Background: Artificial intelligence (AI) is increasingly being applied in various health care services due to its enhanced efficiency and accuracy. As the population ages, AI-based health technologies could be a potent tool in older adults’ health care to address growing, complex, and challenging health needs. This study aimed to investigate perspectives on and acceptability of the use of AI-led health technologies among older adults and the potential challenges that they face in adopting them. The findings from this inquiry could inform the designing of more acceptable and user-friendly AI-based health technologies. Objective: The objectives of the study were (1) to investigate the attitudes and perceptions of older adults toward the use of AI-based health technologies; (2) to identify potential facilitators, barriers, and challenges influencing older adults’ preferences toward AI-based health technologies; and (3) to inform strategies that can promote and facilitate the use of AI-based health technologies among older adults. Methods: This study adopted a qualitative descriptive design. A total of 27 community-dwelling older adults were recruited from a local community center. Three sessions of semistructured interviews were conducted, each lasting 1 hour. The sessions covered five key areas: (1) general impressions of AI-based health technologies; (2) previous experiences with AI-based health technologies; (3) perceptions and attitudes toward AI-based health technologies; (4) anticipated difficulties in using AI-based health technologies and underlying reasons; and (5) willingness, preferences, and motivations for accepting AI-based health technologies. Thematic analysis was applied for data analysis. The Theoretical Domains Framework and the Capability, Opportunity, Motivation, and Behavior (COM-B) model behavior change wheel were integrated into the analysis. Identified theoretical domains were mapped directly to the COM-B model to determine corresponding strategies for enhancing the acceptability of AI-based health technologies among older adults. Results: The analysis identified 9 of the 14 Theoretical Domains Framework domains—knowledge, skills, social influences, environmental context and resources, beliefs about capabilities, beliefs about consequences, intentions, goals, and emotion. These domains were mapped to 6 components of the COM-B model. While most participants acknowledged the potential benefits of AI-based health technologies, they emphasized the irreplaceable role of human expertise and interaction. Participants expressed concerns about the usability of AI technologies, highlighting the need for user-friendly and tailored AI solutions. Privacy concerns and the importance of robust security measures were also emphasized as critical factors affecting their willingness to adopt AI-based health technologies. Conclusions: Integrating AI as a supportive tool alongside health care providers, rather than regarding it as a replacement, was highlighted as a key strategy for promoting acceptance. Government support and clear guidelines are needed to promote ethical AI implementation in health care. These measures can improve health outcomes in the older adult population by encouraging the adoption of AI-driven health technologies.

Background: The prevalence of mental distress and health risk behaviors among adolescents and young adults has emerged as a pressing public health concern. Artificial intelligence (AI) chatbots have been increasingly recognized for their potential to provide scalable and accessible mental health support and health education; however, questions remain about their effectiveness in addressing the unique challenges faced by adolescents and young adults. Objective: This study aimed to synthesize evidence from randomized controlled trials (RCTs) on the effectiveness of AI chatbots in alleviating mental distress and promoting health behaviors among adolescents and young adults. Methods: Eight databases (PubMed, PsycINFO, Cochrane Library, CINAHL, Embase, Web of Science, Scopus, and IEEE Xplore) were searched for RCTs published in English between January 1, 2014, and January 26, 2025. Eligible studies assessed the effects of AI chatbots on mental distress and health behavior outcomes among adolescents and young adults (15‐39 years). Extracted data were synthesized narratively or meta-analyzed as appropriate; subgroup and meta-regression analyses were performed to explore moderators of chatbot effectiveness. Risk of bias was evaluated using the revised Cochrane risk-of-bias 2 (RoB 2) tool for randomized trials. Evidence quality was evaluated using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach. Results: Out of 2495 records retrieved, 31 RCTs were included, comprising 29,637 participants; 26 studies were eligible for meta-analysis. Overall, AI chatbots demonstrated small-to-moderate effects in mitigating mental distress (standard mean difference [SMD] −0.35, 95% CI −0.46 to −0.24; P <.001) and promoting health behaviors (SMD 0.11, 95% CI 0.03 to 0.19; P =.006) in adolescents and young adults. Significant improvements were observed for depressive (SMD −0.43, 95% CI −0.62 to −0.23; P <.001), anxiety (SMD −0.37, 95% CI −0.58 to −0.17; P <.001), stress (SMD −0.41, 95% CI −0.50 to −0.31; P <.001), and psychosomatic symptoms (SMD −0.48, 95% CI −0.82 to −0.14; P =.006); negative affect (SMD −0.27, 95% CI −0.53 to −0.01; P =.04); and self-ambivalence and appearance distress (SMD −0.25, 95% CI −0.34 to −0.17; P =.01). While AI chatbots contributed to modest enhancements in life satisfaction and well-being, their impacts on positive affect and self-efficacy were limited. The effectiveness of AI chatbots varied depending on target samples, control conditions, and design features such as dialog system methods, deployment formats, and the use of reminders. User engagement emerged as a critical factor for success, with repetitive content and technical issues noted as primary barriers to adherence. Conclusions: This systematic review and meta-analysis highlights the potential of AI chatbots to address mental health challenges and promote health behaviors among adolescents and young adults. Retrieval-based dialog systems demonstrated consistent and reliable effects, while generative systems showed promise, but their overall effectiveness was inconclusive. Future research should prioritize developing safety protocols and evaluation frameworks for generative systems and validating their long-term impacts on mental health and behavior change in adolescents and young adults.

Domestic violence (DV) is a public health crisis that threatens both the mental and physical health of people. With the unprecedented surge in data available on the internet and electronic health record systems, leveraging machine learning (ML) to detect obscure changes and predict the likelihood of DV from digital text data is a promising area health science research. However, there is a paucity of research discussing and reviewing ML applications in DV research. Methods: We extracted 3588 articles from four databases. Twenty-two articles met the inclusion criteria. Results: Twelve articles used the supervised ML method, seven articles used the unsupervised ML method, and three articles applied both. Most studies were published in Australia (n = 6) and the United States (n = 4). Data sources included social media, professional notes, national databases, surveys, and newspapers. Random forest (n = 9), support vector machine (n = 8), and naïve Bayes (n = 7) were the top three algorithms, while the most used automatic algorithm for unsupervised ML in DV research was latent Dirichlet allocation (LDA) for topic modeling (n = 2). Eight types of outcomes were identified, while three purposes of ML and challenges were delineated and are discussed. Conclusions: Leveraging the ML method to tackle DV holds unprecedented potential, especially in classification, prediction, and exploration tasks, and particularly when using social media data. However, adoption challenges, data source issues, and lengthy data preparation times are the main bottlenecks in this context. To overcome those challenges, early ML algorithms have been developed and evaluated on DV clinical data.

Background: Intimate partner violence (IPV) is a pressing phenomenon whose consequences are associated with severe physical and mental health outcomes. Every minute, around 24 people in the United States are raped, physically injured, or emotionally abused by their intimate partner. Although having experienced IPV is not modifiable, emotional support is a protective factor to prevent victims from committing suicide. The psychological state of IPV victims is critical in post-traumatic events and this is evidenced in numerous qualitative interviews. Therefore, the objective of this study is to explore the association between IPV with emotional support, life satisfaction, and perceived health status in the United States. Methods: This study analyzed the data from the 2007 Behavioral Risk Factor Surveillance System. Univariate analyses, multivariable logistic regression analyses, and ordinal logistic regression analyses were used to estimate the adjusted odds ratios (AORs) and 95% confidence intervals (95% CIs) for factors associated with IPV. Analyses were conducted using SPSS version 25. Results: The analyses show that there is a strong association between IPV experience and emotional support (AOR:1.810; 95% CI = 1.626–2.015). Participants who had either physical violence or unwanted sex with an intimate partner in the past 12 months have 2.28 higher odds to receive less emotional support and 2.05 higher odds to perceive poor life satisfaction. Also, participants who reported experiencing IPV were associated with (AOR: 3.12; 95% CI =2.68–3.62) times the odds of having ≥6 days more mentally unhealthy days in a month. For perceived health outcomes, people who had been threatened with violence by a sex partner have 1.74 (95% CI =1.54–1.96) times the odds of having poor perceived general health status. IPV survivors have 3.12 (95% CI =2.68–3.62) times the odds of having ≥6 days more mentally unhealthy days in a month. Conclusions: People reported with any IPV experience are more likely to receive less emotional support, perceive dissatisfaction in life, and poor health outcomes. This study shows the need for policies centered on the development of interventions that focus on mental health for those who have experienced IPV.

Background: Intimate partner violence (IPV) is a serious public health problem associated with countless adverse physical and mental health outcomes. It places an enormous economic and public health burden on communities. The aim of this study was to examine the associations between psychological states (such as depression or hopeless) and help-seeking experiences of IPV survivors after experiencing IPV, based on the Allegheny County Health Survey (ACHS). Methods: Data from 2015 to 2016 Allegheny County Health Survey with N = 8,012 adults were analyzed. The 6-item version of the Kessler Psychological Stress Scale, located in Module 11 of the ACHS questionnaire, was used to measure psychological stress in participants. Module 12 of the ACHS questionnaire collected information on participants’ experiences of intimate partner violence and help-seeking in the past 12 months. Descriptive statistical analysis, Pearson’s chi-square or two sample independent t-tests statistical analysis, and multivariate binary logistic regression models were used to analyze the relationship between IPV experience and psychological distress. Results: A total of 212 of the 8,012 participants had IPV experience, with age, marital status, education, income, and race significantly different from those without IPV experience. The psychological stress of participants feeling hopeless (OR = 2.02, 95% CI = 1.37–2.99), restless or fidgety (OR = 1.83, 95% CI = 1.27–2.65), perceiving everything was an effort (OR = 1.55, 95% CI = 1.08–2.22) and worthless (OR = 1.49, 95% CI = 1.01–2.20) was associated with the IPV experience. Help-seeking behaviors of IPV survivors were associated with psychological distress, such as hopelessness (OR = 6.71, 95% CI = 1.38–32.60). Conclusions: This study explored the association between IPV experience, help-seeking and psychological distress, and the need to expand community support. It is necessary to implement targeted interventions, enhance training of professionals, and promote the identification of early IPV cases as well as collaboration between healthcare and social support departments to reduce the occurrence of IPV or psychological distress following IPV.

The intersection of homelessness and transportation is an overlooked component of transport-related exclusion research, yet mobility is recognized as playing a significant role in anyone’s well-being. The lack of research in this field is concurrent with the lack of understanding of homeless individuals’ mobility needs and travel behavior. As a result, there is little that can guide the policy-making process for the travel needs of homeless individuals. In 2015, as a response to this research gap, an interview was designed and then conducted with 159 homeless individuals across eight shelters and drop-in agencies in the city of Toronto, Canada. The interview included stated adaptation experiments to reveal how transportation affected or hindered homeless individuals’ activity participation processes. Decision choice models were estimated with econometric modeling methods, which indicated the influence of variables such as age, income, and duration of homelessness on the decision to reject or accept employment opportunities. The results also showed that a person’s accustomed mode choice and daily number of trips had an effect on the decision to travel for social activity purposes.