Background: For decades, the measurement of sleep and wake has relied upon watch-based actigraphy as an alternative to expensive, obtrusive clinical monitoring. At the time of this publication, we have relied upon a handful of algorithms to score actigraphy data as sleep or wake. However, these algorithms have largely been tested and validated with only small samples of young, healthy individuals.
Objective: This study aimed to establish the accuracy and agreement of conventional and traditional actigraphy algorithms against polysomnography, the clinical standard, using the diverse Multi-Ethnic Study of Atherosclerosis (MESA) sleep dataset. As a secondary objective, we examined algorithm and polysomnography agreement for key sleep metrics including total sleep time (TST), sleep efficiency (SE), and wake after sleep onset (WASO).
Methods: We assessed 5 well-established algorithms, including Cole-Kripke, University of California San Diego (UCSD) scoring, Kripke 2010, Philips-Respironics, and Sadeh, with and without rescoring across 1440 individuals (Mage=mean 69.36, SD 8.97) from the MESA sleep dataset. We conducted epoch-by-epoch comparisons assessing accuracy, confusion matrix analyses, receiver operator characteristic curves (ROC), area under the curve (AUC), and Bland-Altman analyses for agreement.
Results: Primary results indicated all algorithms demonstrated accuracy between 78%-80% with the highest accuracy by the Kripke 2010 (80%) algorithm followed closely by the Cole-Kripke (80%) and Philips-Respironics (80%-79%) algorithms. In addition, moderate Cohen κ agreement and moderate positive Matthews correlations were demonstrated by all algorithms. Further, all algorithms demonstrated significant mean difference across sleep metrics.
Conclusions: The findings of this study establish that these traditional actigraphy algorithms can, with high accuracy, detect sleep and wake in large, diverse population samples, including older adults or populations at risk of health conditions. However, these algorithms may carry difficulty for precise assessment of sleep metrics, especially in cases of sleep disorders or irregular sleep.
This pilot study offers preliminary evidence that a virtual meal-preparation task is feasible for older adults and highlights that the community engagement studios are an effective approach to generate community-informed strategies to enhance intervention designs and reach.
Background: While digital health solutions are becoming increasingly sophisticated, simple forms of everyday digital support may offer underexplored opportunities to promote health among older adults. However, evidence remains scarce on whether such teleassistance-based approaches can effectively enhance health literacy and daily self-care, particularly among populations facing socioeconomic and educational disparities.
Objective: This study examined whether a 14-week mobile teleassistance intervention could support daily health promotion and improve health literacy and quality of life among older adults, and whether different levels of user engagement were associated with differences in outcomes.
Methods: This randomized digital pilot study involved 21 older adults (aged ≥60 years) from Ribeirão Preto, Brazil. All participants were assigned to the intervention arm and subsequently categorized into high-engagement (n=11) and low-engagement (n=10) subgroups according to platform-use metrics. The intervention combined weekly teleconsultations, gamified educational quizzes, and guided health-related activities delivered through a mobile app. Outcomes included health literacy (Health Literacy Questionnaire), quality of life (36-Item Short-Form Health Survey), physical activity, and sedentary behavior, assessed at baseline and postintervention. Analyses appropriate for small samples were applied, including frequentist and Bayesian models.
Results: Participants in the high-engagement subgroup showed greater improvements in health literacy compared with those in the low-engagement subgroup (mean change +9.5 vs +9.1 points; time × group: P<.001; Bayes Factors [BF₁₀]=15). Significant interactions also favored higher engagement for selected quality-of-life domains: vitality (P≤.001), functional capacity (P=.02), and general health (P=.02). A group effect was observed for the mental component (P<.001). Physical activity (F2,38=0.95; P=.39; BF_incl=0.68) and sedentary behavior (F1,19=1.12; P=.32; BF_incl=0.53) did not differ significantly between subgroups. Engagement analytics confirmed higher overall platform use in the high-engagement subgroup (mean 6483.8, SD 807.0 vs mean 3345.3, SD 742.7; t19=6.238; P<.001; d=2.73) and more weekly health-activity minutes (mean 5124.3, SD 757.9 vs mean 3120.7, SD 704.3; t19=6.256; P<.001; d=2.73).
Conclusions: This 14-week randomized digital pilot trial suggests that everyday digital teleassistance may enhance health literacy and specific quality-of-life domains among older adults when engagement is high. However, such support alone appears insufficient to modify physical activity or sedentary behavior in the short term. Larger and longer trials are needed to assess sustainability, scalability, and strategies to address structural inequalities in digital health adoption.
Background: Early cancer detection is crucial, but recognizing the significance of associated symptoms such as unintended weight loss in primary care remains challenging. Clinical decision support systems (CDSSs) can aid cancer detection but face implementation barriers and low uptake in real-world settings. To address these issues, simulation environments offer a controlled setting to study CDSS usage and improve their design for better adoption in clinical practice.
Objective: This study aimed to evaluate a CDSS integrated within general practice electronic health records aimed at identifying patients at risk of undiagnosed cancer.
Methods: The evaluation of a CDSS to identify patients with unintended weight loss was conducted in a simulated primary care environment where general practitioners (GPs) interacted with the CDSS in simulated clinical consultations. There were four possible clinical scenarios based on patient gender and risk of cancer. Data collection included interviews with GPs, cancer survivors (lived-experience community advocates), and patient actors, as well as video analysis of GP-CDSS interactions. Two theoretical frameworks were employed for thematic interpretation of the data.
Results: We recruited 10 GPs and 6 community advocates, conducting 20 simulated consultations with 2 patient actors (2 consultations per GP: 1 high-risk consultation and 1 low-risk consultation). All participants found the CDSS acceptable and unobtrusive. GPs utilized CDSS recommendations in three distinct ways: as a communication aid when discussing follow-up with the patient, as a reminder for differential diagnoses and recommended investigations, and as an aid to diagnostic decision-making without sharing with patients. The CDSS's impact on patient-doctor communication varied, facilitating and hindering interactions depending on the GP's communication style.
Conclusions: We developed and evaluated a CDSS for identifying cancer risk in patients with unintended weight loss in a simulated environment, revealing its potential to aid clinical decision-making and communication while highlighting implementation challenges and the need for context-sensitive application.
Background: The rapid expansion of mobile health (mHealth) apps has transformed health care delivery worldwide. Despite their potential to improve epilepsy care, a substantial treatment gap remains, especially in low- and middle-income countries, due to limited resources, stigma, and low adoption of digital technologies. Although mHealth apps can bridge these disparities, their impact depends on acceptance and use by the target population.
Objective: We aimed to assess the awareness, feasibility, willingness, perception, and factors influencing these behaviors for the usage of mHealth apps among people living with epilepsy in Pakistan.
Methods: We conducted a cross-sectional analytical survey between March and July 2024 among people living with epilepsy attending the Pakistan Institute of Medical Sciences (PIMS). Participants completed a validated, self-administered questionnaire with 33 items across 5 domains. We recruited 406 participants through convenience sampling and analyzed the data using SPSS version 23.0 (IBM Corp). Through multivariable linear regression analysis, we explored factors associated with people living with epilepsy willingness to use mHealth apps. Correlation analysis was used to elucidate the association among awareness, perception, feasibility, and willingness.
Results: Among 406 participants, 53.7% (n=218) were male, 64.5% (n=262) were married, and 89.2% (n=362) were identified as Muslim. Although 86.2% (n=350) of participants have heard about mHealth apps for epilepsy management, 78.1% (n=317) expressed negative perceptions of their use. More than half, 69% (n=280), reported concerns about the privacy of their medical information online, and 78.1% (n=317) were not comfortable using mHealth apps on smartphones or tablets. Multivariable linear regression analysis revealed that rural residents (P=.05), those with a college education (P<.001), and participants with a treatment duration of 2-3 years (P<.001) significantly influenced participants' willingness. Correlation analysis showed a weak negative relationship between awareness and feasibility (ρ=-0.124; P=.01) and a weak positive relationship between awareness and willingness (ρ=0.013; P=.07).
Conclusions: To expand mHealth use for epilepsy care in Pakistan, stakeholders must address concerns about digital literacy, data privacy, and trust. Collaborative efforts involving government, technologists, nongovernmental organizations, academia, and health care providers can improve education, enhance data security, and adapt mHealth tools to local needs, ultimately improving treatment access and outcomes for people living with epilepsy.
Background: Google Street View (GSV) images offer a unique and scalable alternative to in-person audits for examining neighborhood built environment characteristics. Additionally, most prior neighborhood studies have relied on cross-sectional designs.
Objective: This study aimed to use GSV images and computer vision to examine longitudinal changes in the built environment, demographic shifts, and health outcomes in Washington, DC, from 2014 to 2019.
Methods: In total, 434,115 GSV images were systematically sampled at 100 m intervals along primary and secondary road segments. Convolutional neural networks, a type of deep learning algorithm, were used to extract built environment features from images. Census tract summaries of the neighborhood built environment were created. Multilevel mixed-effects linear models with random intercepts for years and census tracts were used to assess associations between built environment changes and health outcomes, adjusting for covariates, including median age, percentage male, percentage Hispanic, percentage African American, percentage college educated, percentage owner-occupied housing, and median household income.
Results: Washington, DC, experienced a shift toward higher-density housing, with non-single-family homes rising from 66% to 72% of the housing stock. Single-lane roads increased from 37% to 42%, suggesting a shift toward more sustainable and compact urban forms. Gentrification trends were reflected in a rise in college-educated residents (16%-41%), a US $17,490 increase in the median household income, and a US $159,600 increase in property values. Longitudinal analyses revealed that increased construction activity was associated with lower rates of obesity, diabetes, high cholesterol, and cancer, while growth in non-single-family housing was correlated with reductions in the prevalence of obesity and diabetes. However, neighborhoods with higher proportions of African American residents experienced reduced construction activity.
Conclusions: Washington, DC, has experienced significant urban transformation, marked by substantial changes in neighborhood built environments and demographic shifts. Urban development is associated with reduced prevalence of chronic conditions. These findings highlight the complex interplay between urban development, demographic changes, and health, underscoring the need for future research to explore the broader impacts of neighborhood built environment changes on community composition and health outcomes. GSV imagery, along with advances in computer vision, can aid in the acceleration of neighborhood studies.
Background: Robotic-assisted surgery (RAS) has grown rapidly in recent decades, and several RAS procedures have become the standard. However, the physical and mental demands of minimally invasive surgery (MIS) techniques can lead to ergonomic shortcomings for surgeons. Advances in wearable technology and artificial intelligence favor the development of innovative solutions to analyze and improve ergonomic conditions during surgical practice.
Objective: The main objective is the development and validation of a predictive model of localized muscle fatigue from electromyography (EMG) data during conventional laparoscopic surgery (LAP) and RAS.
Methods: Four different tasks were performed on LAP and RAS: dissection, labyrinth, peg transfer, and suturing. A wireless EMG sensor system was used to record muscle activity. Joint analysis of the spectrum and analysis graphs was used to evaluate the localized muscle fatigue. A dataset was generated for each task as a function of surgeons' expertise level and surgical type. Each dataset was scaled as preprocessing and divided into 2 datasets: 80% for training and 20% for testing. Multiple linear regression (MLR) and multilayer perceptron (MLP) were applied as predictive techniques and validated on all test datasets. R2 coefficient and root-mean-square error were used to measure the accuracy of the models.
Results: RAS showed less muscle fatigue for novice surgeons compared to LAP practice, although it was higher for expert surgeons. The predictive model achieved satisfactory R2 and root-mean-square error coefficients for all parameters extracted from the EMG signal, predicting with high accuracy localized muscle fatigue values. The MLR predictive model demonstrated superior performance relative to the MLP model.
Conclusions: Wearable technology and artificial intelligence techniques have been successfully applied for the development and validation of a novel predictive model based on MLR and MLP to predict localized muscle fatigue in MIS.
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