Comparing preferences for skin cancer screening: AI-enabled app vs dermatologist.

BACKGROUND AND AIM: Skin cancer is a major public health issue. While self-examinations and professional screenings are recommended, they are rarely performed. Mobile health (mHealth) apps utilising artificial intelligence (AI) for skin cancer screening offer a potential solution to aid self-examinations; however, their uptake is low. Therefore, the aim of this research was to examine provider and user characteristics influencing people's decisions to seek skin cancer screening performed by a mHealth app or a dermatologist. METHODS: Two forced-choice conjoint experiments with Nmain = 1591 and Nreplication = 308 participants from the United States were conducted online to investigate preferences for screening providers. In addition to the provider type (mHealth app vs dermatologist), the following provider attributes were manipulated: costs, expertise, privacy policy, and result details. Subsequently, a questionnaire assessed various user characteristics, including demographics, attitudes toward AI technology and medical mistrust. RESULTS: Outcomes were consistent across the two studies. The provider type was the most influential factor, with the dermatologist being selected more often than the mHealth app. Cost, expertise, and privacy policy also significantly impacted decisions. Demographic subgroup analyses showed rather consistent preference trends across various age, gender, and ethnicity groups. Individuals with greater medical mistrust were more inclined to choose the mHealth app. Trust, accuracy, and quality ratings were higher for the dermatologist, whether selected or not. CONCLUSION: Our results offer valuable insights for technology developers, healthcare providers, and policymakers, contributing to unlocking the potential of skin cancer screening apps in bridging healthcare gaps in underserved communities.

Attitudes Toward the Adoption of 2 Artificial Intelligence-Enabled Mental Health Tools Among Prospective Psychotherapists: Cross-sectional Study.

BACKGROUND: Despite growing efforts to develop user-friendly artificial intelligence (AI) applications for clinical care, their adoption remains limited because of the barriers at individual, organizational, and system levels. There is limited research on the intention to use AI systems in mental health care. OBJECTIVE: This study aimed to address this gap by examining the predictors of psychology students' and early practitioners' intention to use 2 specific AI-enabled mental health tools based on the Unified Theory of Acceptance and Use of Technology. METHODS: This cross-sectional study included 206 psychology students and psychotherapists in training to examine the predictors of their intention to use 2 AI-enabled mental health care tools. The first tool provides feedback to the psychotherapist on their adherence to motivational interviewing techniques. The second tool uses patient voice samples to derive mood scores that the therapists may use for treatment decisions. Participants were presented with graphic depictions of the tools' functioning mechanisms before measuring the variables of the extended Unified Theory of Acceptance and Use of Technology. In total, 2 structural equation models (1 for each tool) were specified, which included direct and mediated paths for predicting tool use intentions. RESULTS: Perceived usefulness and social influence had a positive effect on the intention to use the feedback tool (P<.001) and the treatment recommendation tool (perceived usefulness, P=.01 and social influence, P<.001). However, trust was unrelated to use intentions for both the tools. Moreover, perceived ease of use was unrelated (feedback tool) and even negatively related (treatment recommendation tool) to use intentions when considering all predictors (P=.004). In addition, a positive relationship between cognitive technology readiness (P=.02) and the intention to use the feedback tool and a negative relationship between AI anxiety and the intention to use the feedback tool (P=.001) and the treatment recommendation tool (P<.001) were observed. CONCLUSIONS: The results shed light on the general and tool-dependent drivers of AI technology adoption in mental health care. Future research may explore the technological and user group characteristics that influence the adoption of AI-enabled tools in mental health care.

Personal condition but social cure: Agentic ingroups elevate well-being in chronically ill patients through perceptions of personal control.

OBJECTIVES: Social-cure research has shown that ingroup identification can be beneficial for personal health and well-being. Initial evidence for healthy participants suggests that this might be due to group membership providing a sense of personal control. In this research, we investigate this pathway for chronically ill patients, assuming that any ingroup (even patient identity) can serve as social cure by increasing control as long as the ingroup is perceived as agentic (i.e., effective). DESIGN: We conducted six correlational field studies with patients suffering from different chronic conditions, e.g., cancer (Ntotal = 795). METHODS: All participants were asked about one specific ingroup, e.g., their self-help group. Our main measures were ingroup identification, ingroup agency, personal control and well-being, as well as self-esteem and social support (both discussed as alternative mediators). We performed simple mediation and/or moderated mediation analyses for each study and across studies (merging Studies 2-6). RESULTS: Overall, the impact of ingroup identification on personal well-being was uniquely mediated via personal control (Studies 1, 2, 3, 6) but, as expected, only for those perceiving their ingroup as highly agentic (Studies 4, 5, 6). CONCLUSIONS: Ingroup agency is a boundary condition for the control-based pathway of the social cure effect supporting the model of group-based control. This has practical implications for clinical interventions with chronically ill patients.

Psychological interventions targeting partners of cancer patients: A systematic review.

PURPOSE: Cancer patients' intimate partners often experience levels of psychological burden that are comparable to or even exceed that of the patients, making it imperative that they too be provided with appropriate psychological support. This review aimed to present the content and the effects of interventions delivered to caregiving partners of cancer patients on both partners and patients. Furthermore, we provide information about the acceptability of the interventions and study quality. METHODS: An initial search in Web of Science, PsycINFO, and PubMed databases was conducted. We included RCTs as well as pre-post studies that focused on enhancing partners' wellbeing or diminishing partners' distress. To be included, interventions had to have been offered to partners either only or predominantly. We included studies published until December 2017. The methodological quality of the trials was assessed with the EPHPP assessment tool. RESULTS: Nine studies met the inclusion criteria. Intervention topics included social support, short-term problem solving, the marital relationship quality, role expectations, emotional resilience, and coping strategies. Positive intervention effects were found with regard to social support, emotional distress, improved communication, posttraumatic growth, self-efficacy, and coping. Despite considerably low response rates, the interventions were generally well accepted. Most of the studies suffer limitations because of methodological flaws, the lack of randomization, and small sample sizes. CONCLUSION: Interventions delivered to partners of cancer patients may have positive effects on both partners and patients. We derive several implications for future research: Intervention programs should be tailored to the specific needs of caregiving partners with regard to the cancer trajectory and gender. Effort has to be made to increase sample sizes as well as to include particularly burdened individuals. Selected measurement instruments should be sensitive to specific intervention effects. Finally, information on both statistical as well as clinical relevance of research findings should be provided.