups; therefore, the group-by-time interaction was not significant. The findings suggest that the novel Emotion-Focused Training for Helping Professions intervention holds promise in effectively mitigating self-criticism while fostering greater compassion satisfaction and self-reassurance/self-compassion among helping professionals.

Recent years have seen a sharply rising interest in the scientific area dedicated to the study of the use of Artificial Intelligence (AI) for Cognitive Behavioral Therapy (CBT) research and applications (AI4CBT for brevity). Yet, little is known about how this interest is realized and hence the overall status, prospects, and possible challenges of AI4CBT as a field (e.g. breadth of the field, key topics and methods, key producing countries/institutions/authors, interdisciplinary grounding). This paper addresses this gap by developing a broad-spectrum bibliometric analysis towards acquiring a comprehensive overview of the AI4CBT field. Four key dimensions are analyzed (productivity, producers, productions, and contents) along the array of bibliographic metrics, including production trends over time, leading contributors at various levels, co-authorship, citation, and keywords co-occurrence networks, publication formats, key venues, methodological trends, and disciplinary assessment. The paper concludes by framing the status of AI4CBT as a scientific field, allowing to tie it to scientific and applicative challenges and opportunities that AI4CBT may encounter and offer as it further develops.