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Abstract With the growth of the older adult population (age ≥ 65 years), age-associated neurocognitive disorders, such as Alzheimer’s disease and related dementias (ADRD), are becoming more common in America and worldwide. The increasing number of patients has surpassed the capacity of healthcare systems. Thus, there is a need for time-efficient, cost-effective solutions for better allocation of limited clinical resources that can benefit clinicians and patients. Here, we conducted a quality improvement study in collaboration with Savannah Neurology Specialists and Dent Neurologic Institute. We evaluated users’ perspectives on the usability and feasibility of implementing BrainCheck, a computerized tool for cognitive assessment and care planning. One neurologist and their team from each institute received 45 minutes of training on BrainCheck and used it on an iPad from mid-November to the end of December 2022. In total, 49 patients have completed BrainCheck Cognitive Assessment, and 7 of them also completed BrainCheck Cognitive Care Planning during the study period. On average, it took 13-18 minutes to administer BrainCheck Cognitive Assessment, interpret the result, and document it in EHR. The majority of patients (96%) reported satisfaction with the testing experience and found the instructions easy to understand. Both providers reported high usability of BrainCheck (6.2/7.0) as measured by the Post-Study System Usability Questionnaire. These results demonstrate the great potential of BrainCheck for clinical use, as it can support providers in assessing the cognitive function of their patients in a timely and efficient manner.

Background: The System Usability Scale (SUS) is a widely used scale that has been used to quantify the usability of many software and hardware products. However, the SUS was not specifically designed to evaluate mobile apps, or in particular digital health apps (DHAs). Objective: The aim of this study was to examine whether the widely used SUS distribution for benchmarking (mean 68, SD 12.5) can be used to reliably assess the usability of DHAs. Methods: A search of the literature was performed using the ACM Digital Library, IEEE Xplore, CORE, PubMed, and Google Scholar databases to identify SUS scores related to the usability of DHAs for meta-analysis. This study included papers that published the SUS scores of the evaluated DHAs from 2011 to 2021 to get a 10-year representation. In total, 117 SUS scores for 114 DHAs were identified. R Studio and the R programming language were used to model the DHA SUS distribution, with a 1-sample, 2-tailed t test used to compare this distribution with the standard SUS distribution. Results: The mean SUS score when all the collected apps were included was 76.64 (SD 15.12); however, this distribution exhibited asymmetrical skewness (–0.52) and was not normally distributed according to Shapiro-Wilk test (P=.002). The mean SUS score for “physical activity” apps was 83.28 (SD 12.39) and drove the skewness. Hence, the mean SUS score for all collected apps excluding “physical activity” apps was 68.05 (SD 14.05). A 1-sample, 2-tailed t test indicated that this health app SUS distribution was not statistically significantly different from the standard SUS distribution (P=.98). Conclusions: This study concludes that the SUS and the widely accepted benchmark of a mean SUS score of 68 (SD 12.5) are suitable for evaluating the usability of DHAs. We speculate as to why physical activity apps received higher SUS scores than expected. A template for reporting mean SUS scores to facilitate meta-analysis is proposed, together with future work that could be done to further examine the SUS benchmark scores for DHAs.

Abstract Introduction We created Sleepfect Tracker, a researchkit-based cross platform app to explore the feasibility and acceptability of a sleep tracking app for sleep self-management. Methods We developed Sleepfect Tracker app on Appbakery, a DIY app making platform using ResearchKit for iOS and ResearchDroid for Android users. Sleepfect allows participants to track their sleep behavior (weekly via sleep diary) and environment (monthly), as well as their total sleep time and step counts data via Apple’s HealthKit, Android step count sensors, or Fitbit (wearable). Three hundred and ninety-five (395) individual from around the globe downloaded the app and 163 unique users answered surveys on their sleep behavior, environment, and architecture. In total we collected 6429 sleep and 2882 step data points and provided insights into user sleep behavior and sleep environment. We also analyzed whether sleep duration was associated with steps. Results Regarding immediate sleep environment and behavior, 11.1% worked or studied in bed, 27.0% reported having pets in bed, 39.7% red in bed prior to sleep, 40.3% watched TV in bed prior to sleep, 11.3% drank alcohol prior to bed, 9.8% smoked prior to bed or wake during night, 8.1% ate snack at bedtime, and 6.5% ate when they awake at night. 74.3% of the participants used electronic devices in their bedroom. Of the participants who used electronic devices in the bedroom, 52.1% had an average sleep duration of 6-8 hours and 29.8% reported sleeping 4-6 hours. Of the participants who did not use electronic devices in bedroom, 30.1% slept 4-6 hours, 31.8% slept 6-8 hours, and 32.45% slept 8-10 hours, on average. The relationship between steps count and sleep hours was trending, r=.16, p=.07. Conclusion Users can evaluate their sleep habits, monitor daily sleep-related behaviors through Sleepfect tracker. The app demonstrated initial usability and feasibility, but long-term usability and effectiveness must be evaluated. Further investigations on which functions will be more useful to help user to improve their sleep and engage users should be considered. Support K01HL135452, R01MD007716, R01HL142066, and K07AG052685

Developing user-centered digital health hardware requires systematic design methods applicable across clinical contexts. As diabetes mellitus continues to rise globally and contributes to morbidity, mortality, and costs, effective nutritional management remains essential-yet adherence is often poor. Digital health interventions grounded in human-centered design may enhance adherence by better aligning solutions with patients' real needs. This tutorial aims to provide replicable guidance on applying the design thinking approach to health care hardware development, illustrated through the design, development, and preliminary usability evaluation of SMARTCLOTH (GA-16: Lifestyles, Innovation, and Health), a smart tablecloth prototype intended to facilitate dietary management and support adherence to nutritional recommendations among individuals with diabetes. We demonstrate a systematic design thinking approach adaptable to other hardware contexts, using the Double Diamond model. In mapping, we performed a structured preassessment to define project scope and feasible functionalities. To characterize end user needs, we conducted 6 in-depth interviews with health care professionals and applied persona, empathy map, and customer journey map tools. In exploring, 5 focus groups (patients and diabetes educators) identified barriers, facilitators, and desired functionalities for dietary self-management. In building, we created low- and high-fidelity wireframes and interactive web prototypes using Phaser 3 (HTML5/JS) to simulate a kitchen workspace for meal assembly. In testing, 7 patients with different diabetes profiles participated in 3 iterative usability sessions. Using think-aloud, video analysis, and structured tasks, we documented completion times, errors, and the level of required assistance, enabling refinements. Development progressed through 15 internal versions and 3 user-tested prototypes with real-time adjustments when feasible. Interviews and focus groups yielded three user profiles guiding design: (1) adolescents with type 1 diabetes navigating social and dietary challenges, (2) working-age adults with type 2 diabetes who were motivated but inconsistent, and (3) older adults with type 2 diabetes showing low adherence due to entrenched habits. Iterative usability testing indicated that the system was intuitive, with improvements in layout, labeling, and navigation. Quantitative metrics showed refinement, with simple tasks being completed in under 1 minute in later iterations, while complex meal simulations took longer. Error rates and required guidance decreased as prototypes evolved. Qualitative feedback highlighted clarity, motivational value, and educational potential, while older participants requested larger text and simplified controls. Despite usability gains, motivational barriers persisted among low-adherence older adults. This tutorial demonstrates that systematic human-centered design can yield feasible and well-accepted digital health hardware. SMARTCLOTH emerged as a promising tool for dietary management in diabetes, though effectiveness and clinical outcomes were not evaluated. The methodology can be adapted by teams developing hardware for chronic disease management.

Introduction The Philips Respironics Actiwatch has become a gold standard for actigraphy data collection. With the announcement of their discontinuation, there has been increased momentum to identify an alternative, particularly with consumer-based devices. One promising solution is the Apple Watch because it allows user access to raw accelerometer data, thus eliminating the long-standing problem of the “black box algorithm” with wearable technology. This study compared the activity counts derived from Apple Watch data with that from the Actiwatch. Methods Adults wore an Actiwatch and Apple Watch on the same non-dominant wrist for 7 to 14 days (mean = 9). Accelerometer data were recorded and activity counts were derived from accelerometer data (algorithm for Apple Watch from Lindert et al, 2013). Daily sleep diaries were also completed. Actigraphy activity counts were binned into 2, 5, 10, 30, and 60 minute bins to examine concordance by bin sizes. Agreement between Apple Watch and Actiwatch were quantified with Lin’s Concordance Correlation Coefficients (CCC) across bins. Usability was assessed utilizing an exit survey. Results In increasing order of bins, the CCCs were: .63 (2 mins.), .70 (5 mins.), .78 (10 mins.), .90 (30 mins.), and .92 (60 mins.). Agreement was substantial for bins of 5 minutes or greater with 30 and 60 minutes showing the strongest agreement. The y-intercepts for all bins were positive indicating that Apple Watches were more likely to detect activity counts compared to Actiwatches. In terms of usability, a large majority (88%-100%) of participants favored the Apple Watch for comfort, convenience, preference to wear in public, and preference to wear again. The only dimension where there was not a clear preference was ease of use, with 56% indicating a preference for the Apple Watch. Conclusion This is the first study supporting Apple Watch as a potentially feasible alternative to an Actiwatch. Given that the Apple Watch’s accelerometer appears to be more sensitive, use of a different algorithm for determining activity counts from raw accelerometer data may improve the concordance at smaller bins. Future research should explore the effectiveness of the Apple Watch for sleep/wake detection in clinical samples. Support (if any)

Background: Technology can benefit older adults in many ways, including by facilitating remote access to services, communication, and socialization for convenience or out of necessity when individuals are homebound. As people, especially older adults, self-quarantined and sheltered in place during the COVID-19 pandemic, the importance of usability-in-place became clear. To understand the remote use of technology in an ecologically valid manner, researchers and others must be able to test usability remotely. Objective: Our objective was to review practical approaches for and findings about remote usability testing, particularly remote usability testing with older adults. Methods: We performed a rapid review of the literature and reported on available methods, their advantages and disadvantages, and practical recommendations. This review also reported recommendations for usability testing with older adults from the literature. Results: Critically, we identified a gap in the literature—a lack of remote usability testing methods, tools, and strategies for older adults, despite this population’s increased remote technology use and needs (eg, due to disability or technology experience). We summarized existing remote usability methods that were found in the literature as well as guidelines that are available for conducting in-person usability testing with older adults. Conclusions: We call on the human factors research and practice community to address this gap to better support older adults and other homebound or mobility-restricted individuals.

This paper presents a systematic literature review characterizing the methodological properties of usability studies conducted on educational and learning technologies in the past 20 years. PRISMA guidelines were followed to identify, select, and review relevant research and report results. Our rigorous review focused on (1) categories of educational and learning technologies that have been the focus of usability evaluation, (2) specific usability evaluation methods used, (3) outcome measures, and (4) research limitations. Findings revealed a diverse range of usability evaluation methods employed for different types of educational/learning technologies and the contexts in which those methods were used, with the majority of usability studies being performed on e-learning technologies within higher education contexts. Specific methods, instrumentation, and types of usability research found to be dominant in reviewed studies were further analyzed and classified, with findings suggesting inquiry methods using questionnaires were most prevalent. Prevalent outcome measures were also synthesized, with findings suggesting that the majority of usability research focuses on issues of technological usability, with very few studies considering pedagogical and socio-cultural aspects of usability. A number of limitations were found, including conceptual and procedural flaws, fundamental misunderstanding of usability evaluation methods, and inappropriate application of usability methods, suggesting potentially problematic and unreliable results. These findings are discussed in-depth, and implications for future research are provided.

Abstract Bio-experiential approaches offer an interactive, multisensory, immersive environmental experience using technology and physical design to promote mind-body wellness. These designs have the potential to provide an in-home solution to help persons living with dementia and their care-partners manage challenges including agitation, conflict, stress, and difficult emotions (e.g., grief). This presentation discusses a partnership between an interdisciplinary academic team and a creative design and technology studio that has pioneered bio-experiential designs. This partnership’s mission is to develop a free interactive, immersive bio-experiential platform that offers a positive and engaging experience for both persons living with dementia and their care-partner(s). Our team iteratively developed an initial prototype using multiple strategies rooted in user-centered design, including a literature review, design thinking workshop with the full team, and two focus groups with clinicians (N=10; neurologists, geriatric psychiatrists, geriatricians, neuropsychologists, social workers, occupational therapists). Early development emphasized safety to minimize risk of increased agitation or other neuropsychiatric symptoms (e.g., hallucinations, sleep impairments). Key recommendations to enhance safety and usability included simple calming visuals and sounds, personalized experiences, familiar stimuli, and multisensory engagement (e.g., music, tactile stimuli). Increasing accessibility was also discussed related to ease of use and availability of platforms (e.g., laptop, television). We are now preparing for “live” iterative usability testing of our prototype (i.e., beta testing) with a diverse group of persons living with dementia and care-partner(s) before efficacy testing. The larger goal is implementation and wide dissemination as well as adaptations for other neurodegenerative conditions. *Dr. Vranceanu and Ritchie are co-senior authors.