Poor sleeping has underrepresented medical, healthcare, and social costs?

Assessing the Accuracy of Generative Conversational Artificial Intelligence in Debunking Sleep Health Myths: Mixed Methods Comparative Study With Expert Analysis

BACKGROUND

Adequate sleep is essential for maintaining individual and public health, positively affecting cognition and well-being, and reducing chronic disease risks. It plays a significant role in driving the economy, public safety, and managing health care costs. Digital tools, including websites, sleep trackers, and apps, are key in promoting sleep health education. Conversational artificial intelligence (AI) such as ChatGPT (OpenAI, Microsoft Corp) offers accessible, personalized advice on sleep health but raises concerns about potential misinformation. This underscores the importance of ensuring that AI-driven sleep health information is accurate, given its significant impact on individual and public health, and the spread of sleep-related myths.

OBJECTIVE

This study aims to examine ChatGPT's capability to debunk sleep-related disbeliefs.

METHODS

A mixed methods design was leveraged. ChatGPT categorized 20 sleep-related myths identified by 10 sleep experts and rated them in terms of falseness and public health significance, on a 5-point Likert scale. Sensitivity, positive predictive value, and interrater agreement were also calculated. A qualitative comparative analysis was also conducted.

RESULTS

ChatGPT labeled a significant portion (n=17, 85%) of the statements as "false" (n=9, 45%) or "generally false" (n=8, 40%), with varying accuracy across different domains. For instance, it correctly identified most myths about "sleep timing," "sleep duration," and "behaviors during sleep," while it had varying degrees of success with other categories such as "pre-sleep behaviors" and "brain function and sleep." ChatGPT's assessment of the degree of falseness and public health significance, on the 5-point Likert scale, revealed an average score of 3.45 (SD 0.87) and 3.15 (SD 0.99), respectively, indicating a good level of accuracy in identifying the falseness of statements and a good understanding of their impact on public health. The AI-based tool showed a sensitivity of 85% and a positive predictive value of 100%. Overall, this indicates that when ChatGPT labels a statement as false, it is highly reliable, but it may miss identifying some false statements. When comparing with expert ratings, high intraclass correlation coefficients (ICCs) between ChatGPT's appraisals and expert opinions could be found, suggesting that the AI's ratings were generally aligned with expert views on falseness (ICC=.83, P<.001) and public health significance (ICC=.79, P=.001) of sleep-related myths. Qualitatively, both ChatGPT and sleep experts refuted sleep-related misconceptions. However, ChatGPT adopted a more accessible style and provided a more generalized view, focusing on broad concepts, while experts sometimes used technical jargon, providing evidence-based explanations.

CONCLUSIONS

ChatGPT-4 can accurately address sleep-related queries and debunk sleep-related myths, with a performance comparable to sleep experts, even if, given its limitations, the AI cannot completely replace expert opinions, especially in nuanced and complex fields such as sleep health, but can be a valuable complement in the dissemination of updated information and promotion of healthy behaviors.

Circadian Rhythms, Sleep, and Aging

Abstract. Circadian mechanisms and the sleep-wakefulness rhythms guarantee survival, adaptation, efficient action in everyday life or in emergencies and well-being. Disordered circadian processes at central and/or cellular levels, sleep disorders, and unhealthy wakefulness/sleep rhythms can impair the physiological circadian organization and result in subjective, professional, or behavioral changes ranging from functional inadequacy to higher risks at work or on the road to medical relevance. Circadian rhythms and the sleep organization change ontogenetically; major changes result from normal aging and from the multiple diseases that are often associated. There are circular functional interactions involving sleep/sleep disorders, the autonomic and immune systems, and the functional changes in the circadian system due to aging that deserve attention but have been overlooked thus far.

Sleep pattern, obesity and healthcare expenditures in Brazilian adults

The aim of this study was to analyze the relationship between sleep pattern and healthcare expenditures in adults, as well as to identify whether physical activity, biochemical markers and obesity affect this relationship. The sample was composed of 168 adults aged ≥ 50 years attended by two Basic Healthcare Units in Presidente Prudente, SP, Brazil. Health expenditure, sleep pattern, anthropometry, adiposity index, physical activity, metabolic and cardiovascular variables were assessed. Statistical analyses were performed using Kruskal-Wallis, Mann-Whitney and Spearman tests. Sleep disorders were positively correlated to higher costs with medicines and negatively correlated to costs with laboratory tests, even after adjusts by confounders. In addition, healthcare costs were also correlated to physical activity score, blood pressure, obesity and metabolic variables. Severe sleep disorders and high percentage of body fat were associated with increased use of medications. Sleep pattern is correlated to primary care healthcare costs, obesity and physical activity level.

Sleep Problems and Workplace Violence: A Systematic Review and Meta-Analysis

Background: This systematic review with meta-analysis was carried out to study the relationship between workplace violence and sleep problems.

Methods: The PRISMA statement was used to conduct a systematic search of the literature on PubMed/MEDLINE, Scopus, Sociological abstract, DOAJ, Web of Science, and Google Scholar databases. Of the original number of 749 studies, 34 were included in the systematic review, and 7 in the meta-analysis.

Results: A total of 119,361 participants from 15 different countries took part in these studies which were published between 1999 and 2019. Significant heterogeneity was observed among the studies (I² = 96%). In a random-effects meta-analysis model, pooled odds ratio (OR) analysis revealed that there was a direct relationship between occupational exposure to violence and sleep problems (OR = 2.55; 95% CI = 1.77–3.66). According to the GRADE guidelines, the quality of evidence of the association was low.

Conclusions: The findings of this study demonstrate that occupational exposure to physical, verbal, or sexual violence is associated with sleep problems. Further research on the relationship between violence and sleep is needed so that appropriate measures can be taken to prevent violence and improve sleep hygiene in the workplace.

Trial Registration Number: PROSPERO International prospective register of systematic reviews (CRD42019124903) February 9, 2019.

SleepOMICS: How Big Data Can Revolutionize Sleep Science

Sleep disorders have reached epidemic proportions worldwide, affecting the youth as well as the elderly, crossing the entire lifespan in both developed and developing countries. "Real-life" behavioral (sensor-based), molecular, digital, and epidemiological big data represent a source of an impressive wealth of information that can be exploited in order to advance the field of sleep research. It can be anticipated that big data will have a profound impact, potentially enabling the dissection of differences and oscillations in sleep dynamics and architecture at the individual level ("sleepOMICS"), thus paving the way for a targeted, "one-size-does-not-fit-all" management of sleep disorders ("precision sleep medicine").

Sleep and Mental Health in Truck Drivers: Descriptive Review of the Current Evidence and Proposal of Strategies for Primary Prevention

Background: Professional truck drivers (TDs) are exposed to stressful working (and living) conditions and are vulnerable. They report physical and mental health problems and psychological distress more frequently than the general population and their problems can affect safety on the roads. Actions to improve TDs’ health and reduce the risks of (co-)morbidity or unsafe driving are imperative. Methods: The published studies dealing with the TDs’ sleep habits and mental health were reviewed to define the scenario and organize the preventive strategies proposed thus far. Results: Awareness among TDs of the high risk for health and safety due to (often co-existing) untreated sleep and mental health problems is critical. Alcohol and prescribed or illicit drugs are often misused to compensate for depression, anxiety, job strain, fatigue, and social isolation. Polypharmacy and dependence increase the chance of unsafe behaviors on the road. The TDs’ access to healthcare services is scant, and participation in industry-sponsored wellness programs is limited. Conclusions: Primary prevention is a first unavoidable step to deal with sleep and mental health problems. Educational programs, online support and tele-health assessment/monitoring would help improve the well-being, safety and health of professional TDs and increase safety on the road.