Examining Engagement and Usability in an Online Discussion Platform for Older Adults: Findings From Pilot Studies.

Social media may facilitate older adults' ability to engage socially and explore health information, but it can present difficulties for older adults. Therefore, it is important to explore older adults' experience of usability and user engagement. We conducted two rounds of pilot studies where we used Facebook to engage older adults. We performed a mixed-methods evaluation of user engagement and usability. A directed content analysis of qualitative data from the pilot studies was used to explore engagement and perceived usability, and the Mann-Whitney U test was used to examine differences in feature usage and engagement. We analyzed qualitative data from 13 participants. Qualitative data analysis yielded themes pertaining to three main domains: user engagement , usability , and usability related to aging-related changes . In terms of user engagement and usability, participants in both pilot studies reported positive feedback on felt involvement and endurability, and the second pilot group reported more positive comments regarding perceived usefulness compared with the first pilot group. There was no statistically significant difference in usage over the two studies. The findings of this study suggest opportunities to improve older adults' experience of online discussion platforms. Considering changes that improve perceived aesthetic appeal and focused attention will be helpful.

Potential sources of moral distress during COVID-19: Perspectives of hospice interdisciplinary teams.

OBJECTIVE: This study aimed to examine the impact of COVID-19 on hospice Interdisciplinary team (IDT) members' self-reported stress and identify possible sources of moral distress. METHODS: A cross-sectional survey was conducted using Qualtrics to understand the impact of COVID-19 on quality improvement initiative implementation and hospice IDT members' general and dementia-specific care provision. Directed qualitative content analysis was used to analyze hospice IDT members' responses from five open-ended survey questions that were indicative of stress and possible moral distress. RESULTS: The final sample consisted of 101 unique respondents and 175 comments analyzed. Three categories related to sources of moral distress based on hospice IDT member survey responses were identified: (1) impact of telehealth, personal protective equipment (PPE), and visit restrictions on relationships; (2) lack of COVID-19-specific skills; and (3) organizational climate. Sources of moral distress were categorized in 40% of all responses analyzed. SIGNIFICANCE OF RESULTS: This study is one of the first to document and confirm evidence of potential stress and moral distress amongst hospice IDT members during COVID-19. It is imperative given the possible negative impact on patient care and clinician well-being, that future research and interventions incorporate mechanisms to support clinicians' emotional and ethical attunement and support organizations to actively engage in practices that address clinician moral distress resulting from restrictive environments, such as the one necessitated by COVID-19.

Using an Innovative Discussion Platform to Give Voice to Aging-Related Experiences: A Pilot Study.

Exchanging information with peers may support older adults' management of aging-related health changes, including frailty. The current pilot study used a mixed-methods approach to develop and evaluate an online virtual community for older adults to discuss aging-related health issues and management strategies. Eight older adults (mean age = 84) were enrolled at the start of the study. During a 10-week moderated discussion, participants contributed a total of 133 responses. Common themes included (a) symptoms (e.g., pain, weakness/tiredness, sleep difficulties) and (b) management strategies (e.g., health behavior changes, psychosocial support). A positive trend of change was noted in participants' average self-reported health and chronic disease management self-efficacy scores. This platform could facilitate information exchange among older adults, empowering them to leverage their own knowledge to improve their health management strategies. Future research should expand on this study to include older adults of diverse racial, educational, and cultural backgrounds. [Journal of Gerontological Nursing, 45(12), 33-40.].

Pilot testing a digital pet avatar for older adults.

Social isolation in older adults is a major public health concern. An embodied conversational agent (ECA) has the potential to enhance older adults' social interaction. However, little is known about older adults' experience with an ECA. In this paper, we conducted a pilot study to examine the perceived acceptance and utility of a tablet-based conversational agent in the form of an avatar (termed "digital pet") for older adults. We performed secondary analysis of data collected from a study that employed the use of a digital pet in ten older adults' homes for three months. Most of the participants enjoyed the companionship, entertainment, reminders, and instant assistance from the digital pet. However, participants identified limited conversational ability and technical issues as system challenges. Privacy, dependence, and cost were major concerns. Future applications should maximize the agent's conversational ability and the system's overall usability. Our results can inform future designs of conversational agents for older adults, which need to include older adults as system co-designers to maximize usability and acceptance.

Public perceptions about risk and protective factors for cognitive health and impairment: a review of the literature.

BACKGROUND: Preventing and/or delaying cognitive impairment is a public health priority. To increase awareness of and participation in behaviors that may help maintain cognitive function or reduce risk of impairment, we need to understand public perceptions about risk and protective factors. METHODS: We conducted a scoping review of studies examining the public's perceptions about risk and protective factors related to cognitive health and impairment published since the 2007 National Public Health Road Map to Maintaining Cognitive Health. RESULTS: A search of five databases yielded 1,115 documents published between June 2007 and December 2013. Initial review of abstracts identified 90 potentially eligible studies. After full-article review, 30 met inclusion criteria; four additional articles identified in reference lists also met inclusion criteria. Of the 34, 16 studies addressed Alzheimer's disease (AD) specifically, 15 dementia broadly, 5 mild to moderate cognitive impairment, and 8 normal functioning, with some content overlap. Across studies, respondents reported genetics (n = 14 studies), older age (n = 8), stress (n = 7), brain/head injury (n = 6), and mental illness/brain disease (n = 6) as perceived risk factors for AD and dementia. Protective factors most commonly identified for maintaining cognitive health were intellectual/mental stimulation (n = 13), physical activity (n = 12), healthy diet (n = 10), and social/leisure activities (n = 10). CONCLUSIONS: Studies identified genetics and older age as key perceived risk factors more so than behaviors such as smoking. Individuals perceived that numerous lifestyle factors (e.g. intellectual stimulation, physical activity) could protect against cognitive impairment, AD, and/or dementia. Results can inform national and international education efforts about AD and other dementias.

Optimizing ChatGPT's Interpretation and Reporting of Delirium Assessment Outcomes: Exploratory Study.

BACKGROUND: Generative artificial intelligence (AI) and large language models, such as OpenAI's ChatGPT, have shown promising potential in supporting medical education and clinical decision-making, given their vast knowledge base and natural language processing capabilities. As a general purpose AI system, ChatGPT can complete a wide range of tasks, including differential diagnosis without additional training. However, the specific application of ChatGPT in learning and applying a series of specialized, context-specific tasks mimicking the workflow of a human assessor, such as administering a standardized assessment questionnaire, followed by inputting assessment results in a standardized form, and interpretating assessment results strictly following credible, published scoring criteria, have not been thoroughly studied. OBJECTIVE: This exploratory study aims to evaluate and optimize ChatGPT's capabilities in administering and interpreting the Sour Seven Questionnaire, an informant-based delirium assessment tool. Specifically, the objectives were to train ChatGPT-3.5 and ChatGPT-4 to understand and correctly apply the Sour Seven Questionnaire to clinical vignettes using prompt engineering, assess the performance of these AI models in identifying and scoring delirium symptoms against scores from human experts, and refine and enhance the models' interpretation and reporting accuracy through iterative prompt optimization. METHODS: We used prompt engineering to train ChatGPT-3.5 and ChatGPT-4 models on the Sour Seven Questionnaire, a tool for assessing delirium through caregiver input. Prompt engineering is a methodology used to enhance the AI's processing of inputs by meticulously structuring the prompts to improve accuracy and consistency in outputs. In this study, prompt engineering involved creating specific, structured commands that guided the AI models in understanding and applying the assessment tool's criteria accurately to clinical vignettes. This approach also included designing prompts to explicitly instruct the AI on how to format its responses, ensuring they were consistent with clinical documentation standards. RESULTS: Both ChatGPT models demonstrated promising proficiency in applying the Sour Seven Questionnaire to the vignettes, despite initial inconsistencies and errors. Performance notably improved through iterative prompt engineering, enhancing the models' capacity to detect delirium symptoms and assign scores. Prompt optimizations included adjusting the scoring methodology to accept only definitive "Yes" or "No" responses, revising the evaluation prompt to mandate responses in a tabular format, and guiding the models to adhere to the 2 recommended actions specified in the Sour Seven Questionnaire. CONCLUSIONS: Our findings provide preliminary evidence supporting the potential utility of AI models such as ChatGPT in administering standardized clinical assessment tools. The results highlight the significance of context-specific training and prompt engineering in harnessing the full potential of these AI models for health care applications. Despite the encouraging results, broader generalizability and further validation in real-world settings warrant additional research.

Using artificial intelligence to improve pain assessment and pain management: a scoping review.

CONTEXT: Over 20% of US adults report they experience pain on most days or every day. Uncontrolled pain has led to increased healthcare utilization, hospitalization, emergency visits, and financial burden. Recognizing, assessing, understanding, and treating pain using artificial intelligence (AI) approaches may improve patient outcomes and healthcare resource utilization. A comprehensive synthesis of the current use and outcomes of AI-based interventions focused on pain assessment and management will guide the development of future research. OBJECTIVES: This review aims to investigate the state of the research on AI-based interventions designed to improve pain assessment and management for adult patients. We also ascertain the actual outcomes of Al-based interventions for adult patients. METHODS: The electronic databases searched include Web of Science, CINAHL, PsycINFO, Cochrane CENTRAL, Scopus, IEEE Xplore, and ACM Digital Library. The search initially identified 6946 studies. After screening, 30 studies met the inclusion criteria. The Critical Appraisals Skills Programme was used to assess study quality. RESULTS: This review provides evidence that machine learning, data mining, and natural language processing were used to improve efficient pain recognition and pain assessment, analyze self-reported pain data, predict pain, and help clinicians and patients to manage chronic pain more effectively. CONCLUSIONS: Findings from this review suggest that using AI-based interventions has a positive effect on pain recognition, pain prediction, and pain self-management; however, most reports are only pilot studies. More pilot studies with physiological pain measures are required before these approaches are ready for large clinical trial.

Treatment of class II malocclusion with Invisalign®: A pilot study using digital model-integrated maxillofacial cone beam computed tomography.

Background/purpose: The treatment effects of Invisalign® are still obscure due to methodological limitations of previous studies. We introduced a method to comprehensively evaluate the dental and skeletal changes of Class II malocclusion treated non-extraction with Invisalign® and compare with the virtual simulation of ClinCheck® using digital models integrated into maxillofacial cone-beam computed tomography (CBCT). Materials and methods: The pretreatment (T1) and posttreatment (T2) scanned digital images of actual dentitions were integrated into maxillofacial CBCT images. To evaluate three-dimensional movement of maxillary teeth and change of mandible position, T1 and T2 digital model-integrated maxillofacial CBCT images were superimposed using voxel-based registrations of stable cranial base structures. To evaluate movement of mandibular teeth, model-integrated mandibular CBCT superimposition was registered on mandibular basal bone. To compare achieved and predicted tooth movements, the actual dental images and the virtual digital models created by ClinCheck® were registered on the T1 dentitions. Results: For simulated upper first molar (U6) distalization of more than 1 mm, treatment accuracy ranged from 31.1% to 40.1%, which was significantly less than virtual planning and previous reports. In unilateral Class II subjects, the amount of U6 distalization on the Class II side was not significantly different from contralateral side, indicating efficacy of sequential distalization was questionable. Those with favorable overjet correction showed evidence of condylar distraction. Conclusion: Digital model-integrated CBCT superimpositions reflected the actual treatment changes in comparison with the virtual simulation, and showed that ideal occlusion was not achieved in mild to moderate Class II adult patients treated non-extraction with Invisalign®.