JMIR mHealth and uHealth

Abstract Objective Wrist-worn accelerometers are common in large-scale epidemiological studies, but their ability to measure sedentary behaviour in free-living environments is unknown. We therefore aimed to evaluate the accuracy of openly-available methods to infer sedentary time from wrist-worn accelerometers. Methods We analysed data from 662 working-age adults in the SMART Work & Life study (20-70 years; mean age 45 years; 72% female) who concurrently wore wrist- and thigh-worn accelerometers for up to eight free-living days. Reference measurements of sedentary time were derived from the thigh accelerometer data using proprietary algorithms. Wrist accelerometer data were processed using widely used, publicly available activity recognition models. Performance was evaluated at 30-second epochs to generate per-participant metrics, alongside comparisons of mean daily sedentary time, mean daily number of prolonged sedentary bouts ([≥] 30 minutes) and proportion of sedentary time in prolonged bouts. Model performance was examined across subgroups defined by age, sex, body mass index, season, recruitment centre, and in sensitivity analyses restricted to daytime hours (08:00-22:00). Results The best performing machine learning model (Actinet) accurately classified sedentary time from wrist-worn accelerometer data with a mean per-participant accuracy of 0.87 and F1 score of 0.85. Cut point-based approaches demonstrated lower accuracy of 0.80 (F1 score of 0.79). The ActiNet machine learning model showed strong agreement in daily sedentary time, daily number of prolonged sedentary bouts and proportion of sedentary time in prolonged bouts, all within 10% of the free-living thigh reference. Findings were consistent across subgroups and in analyses restricted to daytime hours. Conclusion Wrist-worn accelerometers can provide accurate measurements of sedentary behaviour in free-living settings, when assessed using current machine learning models, particularly ActiNet. This work provides confidence in future epidemiological research to examine sedentary behaviour patterns from wrist-worn accelerometers and their associations with health outcomes.

Vayu is a mobile breathwork system comprising an iOS companion app and Apple Watch application that delivers slow, resonant breathing using screen-free haptic cues, HRV-adaptive pacing, and reflective journaling grounded in Patanjali's five states of mind. The watchOS component provides tactile phase guidance and real-time biometric sensing (heart rate, HRV), while the iOS interface supports analytics and personalized recommendations. In a 4-6-week naturalistic pilot involving 199 adults (ages 22-65) across Canada, the United States, and India, participants engaged in daily 5-10-minute sessions guided by on-wrist haptics. Average adherence was 4.1 +/- 2.3 sessions per week, with 71% of active users maintaining at least 3 sessions per week. By week four, perceived stress (PSS-10) decreased by 2.5 points, resting heart rate declined by 7.4 bpm, and HRV increased by a median of 28.6% relative to baseline, accompanied by mood improvements. No adverse events were reported. HRV metrics are derived from Apple Watch PPG-based proxies and interpreted as relative trends. These findings suggest Vayu is effective and well-tolerated, demonstrating strong engagement and early efficacy signals.

Background: Public services are increasingly delivered through digital platforms. Although digital health may improve access and scalability, they may also widen inequalities for people who lack reliable access, confidence, skills, affordability or trust. Objective: This study examined the prevalence of self-reported digital exclusion among UK veterans and assessed its association with depression, anxiety and loneliness. Methods: A cross-sectional online survey was conducted between July 2025 and March 2026. Participants were UK Armed Forces veterans and resident in the UK. The survey collected sociodemographic, military service, digital access and health data. Self-reported digital exclusion was defined as reporting feeling excluded or disadvantaged due to lack of digital access or skills. Probable depression, anxiety and loneliness were assessed using the PHQ-2, GAD-2 and three-item UCLA Loneliness Scale, respectively. Associations between digital exclusion and each outcome were examined using adjusted multivariable logistic regression. Results: Of 1,911 responses received, 1,607 were included after data quality exclusions. Among participants with valid responses to the primary digital exclusion item, 553 (41.7%) reported digital exclusion. Digital exclusion was more common among females, younger veterans and those with lower household income. Probable depression, anxiety and loneliness were more prevalent among digitally excluded participants than among non-excluded participants. In adjusted models, self-reported digital exclusion was associated with higher odds of probable depression (AOR 1.38; 95% CI 1.04 to 1.83; p=0.028), probable anxiety (AOR 1.63, 95% CI 1.23 to 2.16; p<0.001), and probable loneliness (AOR 1.85; 95% CI 1.43 to 2.40; p<0.001). Conclusion: More than two-fifths of veterans with valid exposure data reported digital exclusion, despite high reported device access and confidence. Self-reported digital exclusion was associated with poorer mental health and loneliness, although causality cannot be inferred from these cross-sectional data. Digital-first services for veterans should include routine digital needs screening, targeted support and clear non-digital routes to care.

Existing interfaces modulate user experience through visual, auditory, and haptic channels, but direct physiological modulation, which programmatically alters a user's internal state, remains largely underexplored. We present a wearable sonophoresis patch that uses low-frequency acoustic stimulation to deliver psychoactive substances transdermally, and evaluate its potential for programmable physiological modulation in HCI. We tested this in a double-blinded study (N=26) delivering 100 mg caffeine versus sham control, recording physiological signals during rest and a sustained attention task (SART). The planned comparison for heart rate standard deviation during rest was significant (HR-SD p=0.025, d=1.48), with the caffeine group showing suppressed HR~SD consistent with sympathetic activation. Mean heart rate at rest was not significant (p=0.365), but exploratory analyses during the cognitive task revealed significant cardiovascular divergence: heart rate (p=0.003) and heart rate standard deviation (p=0.027) both moved in directions consistent with systemic caffeine delivery, with effects emerging within minutes of device activation and a sustained group effect across all task rounds (p<0.001). These results provide indirect evidence that wearable sonophoresis can deliver substances to modulate user physiology, opening the design space for on-skin chemical interfaces that adapt delivery in real time to change the user's physiological state on demand.

Study Objectives: To evaluate a framework using wearable data to personalize the definition of short sleep, comparing its temporal and functional characteristics against a fixed threshold. Methods: 462 healthy adults wore sleep trackers and provided daily ecological momentary assessments for a year. Short sleep was defined using either a fixed threshold of <6 h/night (fSS) or personalized thresholds anchored to individual sleep-duration distributions (pSS). Temporal patterns of consecutive short-sleep nights were characterized. Linear mixed-effects models examined associations between accumulating short-sleep nights and short- and long-term markers. Sleep patterns across six other countries were also evaluated. Results: pSS and fSS produced similar average thresholds and overall prevalence of short-sleep nights. However, pSS showed larger effect estimates for short-term outcomes, including alertness, sleep satisfaction, stress, sleep heart rate, HRV, and sedentariness. Effects increased with successive short-sleep nights. Proportion of pSS showed stronger association with blood pressure and arterial stiffness. Isolated short nights were common, whereas longer runs were uncommon and typically followed by incomplete recovery sleep. Personalized thresholds distinguished stable short sleepers with few pSS nights from individuals experiencing recurrent sleep shortfall and highlighted vulnerability among those achieving recommended sleep duration but with high variability. Despite marked cross-country differences in sleep habits, the distribution of short-sleep runs, and termination patterns were remarkably similar. Conclusion: Anchoring short sleep to individual habitual sleep distribution captures relative sleep shortfall beyond absolute duration, better characterizing the functional impact of short sleep. Preventive strategies may benefit from limiting pSS accumulation together with addressing sporadic inadequate sleep.

Background: Behavioral crises are common and distressing in children with neurodevelopmental or behavioral conditions, and many escalate to emergency service use. Access to behavioral therapy is often constrained. Smartphone applications, in-home systems, and wearable sensors that could support caregivers during crises at home are in active development, but few studies have asked caregivers what they would accept or want from such tools. Methods: We conducted a single-center cross-sectional online survey (REDCap) of caregivers of children aged 5-17 years with neurodevelopmental or behavioral conditions, recruited as a convenience sample through flyers, email invitations, and in-person invitations during clinic visits from the neurobehavioral continuum of care at Cincinnati Children's Hospital Medical Center. The response rate is undetermined due to the open-ended recruitment process. Prior behavioral-crisis experience was not an eligibility requirement. The 24-item instrument covered crisis burden, service utilization, caregiver confidence and training, therapy access and barriers, and technology preferences. Analyses were estimation-first (proportions with Wilson 95% confidence intervals [CIs]; medians with interquartile ranges [IQRs]); three pre-specified bivariate analyses used ordinal methods (Kendall's tau-b and Jonckheere-Terpstra for ordinal pairs; Friedman for repeated ratings of five support functions). Recruitment is ongoing toward a target of 75; this interim analysis includes the first 55 respondents, and all findings are hypothesis-generating. Results: All 55 respondents reported that their child had experienced a behavioral crisis; 44% (95% CI 31-57%) reported crises at least weekly, and 35% (95% CI 23-48%) had ever used 911 or an emergency department for a crisis. Half of caregivers (51%) felt not at all or only a little confident managing crises, and only 46% (95% CI 33-59%) had received informal or formal crisis-management training. The most frequent barrier to behavioral therapy was long waitlists (51%; 95% CI 38-64%). Stated openness to hypothetical technology-based crisis support was high, with 64% (95% CI 50-75%) very interested in a smartphone app or in-home support system, 80% (95% CI 68-88%) willing to have their child use a wearable sensor (1 of 55 declined), and 49% (95% CI 36-62%) willing to share video or audio with a future support tool (a further 42% answered "maybe"; 9% declined). The most-valued features were a personalized crisis plan (58%) and safe de-escalation scripts (49%); the most-cited concern was privacy and data security (36%). Conclusions: In this small, self-selected, single-center sample, caregivers of children with neurodevelopmental or behavioral conditions reported substantial crisis burden, limited training, and constrained access to therapy, alongside high stated openness to technology-based crisis support; personalization and privacy were their leading priorities. These preliminary, hypothesis-generating findings can inform the design of caregiver-facing crisis-support technologies and larger representative studies.

Objective. To evaluate the perceived usability, acceptability, and user experience (rather than the clinical effectiveness) of Boora, an AI-assisted, human-supervised digital platform prototype for longitudinal overweight and obesity care, among users and health professionals in Brazilian primary care. Design. Convergent mixed-methods formative evaluation. Perceived usability was measured with the System Usability Scale (SUS) and summarised descriptively; semi-structured interviews conducted after hands-on use were analysed with codebook thematic analysis (Braun and Clarke); the two strands were integrated through a joint display. Qualitative reporting followed the Consolidated Criteria for Reporting Qualitative Research (COREQ). Setting. Primary health care network of Ananindeua, Para, within the Brazilian Unified Health System (January to February 2026). Participants. Fifteen adults with overweight or obesity (BMI at least 25 kg/m2, confirmed via electronic health records) who used the patient application on their own smartphones for 24 hours, and eight primary care professionals (nurses, physicians, and a dietitian) who used the professional dashboard for approximately 20 minutes on predefined tasks with synthetic data. Main outcome measures. SUS scores and qualitative themes addressing usability, acceptability, perceived usefulness, barriers, and perceived clinical and workflow fit. Results. Boora showed good perceived usability in both cohorts (users mean 76.5, SD 10.3; professionals mean 77.5, SD 4.6; both above the SUS normative average of 68). Four themes emerged per cohort. Users valued an accessible interface and visible progress but described daily logging burden, fragile anticipated engagement, and digital-literacy and accessibility barriers. Professionals valued a clear interface and the prospect of panel-managed, proactive follow-up, while requiring training, AI governance, protected time, and interoperability with the national record. Integration indicated that the disengagement users anticipated was the risk professionals perceived the dashboard could help identify, whereas the educational AI assistant was the weakest and most ambiguous component for both groups. Conclusions. Boora was perceived as usable and acceptable, with perceived value concentrated in human-supervised, longitudinal follow-up rather than autonomous self-tracking or AI advice. These findings concern perceived usability and acceptability, not clinical effectiveness or sustained engagement. Real-world adoption would depend on accessibility refinements, electronic-record integration, and clear AI governance aligned with the principles of Brazil's proposed risk-based AI framework and the LGPD.

Objectives To identify stakeholder-perceived design tensions in a two-sided marketplace for reusable digital therapeutics (DTx) software components and to use these tensions to propose alternative marketplace concepts. Methods We conducted 24 semi-structured interviews with digital health researchers and professionals. Data were analysed using hybrid deductive-inductive codebook thematic analysis. The Magic Triangle provided the initial deductive structure. One researcher coded all transcripts; a second independently applied the developing codebook to five transcripts to refine definitions and consistency. Seventeen parent themes were synthesized into 12 design tensions, which informed three author-generated marketplace concepts. Results Participants described trade-offs concerning target users and host, component scope and customization, quality labels, verification, geographic scope, pricing, interoperability, platform launch, risks and market niche. The resulting concepts emphasized a regional startup ecosystem, a research-oriented hybrid marketplace or a global marketplace with stricter entry requirements. Discussion The concepts combine the tensions in different ways and highlight competing priorities in governance, openness, assurance, scalability and early platform growth. Conclusion Stakeholders identified recurring design choices for a DTx software-component marketplace. The concepts provide hypotheses for prototyping and evaluation; the study did not test technical feasibility, market demand, regulatory acceptability or effects on development cost or time.

Digital technologies can support meaningful social interaction by providing personally relevant prompts for memory, communication, and shared reflection. In later life, mobile phone photography may offer an accessible medium through which older adults and caregivers construct stories, express meaning, and participate in relational engagement. However, limited psychophysiological evidence is available on how digital photo supported storytelling engages cognitive and social processes in older adult caregiver dyads. This study examined alpha band EEG dynamics during digital photo elicited storytelling in two museum settings. Thirty two older adult caregiver dyads completed cognitive and psychological screening and participated in a museum-based storytelling protocol. During the museum visit, participants used mobile-phone photography to capture personally meaningful objects, scenes, or exhibition spaces. Each participant then selected one photograph as a digital prompt for a structured but naturalistic storytelling interaction. EEG was recorded during eyes closed resting, eyes open resting, storytelling, and listening conditions. Relative alpha power was analyzed using a predefined 10 electrode sensor level set. Task related alpha modulation was examined relative to eyes open resting. Associations between Cz alpha power and MoCA scores were tested, and dyad level alpha band inter brain similarity was explored using spatial alpha power patterns with within site shuffled dyad surrogate comparisons. Alpha power was higher during eyes closed resting and lower during storytelling and listening relative to eyes-open resting, indicating task-related alpha modulation during digital photo supported narrative interaction. Associations between MoCA scores and Cz alpha power were weak, condition-specific, and did not survive false discovery rate correction. During storytelling, dyad level alpha-band inter brain similarity was modestly higher than within site shuffled dyad estimates, but this effect did not remain significant after correction across conditions. These findings suggest that digital photo elicited storytelling can provide a meaningful medium for studying cognitive and social engagement in older adult caregiver dyads. Alpha band EEG activity was sensitive to storytelling and listening, although cognition related and dyadic similarity effects were modest. The study contributes to research on technology supported human behavior by showing how digital image prompts can structure naturalistic social interaction while enabling psychophysiological measurement in real-world contexts.

Whether diverse populations outside Western settings are behaviourally ready to integrate wearable-derived data into clinical care remains poorly understood. This study examines sociotechnical determinants of wearable adoption and digital health data-sharing readiness in a large, highly diverse multinational population in Qatar, a rapidly digitising health ecosystem with advanced eHealth infrastructure. We conducted a cross-sectional community-based survey of 3,004 adults across Qatar, assessing wearable device use, behavioural engagement, and willingness to integrate wearable-generated data into healthcare workflows. Multivariable logistic regression identified independent predictors of wearable adoption. Wearable device use prevalence was 34.1%. Behavioural factors were the strongest independent predictors of adoption: daily exercisers had more than four times the odds of wearable use compared with rarely active participants, and willingness to share data with healthcare providers was independently associated with adoption after full adjustment. Notably, education level was not independently associated with wearable use, suggesting that behavioural readiness outweighs traditional socioeconomic indicators as a determinant of digital health engagement. Older age ([&ge;]56 years) and African ethnicity were associated with lower adoption odds, highlighting persistent digital inequities. These findings challenge the assumption that digital health equity is primarily an education or access problem, repositioning it as a behavioural engagement challenge. Health systems scaling remote monitoring programmes should prioritise identifying behaviourally engaged subpopulations rather than relying solely on demographic targeting. Targeted digital engagement strategies addressing older adults and underrepresented ethnic groups are essential for equitable implementation of digital medicine.

In Mongolia, an average of 65,000 women become pregnant each year, and about 59,500 babies are born. Although the number of pregnancies is decreasing by 8-12 percent each year, the level of fetal monitor usage remains high. The capital's maternity hospital currently has 27 fetal monitors in use, and an average of 30-35 calls are recorded per month. However, there is a lack of research on the use of fetal monitors, the causes and influencing factors of damage, and the organization of technical services. Therefore, this topic was chosen to determine the usage status of fetal monitors, the causes of malfunctions, and ways to improve them. Purpose To study the causes and factors affecting possible damage and injury during the use of fetal monitors, and to identify ways to reduce them. Materials and methods A one-time study was conducted on 10 MT-610 fetal monitors that were put into operation in 2019 at the Urgo Maternity Hospital in the capital. Data were collected and processed using document analysis methods from the technical passports and call logs of these devices. The factors contributing to common failures were identified using focus group interviews with the engineers and technicians responsible for the equipment. Results This study found that fetal monitor failures are caused by improper use, lack of regular calibration, electrical fluctuations, ambient temperature and humidity, and insufficient medical staff skills, training, and knowledge of how to use the device, all of which contribute to failures and measurement errors. It is also observed that when a replacement part is needed for a monitor that frequently breaks, the monitor is more likely to break again if it is used as a replacement from a previously broken monitor. Therefore, training doctors and nurses who replace spare parts on their use has been observed to significantly reduce future breakdowns. Conclusion According to the study results, the breakdowns and failures of fetal monitoring devices are mainly related to internal system failures, unstable power supply, and wear and tear of accessories and mechanical parts. The highest percentage of device failures indicates the need for special attention to the reliability of the device's basic functions. Additionally, the high percentage of accessory and printer failures indicates the need for proper use and monitoring of the entire device. In addition to technical factors, human misuse, lack of maintenance, and environmental influences also play a significant role in damage. Therefore, it is concluded that to ensure the reliable operation of fetal monitors, it is necessary to perform regular maintenance, stabilize the power supply, improve the quality of accessories, and increase the knowledge and skills of medical staff. Keywords: Fetal Monitoring, Equipment Failure, Risk Factors

Introduction: The Diabetes Prevention Program Outcomes Study (DPPOS) is an established cohort of aging persons with pre-diabetes and type 2 diabetes with 25 years of median follow-up. In 2022 DPPOS added Alzheimer's disease (AD), and AD related dementias (ADRD) phenotyping using the National Alzheimer's Coordinating Center (NACC) Uniform Data Set (UDSv3), which included a standardized neurological examination across 25 clinical sites, administered by clinical staff and interpreted centrally by clinicians. Methods: A DPPOS video-based asynchronous neurological examination (DPPOS-VANE) was developed iteratively through consensus from research clinicians and staff feedback to harmonize with UDSv3 to identify common neurological diagnoses aside from dementia including diabetic cranial neuropathies, stroke and parkinsonism. DPPOS-VANE was designed to be conducted without direct participant contact by the examiner, reproducible, and independent of clinical skills of PCs. An iPad camera recorded the video exam, comprised of assessments of extraocular and facial movements, visual fields, speech, gross motor strength, pronator drift, praxis and parkinsonism. A 10-minute training video demonstrated the examination step-by-step with scripts and instructions in English and Spanish. Site-specific performance review, feedback, and staff certification preceded central reading of video recordings by physicians. After two years of implementation, 1286 DPPOS-VANEs led to 1284 examination reviews. Of these, 1204 (93%) were completed by having the examiner follow the standard script. Overall, 1237 examinations (96%) were delivered as planned, 41 (3%) had minor errors but were still usable, and 6 (0.4%) had major deviations in exam technique; two additional recorded evaluations were not usable as recorded videos were inaccessible due to technical errors. Each examination was completed within 10-15 minutes. Each site on average completed 51.4 examinations (range 14-92). Discussion: Engaging 55 research staff across 25 sites and 3 physician-reviewers, this study is the first to demonstrate feasibility of a VANE as an efficient neurological examination model enabled by commonly used devices. Such a multisite standardized VANE represents a novel paradigm for large epidemiological studies.

Background. Maximal oxygen uptake (VO2max) is a leading marker of cardiorespiratory fitness and a strong predictor of all-cause mortality. Cardiopulmonary exercise testing (CPET) is the reference method but is resource-intensive, so consumer wearables estimate VO2max from passively collected signals; these estimates compress the fitness range, returning near-correct group averages while ranking individuals poorly. No peer-reviewed validation of a smart-ring VO2max estimate against CPET has been reported, and none in a South Asian cohort. Objective. To validate the Ultrahuman Ring AIR VO2max estimate against laboratory CPET, benchmark it against published prediction equations, and assess its generalization and construct validity. Methods. In a single-site paired ring-CPET cohort (N = 101; mean CPET peak VO2 43.3 mL{middle dot}kg-{superscript 1}{middle dot}min-{superscript 1}, SD 9.9), peak oxygen uptake was measured by treadmill or cycle-ergometer CPET, and the Ultrahuman Ring AIR estimate was computed from passively collected signals using a transparent ensemble based on published equations. Ensemble weights and calibration were selected on an 85-subject development set by an automated search minimizing a composite 5-fold cross-validated error criterion; the locked estimate was evaluated on a 16-subject held-out test set. The calibrated coefficients are proprietary. Agreement was quantified with mean absolute error (MAE), bias, Pearson r, regression slope and Lin's concordance correlation coefficient (CCC; bootstrap 95% CIs), and Bland-Altman limits of agreement. Separately, in 181,133 de-identified Ring users (no CPET reference), construct validity was assessed against ring-measured sleep, continuous glucose monitoring (n = 2,597), and a venous blood panel (n up to 15,203), adjusted for age, sex, and BMI, with lipoprotein(a) as a pre-specified negative control. Reporting followed TRIPOD and STARD. Results. With a self-reported fitness level provided, the estimate agreed with CPET peak VO2 at MAE 4.68 mL{middle dot}kg-{superscript 1}{middle dot}min-{superscript 1} (95% CI 3.93 to 5.49), Pearson r 0.79, CCC 0.79, and slope 0.71. The five published equations were worse on every metric (MAE 6.2 to 10.6, CCC 0.28 to 0.56, slope 0.32 to 0.42), each compressing the fitness range. On the held-out test set (n = 16), agreement held (r 0.84, slope 0.81, MAE essentially unchanged). Without the fitness input, full-cohort MAE was 5.16, still ahead of every published equation. At population scale, higher estimated fitness tracked a healthier profile on measurements the estimate does not use: better ring-measured sleep; higher continuous-glucose time in target range (79.6% versus 61.5%, top versus bottom decile; n = 222 and 399 of 2,597 users); and lower triglycerides, fasting glucose, and HOMA-IR (n up to 15,203 assayed per marker). These associations held after adjustment for age, sex, and BMI, whereas the pre-specified negative control lipoprotein(a) did not separate the deciles. Conclusions. The Ultrahuman Ring AIR VO2max estimate agreed with laboratory CPET substantially better than published prediction equations, held its agreement on held-out subjects, and ordered a large population along independent cardiometabolic gradients consistent with true fitness.

Background: Globally, at least 2.6 billion people need rehabilitation services and more than 2.5 billion people need assistive technology (AT). However, reliable data are lacking on population level need for rehabilitation services and assistive products (AP) in different settings for evidence-based policy and programme planning. This first study paper describes the development of the Functional Needs Assessment Tool (FNAT), a new survey tool developed to fill this data gap between 2018 and 2023. Objective: To develop a new multidomain tool to assess population-level functional difficulties and need for service and AP utilising both self-report and clinical assessment methodologies. Development stages: FNAT was developed based upon primary and secondary data analysis, existing survey tools and expert consultation through a series of four steps: Step 1 Inform, Step 2 Build, Step 3 Draft and Step 4 Develop. FNAT uses both self-reported and clinical assessment tools to estimate the prevalence of functional difficulties/impairment and the need for services and AP in the following seven domains: vision, hearing, mobility, communication, cognition, self-care and mental health. It uses a two-stage population-based assessment with data collection through a bespoke tablet-based mobile application and web-based platform. Discussion: FNAT is a new multi-domain modular tool developed to address data gaps by estimating prevalence of functional difficulties and service/AP needs in a population. Potential advantages and disadvantages were highlighted during the development stages, and the tool needs to be pilot tested to assess the feasibility of the methodology and the functionality of the tablet-based mobile data collection application.

Background: Polypharmacy is common in people living with dementia (PLwD) and associated with adverse outcomes. Although Structured Medication Reviews (SMRs) are recommended to optimise medication regimens, their delivery is often constrained by limited healthcare resources. Artificial intelligence (AI) may support SMRs, yet little is known about how it is perceived by PLwD and their carers. This study explored their experiences of polypharmacy, views on SMRs, and attitudes towards use of AI tools in SMRs. Methods: Semi-structured interviews with 12 PLwD experiencing polypharmacy and two focus groups with 14 carers were conducted via Microsoft Teams or telephone and analysed using Reflexive Thematic Analysis. Results: Two themes were constructed: experiences of SMRs, and attitudes towards AI in SMRs. Participants described challenges in managing polypharmacy, with carers often playing a central role in supporting adherence and monitoring side effects. Experiences of SMRs varied widely. SMRs were most valued when clinicians were empathetic and able to offer personalised guidance. Participants viewed AI use in SMRs positively, provided that such tools were well validated and used to assist rather than replace healthcare professionals. AI was viewed as having the potential to reduce administrative burden and support more person-centred care. However, some had concerns regarding patient safety and data security, highlighting the need for appropriate regulation and human oversight. Conclusion: Participants were supportive of AI use in SMRs, despite concerns about safety, data security and disclosure of AI use, and emphasised the importance of patient-clinician interactions and lived experience involvement in AI tool development.

Background: Cognitive rehabilitation (CR) is an established behavioral intervention that improves daily functioning for individuals with mild cognitive impairment (MCI) and early-stage dementia. Traditional models of in-person delivery limit access, particularly for individuals living in rural areas. This study evaluated the efficacy of a novel telephone-based virtual CR model combining speech-language pathologist (SLP)-led sessions with cognitive exercises delivered by an automated voice agent between visits. Methods: We conducted a retrospective observational analysis of 141 older adults who completed treatment to discharge (58% female; mean age 71.2, standard deviation 10.8 years; MCI diagnosis rate 61.7%, dementia diagnosis rate 29.1%; Montreal Cognitive Assessment mean score 20.8, standard deviation 4.3). Changes in four outcome measures from initiation of treatment to discharge were evaluated for statistical significance. The four outcomes studied were patient-reported quality of life and three therapist-rated Functional Communication Measures (FCMs): overall cognition, spoken language, and language comprehension. Changes were compared to FCM averages from the American Speech-Language-Hearing Association National Outcomes Measurement System (ASHA NOMS). Models were developed to predict changes in outcome measures based on patient demographics, clinical status, program engagement and treating therapist. Results: All four outcomes improved significantly over the course of treatment (p<0.05), with medium to very large effect sizes. Mean changes in the three FCM outcomes exceeded ASHA NOMS benchmarks for in-person outpatient care. A majority of patients saw an improvement in each clinical outcome measure. Models with meaningful predictive power were identified for changes in all outcome measures except the FCM for language comprehension. Baseline cognitive function was the most influential and negatively correlated predictor of an improvement in overall cognitive abilities and language expression. Baseline quality of life was the dominant and negatively correlated predictor of improvement in quality of life. Conclusions: Telephone-based virtual CR led by SLPs with automated exercises delivered by a voice agent produced clinically meaningful functional and quality of life gains relative to external benchmarks for in-person clinical practice. These results support the use of virtual CR within post-diagnostic care for older adults experiencing cognitive impairment, particularly for rural and underserved communities.

Objective: To evaluate the efficacy, engagement, and usability of Tune Out, an unguided, self-paced online tinnitus management program, for reducing tinnitus severity in adults with tinnitus. Design: A two-arm, parallel-group randomised controlled trial was conducted with Australian adults reporting diagnosed or self-reported tinnitus. Participants were randomised to immediate access to Tune Out or a waitlist control group. Outcomes were assessed at baseline, 6 weeks, and 12 weeks. The primary outcome was tinnitus severity measured using the Tinnitus Functional Index (TFI). Secondary outcomes included tinnitus handicap, psychological symptoms, program engagement, self-efficacy, and usability. Results: Eighty-eight participants were randomised: 43 to the intervention group and 45 to the waitlist control group. The primary outcome analysis included 63 participants at 12 weeks. A significant Group x Time interaction was observed for TFI total score, indicating greater reductions in tinnitus severity over time in the intervention group compared with waitlist control, F(2, 102.57) = 5.95, p = .004, partial 2= .104. Significant effects were also observed for tinnitus handicap, F(2, 106.76) = 4.12, p = .019, partial 2 = .072. Effects on psychological symptoms were less consistent, although anxiety showed a significant Group x Time interaction, F(2, 116.85) = 3.63, p = .030, partial 2 = .059. At 12 weeks, 23.1% of intervention participants achieved a clinically meaningful reduction in tinnitus severity compared with 5.4% of controls. Program use was highly variable, with a median use of 1.10 hours, and 25.6% of intervention participants recording no use. Usability ratings were favourable among respondents, with a mean System Usability Scale score of 73.13. Conclusions: Tune Out demonstrated preliminary efficacy for reducing tinnitus severity and tinnitus handicap compared with waitlist control. Effects on broader psychological symptoms were less consistent. Although usability was rated positively, low and variable engagement highlights the need for strategies to support uptake and sustained use in unguided digital tinnitus interventions.

Background: Motor disturbances are common in neurologic and neurodegenerative syndromes. A standard motor speed and dexterity measure is the finger tapping test (FTT). The FTT has traditionally been administered in clinic using a mechanical FTT, limiting accessibility and early motor change quantification. This study assessed the validity of a smartphone app-based FTT, which may expand access and enable more frequent testing. Methods: The cohort was diagnostically diverse, including participants with frontotemporal dementia (FTD), progressive supranuclear palsy (PSP), corticobasal syndrome, primary progressive aphasia, multiple sclerosis, and clinically unimpaired controls. Participants completed a 20-second ALLFTD Mobile App (mApp)-FTT with each hand. Tapping speed metrics were extracted. Participants completed the gold-standard mechanical FTT, a neurologist-administered finger tapping exam, the PSP Rating Scale (PSPRS) and the Unified Parkinson`s Disease Rating Scale (UPDRS). Correlations assessed mApp-FTT and mechanical FTT relationships; regressions evaluated associations with neurologist-rated finger tapping impairment, PSPRS and UPDRS, adjusting for age and sex. Results: The mApp-FTT showed moderate-to-strong correlations with the mechanical FTT (dominant: r=0.63, p<0.001; non-dominant: r=0.55, p<0.001). Taps per second were associated with PSPRS motor severity (dominant hand: std. {beta}=-0.59, 95% CI [-0.91, -0.27], p<0.001) and the UPDRS (dominant hand: std. {beta}=-0.41, 95% CI [-0.82, 0.00], p=0.049). Flight time was modestly associated with neurologist-rated finger tapping impairment (dominant hand: std. {beta}=0.15, 95% CI [0.00, 0.29], p=0.044). Conclusion: These findings support mApp-FTT validity as a measure of motor function across neurodegenerative conditions. Validation in longitudinal and unsupervised remote settings is warranted to understand scalability and evaluate change over time.

Cognitive impairment is a prevalent symptom extending from physiological ageing to disease. It commonly manifests itself in initial memory problems, progressing and co-occurring in more severe conditions such as Mild Cognitive Impairment, Alzheimer's Disease and Major Depressive Disorder. However, current non-invasive screening assessments either lack biological information or are invasive and restricted to specialized centers with complex and cost-intensive set-ups. Here, we conducted an initial validation of mobile pupillometry with Virtual Reality (VR) under experimental conditions as a digital biomarker for cognitive impairment by testing required biomarker-specific properties. For this purpose, we first assessed its construct validity by testing healthy participants (n=43) on an n-back task in VR while pupil size was measured. Mixed effects models revealed that similar to lab-based eye-tracking systems, pupil size increased in a sensible and distinguishable fashion as a function of working memory load. Second, to test the signal's reliability, the same participants were tested on the identical set-up two to three months after their first visit. We observed that the pupil response profile was highly stable over this period. Third, for its clinical validity, we examined patients (n=89) from three different cohorts with varying degrees of cognitive impairment and compared them to healthy control participants (n=81). Mixed-effects models indicated that pupil size was reduced as a function of cognitive impairment levels at higher cognitive load and that this effect was stronger pronounced with increasing age. In conclusion, we provide initial evidence for mobile pupillometry being a sensitive, reliable and clinically valid digital biomarker for cognitive functioning and impairment, which offers desirable properties due to its quick, automatized and location-independent set-up. Keywords: digital biomarker, mobile pupillometry, Virtual Reality, cognition, , Major Depressive Disorder, Mild Cognitive Impairment, Alzheimer's Disease

Background: Automatic sleep staging algorithms are increasingly applied in clinical and home-based recordings. However, their performance may degrade when transferred to new montages and clinical populations. This is particularly relevant in reduced-channel portable PSG and in disorders such as REM sleep behaviour disorder (RBD), where altered sleep architecture may challenge pretrained models. Objective: To evaluate and compare multiple open-source sleep staging algorithms on a minimal portable PSG setup in controls and patients with and without RBD, and to assess the impact of fine-tuning on clinic-ascertained data. Methods: Six open-source models were applied to 76 subjects recruited from three clinical sleep medicine sites. Performance was assessed using accuracy, F1 scores, and Cohen's kappa, both overall and per sleep stage. Each model was evaluated out-of-the-box and after fine-tuning on clinical data. Results: Out-of-the-box performance varied substantially across models (Cohen's kappa 0.21-0.54). Fine-tuning consistently improved agreement, with the best-performing model (GSSC) reaching Cohen's kappa = 0.58 indicating moderate to good agreement. Performance was highest in controls and lower in patient groups. N3 was the most reliably classified stage across models, whereas N1 remained consistently challenging. REM classification improved after fine-tuning in several architectures but remained model, and subgroup-dependent, particularly in RBD subjects. Conclusion: Fine-tuning substantially mitigates domain shift, updating model parameters to align with new data distributions, when applying automatic sleep staging algorithms to portable clinical recordings. Model architecture influences robustness, with feature-learning approaches demonstrating greater adaptability than fixed-feature models. Despite moderate agreement after adaptation, performance, especially for REM and N1 remains insufficient for fully automated diagnostic use in clinical populations.