Background: The COVID-19 pandemic compelled older adults to engage with technology to a greater extent given emergent public health observance and home-sheltering restrictions in the United States. This study examined subjective experiences of technology use among older adults as a result of unforeseen and widespread public health guidance catalyzing their use of technology differently, more often, or in new ways.
Objective: This study aimed to explore whether older adults scoring higher on the Unified Theory of Acceptance and Use of Technology questionnaire fared better in aspects of technology use, and reported better subjective experiences, in comparison with those scoring lower.
Methods: A qualitative study using prevalence and thematic analyses of data from 18 older adults (mean age 79 years) in 2 groups: 9 scoring higher and 9 scoring lower on the Unified Theory of Acceptance and Use of Technology questionnaire.
Results: Older adults were fairly competent technology users across both higher- and lower-scoring groups. The higher-scoring group noted greater use of technology in terms of telehealth and getting groceries and household items. Cognitive difficulty was described only among the lower-scoring group; they used technology less to get groceries and household items and to obtain health information. Qualitative themes depict the role of habit in technology use, enthusiasm about technology buttressed by the protective role of technology, challenges in technology use, and getting help regardless of technology mastery.
Conclusions: Whereas the pandemic compelled older adults to alter or increase technology use, it did not change their global outlook on technology use. Older adults' prepandemic habits of technology use and available help influenced the degree to which they made use of technology during the COVID-19 pandemic.
As artificial intelligence (AI) technologies occupy a bigger role in psychiatric and psychological care and become the object of increased research attention, industry investment, and public scrutiny, tools for evaluating their clinical, ethical, and user-centricity standards have become essential. In this paper, we first review the history of rating systems used to evaluate AI mental health interventions. We then describe the recently introduced Framework for AI Tool Assessment in Mental Health (FAITA-Mental Health), whose scoring system allows users to grade AI mental health platforms on key domains, including credibility, user experience, crisis management, user agency, health equity, and transparency. Finally, we demonstrate the use of FAITA-Mental Health scale by systematically applying it to OCD Coach, a generative AI tool readily available on the ChatGPT store and designed to help manage the symptoms of obsessive-compulsive disorder. The results offer insights into the utility and limitations of FAITA-Mental Health when applied to "real-world" generative AI platforms in the mental health space, suggesting that the framework effectively identifies key strengths and gaps in AI-driven mental health tools, particularly in areas such as credibility, user experience, and acute crisis management. The results also highlight the need for stringent standards to guide AI integration into mental health care in a manner that is not only effective but also safe and protective of the users' rights and welfare.
Background: Diabetes distress refers to the negative emotional reaction to living with the demands of diabetes; it occurs in >40% of adults with type 1 diabetes (T1D). However, no interventions to reduce diabetes distress are specifically designed to be an integral part of diabetes care.
Objective: This study aims to modify and adapt existing evidence-based methods into a nurse-led group intervention to reduce diabetes distress among adults with T1D and moderate to severe diabetes distress.
Methods: The overall framework of this study was informed by the initial phase of the Medical Research Council's complex intervention framework that focused on undertaking intervention identification and development to guide the adaptation of the intervention. This study took place at 2 specialized diabetes centers in Denmark from November 2019 to June 2021. A total of 36 adults with T1D participated in 10 parallel workshops. A total of 12 diabetes-specialized nurses were interviewed and participated in 1 cocreation workshop; 12 multidisciplinary specialists, including psychologists, educational specialists, and researchers, participated in 4 cocreation workshops and 14 feedback meetings. Data were analyzed by applying a deductive analytic approach.
Results: The intervention included 5 biweekly 2.5-hour small group sessions involving adults with T1D and diabetes distress. Guided by a detailed step-by-step manual, the intervention was delivered by 2 trained diabetes specialist nurses. The intervention material included visual conversation tools covering seven diabetes-specific sources derived from the 28-item Type 1 Diabetes Distress Scale for measuring diabetes distress: (1) powerlessness, (2) self-management, (3) fear of hypoglycemia, (4) food and eating, (5) friends and family, (6) negative social perception, and (7) physician distress. The tools are designed to kick-start awareness and sharing of diabetes-specific challenges and strengths, individual reflections, as well as plenary and peer-to-peer discussions about strategies to manage diabetes distress, providing new perspectives on diabetes worries and strategies to overcome negative emotions. Diabetes specialist nurses expressed a need for a manual with descriptions of methods and detailed guidelines for using the tools. To deliver the intervention, nurses need increased knowledge about diabetes distress, how to support diabetes distress reduction, and training and supervision to improve skills.
Conclusions: This co-design study describes the adaptation of a complex intervention with a strong evidence base, including detailed reporting of the theoretical underpinnings and core mechanisms.
Background: Patients often struggle with determining which outpatient specialist to consult based on their symptoms. Natural language processing models in health care offer the potential to assist patients in making these decisions before visiting a hospital.
Objective: This study aimed to evaluate the performance of ChatGPT in recommending medical specialties for medical questions.
Methods: We used a dataset of 31,482 medical questions, each answered by doctors and labeled with the appropriate medical specialty from the health consultation board of NAVER (NAVER Corp), a major Korean portal. This dataset includes 27 distinct medical specialty labels. We compared the performance of the fine-tuned Korean Medical bidirectional encoder representations from transformers (KM-BERT) and ChatGPT models by analyzing their ability to accurately recommend medical specialties. We categorized responses from ChatGPT into those matching the 27 predefined specialties and those that did not. Both models were evaluated using performance metrics of accuracy, precision, recall, and F1-score.
Results: ChatGPT demonstrated an answer avoidance rate of 6.2% but provided accurate medical specialty recommendations with explanations that elucidated the underlying pathophysiology of the patient's symptoms. It achieved an accuracy of 0.939, precision of 0.219, recall of 0.168, and an F1-score of 0.134. In contrast, the KM-BERT model, fine-tuned for the same task, outperformed ChatGPT with an accuracy of 0.977, precision of 0.570, recall of 0.652, and an F1-score of 0.587.
Conclusions: Although ChatGPT did not surpass the fine-tuned KM-BERT model in recommending the correct medical specialties, it showcased notable advantages as a conversational artificial intelligence model. By providing detailed, contextually appropriate explanations, ChatGPT has the potential to significantly enhance patient comprehension of medical information, thereby improving the medical referral process.
Background: Intake24, a web-based 24-hour dietary recall tool developed in the United Kingdom, was adapted for use in New Zealand (Intake24-NZ) through the addition of a New Zealand food list, portion size images, and food composition database. Owing to the customizations made, a thorough evaluation of the tool's usability was required. Detailed qualitative usability studies are well suited to investigate any challenges encountered while completing a web-based 24-hour recall and provide meaningful data to inform enhancements to the tool.
Objective: This study aims to evaluate the usability of Intake24-NZ and identify improvements to enhance both the user experience and the quality of dietary intake data collected.
Methods: We used a mixed methods approach comprising two components: (1) completion of a single 24-hour dietary recall using Intake24-NZ with both screen observation recordings and collation of verbal participant feedback on their experience and (2) a survey.
Results: A total of 37 participants aged ≥11 years self-completed the dietary recall and usability survey (men and boys: 14/37, 38% and women and girls: 23/37, 62%; Māori: 10/37, 27% and non-Māori: 27/37, 73%). Although most (31/37, 84%) reported that Intake24-NZ was easy to use and navigate, data from the recorded observations and usability survey revealed challenges related to the correct use of search terms, search results obtained (eg, type and order of foods displayed), portion size estimation, and associated food prompts (eg, did you add milk to your tea?).
Conclusions: This comprehensive usability study identified challenges experienced by users in completing a dietary recall in Intake24-NZ. The results informed a series of improvements to enhance user experience and the quality of dietary data collected with Intake24-NZ, including adding new foods to the food list, optimizing the search function and ordering of search results, creating new portion size images, and providing clearer instructions to the users.
Background: Poor physical fitness, stress, and fatigue are factors impacting military readiness, national security, and economic burden for the United States Department of Defense. Improved accuracy of wearable biosensors and remote field biologic sample collection strategies could make critical contributions to understanding how physical readiness and occupational stressors result in on-the-job and environment-related injury, sleep impairments, diagnosis of mental health disorders, and reductions in performance in war-fighters.
Objective: This study aimed to evaluate the feasibility and acceptability of intensive biomarker and biometric data collection to understand physiological and psychological stress in Army Reserved Officer Training Corps cadets before, during, and after a 96-hour field training exercise (FTX).
Methods: A prospective pilot study evaluated the feasibility and acceptability of multimodal field data collection using passive drool saliva sampling, sweat sensors, accelerometry, actigraphy, and photoplethysmography. In addition, physical fitness (Army Combat Fitness Test), self-reported injury, and psychological resilience (Brief Resilience Scale) were measured.
Results: A total of 22 cadets were included. Two were lost to follow-up due to injury during FTX, for a retention rate of 91%. Assessments of performance and psychological resilience were completed for all remaining participants, resulting in 100% testing adherence. All participants provided saliva samples before the FTX, with 98% adherence at the second time point and 91% at the third. For sweat, data collection was not possible. Average daily wear time for photoplethysmography devices was good to excellent, meeting a 70% threshold with data collected for ≥80% of person-days at all time points. Of the participants who completed the FTX and 12 completed a post-FTX acceptability survey for a response rate of 60%. Overall, participant acceptance was high (≥80%) for all metrics and devices.
Conclusions: This study demonstrates that wearable biosensors and remote field biologic sample collection strategies during a military FTX have the potential to be used in higher stakes tactical environments in the future for some, but not all, of the strategies. Overall, real-time biometric and biomarker sampling is feasible and acceptable during field-based training and provides insights and strategies for future interventions on military cadet and active-duty readiness, environmental stress, and recovery.
Background: Oral diabetes medications are important for glucose management in people with diabetes. Although there are many health-related videos on Douyin (the Chinese version of TikTok), the quality of information and the effects on user comment attitudes are unclear.
Objective: The purpose of this study was to analyze the quality of information and user comment attitudes related to oral diabetes medication videos on Douyin.
Methods: The key phrase "oral diabetes medications" was used to search Douyin on July 24, 2023, and the final samples included 138 videos. The basic information in the videos and the content of user comments were captured using Python. Each video was assigned a sentiment category based on the predominant positive, neutral, or negative attitude, as analyzed using the Weiciyun website. Two independent raters assessed the video content and information quality using the DISCERN (a tool for assessing health information quality) and PEMAT-A/V (Patient Education Materials Assessment Tool for Audiovisual Materials) instruments.
Results: Doctors were the main source of the videos (136/138, 98.6%). The overall information quality of the videos was acceptable (median 3, IQR 1). Videos on Douyin showed relatively high understandability (median 75%, IQR 16.6%) but poor actionability (median 66.7%, IQR 48%). Most content on oral diabetes medications on Douyin related to the mechanism of action (75/138, 54.3%), precautions (70/138, 50.7%), and advantages (68/138, 49.3%), with limited content on indications (19/138, 13.8%) and contraindications (14/138, 10.1%). It was found that 10.1% (14/138) of the videos contained misinformation, of which 50% (7/14) were about the method of administration. Regarding user comment attitudes, the majority of videos garnered positive comments (81/138, 58.7%), followed by neutral comments (46/138, 33.3%) and negative comments (11/138, 8%). Multinomial logistic regression revealed 2 factors influencing a positive attitude: user comment count (adjusted odds ratio [OR] 1.00, 95% CI 1.00-1.00; P=.02) and information quality of treatment choices (adjusted OR 1.49, 95% CI 1.09-2.04; P=.01).
Conclusions: Despite most videos on Douyin being posted by doctors, with generally acceptable information quality and positive user comment attitudes, some content inaccuracies and poor actionability remain. Users show more positive attitudes toward videos with high-quality information about treatment choices. This study suggests that health care providers should ensure the accuracy and actionability of video content, enhance the information quality of treatment choices of oral diabetes medications to foster positive user attitudes, help users access accurate health information, and promote medication adherence.
Background: Older adults with multiple chronic conditions (MCC) and polypharmacy often face challenges with medication adherence. Nonadherence can lead to suboptimal treatment outcomes, adverse drug events, and poor quality of life.
Objective: To facilitate medication adherence among older adults with MCC and polypharmacy in primary care, we are adapting a technology-enabled intervention previously implemented in a specialty clinic. The objective of this study was to obtain multilevel feedback to inform the adaptation of the proposed intervention (Phenotyping Adherence Through Technology-Enabled Reports and Navigation [PATTERN]).
Methods: We conducted a formative qualitative study among patients, clinicians, and clinic administrators affiliated with a large academic health center in Chicago, Illinois. Patient eligibility included being aged 65 years or older, living with MCC, and contending with polypharmacy. Eligibility criteria for clinicians and administrators included being employed by any primary care clinic affiliated with the participating health center. Individual semistructured interviews were conducted remotely by a trained member of the study team using interview guides informed by the Exploration, Preparation, Implementation, and Sustainment Framework. Thematic analysis of interview audio recordings drew from the Rapid Identification of Themes from Audio Recordings procedures.
Results: In total, we conducted 25 interviews, including 12 with clinicians and administrators, and 13 with patients. Thematic analysis revealed participants largely found the idea of technology-based medication adherence monitoring to be acceptable and appropriate for the target population in primary care, although several concerns were raised; we discuss these in detail.
Conclusions: Our medication adherence monitoring intervention, adapted from specialty care, will be implemented in primary care. Formative interviews, informed by the Exploration, Preparation, Implementation, and Sustainment Framework and conducted among patients, clinicians, and administrators, have identified intervention adaptation needs. Results from this study could inform other interventions using the patient portal with older adults.
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