Background: Mental disorders have ranked among the top 10 prevalent causes of burden on a global scale. Generative artificial intelligence (GAI) has emerged as a promising and innovative technological advancement that has significant potential in the field of mental health care. Nevertheless, there is a scarcity of research dedicated to examining and understanding the application landscape of GAI within this domain.
Objective: This review aims to inform the current state of GAI knowledge and identify its key uses in the mental health domain by consolidating relevant literature.
Methods: Records were searched within 8 reputable sources including Web of Science, PubMed, IEEE Xplore, medRxiv, bioRxiv, Google Scholar, CNKI and Wanfang databases between 2013 and 2023. Our focus was on original, empirical research with either English or Chinese publications that use GAI technologies to benefit mental health. For an exhaustive search, we also checked the studies cited by relevant literature. Two reviewers were responsible for the data selection process, and all the extracted data were synthesized and summarized for brief and in-depth analyses depending on the GAI approaches used (traditional retrieval and rule-based techniques vs advanced GAI techniques).
Results: In this review of 144 articles, 44 (30.6%) met the inclusion criteria for detailed analysis. Six key uses of advanced GAI emerged: mental disorder detection, counseling support, therapeutic application, clinical training, clinical decision-making support, and goal-driven optimization. Advanced GAI systems have been mainly focused on therapeutic applications (n=19, 43%) and counseling support (n=13, 30%), with clinical training being the least common. Most studies (n=28, 64%) focused broadly on mental health, while specific conditions such as anxiety (n=1, 2%), bipolar disorder (n=2, 5%), eating disorders (n=1, 2%), posttraumatic stress disorder (n=2, 5%), and schizophrenia (n=1, 2%) received limited attention. Despite prevalent use, the efficacy of ChatGPT in the detection of mental disorders remains insufficient. In addition, 100 articles on traditional GAI approaches were found, indicating diverse areas where advanced GAI could enhance mental health care.
Conclusions: This study provides a comprehensive overview of the use of GAI in mental health care, which serves as a valuable guide for future research, practical applications, and policy development in this domain. While GAI demonstrates promise in augmenting mental health care services, its inherent limitations emphasize its role as a supplementary tool rather than a replacement for trained mental health providers. A conscientious and ethical integration of GAI techniques is necessary, ensuring a balanced approach that maximizes benefits while mitigating potential challenges in mental health care practices.
Climate change, local epidemics, future pandemics, and forced displacements pose significant public health threats worldwide. To cope successfully, people and communities are faced with the challenging task of developing resilience to these stressors. Our viewpoint is that the powerful capabilities of modern informatics technologies including artificial intelligence, biomedical and environmental sensors, augmented or virtual reality, data science, and other digital hardware or software, have great potential to promote, sustain, and support resilience in people and communities. However, there is no "one size fits all" solution for resilience. Solutions must match the specific effects of the stressor, cultural dimensions, social determinants of health, technology infrastructure, and many other factors.
Background: Previous research and safety advocacy groups have proposed various behaviors for older adults to actively engage in medication safety. However, little is known about how older adults perceive the importance and reasonableness of these behaviors in ambulatory settings.
Objective: This study aimed to assess older adults' perceptions of the importance and reasonableness of 8 medication safety behaviors in ambulatory settings and compare their responses with those of younger adults.
Methods: We conducted a survey of 1222 adults in the United States using crowdsourcing to evaluate patient behaviors that may enhance medication safety in community settings. A total of 8 safety behaviors were identified based on the literature, such as bringing medications to office visits, confirming medications at home, managing medication refills, using patient portals, organizing medications, checking medications, getting help, and knowing medications. Respondents were asked about their perception of the importance and reasonableness of these behaviors on a 5-point Likert rating scale in the context of collaboration with primary care providers. We assessed the relative ranking of behaviors in terms of importance and reasonableness and examined the association between these dimensions across age groups using statistical tests.
Results: Of 1222 adult participants, 125 (10.2%) were aged 65 years or older. Most participants were White, college-educated, and had chronic conditions. Older adults rated all 8 behaviors significantly higher in both importance and reasonableness than did younger adults (P<.001 for combined behaviors). Confirming medications ranked highest in importance (mean score=3.78) for both age groups while knowing medications ranked highest in reasonableness (mean score=3.68). Using patient portals was ranked lowest in importance (mean score=3.53) and reasonableness (mean score=3.49). There was a significant correlation between the perceived importance and reasonableness of the identified behaviors, with coefficients ranging from 0.436 to 0.543 (all P<.001).
Conclusions: Older adults perceived the identified safety behaviors as more important and reasonable than younger adults. However, both age groups considered a behavior highly recommended by professionals as the least important and reasonable. Patient engagement strategies, common and specific to age groups, should be considered to improve medication safety in ambulatory settings.
Background: A common challenge for individuals caring for people with Alzheimer disease and related dementias is managing the behavioral and psychological symptoms of dementia (BPSD). Effective management of BPSD will increase the quality of life of people living with dementia, lessen caregivers' burden, and lower health care cost.
Objective: In this review, we seek to (1) examine how indoor environmental quality parameters pertaining to light, noise, temperature, and humidity are associated with BPSD and how controlling these parameters can help manage these symptoms and (2) identify the current state of knowledge in this area, current gaps in the research, and potential future directions.
Methods: Searches were conducted in the CINAHL, Embase, MEDLINE, and PsycINFO databases for papers published from January 2007 to February 2024. We searched for studies examining the relationship between indoor environmental quality parameters pertaining to light, noise, temperature, and humidity and BPSD.
Results: A total of 3123 papers were identified in the original search in October 2020. After an additional 2 searches and screening, 38 (0.69%) of the 5476 papers were included. Among the included papers, light was the most studied environmental factor (34/38, 89%), while there were fewer studies (from 5/38, 13% to 11/38, 29%) examining the relationships between other environmental factors and BPSD. Of the 38 studies, 8 (21%) examined multiple indoor environmental quality parameters. Subjective data were the only source of environmental assessments in 6 (16%) of the 38 studies. The findings regarding the relationship between agitation and light therapy are conflicted, while the studies that examined the relationship between BPSD and temperature or humidity are all observational. The results suggest that when the environmental factors are deemed overstimulating or understimulating for an individual with dementia, the behavioral symptoms tend to be exacerbated.
Conclusions: The findings of this scoping review may inform the design of long-term care units and older adult housing to support aging in place. More research is still needed to better understand the relationship between indoor environmental quality parameters and BPSD, and there is a need for more objective measurements of both the indoor environmental quality parameters and behavioral symptoms. One future direction is to incorporate objective sensing and advanced computational methods in real-time assessments to initiate just-in-time environmental interventions. Better management of BPSD will benefit patients, caregivers, and the health care system.
Background: The rehabilitation of children with disabilities has received considerable attention from the United Nations. However, the state of rehabilitation services for children with disabilities worldwide remains far from optimistic, even in economically affluent middle- and high-income countries.
Objective: This scoping review aimed to identify the rehabilitation needs of children with disabilities and their barriers to rehabilitation services in middle- and high-income countries.
Methods: A systematic search was conducted using MEDLINE and Web of Science for papers published from January 2013 to December 2023. Studies were included if they were peer-reviewed, full-text articles related to children with disabilities, reporting on their access to rehabilitation services, and conducted in countries classified by the World Bank 2023 as middle- and high-income economies. Exclusion criteria included duplicates, unavailable full texts, and studies without distinct outcomes. A total of 27 studies were selected following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, focusing on children, their families, or service providers.
Results: The suitability, availability, and affordability of rehabilitation services were identified as the major needs and barriers for children with disabilities in middle- and high-income countries. This included communication barriers, a need for more personnel and facilities, and the stagnation and inadequacy of economic subsidies.
Conclusions: Middle- and high-income countries have relatively well-established rehabilitation infrastructure and support systems. They are nevertheless insufficient for meeting the needs of children with disabilities. More attention should be paid to these issues to improve the well-being of children with disabilities. The data provided by this review can help raise awareness of rehabilitation needs and barriers at the policy level.
Background: The impact of missing data on individual continuous glucose monitoring (CGM) data is unknown but can influence clinical decision-making for patients.
Objective: We aimed to investigate the consequences of data loss on glucose metrics in individual patient recordings from continuous glucose monitors and assess its implications on clinical decision-making.
Methods: The CGM data were collected from patients with type 1 and 2 diabetes using the FreeStyle Libre sensor (Abbott Diabetes Care). We selected 7-28 days of 24 hours of continuous data without any missing values from each individual patient. To mimic real-world data loss, missing data ranging from 5% to 50% were introduced into the data set. From this modified data set, clinical metrics including time below range (TBR), TBR level 2 (TBR2), and other common glucose metrics were calculated in the data sets with and that without data loss. Recordings in which glucose metrics deviated relevantly due to data loss, as determined by clinical experts, were defined as expert panel boundary error (εEPB). These errors were expressed as a percentage of the total number of recordings. The errors for the recordings with glucose management indicator <53 mmol/mol were investigated.
Results: A total of 84 patients contributed to 798 recordings over 28 days. With 5%-50% data loss for 7-28 days recordings, the εEPB varied from 0 out of 798 (0.0%) to 147 out of 736 (20.0%) for TBR and 0 out of 612 (0.0%) to 22 out of 408 (5.4%) recordings for TBR2. In the case of 14-day recordings, TBR and TBR2 episodes completely disappeared due to 30% data loss in 2 out of 786 (0.3%) and 32 out of 522 (6.1%) of the cases, respectively. However, the initial values of the disappeared TBR and TBR2 were relatively small (<0.1%). In the recordings with glucose management indicator <53 mmol/mol the εEPB was 9.6% for 14 days with 30% data loss.
Conclusions: With a maximum of 30% data loss in 14-day CGM recordings, there is minimal impact of missing data on the clinical interpretation of various glucose metrics.
Trial registration: ClinicalTrials.gov NCT05584293; https://clinicaltrials.gov/study/NCT05584293.
Background: The interrelation between COVID-19 and various cardiovascular and metabolic disorders has been a critical area of study. There is a growing need to understand how comorbidities such as cardiovascular diseases (CVDs) and metabolic disorders affect the risk and severity of COVID-19.
Objective: The objective of this study is to systematically analyze the association between COVID-19 and cardiovascular and metabolic disorders. The focus is on comorbidity, examining the roles of CVDs such as embolism, thrombosis, hypertension, and heart failure, as well as metabolic disorders such as disorders of glucose and iron metabolism.
Methods: Our study involved a systematic search in PubMed for literature published from 2000 to 2022. We established 2 databases: one for COVID-19-related articles and another for CVD-related articles, ensuring all were peer-reviewed. In terms of data analysis, statistical methods were applied to compare the frequency and relevance of MeSH (Medical Subject Headings) terms between the 2 databases. This involved analyzing the differences and ratios in the usage of these terms and employing statistical tests to determine their significance in relation to key CVDs within the COVID-19 research context.
Results: The study revealed that "Cardiovascular Diseases" and "Nutritional and Metabolic Diseases" were highly relevant as level 1 Medical Subject Headings descriptors in COVID-19 comorbidity research. Detailed analysis at level 2 and level 3 showed "Vascular Disease" and "Heart Disease" as prominent descriptors under CVDs. Significantly, "Glucose Metabolism Disorders" were frequently associated with COVID-19 comorbidities such as embolism, thrombosis, and heart failure. Furthermore, iron deficiency (ID) was notably different in its occurrence between COVID-19 and CVD articles, underlining its significance in the context of COVID-19 comorbidities. Statistical analysis underscored these differences, highlighting the importance of both glucose and iron metabolism disorders in COVID-19 research.
Conclusions: This work lays the foundation for future research that utilizes a knowledge-based approach to elucidate the intricate relationships between these conditions, aiming to develop more effective health care strategies and interventions in the face of ongoing pandemic challenges.
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