Weekly minutes of moderate to vigorous physical activity is associated with movement quality in overweight and obese older adults, independent of age.

BACKGROUND: Physical activity can improve function and decrease healthcare spending among overweight and obese older adults. Although unstructured physical activity has been related to cardiometabolic improvements, the relationship between unstructured activity and movement quality is unclear. AIMS: This study aimed to evaluate the association of amount of unstructured free-living moderate-vigorous physical activity (MVPA) with measures of movement quality in overweight and obese older adults. METHODS: The association of MVPA with movement quality was assessed in 165 overweight and obese older adults (Age: 77.0(8.0) years; Body mass index (BMI): 29.2(5.3) kg/m2). Participants performed overground walking, the Figure of 8 Walk test, and the Five-Times Sit to Stand. Weekly physical activity was measured using a waist-worn Actigraph activity monitor. RESULTS: Movement quality during straight path [gait speed (ρ = 0.30, p < 0.01), stride length (ρ = 0.33, p < 0.01), double-limb support time (ρ = -0.26, p < 0.01), and gait symmetry (ρ = 0.17, p = 0.02)] and curved path [F8W time (ρ = -0.22, p < 0.01) and steps (ρ = -0.22, p < 0.01)] walking were associated with weekly minutes of MVPA after controlling for age. Five-Times Sit to Stand performance was not significantly associated with weekly minutes of MVPA (ρ = -0.10, p = 0.13). CONCLUSIONS: Older adults with high BMIs who are less active also demonstrate poorer movement quality, independent of age. Physical activity engagement and task-specific training should be targeted in interventions to promote healthy aging, decrease falls, and delay disability development. Future work should consider the interconnected nature of movement quality with physical activity engagement and investigate if targeting one influences the other.

Facilitators and barriers to real-life mobility in community-dwelling older adults: a narrative review of accelerometry- and global positioning system-based studies.

Real-life mobility, also called "enacted" mobility, characterizes an individual's activity and participation in the community. Real-life mobility may be facilitated or hindered by a variety of factors, such as physical abilities, cognitive function, psychosocial aspects, and external environment characteristics. Advances in technology have allowed for objective quantification of real-life mobility using wearable sensors, specifically, accelerometry and global positioning systems (GPSs). In this review article, first, we summarize the common mobility measures extracted from accelerometry and GPS. Second, we summarize studies assessing the associations of facilitators and barriers influencing mobility of community-dwelling older adults with mobility measures from sensor technology. We found the most used accelerometry measures focus on the duration and intensity of activity in daily life. Gait quality measures, e.g., cadence, variability, and symmetry, are not usually included. GPS has been used to investigate mobility behavior, such as spatial and temporal measures of path traveled, location nodes traversed, and mode of transportation. Factors of note that facilitate/hinder community mobility were cognition and psychosocial influences. Fewer studies have included the influence of external environments, such as sidewalk quality, and socio-economic status in defining enacted mobility. Increasing our understanding of the facilitators and barriers to enacted mobility can inform wearable technology-enabled interventions targeted at delaying mobility-related disability and improving participation of older adults in the community.

Fear of falling in community-dwelling older adults: What their gait acceleration pattern reveals.

BACKGROUND: Fear of Falling (FOF) is common among community-dwelling older adults and is associated with increased fall-risk, reduced activity, and gait modifications. OBJECTIVE: In this cross-sectional study, we examined the relationships between FOF and gait quality. METHODS: Older adults (N=232; age 77±6; 65 % females) reported FOF by a single yes/no question. Gait quality was quantified as (1) harmonic ratio (smoothness) and other time-frequency spatiotemporal variables from triaxial accelerometry (Vertical-V, Mediolateral-ML, Anterior-Posterior -AP) during six-minute walk; (2) gait speed, step-time CoV (variability), and walk-ratio (step-length/cadence) on a 4-m instrumented walkway. Mann Whitney U-tests and Random forest classifier compared gait between those with and without FOF. Selected gait variables were used to build Support Vector Machine (SVM) classifier and performance was evaluated using AUC-ROC. RESULTS: Individuals with FOF had slower gait speed (103.66 ± 17.09 vs. 110.07 ± 14.83 cm/s), greater step time CoV (4.17 ± 1.66 vs. 3.72 ± 1.24 %), smaller walk-ratio (0.53 ± 0.08 vs. 0.56 ± 0.07 cm/steps/minute), smaller standard deviation V (0.15 ± 0.06 vs. 0.18 ± 0.09 m/s2), and smaller harmonic-ratio V (2.14 ± 0.73 vs. 2.38 ± 0.58), all p<.01. Linear SVM yielded an AUC-ROC of 67 % on test dataset, coefficient values being gait speed (-0.19), standard deviation V (-0.23), walk-ratio (-0.36), and smoothness V (-0.38) describing associations with presence of FOF. CONCLUSION: Older adults with FOF have reduced gait speed, acceleration adaptability, walk-ratio, and smoothness. Disrupted gait patterns during fear of falling could provide insights into psychosocial distress in older adults. Longitudinal studies are warranted.

Association of Gait Quality With Daily-Life Mobility: An Actigraphy and Global Positioning System Based Analysis in Older Adults.

OBJECTIVE: Walking is a key component of daily-life mobility. We examined associations between laboratory-measured gait quality and daily-life mobility through Actigraphy and Global Positioning System (GPS). We also assessed the relationship between two modalities of daily-life mobility i.e., Actigraphy and GPS. METHODS: In community-dwelling older adults (N = 121, age = 77±5 years, 70% female, 90% white), we obtained gait quality from a 4-m instrumented walkway (gait speed, walk-ratio, variability) and accelerometry during 6-Minute Walk (adaptability, similarity, smoothness, power, and regularity). Physical activity measures of step-count and intensity were captured from an Actigraph. Time out-of-home, vehicular time, activity-space, and circularity were quantified using GPS. Partial Spearman correlations between laboratory gait quality and daily-life mobility were calculated. Linear regression was used to model step-count as a function of gait quality. ANCOVA and Tukey analysis compared GPS measures across activity groups [high, medium, low] based on step-count. Age, BMI, and sex were used as covariates. RESULTS: Greater gait speed, adaptability, smoothness, power, and lower regularity were associated with higher step-counts (0.20<|ρp| < 0.26, p < .05). Age(ß = -0.37), BMI(ß = -0.30), speed(ß = 0.14), adaptability(ß = 0.20), and power(ß = 0.18), explained 41.2% variance in step-count. Gait characteristics were not related to GPS measures. Participants with high (>4800 steps) compared to low activity (steps<3100) spent more time out-of-home (23 vs 15%), more vehicular travel (66 vs 38 minutes), and larger activity-space (5.18 vs 1.88 km2), all p < .05. CONCLUSIONS: Gait quality beyond speed contributes to physical activity. Physical activity and GPS-derived measures capture distinct aspects of daily-life mobility. Wearable-derived measures should be considered in gait and mobility-related interventions.

Weekly minutes of moderate to vigorous physical activity is associated with movement quality in overweight and obese older adults.

Background: Physical activity can improve function and decrease healthcare spending among overweight and obese older adults. Although unstructured physical activity has been related to cardiometabolic improvements, the relationship between unstructured activity and movement quality is unclear. Aims: This study aimed to evaluate the association of amount of unstructured free-living moderate-vigorous physical activity (MVPA) with measures of movement quality in overweight and obese older adults. Methods: The association of MVPA with movement quality was assessed in 165 overweight and obese older adults (Age: 77.0(8.0) years; Body mass index (BMI): 29.2(5.3) kg/m2). Participants performed overground walking, the Figure of 8 Walk test, and the Five-Times Sit to Stand. Weekly physical activity was measured using a waist-worn Actigraph activity monitor. Results: Movement quality during straight path (gait speed (ρ = 0.30, p < 0.01), stride length (ρ = 0.33, p < 0.01), double-limb support time (ρ=-0.26, p < 0.01), and gait symmetry (ρ = 0.17, p = 0.02)) and curved path (F8W time (ρ=-0.22, p < 0.01) and steps (ρ=-0.22, p < 0.01)) walking were associated with weekly minutes of MVPA after controlling for age. Five-Times Sit to Stand performance was not significantly associated with weekly minutes of MVPA (ρ=-0.10, p = 0.13). Conclusions: Older adults with high BMIs who are less active also demonstrate poorer movement quality which should be targeted in interventions to promote healthy aging, decrease falls, and delay disability development. Future work should explore if these associations are observed in middle-aged adults so targeted interventions can be implemented even earlier in the disability development continuum.

Uneven surface and cognitive dual-task independently affect gait quality in older adults.

BACKGROUND: Real-world mobility involves walking in challenging conditions. Assessing gait during simultaneous physical and cognitive challenges provides insights on cognitive health. RESEARCH QUESTION: How does uneven surface, cognitive task, and their combination affect gait quality and does this gait performance relate to cognitive functioning? METHODS: Community-dwelling older adults (N = 104, age=75 ± 6 years, 60 % females) performed dual-task walking paradigms (even and uneven surface; with and without alphabeting cognitive task (ABC)) to mimic real-world demands. Gait quality measures [speed(m/s), rhythmicity(steps/minute), stride time variability (%), adaptability (m/s2), similarity, smoothness, power (Hz) and regularity] were calculated from an accelerometer worn on the lower back. Linear-mixed modelling and Tukey analysis were used to analyze independent effects of surface and cognitive task and their interaction on gait quality. Partial Spearman correlations compared gait quality with global cognition and executive function. RESULTS: No interaction effects between surface and cognitive task were found. Uneven surface reduced gait speed(m/s) (ß = -0.07). Adjusted for speed, uneven surface reduced gait smoothness (ß = -0.27) and increased regularity (ß = 0.09), Tukey p < .05, for even vs uneven and even-ABC vs uneven-ABC. Cognitive task reduced gait speed(m/s) (ß = -0.12). Adjusted for speed, cognitive task increased variability (ß = 7.60), reduced rhythmicity (ß = -6.68) and increased regularity (ß = 0.05), Tukey p < .05, for even vs even-ABC and uneven vs uneven-ABC. With demographics as covariates, gait speed was not associated with cognition. Gait quality [lower variability during even-ABC (ρp =-.31) and uneven-ABC (ρp =-.28); greater rhythmicity (ρp between.22 and.29) and greater signal-adaptability AP (ρp between.22 and.26) during all walking tasks] was associated with better global cognition. Gait adaptability during even (ρp =-0.21, p = 0.03) and uneven(ρp =-0.19, p = 0.04) walking was associated with executive function. SIGNIFICANCE: Surface and cognitive walking tasks independently affected gait quality. Our study with high-functioning older adults suggests that task-related changes in gait quality are related to subtle changes in cognitive functioning.

A large, curated, open-source stroke neuroimaging dataset to improve lesion segmentation algorithms.

Accurate lesion segmentation is critical in stroke rehabilitation research for the quantification of lesion burden and accurate image processing. Current automated lesion segmentation methods for T1-weighted (T1w) MRIs, commonly used in stroke research, lack accuracy and reliability. Manual segmentation remains the gold standard, but it is time-consuming, subjective, and requires neuroanatomical expertise. We previously released an open-source dataset of stroke T1w MRIs and manually-segmented lesion masks (ATLAS v1.2, N = 304) to encourage the development of better algorithms. However, many methods developed with ATLAS v1.2 report low accuracy, are not publicly accessible or are improperly validated, limiting their utility to the field. Here we present ATLAS v2.0 (N = 1271), a larger dataset of T1w MRIs and manually segmented lesion masks that includes training (n = 655), test (hidden masks, n = 300), and generalizability (hidden MRIs and masks, n = 316) datasets. Algorithm development using this larger sample should lead to more robust solutions; the hidden datasets allow for unbiased performance evaluation via segmentation challenges. We anticipate that ATLAS v2.0 will lead to improved algorithms, facilitating large-scale stroke research.

Mobility of Older Adults: Gait Quality Measures Are Associated With Life-Space Assessment Scores.

BACKGROUND: The relation of gait quality to real-life mobility among older adults is poorly understood. This study examined the association between gait quality, consisting of step variability, smoothness, regularity, symmetry, and gait speed, and the Life-Space Assessment (LSA). METHOD: In community-dwelling older adults (N = 232, age 77.5 ± 6.6, 65% females), gait quality was derived from (i) an instrumented walkway: gait speed, variability, and walk ratio and (ii) accelerometer: signal variability, smoothness, regularity, symmetry, and time-frequency spatiotemporal variables during 6-minute walk. In addition to collecting LSA scores, cognitive functioning, walking confidence, and falls were recorded. Spearman correlations (speed as covariate) and random forest regression were used to assess associations between gait quality and LSA, and Gaussian mixture modeling (GMM) was used to cluster participants. RESULTS: Spearman correlations of ρ p = .11 (signal amplitude variability mediolateral [ML] axis), ρ p = .15 and ρ p = -.13 (symmetry anterior-posterior-vertical [AP-V] and ML-AP axes, respectively), ρ p = .16 (power V), and ρ = .26 (speed), all p <.05 and marginally related, ρ p = -.12 (regularity V), ρ p = .11 (smoothness AP), and ρ p = -.11 (step-time variability), all p <.1, were obtained. The cross-validated random forest model indicated good-fit LSA prediction error of 17.77; gait and cognition were greater contributors than age and gender. GMM indicated 2 clusters. Group 1 (n = 189) had better gait quality than group 2 (n = 43): greater smoothness AP (2.94 ± 0.75 vs 2.30 ± 0.71); greater similarity AP-V (.58 ± .13 vs .40 ± .19); lower regularity V (0.83 ± 0.08 vs 0.87 ± 0.10); greater power V (1.86 ± 0.18 vs 0.97 ± 1.84); greater speed (1.09 ± 0.16 vs 1.00 ± 0.16 m/s); lower step-time coefficient of variation (3.70 ± 1.09 vs 5.09 ± 2.37), and better LSA (76 ± 18 vs 67 ± 18), padjusted < .004. CONCLUSIONS: Gait quality measures taken in the clinic are associated with real-life mobility in the community.