Explore the vast range of titles available.

Accurate and scalable gait assessment is essential for clinical and research applications, including fall risk evaluation, rehabilitation monitoring, and early detection of neurodegenerative diseases. While electronic walkways remain the clinical gold standard, their high cost and limited portability restrict widespread use. Wearable inertial measurement units (IMUs) and markerless depth cameras have emerged as promising alternatives; however, prior studies have typically assessed these systems under tightly controlled conditions, with single participants in view, limited marker sets, and without direct cross-technology comparisons. This study addresses these gaps by simultaneously evaluating three sensing technologies—APDM wearable IMUs (tested in two separate configurations: foot-mounted and lumbar-mounted) and the Azure Kinect depth camera—against ProtoKinetics Zeno™ Walkway Gait Analysis System in a realistic clinical environment where multiple individuals were present in the camera’s field of view. Gait data from 20 older adults (mean age 70.06±9.45 years) performing Single-Task and Dual-Task walking trials were synchronously captured using custom hardware for precise temporal alignment. Eleven gait markers spanning macro, micro-temporal, micro-spatial, and spatiotemporal domains were compared using mean absolute error (MAE), Pearson correlation (r), and Bland–Altman analysis. Foot-mounted IMUs demonstrated the highest accuracy (MAE =0.00–6.12, r=0.92–1.00), followed closely by the Azure Kinect (MAE =0.01–6.07, r=0.68–0.98). Lumbar-mounted IMUs showed consistently lower agreement with the reference system. These findings provide the first comprehensive comparison of wearable and depth-sensing technologies with a clinical gold standard under real-world conditions and across an extensive set of gait markers. The results establish a foundation for deploying scalable, low-cost gait assessment systems in diverse healthcare contexts, supporting early detection, mobility monitoring, and rehabilitation outcomes across multiple patient populations.

Alzheimer's disease and related dementias (ADRD) currently affect over 5.7 million Americans and over 35 million people worldwide. At the same time, over 31 million older adults are physically inactive with impaired physical performance interfering with activities of daily living. Low physical activity is a risk factor for ADRD. We examined the utility of a new measure, the Quick Physical Activities Rating (QPAR) as an informant-rated instrument to quantify the dosage of physical activities in healthy controls, MCI and ADRD compared with Gold Standard assessments of objective measures of physical performance, fitness, and functionality. This study analyzed 390 consecutive patient-caregiver dyads who underwent a comprehensive evaluation including the Clinical Dementia Rating (CDR), mood, neuropsychological testing, caregiver ratings of patient behavior and function, and a comprehensive physical performance and gait assessment. The QPAR was completed prior to the office visit and was not considered in the clinical evaluation, physical performance assessment, staging or diagnosis of the patient. Psychometric properties including item variability and distribution, floor and ceiling effects, strength of association, known-groups performance, and internal consistency were determined. The patients had a mean age of 75.3±9.2 years, 15.7±2.8 years of education and were 46.9% female. The patients had a mean CDR-SB of 4.8±4.7 and a mean MoCA score of 18.6±7.1 and covered a range of healthy controls (CDR 0 = 54), MCI or very mild dementia (CDR 0.5 = 161), mild dementia (CDR 1 = 92), moderate dementia (CDR 2 = 64), and severe dementia (CDR 3 = 29). The mean QPAR score was 20.2±18.9 (range 0-132) covering a wide range of physical activity. The QPAR internal consistency (Cronbach alpha) was very good at 0.747. The QPAR was correlated with measures of physical performance (dexterity, grip strength, gait, mobility), physical functionality rating scales, measures of activities of daily living and comorbidities, the UPDRS, and frailty ratings (all p < .001). The QPAR report of physical activities was able to discriminate between individuals with impaired physical functionality (32.2±23.9 vs 15.2±13.8, p < .001), falls risk (28.4±21.6 vs. 14.5±13.2, p < .001), and the presence of frailty (28.1±22.7 vs. 11.8±9.4, p < .001). The QPAR showed strong psychometric properties and excellent data quality, and worked equally well across different patient ages, sexes, informant relationships, and in individuals with and without cognitive impairment. The QPAR is a brief detection tool that captures informant reports of physical activities and differentiates individuals with normal physical functionality from those individuals with impaired physical functionality. The QPAR correlated with Gold Standard assessments of strength and sarcopenia, activities of daily living, gait and mobility, fitness, health related quality of life, frailty, global physical performance, and provided good discrimination between states of physical functionality, falls risk, and frailty. The QPAR performed well in comparison to standardized scales of objective physical performance, but in a brief fashion that could facilitate its use in clinical care and research.

The Health Brain Initiative (HBI), established by University of Miami's Comprehensive Center for Brain Health (CCBH), follows racially/ethnically diverse older adults without dementia living in South Florida. With dementia prevention and brain health promotion as an overarching goal, HBI will advance scientific knowledge by developing novel assessments and non-invasive biomarkers of Alzheimer's disease and related dementias (ADRD), examining additive effects of sociodemographic, lifestyle, neurological and biobehavioral measures, and employing innovative, methodologically advanced modeling methods to characterize ADRD risk and resilience factors and transition of brain aging. HBI is a longitudinal, observational cohort study that will follow 500 deeply-phenotyped participants annually to collect, analyze, and store clinical, cognitive, behavioral, functional, genetic, and neuroimaging data and biospecimens. Participants are ≥50 years old; have no, subjective, or mild cognitive impairment; have a study partner; and are eligible to undergo magnetic resonance imaging (MRI). Recruitment is community-based including advertisements, word-of-mouth, community events, and physician referrals. At baseline, following informed consent, participants complete detailed web-based surveys (e.g., demographics, health history, risk and resilience factors), followed by two half-day visits which include neurological exams, cognitive and functional assessments, an overnight sleep study, and biospecimen collection. Structural and functional MRI is completed by all participants and a subset also consent to amyloid PET imaging. Annual follow-up visits repeat the same data and biospecimen collection as baseline, except that MRIs are conducted every other year after baseline. HBI has been approved by the University of Miami Miller School of Medicine Institutional Review Board. Participants provide informed consent at baseline and are re-consented as needed with protocol changes. Data collected by HBI will lead to breakthroughs in developing new diagnostics and therapeutics, creating comprehensive diagnostic evaluations, and providing the evidence base for precision medicine approaches to dementia prevention with individualized treatment plans.

Accurate balance assessment is important in healthcare for identifying and managing conditions affecting stability and coordination. It plays a key role in preventing falls, understanding movement disorders, and designing appropriate therapeutic interventions across various age groups and medical conditions. However, traditional balance assessment methods often suffer from subjectivity, lack of comprehensive balance assessments and remote assessment capabilities, and reliance on specialized equipment and expert analysis. In response to these challenges, our study introduces an innovative approach for estimating scores on the Modified Clinical Test of Sensory Interaction on Balance (m-CTSIB). Utilizing wearable sensors and advanced machine learning algorithms, we offer an objective, accessible, and efficient method for balance assessment. We collected comprehensive movement data from 34 participants under four different sensory conditions using an array of inertial measurement unit (IMU) sensors coupled with a specialized system to evaluate ground truth m-CTSIB balance scores for our analysis. This data was then preprocessed, and an extensive array of features was extracted for analysis. To estimate the m-CTSIB scores, we applied Multiple Linear Regression (MLR), Support Vector Regression (SVR), and XGBOOST algorithms. Our subject-wise Leave-One-Out and 5-Fold cross-validation analysis demonstrated high accuracy and a strong correlation with ground truth balance scores, validating the effectiveness and reliability of our approach. Key insights were gained regarding the significance of specific movements, feature selection, and sensor placement in balance estimation. Notably, the XGBOOST model, utilizing the lumbar sensor data, achieved outstanding results in both methods, with Leave-One-Out cross-validation showing a correlation of 0.96 and a Mean Absolute Error (MAE) of 0.23 and 5-fold cross-validation showing comparable results with a correlation of 0.92 and an MAE of 0.23, confirming the model’s consistent performance. This finding underlines the potential of our method to revolutionize balance assessment practices, particularly in settings where traditional methods are impractical or inaccessible.

The recent approval of treatments for tardive dyskinesia (TD) has rekindled interest in this chronic and previously recalcitrant condition. A large proportion of patients with chronic mental illness suffer from various degrees of TD. Even the newer antipsychotics constitute a liability for TD, and their liberal prescription might lead to emergence of new TD in patient populations previously less exposed to antipsychotics, such as those with depression, bipolar disorder, autism, or even attention deficit hyperactivity disorder. The association of TD with activity limitations remains poorly understood. We review potential new avenues of assessing the functional sequelae of TD, such as the performance of instrumental activities of daily living, residential status, and employment outcomes. We identify several mediating aspects, including physical performance measures and cognition, that may represent links between TD and everyday performance, as well as potential treatment targets.

Background Gait and balance deficiencies may be important indicators of cognitive impairment, distinguishing dementia from normal cognition (NC). It is unclear whether this extends to pre‐dementia stages of disease. Study goals were to: assess patterns of mobility across early stages of disease and identify specific measures that distinguish individuals with subjective cognitive impairment (SCI) and mild cognitive impairment (MCI). Method We assessed whether mobility differentiates SCI (n = 35) from NC (n = 78) and MCI (n = 41). Using ANCOVA, we compared diagnostic groups on global (mini‐PPT; TUG) and individual (gait speed (GS), flexibility, balance, step length, single‐leg support) measures of mobility, controlling for age and baseline physical activity levels. Group separation was evaluated with logistic regression. Group differences in mobility while distinguishing between MCI etiology (AD vs non‐AD) and relationships between mobility and MRI cortical atrophy score (CAS) were analyzed and results validated against biomarker‐confirmed diagnoses (true controls/preclinical/MCI). Result Participants were 69±9yr old, highly educated (16±3y), mostly non‐Hispanic White (77%), and performed within normal parameters for mobility. Mobility performance decreased with disease stage (MCI>SCI>NC). All mobility measures distinguished MCI from NC (p<0.01). Dual task mobility distinguished MCI from SCI (e.g., ORTUG = 1.24, 95%CI:1.055‐1.457). AD vs non‐AD etiology accounted for most group differences in mobility. Higher mini‐PPT score was linked to lower odds of SCI vs NC (OR = 0.705 (0.543‐0.916)) stemming from significantly poorer balance in SCI (β = ‐0.3±0.1, p = 0.041). Lower GS explained MCI differences from NC (β = ‐0.1±0.03, p = 0.003) and SCI (β = ‐0.1±0.04, p = 0.027). Results were not explained by differences in age and physical activity. Small‐to‐moderate correlations between CAS and mobility (e.g., rTUG dual = 0.34, p<0.001) were found. In analyses using biomarker‐confirmed diagnoses, mobility was lower in MCI vs true controls (all p<0.01) and vs preclinical disease (p<0.05), with a trend for preclinical vs true controls (p = 0.054) validating findings on clinical diagnoses. Conclusion Mobility parameters can distinguish NC from SCI and MCI highlighting: 1) dual task testing and subtle changes in balance help detect early cognitive impairments; 2) changes in GS mark more significant cognitive impairment. Mobility testing may be an important tool in early detection of ADRD in the clinical setting and can help identify targets for early intervention.

Gait and balance deficiencies may be important indicators of cognitive impairment, distinguishing dementia from normal cognition (NC). It is unclear whether this extends to pre-dementia stages of disease. Study goals were to: assess patterns of mobility across early stages of disease and identify specific measures that distinguish individuals with subjective cognitive impairment (SCI) and mild cognitive impairment (MCI). We assessed whether mobility differentiates SCI (n = 35) from NC (n = 78) and MCI (n = 41). Using ANCOVA, we compared diagnostic groups on global (mini-PPT; TUG) and individual (gait speed (GS), flexibility, balance, step length, single-leg support) measures of mobility, controlling for age and baseline physical activity levels. Group separation was evaluated with logistic regression. Group differences in mobility while distinguishing between MCI etiology (AD vs non-AD) and relationships between mobility and MRI cortical atrophy score (CAS) were analyzed and results validated against biomarker-confirmed diagnoses (true controls/preclinical/MCI). Participants were 69±9yr old, highly educated (16±3y), mostly non-Hispanic White (77%), and performed within normal parameters for mobility. Mobility performance decreased with disease stage (MCI>SCI>NC). All mobility measures distinguished MCI from NC (p<0.01). Dual task mobility distinguished MCI from SCI (e.g., OR = 1.24, 95%CI:1.055-1.457). AD vs non-AD etiology accounted for most group differences in mobility. Higher mini-PPT score was linked to lower odds of SCI vs NC (OR = 0.705 (0.543-0.916)) stemming from significantly poorer balance in SCI (β = -0.3±0.1, p = 0.041). Lower GS explained MCI differences from NC (β = -0.1±0.03, p = 0.003) and SCI (β = -0.1±0.04, p = 0.027). Results were not explained by differences in age and physical activity. Small-to-moderate correlations between CAS and mobility (e.g., r = 0.34, p<0.001) were found. In analyses using biomarker-confirmed diagnoses, mobility was lower in MCI vs true controls (all p<0.01) and vs preclinical disease (p<0.05), with a trend for preclinical vs true controls (p = 0.054) validating findings on clinical diagnoses. Mobility parameters can distinguish NC from SCI and MCI highlighting: 1) dual task testing and subtle changes in balance help detect early cognitive impairments; 2) changes in GS mark more significant cognitive impairment. Mobility testing may be an important tool in early detection of ADRD in the clinical setting and can help identify targets for early intervention.

The Health Brain Initiative (HBI), established by University of Miami's Comprehensive Center for Brain Health (CCBH), follows racially/ethnically diverse older adults without dementia living in South Florida. With dementia prevention and brain health promotion as an overarching goal, HBI will advance scientific knowledge by developing novel assessments and non-invasive biomarkers of Alzheimer's disease and related dementias (ADRD), examining additive effects of sociodemographic, lifestyle, neurological and biobehavioral measures, and employing innovative, methodologically advanced modeling methods to characterize ADRD risk and resilience factors and transition of brain aging. HBI is a longitudinal, observational cohort study that will follow 500 deeply-phenotyped participants annually to collect, analyze, and store clinical, cognitive, behavioral, functional, genetic, and neuroimaging data and biospecimens. Participants are [greater than or equal to]50 years old; have no, subjective, or mild cognitive impairment; have a study partner; and are eligible to undergo magnetic resonance imaging (MRI). Recruitment is community-based including advertisements, word-of-mouth, community events, and physician referrals. At baseline, following informed consent, participants complete detailed web-based surveys (e.g., demographics, health history, risk and resilience factors), followed by two half-day visits which include neurological exams, cognitive and functional assessments, an overnight sleep study, and biospecimen collection. Structural and functional MRI is completed by all participants and a subset also consent to amyloid PET imaging. Annual follow-up visits repeat the same data and biospecimen collection as baseline, except that MRIs are conducted every other year after baseline.

Alzheimer's disease and related dementias (ADRD) currently affect over 5.7 million Americans and over 35 million people worldwide. At the same time, over 31 million older adults are physically inactive with impaired physical performance interfering with activities of daily living. Low physical activity is a risk factor for ADRD. We examined the utility of a new measure, the Quick Physical Activities Rating (QPAR) as an informant-rated instrument to quantify the dosage of physical activities in healthy controls, MCI and ADRD compared with Gold Standard assessments of objective measures of physical performance, fitness, and functionality. This study analyzed 390 consecutive patient-caregiver dyads who underwent a comprehensive evaluation including the Clinical Dementia Rating (CDR), mood, neuropsychological testing, caregiver ratings of patient behavior and function, and a comprehensive physical performance and gait assessment. The QPAR was completed prior to the office visit and was not considered in the clinical evaluation, physical performance assessment, staging or diagnosis of the patient. Psychometric properties including item variability and distribution, floor and ceiling effects, strength of association, known-groups performance, and internal consistency were determined. The patients had a mean age of 75.3±9.2 years, 15.7±2.8 years of education and were 46.9% female. The patients had a mean CDR-SB of 4.8±4.7 and a mean MoCA score of 18.6±7.1 and covered a range of healthy controls (CDR 0 = 54), MCI or very mild dementia (CDR 0.5 = 161), mild dementia (CDR 1 = 92), moderate dementia (CDR 2 = 64), and severe dementia (CDR 3 = 29). The mean QPAR score was 20.2±18.9 (range 0-132) covering a wide range of physical activity. The QPAR internal consistency (Cronbach alpha) was very good at 0.747. The QPAR was correlated with measures of physical performance (dexterity, grip strength, gait, mobility), physical functionality rating scales, measures of activities of daily living and comorbidities, the UPDRS, and frailty ratings (all p < .001). The QPAR report of physical activities was able to discriminate between individuals with impaired physical functionality (32.2±23.9 vs 15.2±13.8, p < .001), falls risk (28.4±21.6 vs. 14.5±13.2, p < .001), and the presence of frailty (28.1±22.7 vs. 11.8±9.4, p < .001). The QPAR showed strong psychometric properties and excellent data quality, and worked equally well across different patient ages, sexes, informant relationships, and in individuals with and without cognitive impairment. The QPAR is a brief detection tool that captures informant reports of physical activities and differentiates individuals with normal physical functionality from those individuals with impaired physical functionality. The QPAR correlated with Gold Standard assessments of strength and sarcopenia, activities of daily living, gait and mobility, fitness, health related quality of life, frailty, global physical performance, and provided good discrimination between states of physical functionality, falls risk, and frailty. The QPAR performed well in comparison to standardized scales of objective physical performance, but in a brief fashion that could facilitate its use in clinical care and research.