Background: Connected sensor technologies can capture raw data and analyze them using advanced statistical methods such as machine learning or artificial intelligence to generate interpretable behavioral or physiological outcomes. Previous research conducted on connected sensor technologies has focused on design, development, and validation. Published review studies have either summarized general technological solutions to address specific behaviors such as physical activity or focused on remote monitoring solutions in specific patient populations. Objective: This study aimed to map research that focused on using connected sensor technologies to augment rehabilitation services by informing care decisions. Methods: The Population, Concept, and Context framework will be used to define inclusion criteria. Relevant articles published between 2008 to the present will be included if (1) the study enrolled adults (population), (2) the intervention used at least one connected sensor technology and involved data transfer to a clinician so that the data could be used to inform the intervention (concept), and (3) the intervention was within the scope of rehabilitation (context). An initial search strategy will be built in Embase; peer reviewed; and then translated to Ovid MEDLINE ALL, Web of Science Core Collection, and CINAHL. Duplicates will be removed prior to screening articles for inclusion. Two independent reviewers will screen articles in 2 stages: title/abstract and full text. Discrepancies will be resolved through group discussion. Data from eligible articles relevant to population, concept, and context will be extracted. Descriptive statistics will be used to report findings, and relevant outcomes will include the type and frequency of connected sensor used and method of data sharing. Additional details will be narratively summarized and displayed in tables and figures. Key partners will review results to enhance interpretation and trustworthiness. Results: We conducted initial searches to refine the search strategy in February 2024. The results of this scoping review are expected in October 2024. Conclusions: Results from the scoping review will identify critical areas of inquiry to advance the field of technology-augmented rehabilitation. Results will also support the development of a longitudinal model to support long-term health outcomes. Trial Registration: Open Science Framework jys53; https://osf.io/jys53 International Registered Report Identifier (IRRID): DERR1-10.2196/60496

Objectives: Strong cervical musculature is recognized as a protective factor against sports-related concussions. Evidence suggests that jaw clenching may activate cervical muscles, potentially reducing head acceleration during impact. Methods: This observational cohort study examined the immediate effects of a customized interocclusal orthotic (CIO) on cervical muscle performance. Forty-two healthy adults (≥18 years) underwent strength and endurance testing with and without a CIO using a digital pressure gauge and six directional isometric contractions. Descriptive statistics and two-way repeated-measures MANOVA models were applied to evaluate condition effects. Results: CIO use produced significant improvements in cervical muscle strength and endurance across all directions compared to non-use. Forward flexion strength increased by 12.96% (p < 0.001, ηp² = 0.185), backward extension by 10.34% (p = 0.017, ηp² = 0.091), right rotation by 19.03% (p < 0.001, ηp² = 0.333) and left rotation by 19.86% (p < 0.001, ηp² = 0.353). Endurance gains demonstrated large effect sizes, with flexor endurance improving by 44.57% (p < 0.001, ηp² = 0.447). Conclusions: Optimized jaw alignment using a customized orthotic can elicit immediate, clinically meaningful enhancements in cervical strength and endurance, suggesting a promising adjunct for concussion risk mitigation in contact sports.

Objectives: Urinary tract infections (UTIs) are the most common healthcare-associated infections in Skilled Nursing Facilities (SNFs); they are associated with longer lengths of stay, higher levels of care, increased treatment costs, and higher mortality rates. This study aimed to develop a machine learning classification model to predict the risk of high catheter-associated urinary tract infection rates based on SNF characteristics. Methods: We analyzed 94,877 total SNF-year observations from 2019 to 2024, not unique facilities; thus, individual SNFs may appear in multiple years. The factor variables were average length of stay in days, number of staffed beds, total nurse and total physical therapy staffing hours per resident per day, facility ownership, geographic classification, facility accreditation, Accountable Care Organization affiliations, Centers for Medicare and Medicaid Services SNF Overall Star Rating, and the SNF-year of the observations. We utilized three machine learning models for this analysis: Random Forest, XGBoost, and LightGBM. We used Shapley Additive exPlanations to interpret the best-performing machine learning model by visualizing feature importance and examining the relationship between key predictors and the outcome. Results: We found that machine learning models outperformed traditional logistic regression in predicting UTIs in skilled nursing facilities. Using the best-performing model, Random Forest, we identified rural SNFs, and the number of staffed beds as the most influential predictors of high UTI rates, followed by average length of stay, and geographic location. Conclusions: This study demonstrates the value of using facility-level characteristics to predict the risk of UTIs in SNFs with machine learning models. Results from this study can inform infection prevention efforts in post-acute care settings.

BACKGROUND Connected sensor technologies can capture raw data and analyze them using advanced statistical methods such as machine learning or artificial intelligence to generate interpretable behavioral or physiological outcomes. Previous research conducted on connected sensor technologies has focused on design, development, and validation. Published review studies have either summarized general technological solutions to address specific behaviors such as physical activity or focused on remote monitoring solutions in specific patient populations. OBJECTIVE This study aimed to map research that focused on using connected sensor technologies to augment rehabilitation services by informing care decisions. METHODS The Population, Concept, and Context framework will be used to define inclusion criteria. Relevant articles published between 2008 to the present will be included if (1) the study enrolled adults (population), (2) the intervention used at least one connected sensor technology and involved data transfer to a clinician so that the data could be used to inform the intervention (concept), and (3) the intervention was within the scope of rehabilitation (context). An initial search strategy will be built in Embase; peer reviewed; and then translated to Ovid MEDLINE ALL, Web of Science Core Collection, and CINAHL. Duplicates will be removed prior to screening articles for inclusion. Two independent reviewers will screen articles in 2 stages: title/abstract and full text. Discrepancies will be resolved through group discussion. Data from eligible articles relevant to population, concept, and context will be extracted. Descriptive statistics will be used to report findings, and relevant outcomes will include the type and frequency of connected sensor used and method of data sharing. Additional details will be narratively summarized and displayed in tables and figures. Key partners will review results to enhance interpretation and trustworthiness. RESULTS We conducted initial searches to refine the search strategy in February 2024. The results of this scoping review are expected in October 2024. CONCLUSIONS Results from the scoping review will identify critical areas of inquiry to advance the field of technology-augmented rehabilitation. Results will also support the development of a longitudinal model to support long-term health outcomes. CLINICALTRIAL Open Science Framework jys53; https://osf.io/jys53 INTERNATIONAL REGISTERED REPORT DERR1-10.2196/60496