Health Insurance Literacy among Caregivers of Substance Using Young Adults

Exploring factors influencing user perspective of ChatGPT as a technology that assists in healthcare decision making: A cross sectional survey study

As ChatGPT emerges as a potential ally in healthcare decision-making, it is imperative to investigate how users leverage and perceive it. The repurposing of technology is innovative but brings risks, especially since AI's effectiveness depends on the data it's fed. In healthcare, ChatGPT might provide sound advice based on current medical knowledge, which could turn into misinformation if its data sources later include erroneous information. Our study assesses user perceptions of ChatGPT, particularly of those who used ChatGPT for healthcare-related queries. By examining factors such as competence, reliability, transparency, trustworthiness, security, and persuasiveness of ChatGPT, the research aimed to understand how users rely on ChatGPT for health-related decision-making. A web-based survey was distributed to U.S. adults using ChatGPT at least once a month. Bayesian Linear Regression was used to understand how much ChatGPT aids in informed decision-making. This analysis was conducted on subsets of respondents, both those who used ChatGPT for healthcare decisions and those who did not. Qualitative data from open-ended questions were analyzed using content analysis, with thematic coding to extract public opinions on urban environmental policies. Six hundred and seven individuals responded to the survey. Respondents were distributed across 306 US cities of which 20 participants were from rural cities. Of all the respondents, 44 used ChatGPT for health-related queries and decision-making. In the healthcare context, the most effective model highlights 'Competent + Trustworthy + ChatGPT for healthcare queries', underscoring the critical importance of perceived competence and trustworthiness specifically in the realm of healthcare applications of ChatGPT. On the other hand, the non-healthcare context reveals a broader spectrum of influential factors in its best model, which includes 'Trustworthy + Secure + Benefits outweigh risks + Satisfaction + Willing to take decisions + Intent to use + Persuasive'. In conclusion our study findings suggest a clear demarcation in user expectations and requirements from AI systems based on the context of their use. We advocate for a balanced approach where technological advancement and user readiness are harmonized.

Exploring the determinants influencing suicidal ideation and depression in gastrointestinal cancer patients

Studies have shown a heightened prevalence of depression and suicidal ideation among patients with Gastrointestinal Cancer (GIC). GIC patients are at a 1.5- to threefold increased risk of suicide and depression compared to other cancer patients. This study investigates the interplay of internet use, family burden, and emotional support on mental health (depression) and suicidal ideation among patients with GIC. The study involves 202 respondents of which 78 were undergoing GIC treatment during this study. Using structural equation modeling, our findings indicate a substantial negative correlation between mental health and suicidal ideation. Overall, suicidal ideation (median score) was noticeably lower in patient who completed their treatment with noticeable individuals with exceptionally high SI even after completing the treatment. Notably, participants who had completed their treatment demonstrated a significantly stronger correlation between emotional support and mental health compared to those who were still undergoing treatment. Age was found to moderate the mental health-suicidal ideation link significantly. Internet usage for health-related information was also inversely correlated with mental health (directly) and suicidal ideation (indirectly). We noted that the influence of emotional support on mental health was significantly higher among individuals who completed their treatment compared to those who were undergoing their GIC treatment. Family burden emerged as significant negative influences on mental health, while emotional support positively impacted mental health. The findings of this study contribute towards a deeper understanding of suicide risk factors in GIC patients, potentially shaping more effective preventive strategies.

User Intentions to Use ChatGPT for Self-Diagnosis and Health-Related Purposes: Cross-sectional Survey Study

BACKGROUND

With the rapid advancement of artificial intelligence (AI) technologies, AI-powered chatbots, such as Chat Generative Pretrained Transformer (ChatGPT), have emerged as potential tools for various applications, including health care. However, ChatGPT is not specifically designed for health care purposes, and its use for self-diagnosis raises concerns regarding its adoption's potential risks and benefits. Users are increasingly inclined to use ChatGPT for self-diagnosis, necessitating a deeper understanding of the factors driving this trend.

OBJECTIVE

This study aims to investigate the factors influencing users' perception of decision-making processes and intentions to use ChatGPT for self-diagnosis and to explore the implications of these findings for the safe and effective integration of AI chatbots in health care.

METHODS

A cross-sectional survey design was used, and data were collected from 607 participants. The relationships between performance expectancy, risk-reward appraisal, decision-making, and intention to use ChatGPT for self-diagnosis were analyzed using partial least squares structural equation modeling (PLS-SEM).

RESULTS

Most respondents were willing to use ChatGPT for self-diagnosis (n=476, 78.4%). The model demonstrated satisfactory explanatory power, accounting for 52.4% of the variance in decision-making and 38.1% in the intent to use ChatGPT for self-diagnosis. The results supported all 3 hypotheses: The higher performance expectancy of ChatGPT (β=.547, 95% CI 0.474-0.620) and positive risk-reward appraisals (β=.245, 95% CI 0.161-0.325) were positively associated with the improved perception of decision-making outcomes among users, and enhanced perception of decision-making processes involving ChatGPT positively impacted users' intentions to use the technology for self-diagnosis (β=.565, 95% CI 0.498-0.628).

CONCLUSIONS

Our research investigated factors influencing users' intentions to use ChatGPT for self-diagnosis and health-related purposes. Even though the technology is not specifically designed for health care, people are inclined to use ChatGPT in health care contexts. Instead of solely focusing on discouraging its use for health care purposes, we advocate for improving the technology and adapting it for suitable health care applications. Our study highlights the importance of collaboration among AI developers, health care providers, and policy makers in ensuring AI chatbots' safe and responsible use in health care. By understanding users' expectations and decision-making processes, we can develop AI chatbots, such as ChatGPT, that are tailored to human needs, providing reliable and verified health information sources. This approach not only enhances health care accessibility but also improves health literacy and awareness. As the field of AI chatbots in health care continues to evolve, future research should explore the long-term effects of using AI chatbots for self-diagnosis and investigate their potential integration with other digital health interventions to optimize patient care and outcomes. In doing so, we can ensure that AI chatbots, including ChatGPT, are designed and implemented to safeguard users' well-being and support positive health outcomes in health care settings.

Development of a survey measuring parental physical activity support in parents of children with disabilities

PURPOSE

The purposes of the current study were to develop a questionnaire to measure parental physical activity (PA) support in parents of children with disabilities and to examine factors associated with parental PA support.

MATERIALS AND METHODS

A questionnaire measuring parental PA support in parents of children with disabilities (PPSQ for PCD), consisting of three constructs (encouragement, co-PA, and logistic support) with 19 items, was initially developed and evaluated by the Confirmatory Factor Analysis. Three independent multiple regression analyses were conducted to examine associations between the three constructs and parental PA orientation variables.

RESULTS

With the item deletion approach, the final model with 12 items (four items in each construct) was developed, which revealed an excellent model fit (x2= 78.42 [df = 51, p = .008], CFI = 0.98, GFI = 0.94. RMSEA = 0.05). The multiple regression analyses revealed that parental orientation variables were differently associated with each construct of the PPSQ for PCD.

CONCLUSIONS

The PPSQ for PCD consists of three constructs, differently associated with parental orientation variables. This questionnaire can be used as an instrument to assess parental PA support. Implications for RehabilitationThe Parental Physical Activity Questionnaire (PPAQ) can be used to assess parental physical activity support in parents of children with disabilities.The PPAQ measures three constructs (i.e., types) of parental physical activity support including encouragement, co-physical activity, and logistic support.Parental physical activity orientations (e.g., importance, enjoyment, and behavior) are differently associated with each construct of parental physical activity support.