Technology, mind, and behavior最新文献

Leveraging AI Tools to Express Gratitude. 利用人工智能工具表达感激。

IF 3.4

Technology, mind, and behavior Pub Date : 2026-08-20 DOI: 10.1037/tmb0000204

Samantha J Heintzelman, Grace Pearce

{"title":"Leveraging AI Tools to Express Gratitude.","authors":"Samantha J Heintzelman, Grace Pearce","doi":"10.1037/tmb0000204","DOIUrl":"10.1037/tmb0000204","url":null,"abstract":"Generative artificial intelligence (AI) has transformed the way people engage in everyday tasks, including helping with emotional labor. Here, we ask whether AI can effectively assist people in preparing written expressions of the social emotion of gratitude while maintaining the subjective well-being and relational benefits of that practice. In Study 1, participants (N = 246) randomly assigned to an AI-modified version of a gratitude letter task report levels of gratitude, subjective well-being, and relational closeness that are not statistically different than those who manually wrote a gratitude letter. In a subset of participants (n = 115), both groups showed increases in well-being from baseline to post-task. In Study 2, third-party readers (N = 522) rated the AI-generated letters as more authentic expressions of gratitude than the human-generated letters. Further, they tended to believe the AI letters were written by humans. This work presents a novel integration of AI to generate written output, traditionally produced by participants, in a positive activity intervention, extending the application of AI in this area. Importantly, doing so maintained the benefits of the gratitude letter task for participant well-being, suggesting that AI can be leveraged in thoughtful ways to reduce participant burden without sacrificing activity efficacy.","PeriodicalId":74913,"journal":{"name":"Technology, mind, and behavior","volume":" ","pages":""},"PeriodicalIF":3.4,"publicationDate":"2026-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13496258/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148802682","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Heterogeneous Associations Between Objectively Measured Screen Time and Sleep in Early Adolescence. 客观测量的屏幕时间与青少年早期睡眠之间的异质性关联。

IF 3.4

Technology, mind, and behavior Pub Date : 2026-06-25 DOI: 10.1037/tmb0000203

Sierra Clifford, Leah D Doane, Kathryn Lemery-Chalfant

{"title":"Heterogeneous Associations Between Objectively Measured Screen Time and Sleep in Early Adolescence.","authors":"Sierra Clifford, Leah D Doane, Kathryn Lemery-Chalfant","doi":"10.1037/tmb0000203","DOIUrl":"10.1037/tmb0000203","url":null,"abstract":"Early adolescence is a time of risk for disrupted or insufficient sleep, and widespread use of electronic media may exacerbate this risk. However, findings linking screen time to sleep in adolescent samples are inconsistent, rely primarily on self-report, and seldom consider sleep variability. We examined associations between 24-hour total screen time scraped from mobile devices and actigraphy-assessed indicators of sleep timing and quality across one week (means, variability, and day-to-day fluctuations) in a community sample of 677 adolescent twins (Mean age = 13.53, SD = 1.13; 50.96% female; 52.88% non-Hispanic White, 21.27% Hispanic/Latino), of whom 389 had actigraphy data and 488 had screen time data. For a subsample of Android users (n=171), hours of social media and mobile gaming were also considered. We conducted person-level associations using path analysis, with standard errors corrected for clustering of twins within families, and day-level analyses using three-level models. After multiple testing correction, greater person-level screen time was associated with later bedtime, and more variable wake time and duration, but not efficiency, latency, catch-up sleep, or social jetlag. More social media use was associated with later bedtime and greater duration variability. Mobile gaming was unrelated to sleep. Day-level screen time was weakly associated with later bedtime that night, with no other significant associations. Associations were mixed and modest, but not reliant on adolescent self-report. Findings do not provide strong evidence that moderate screen use is detrimental to sleep, but future studies should consider individual and contextual moderators and more nuanced objective measures.","PeriodicalId":74913,"journal":{"name":"Technology, mind, and behavior","volume":" ","pages":""},"PeriodicalIF":3.4,"publicationDate":"2026-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13313597/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148355129","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Readiness Evaluation for AI-Mental Health Deployment and Implementation (READI): A Review and Proposed Framework. 人工智能心理健康部署和实施的准备评估（READI）：一个审查和提议的框架。

IF 3.4

Technology, mind, and behavior Pub Date : 2025-01-01 Epub Date: 2025-04-21 DOI: 10.1037/tmb0000163

Elizabeth C Stade, Johannes C Eichstaedt, Jane P Kim, Shannon Wiltsey Stirman

{"title":"Readiness Evaluation for AI-Mental Health Deployment and Implementation (READI): A Review and Proposed Framework.","authors":"Elizabeth C Stade, Johannes C Eichstaedt, Jane P Kim, Shannon Wiltsey Stirman","doi":"10.1037/tmb0000163","DOIUrl":"10.1037/tmb0000163","url":null,"abstract":"While generative artificial intelligence (AI) may lead to technological advances in the mental health field, it poses safety risks for mental health service consumers. Furthermore, clinicians and healthcare systems must attend to safety and ethical considerations prior to deploying these AI-mental health technologies. To ensure the responsible deployment of AI-mental health applications, a principled method for evaluating and reporting on AI-mental health applications is needed. We conducted a narrative review of existing frameworks and criteria (from the mental health, healthcare, and AI fields) relevant to the evaluation of AI-mental health applications. We provide a summary and analysis of these frameworks, with a particular emphasis on the unique needs of the AI-mental health intersection. Existing frameworks contain areas of convergence (e.g., frequent emphasis on safety, privacy/confidentiality, effectiveness, and equity) that are relevant to the evaluation of AI-mental health applications. However, current frameworks are insufficiently tailored to unique considerations for AI and mental health. To address this need, we introduce the Readiness Evaluation for AI-Mental Health Deployment and Implementation (READI) framework for mental health applications. The READI framework comprises considerations of Safety, Privacy/Confidentiality, Equity, Effectiveness, Engagement, and Implementation. The READI framework outlines key criteria for assessing the readiness of AI-mental health applications for clinical deployment, offering a structured approach for evaluating these technologies and reporting findings.","PeriodicalId":74913,"journal":{"name":"Technology, mind, and behavior","volume":"6 2","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12494062/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145234435","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Brain-based predictive modeling of mind-wandering and involuntary thought: the idiographic approach. 基于大脑的走神和无意识思维的预测模型：具体方法。

IF 3.4

Technology, mind, and behavior Pub Date : 2025-01-01 Epub Date: 2025-03-13 DOI: 10.1037/tmb0000155

Lotus Shareef-Trudeau, David Lydon-Staley, John Medaglia, Aaron Kucyi

{"title":"Brain-based predictive modeling of mind-wandering and involuntary thought: the idiographic approach.","authors":"Lotus Shareef-Trudeau, David Lydon-Staley, John Medaglia, Aaron Kucyi","doi":"10.1037/tmb0000155","DOIUrl":"10.1037/tmb0000155","url":null,"abstract":"A substantial portion of waking life consists of mental experiences that arise involuntarily, including mind-wandering, spontaneous thought and rumination. Given that these experiences are fundamental to cognitive function and mental health, there has been growing scientific and clinical interest in using measures of brain activity to model and predict the momentary occurrence of these various forms of involuntary thought. In the most common machine learning approach in neuroimaging and electrophysiology research, model training data include neural features derived from a population of individuals, and testing is performed on held-out data in one or more individuals. However, sources of idiosyncrasy, such as individual differences in the nature of thought and brain functional organization, raise critical questions regarding whether population-derived neural models can generalize to individuals. In this article, we describe how an idiographic (person-specific) approach has potential to improve theory and practice in predictive neural modeling of involuntary thought. This approach emphasizes dense-sampling of individuals so that a diverse set of brain states and mental experiences can be modeled within a single individual, and rigorous tests of cross-subject generalizability can be performed. We review the advantages and shortcomings of both nomothetic (population-based) and idiographic predictive modeling approaches, including recent insights from dense-sampling neuroimaging studies that demonstrate person-specific brain-based predictions of mind-wandering. We discuss implications for developing personalized clinical biomarkers, strategies to overcome the practical challenges of an idiographic approach, and neuroethical concerns that must be considered as person-specific models may enhance the potential for accurate brain-based predictions of involuntary thought.","PeriodicalId":74913,"journal":{"name":"Technology, mind, and behavior","volume":"6 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12490730/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145234508","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Extending intention to use toward postadoption behavior—Conceptualizing actual usage for information technology-enabled banking services. 将使用意向扩展到采用后行为--对信息技术支持的银行服务实际使用情况的概念化。

Technology, mind, and behavior Pub Date : 2024-06-10 DOI: 10.1037/tmb0000132

Kritika Nagdev, A. Rajesh