Arthritis is a leading cause of joint pain and reduced mobility, particularly among older adults. Current treatments still rely on reactive treatments and often do not offer personalized monitoring or early intervention. This paper addresses emerging wearable technology that could address these shortcomings by enabling more immediate and proactive management of joint pain. The primary objective was to design a low-cost, wearable knee brace system that integrates motion and pressure sensors with machine learning analytics to deliver real-time, personalized monitoring data for gait and joint stress. To achieve this, we conducted a comprehensive review of existing wearable sensor technologies and their applications in joint monitoring. Insights from this review guided the design of a prototype device featuring motion and pressure sensors embedded in an adjustable knee brace. We developed a companion mobile application to collect data, analyze joint health, and provide user-friendly feedback. Results demonstrated that the multi-sensor approach improved accuracy in tracking knee movement and pressure changes compared to single-sensor models. The device successfully streamed real-time data and classified gait patterns with promising accuracy. Our findings support the feasibility of this integrated system to enhance arthritis monitoring beyond current commercial options. This research contributes new knowledge by addressing gaps in existing wearable technologies, particularly for aging populations with arthritis. The proposed system offers a foundation for more personalized, preventative arthritis care that may improve quality of life and alleviate healthcare burdens for those with knee joint arthritis.

Proactive Arthritis Care with a Multi-Sensor Wearable Knee Brace System

People from around the world experience mobility impairments, leading to significant impacts on independence and quality of life. While conventional mobility assistive devices such as wheelchairs, walkers, and prosthetics offer essential support, they often lack adaptability and user responsiveness. Artificial intelligence redefines mobility devices by advancing autonomy, personalization, and user experience. This scoping review maps the current landscape of AI-powered mobility aids for adults with physical impairments. Using the Arksey and O’Malley framework and PRISMA-ScR guidelines, we systematically reviewed 39 peer-reviewed studies, which were identified through specific search strategies developed for each database. Screening and data extraction were conducted in pairs using a predefined criterion. The included studies spanned 17 countries and focused primarily on smart wheelchairs (n=22), followed by walkers (n=8), prosthetics (n=3), rollators (n=2), and individual studies on canes, wheelchairs/scooters, lower-limb braces, and body-weight support systems (n=1 each). AI models were then categorized by learning type. AI-powered mobility devices offer individual benefits like enhanced navigation, motivation, and emotional well-being, but users face challenges such as device complexity, usability issues, and fatigue. At the administrative level, these technologies reduce caregiver workload and improve efficiency, yet are hindered by insufficient training and limited user input, while policy-level barriers include inadequate funding and lack of regulatory support. These findings affirm the potential of AI to enhance mobility and autonomy and promote the urgent need for inclusive, real-world research, standardized evaluation, and equitable access across diverse populations.

AI-powered Mobility Devices for Adults With Impairments: A Scoping Review

Loneliness is increasingly recognized as a non-motor symptom in Parkinson’s disease (PD), impacting mood, cognitive decline, and even mortality. Individuals with PD are vulnerable due to physical limitations, communication challenges, and stigma. This review synthesizes peer-reviewed literature on the intersection of loneliness, social isolation, and PD. Using Covidence, we screened 232 abstracts and subsequently reviewed 102 full-text articles. Twenty contributed contextual or descriptive insights, while only four studies provided structured, analyzable data using validated measurement tools or reporting statistical associations with clinical outcomes. Most studies were observational, published between 2020 and 2024, in English. Where reported, loneliness prevalence in PD populations ranged from 24% to over 30%, and was linked to poor quality of life, depression, and a greater disease burden. One prospective study identified a significant association between loneliness and incident PD (HR=1.25, fully adjusted), while others connected loneliness to reduced autonomy and poorer functional outcomes. However, intervention studies aimed at alleviating loneliness were notably rare and typically descriptive, with no high-quality trials identified. A concern was the limited use of formal risk bias tools, with only 17% of studies employing one. Furthermore, conflict of interest disclosures were present in a mere 13% of the articles included. While 60% of studies did include reviewer's comments on bias, these lacked standardization. This review underscores a growing but methodologically inconsistent body of literature. Improved standardization is crucial to develop evidence-based targeted interventions to address loneliness in individuals with PD.

Bridges in Parkinson’s Disease: Connecting Lives, Ending Loneliness

Osteoarthritis (OA) is a prevalent and disabling condition affecting over one third of Canadians aged 65 and older, with the hand and wrist being among the most impacted joints. This chronic, degenerative joint disease severely limits physical independence by causing synovial inflammation and cartilage degradation, leading to joint stiffness. As a result, seniors diagnosed with OA often experience persistent joint pain, progressive joint immobility, and diminished quality of life. Current treatments such as pharmacotherapy and physiotherapy often provide limited or inconsistent relief due to side effects, accessibility, or patient adherence. In response, this project introduces a novel, multimodal therapeutic glove designed to deliver lasting non-pharmacological relief for individuals with hand and wrist OA. The glove integrates thermal stimulation and localized vibration to reduce pain and joint immobility, improving hand function. The design is grounded in an evidence-based understanding of osteoarthritis pain responses, with therapeutic principles derived from case studies and meta-analyses of both thermotherapy and massage therapy. The glove features vibrating mechanisms that simulate massage movements, providing relief and promoting circulation in affected areas. Furthermore, the glove incorporates heat therapy, which helps reduce stiffness and alleviates pain by increasing blood flow. To evaluate the glove’s effectiveness and usability, a pilot test was conducted with nine participants, where two-thirds of the participants reported hand or wrist pain. Guided sessions were used to measure pain and assess mobility using a numerical rating scale (0–5). The average reported pain relief score was 3.89, while the ease-of-movement score averaged 4.56, suggesting moderate to significant improvement.

Multimodal Wearable Devices: A Non-Pharmacological Treatment of Hand and Wrist Osteoarthritis

Introduction: Breast cancer is predominantly a disease of aging, with most cases occurring in women over the age of 50. A key biological contributor to this age-related risk is “inflammaging”—a chronic, low-grade inflammatory state driven by immunosenescence and dysregulated cytokine signalling. This persistent inflammatory milieu promotes tumour initiation, growth, and progression. Dietary factors, particularly omega-3 polyunsaturated fatty acids (n-3 PUFA), may modulate inflammaging and influence breast cancer development. Methods: This narrative review synthesizes findings from epidemiological, preclinical, and mechanistic studies examining the role of n-3 PUFA, particularly EPA and DHA, in modifying inflammaging and breast cancer risk. Literature was identified through PubMed and reference screening using keywords related to inflammation, aging, dietary fats, and breast cancer. Results: Preclinical and mechanistic studies demonstrate that n-3 PUFA exert anti-inflammatory, anti-proliferative, and pro-apoptotic effects through modulation of NF-κB, cytokine signaling, lipid raft integrity, and immune cell function. Notably, these mechanisms directly target biological processes underlying inflammaging. Early-life exposure to n-3 PUFA appears especially protective against breast cancer. Epidemiologic evidence suggests a modest protective association, particularly in populations with higher marine n-3 PUFA intake and lower n-6/n-3 PUFA ratios. Conclusion: n-3 PUFA may attenuate inflammaging and represent a promising dietary strategy to reduce age-related breast cancer risk. Their integration into the diet, particularly during key windows of susceptibility, could represent a preventive approach targeting the inflammation–aging–breast cancer axis.

The Role of Omega-3 Polyunsaturated Fatty Acids in Inflammaging and Breast Cancer: A Narrative Literature Review

Alzheimer’s disease (AD) is a neurodegenerative disorder driven by diverse mechanisms, including cognitive decline and disrupted gut-brain communication, resulting in a mortality increase of over 145% in North America. Emerging evidence highlights the critical role of gut-derived metabolites, G protein, and short-chain fatty acids, which modulate neuroinflammation, tau phosphorylation, and Aβ aggregation via G protein-coupled receptors (GPCRs), including C3AR, GPR84, and GPR27. These effects are mediated through signalling pathways such as STAT3 and cAMP/PKA, with orphan GPCRs emerging as promising, disease-specific targets. Through examining literature research, we assessed machine learning models, such as support vector machines and random forest models. Insight-generating AI methods and tools such as LIME and SHAP were used to clarify the model decisions, which pinpoint the critical biomarkers and brain regions that are connected to AD progression. We propose a personalized treatment that integrates microbiome sequencing, AI-informed GPCR targeting, and therapeutic strategies such as probiotics, metabolite mimics, and drug repurposing in elderly patients who are living with AD. This system level supports precision neurology by customizing treatments to individual gut and molecular profiles, leading AD patient care towards targeted personalized therapies. Future directions include validation via CRISPR-edited organoids, fecal transplant models, and microbiome-guided clinical trials. This approach holds promise for reducing cognitive decline, long-term care reliance, and healthcare costs, while advancing therapeutic equity and economic resilience in aging populations.

Translational AI Integration and Gut-Brain Axis Biomarkers in Alzheimer’s Disease: Systematic Review

Fragile X syndrome (FXS) is a neurodevelopmental disorder caused by the expansion of cysteine-guanine-guanine (CGG) repeats in the fragile X messenger ribonucleoprotein 1 (FMR1) gene. The increase in repeats silences the transcription of fragile X messenger ribonucleoprotein (FMRP), resulting in intellectual and developmental disabilities. Despite the development of symptom management therapies, there is no cure for FXS. In the past decades, several studies have demonstrated the importance of age-related alterations in the processing of amyloid precursor protein (APP) in FXS pathology. Hence, this systematic review was conducted to summarize and evaluate FXS treatments targeting components of the APP pathway. Using PubMed, a literature search was performed and uncovered 88 results. 9 articles related to the APP pathway were selected, and they were further categorized by their targets: glutamate, metabotropic glutamate receptor 5 (mGluR5), brain cytoplasmic RNA (BC1), and A disintegrin and metalloproteinase 10 (ADAM10). The majority of these results focused on treating the symptoms in young patients, corresponding with peak symptomatology. However, FXS individuals also display age-dependent premature cognitive decline. Since APP processing is altered with age, we suggest these treatments for FXS in younger individuals may be translatable to the aging FXS population. Further research is required to understand the mechanism of age-dependent alternation in APP processing in FXS patients and to investigate the efficiency of treatments with increased sample size and different tissues.

Aging with Fragile X Syndrome: Prospective Treatments Targeting the Amyloid Processing Pathway

Heart disease remains the leading global cause of death, highlighting the urgent need for accurate and scalable early detection methods. This study investigates the effectiveness of supervised machine learning techniques in predicting heart disease from routine clinical data. The objective is to determine which classification model provides the most reliable predictions in a clinical setting. We combined four public heart disease datasets from the UCI Machine Learning Repository and selected 14 relevant clinical features. Missing values were filled in using standard statistical methods (mean, median, or mode as appropriate). We then trained and evaluated four supervised machine learning models: logistic regression, support vector machine, extreme gradient boosting, and a multi-layer neural network. Each model was tuned for optimal performance and assessed using accuracy, precision, recall, and the F1-score. All models achieved higher than 80% accuracy. The support vector machine was the most accurate with 83.2% on the test data and also attained the highest F1-score (73%) for identifying heart disease cases. The neural network and gradient boosting models each reached about 82% accuracy, with the neural network identifying slightly more actual positive cases (higher recall). The logistic regression model trailed slightly at 80.7% accuracy but still showed competitive precision and recall. Machine learning models can effectively predict heart disease, providing a valuable tool for early detection. Among the models tested, the support vector machine performed best. These results indicate that support vector machines may offer stronger predictive performance for clinical applications of heart disease detection.

Evaluating Supervised Machine Learning Models for Heart Disease Prediction

Objective: This scoping review maps the existing global evidence on the met and unmet cognitive, environmental, financial, physical, personal, psychological, and social needs of community-dwelling Black older adults. Methods: Following Arksey and O’Malley’s framework and the Joanna Briggs Institute methodology, we conducted a comprehensive search across 12 databases for peer-reviewed articles published between 2000 and March 2025. Eligible studies reported on care needs within seven domains. Data were extracted, categorized, and summarized descriptively using PRISMA-ScR guidelines. Results: From 30,905 citations, 552 studies across 28 countries (83.5% in the U.S.) identified 5,023 needs, primarily in the personal and physical domains, based on mostly small, urban, quantitative studies. The most unmet needs within their domain were processing speed, transportation by bike, financial status, coordination, apathy,caregiving and assisted burden, and social isolation. In contrast, the most met need within their domains were nationality/birthplace, muscle power, insurance, social capital, self-efficacy, language and land use mix. Conclusion: This scoping review highlights that Black older adults face widespread unmet needs, particularly in physical, cognitive, financial, psychological, and environmental domains, largely due to structural inequities and the erosion of traditional support systems. Addressing these issues requires culturally grounded, equity-driven approaches that center the lived experiences of Black older adults and inform inclusive, responsive aging policies globally.

The Met and Unmet Needs of Black Older Adults: A Scoping Review

Introduction: Acute interventions allow researchers to examine and compare the influence of a single exercise session. While researchers assume the washout period will minimize carryover effects between exercise bouts, early exercise adaptations may still take place, as observed within the repeated-bout effect. Rarely are these early adaptations assessed. This study aimed to assess pre-to-post acute intervention changes in body composition, strength, and cycling peak power output (Max PO) following an acute intervention of four identical exercise bouts. Methods: Eighteen untrained, healthy adults participated in both pre- and post-study testing. Weight and percent body fat were measured using a digital scale and air displacement plethysmography, respectively, pre- and post-study. Estimated 1 repetition maximums (e1RM) for leg press, chest press, seated row testing, and an incremental cycling test was conducted pre- and post-study. Paired t-tests were used to compare pre- and post-study outcomes in the overall group and stratified by sex. Results: Body weight increased overall (p=0.001), but sex-based analyses revealed an increase only in females (p=0.002). There was no change in percent body fat (p>0.05). e1RM increased for leg press, chest press and seated row as did cycling Max PO (p<0.05 for all), with increases in both sexes for leg press and chest press, and males only for seated row. Conclusions: The acute exercise intervention induced strength and performance improvements, indicating possible early adaptations to exercise. These adaptations may have implications on physiological outcomes measures and should be assessed in other acute interventions.

Strength and Body Composition Changes Following Repeated Acute Exercise in Untrained Adults

National Inter-University Health Data and AI Inquiry Program Abstract: Strokes are a leading cause of mortality in Canada and long-term disability worldwide, occurring either through blockage of blood or rupture of blood vessels in the brain. The impact of strokes is expected to increase as global populations age. Although clinical risk factors are prioritized in stroke prediction, lifestyle behaviours and social determinants of health (SDOH) also play a crucial role. Prevention of stroke not only affects populations on a personal and societal level but may also reduce its impact on healthcare systems. This project aims to aid individuals in predicting stroke risk by utilizing machine learning and providing preventative measures as well as promoting healthier aging based on public Canadian health data. The model uses Random Forest Regression to replace empty values in the dataset with an approximation. It tests the accuracy by predicting existing crude rate data and comparing the approximations to the actual value. The model demonstrated accurate predictions for lower crude rate values, but struggled with extreme values due to limited data availability. Results showed statistically significant relationships between stroke and various modifiable factors (p < 0.05), including alcohol consumption, work stress, and physical activity. This project highlights the potential implementation of AI in primary stroke prevention using socioeconomic and behavioural data. In addition to clinical settings, this model can be applied on a larger scale in public health and insurance planning with further refinement and an expanded dataset.

Integrating Artificial Intelligence and Behavioural Risk Profiling for Early Stroke Prevention

Cognitive decline and dementia are increasing public health concerns among Canada’s aging population. Musical interaction has been a potential non-pharmacologic intervention to enhance cognitive and affective well-being. Its benefits may not be widely distributed to all groups, though. This research investigates the association between musical engagement and cognitive function, and social determinants among older Canadians using analysis of two national surveys: the Canadian Community Health Survey (CCHS – Healthy Aging) and the General Social Survey (GSS – Time Use, Cycle 29). Affordable artificial intelligence (AI) techniques, including multiple linear regression and k-means clustering, were employed to explore predictors of depression and musical engagement trends. Depression scores on the Patient Health Questionnaire-9 (PHQ-9) were approximated based on loneliness, income, education, race, and immigration status. Clustering pinpointed high depression groups with limited music access. GSS data illustrated differences in gender and income participation in music despite regression findings indicating poor predictive capability (R² ≈ 0.00). Loneliness was the strongest predictor of depression (coef = 0.36), and the model explained 34% of the variance (R² = 0.34). Findings indicate that racialized, low-income older adults are least likely to have music-based care. These findings identify that AI can uncover disparities and guide culturally responsive, community-based programming to promote equity in healthy aging.

Underline digital video library

Proactive Arthritis Care with a Multi-Sensor Wearable Knee Brace System

AI-powered Mobility Devices for Adults With Impairments: A Scoping Review

Bridges in Parkinson’s Disease: Connecting Lives, Ending Loneliness

Multimodal Wearable Devices: A Non-Pharmacological Treatment of Hand and Wrist Osteoarthritis

The Role of Omega-3 Polyunsaturated Fatty Acids in Inflammaging and Breast Cancer: A Narrative Literature Review

Translational AI Integration and Gut-Brain Axis Biomarkers in Alzheimer’s Disease: Systematic Review

Aging with Fragile X Syndrome: Prospective Treatments Targeting the Amyloid Processing Pathway

Evaluating Supervised Machine Learning Models for Heart Disease Prediction

The Met and Unmet Needs of Black Older Adults: A Scoping Review

Strength and Body Composition Changes Following Repeated Acute Exercise in Untrained Adults

Integrating Artificial Intelligence and Behavioural Risk Profiling for Early Stroke Prevention

Predicting Loneliness-Linked Health Risks by Analyzing Digital Behaviours Using AI

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