Relationship between skeletal mitochondrial function and digital markers of free-living physical activity in older adults.

This study examined the association between in vivo skeletal mitochondrial function and digital free-living physical activity patterns-a measure that summarizes biological, phenotypic, functional, and environmental effects on mobility. Among 459 participants (mean age 68 years; 55% women) in the Baltimore Longitudinal Study of Aging, mitochondrial function was quantified as skeletal muscle oxidative capacity via post-exercise phosphocreatine recovery rate (τPCr) in the vastus lateralis muscle of the left thigh, using 31P magnetic resonance spectroscopy. Accelerometry was collected using a 7-day, 24-h wrist-worn protocol and summarized into activity amount, intensity, endurance, and accumulation patterning metrics. Linear regression, two-part linear and logistic (bout analyses), and linear mixed effects models (time-of-day analyses) were used to estimate associations between τPCr and each physical activity metric. Interactions by age, sex, and gait speed were tested. After covariate adjustment, higher τPCr (or poorer mitochondrial function) was associated with lower activity counts/day (ß = - 6593.7, SE = 2406.0; p = 0.006) and activity intensity (- 81.5 counts, SE = 12.9; p < 0.001). For activity intensity, the magnitude of association was greater for men and those with slower gait speed (interaction p < 0.02 for both). Conversely, τPCr was not associated with daily active minutes/day (p = 0.15), activity fragmentation (p = 0.13), or endurance at any bout length (p > 0.05 for all). Time-of-day analyses show participants with high τPCr were less active from 6:00 a.m. to 12:00 a.m. than those with low τPCr. Results indicate that poorer skeletal mitochondrial function is primarily associated with lower engagement in high intensity activities. Our findings help define the connection between laboratory-measured mitochondrial function and real-world physical activity behavior.

Classification of Free-Living Body Posture with ECG Patch Accelerometers: Application to the Multicenter AIDS Cohort Study.

Purpose: As health studies increasingly monitor free-living heart performance via ECG patches with accelerometers, researchers will seek to investigate cardio-electrical responses to physical activity and sedentary behavior, increasing demand for fast, scalable methods to process accelerometer data. We extend a posture classification algorithm for accelerometers in ECG patches when researchers do not have ground-truth labels or other reference measurements (i.e., upright measurement). Methods: Men living with and without HIV in the Multicenter AIDS Cohort study wore the Zio XT® for up to two weeks (n = 1,250). Our novel extensions for posture classification include (1) estimation of an upright posture for each individual without a reference upright measurement; (2) correction of the upright estimate for device removal and re-positioning using novel spherical change-point detection; and (3) classification of upright and recumbent periods using a clustering and voting process rather than a simple inclination threshold used in other algorithms. As no posture labels exist in the free-living environment, we perform numerous sensitivity analyses and evaluate the algorithm against labelled data from the Towson Accelerometer Study, where participants wore accelerometers at the waist. Results: On average, 87.1% of participants were recumbent at 4am and 15.5% were recumbent at 1pm. Participants were recumbent 54 minutes longer on weekends compared to weekdays. Performance was good in comparison to labelled data in a separate, controlled setting (accuracy = 96.0%, sensitivity = 97.5%, specificity = 95.9%). Conclusions: Posture may be classified in the free-living environment from accelerometers in ECG patches even without measuring a standard upright position. Furthermore, algorithms that fail to account for individuals who rotate and re-attach the accelerometer may fail in the free-living environment.

Digital wearable insole-based identification of knee arthropathies and gait signatures using machine learning.

Gait is impaired in musculoskeletal conditions, such as knee arthropathy. Gait analysis is used in clinical practice to inform diagnosis and monitor disease progression or intervention response. However, clinical gait analysis relies on subjective visual observation of walking as objective gait analysis has not been possible within clinical settings due to the expensive equipment, large-scale facilities, and highly trained staff required. Relatively low-cost wearable digital insoles may offer a solution to these challenges. In this work, we demonstrate how a digital insole measuring osteoarthritis-specific gait signatures yields similar results to the clinical gait-lab standard. To achieve this, we constructed a machine learning model, trained on force plate data collected in participants with knee arthropathy and controls. This model was highly predictive of force plate data from a validation set (area under the receiver operating characteristics curve [auROC] = 0.86; area under the precision-recall curve [auPR] = 0.90) and of a separate, independent digital insole dataset containing control and knee osteoarthritis subjects (auROC = 0.83; auPR = 0.86). After showing that digital insole-derived gait characteristics are comparable to traditional gait measurements, we next showed that a single stride of raw sensor time-series data could be accurately assigned to each subject, highlighting that individuals using digital insoles can be identified by their gait characteristics. This work provides a framework for a promising alternative to traditional clinical gait analysis methods, adds to the growing body of knowledge regarding wearable technology analytical pipelines, and supports clinical development of at-home gait assessments, with the potential to improve the ease, frequency, and depth of patient monitoring.

The way we walk ­ our 'gait' ­ is a key indicator of health. Gait irregularities like limping, shuffling or a slow pace can be signs of muscle or joint problems. Assessing a patient's gait is therefore an important element in diagnosing these conditions, and in evaluating whether treatments are working. Gait is often assessed via a simple visual inspection, with patients being asked to walk back and forth in a doctor's office. While quick and easy, this approach is highly subjective and therefore imprecise. 'Objective gait analysis' is a more accurate alternative, but it relies on tests being conducted in specialised laboratories with large-scale, expensive equipment operated by highly trained staff. Unfortunately, this means that gait laboratories are not accessible for everyday clinical use. In response, Wipperman et al. aimed to develop a low-cost alternative to the complex equipment used in gait laboratories. To do this, they harnessed wearable sensor technologies ­ devices that can directly measure physiological data while embedded in clothing or attached to the user. Wearable sensors have the advantage of being cheap, easy to use, and able to provide clinically useful information without specially trained staff. Wipperman et al. analysed data from classic gait laboratory devices, as well as 'digital insoles' equipped with sensors that captured foot movements and pressure as participants walked. The analysis first 'trained' on data from gait laboratories (called force plates) and then applied the method to gait measurements obtained from digital insoles worn by either healthy participants or patients with knee problems. Analysis of the pressure data from the insoles confirmed that they could accurately predict which measurements were from healthy individuals, and which were from patients. The gait characteristics detected by the insoles were also comparable to lab-based measurements ­ in other words, the insoles provided similar type and quality of data as a gait laboratory. Further analysis revealed that information from just a single step could reveal additional information about the subject's walking. These results support the use of wearable devices as a simple and relatively inexpensive way to measure gait in everyday clinical practice, without the need for specialised laboratories and visits to the doctor's office. Although the digital insoles will require further analytical and clinical study before they can be widely used, Wipperman et al. hope they will eventually make monitoring muscle and joint conditions easier and more affordable.

Validation of a Zio XT Patch Accelerometer for the Objective Assessment of Physical Activity in the Atherosclerosis Risk in Communities (ARIC) Study.

BACKGROUND: Combination devices to monitor heart rate/rhythms and physical activity are becoming increasingly popular in research and clinical settings. The Zio XT Patch (iRhythm Technologies, San Francisco, CA, USA) is US Food and Drug Administration (FDA)-approved for monitoring heart rhythms, but the validity of its accelerometer for assessing physical activity is unknown. OBJECTIVE: To validate the accelerometer in the Zio XT Patch for measuring physical activity against the widely-used ActiGraph GT3X. METHODS: The Zio XT and ActiGraph wGT3X-BT (Actigraph, Pensacola, FL, USA) were worn simultaneously in two separately-funded ancillary studies to Visit 6 of the Atherosclerosis Risk in Communities (ARIC) Study (2016-2017). Zio XT was worn on the chest and ActiGraph was worn on the hip. Raw accelerometer data were summarized using mean absolute deviation (MAD) for six different epoch lengths (1-min, 5-min, 10-min, 30-min, 1-h, and 2-h). Participants who had ≥3 days of at least 10 h of valid data between 7 a.m-11 p.m were included. Agreement of epoch-level MAD between the two devices was evaluated using correlation and mean squared error (MSE). RESULTS: Among 257 participants (average age: 78.5 ± 4.7 years; 59.1% female), there were strong correlations between MAD values from Zio XT and ActiGraph (average r: 1-min: 0.66, 5-min: 0.90, 10-min: 0.93, 30-min: 0.93, 1-h: 0.89, 2-h: 0.82), with relatively low error values (Average MSE × 106: 1-min: 349.37 g, 5-min: 86.25 g, 10-min: 56.80 g, 30-min: 45.46 g, 1-h: 52.56 g, 2-h: 54.58 g). CONCLUSIONS: These findings suggest that Zio XT accelerometry is valid for measuring duration, frequency, and intensity of physical activity within time epochs of 5-min to 2-h.

Musculoskeletal Pain Characteristics and Objectively Measured Physical Activity in Older Adults.

BACKGROUND: Pain is associated with reports of restricted physical activity (PA), yet the association between musculoskeletal pain characteristics and objectively measured PA quantities and patterns in late life is not well understood. METHODS: A total of 553 adults (mean age 75.8 ±â€…8.4 years, 54.4% women) in the Baltimore Longitudinal Study of Aging (BLSA) completed a health interview and subsequent 7-day wrist-worn ActiGraph assessment in the free-living environment between 2015 and 2020. Pain characteristics, including pain presence in 6x sites (ie, shoulders, hands/wrists, low back, hip, knees, and feet), pain laterality in each site, and pain distribution were assessed. PA metrics were summarized into total daily activity counts (TAC), activity fragmentation, active minutes/day, and diurnal patterns of activity. Linear regression models and mixed-effects models examined the association between pain characteristics and PA outcomes, adjusted for demographics and comorbidities. RESULTS: Unilateral knee pain was associated with 184 070 fewer TAC (p = .039) and 36.2 fewer active minutes/day (p = .032) compared to those without knee pain. Older adults with shoulder pain or hand/wrist pain had more active minutes compared to those without pain (p < .05 for all). For diurnal patterns of activity, participants with knee pain had fewer activity counts during the afternoon (12:00 pm to 5:59 pm). Analyses stratified by sex showed that these associations were only significant among women. CONCLUSIONS: Our study highlights the importance of assessing pain laterality in addition to pain presence and suggests that pain interferes with multiple aspects of daily activity. Longitudinal studies are needed to assess the temporality of these findings.

The Association Between Ankle-Brachial Index and Daily Patterns of Physical Activity: Results From the Hispanic Community Health Study/Study of Latinos.

BACKGROUND: Peripheral artery disease (PAD) is associated with lower physical activity but less is known about its association with daily patterns of activity. We examined the cross-sectional association between ankle-brachial index (ABI) and objectively measured patterns of physical activity among Hispanic/Latino adults. METHODS: We analyzed data from 7 688 participants (aged 45-74 years) in the Hispanic Community Health Study/Study of Latinos. ABI was categorized as low (≤0.90, indicating PAD), borderline low (0.91-0.99), normal (1.00-1.40), and high (>1.40, indicating incompressible ankle arteries). Daily physical activity metrics derived from accelerometer data included: log of total activity counts (LTAC), total log-transformed activity counts (TLAC), and active-to-sedentary transition probability (ASTP). Average differences between ABI categories in physical activity, overall and by 4-hour time-of-day intervals, were assessed using linear regression and mixed-effects models, respectively. RESULTS: In Hispanic/Latino adults, 5.3% and 2.6% had low and high ABIs, respectively. After adjustment, having a low compared to a normal ABI was associated with lower volume (LTAC = -0.13, p < .01; TLAC = -74.4, p = .04) and more fragmented physical activity (ASTP = 1.22%, p < .01). Having a low ABI was linked with more fragmented physical activity after 12 pm (p < .01). Having a high ABI was associated with lower volumes of activity (TLAC = -132.0, p = .03). CONCLUSIONS: Having a low or high ABI is associated with lower and more fragmented physical activity in Hispanic/Latino adults. In adults with low ABI, physical activity is more fragmented in the afternoon to evening. Longitudinal research is warranted to expand these findings to guide targeted interventions for PAD or incompressible ankle arteries.

24 h Rest/Activity Rhythms in Older Adults with Memory Impairment: Associations with Cognitive Performance and Depressive Symptomatology.

Little is known about links of circadian rhythm alterations with neuropsychiatric symptoms and cognition in memory impaired older adults. Associations of actigraphic rest/activity rhythms (RAR) with depressive symptoms and cognition are examined using function-on-scalar regression (FOSR). Forty-four older adults with memory impairment (mean: 76.84 ± 8.15 years; 40.9% female) completed 6.37 ± 0.93 days of actigraphy, the Beck depression inventory-II (BDI-II), mini-mental state examination (MMSE) and consortium to establish a registry for Alzheimer's disease (CERAD) delayed word recall. FOSR models with BDI-II, MMSE, or CERAD as individual predictors adjusted for demographics (Models A1-A3) and all three predictors and demographics (Model B). In Model B, higher BDI-II scores are associated with greater activity from 12:00-11:50 a.m., 2:10-5:50 p.m., 8:40-9:40 p.m., 11:20-12:00 a.m., higher CERAD scores with greater activity from 9:20-10:00 p.m., and higher MMSE scores with greater activity from 5:50-10:50 a.m. and 12:40-5:00 p.m. Greater depressive symptomatology is associated with greater activity in midafternoon, evening, and overnight into midday; better delayed recall with greater late evening activity; and higher global cognitive performance with greater morning and afternoon activity (Model B). Time-of-day specific RAR alterations may affect mood and cognitive performance in this population.

The association of vitamin D supplementation and serum vitamin D levels with physical activity in older adults: Results from a randomized trial.

BACKGROUND: To assess whether vitamin D3 supplementation attenuates the decline in daily physical activity in low-functioning adults at risk for falls. METHODS: Secondary data analyses of STURDY (Study to Understand Fall Reduction and Vitamin D in You), a response-adaptive randomized clinical trial. Participants included 571 adults aged 70 years and older with baseline serum 25(OH)D levels of 10-29 ng/mL and elevated fall risk, who wore a wrist accelerometer at baseline and at least one follow-up visit and were randomized to receive: 200 IU/day (control), 1000, 2000, or 4000 IU/day of vitamin D3 . Objective physical activity quantities and patterns (total daily activity counts, active minutes/day, and activity fragmentation) were measured for 7-days, 24-h/day, in the free-living environment using the Actigraph GT9x over up to 24-months of follow-up. RESULTS: In adjusted models, physical activity quantities declined (p < 0.001) and became more fragmented, or "broken up", (p = 0.017) over time. Supplementation with vitamin D3 did not attenuate this decline. Changes in physical activity were more rapid among those with baseline serum 25(OH)D <20 ng/mL compared to those with baseline 25(OH)D levels of 20-29 ng/mL (time*baseline 25(OH)D, p < 0.05). CONCLUSION: In low-functioning older adults with serum 25(OH)D levels 10-29 ng/mL, vitamin D3 supplementation of 1000 IU/day or higher did not attenuate declines in physical activity compared with 200 IU/day. Those with baseline 25(OH)D <20 ng/mL showed accelerated declines in physical activity. Alternative interventions to supplementation are needed to curb declines in physical activity in older adults with low serum 25(OH)D.

Free-Living Gait Cadence Measured by Wearable Accelerometer: A Promising Alternative to Traditional Measures of Mobility for Assessing Fall Risk.

BACKGROUND: Wearable devices have become widespread in research applications, yet evidence on whether they are superior to structured clinic-based assessments is sparse. In this manuscript, we compare traditional, laboratory-based metrics of mobility with a novel accelerometry-based measure of free-living gait cadence for predicting fall rates. METHODS: Using negative binomial regression, we compared traditional in-clinic measures of mobility (6-minute gait cadence, speed, and distance, and 4-m gait speed) with free-living gait cadence from wearable accelerometers in predicting fall rates. Accelerometry data were collected with wrist-worn Actigraphs (GT9X) over 7 days in 432 community-dwelling older adults (aged 77.29 ± 5.46 years, 59.1% men, 80.2% White) participating in the Study to Understand Fall Reduction and Vitamin D in You. Falls were ascertained using monthly calendars, quarterly contacts, and ad hoc telephone reports. Accelerometry-based free-living gait cadence was estimated with the Adaptive Empirical Pattern Transformation algorithm. RESULTS: Across all participants, free-living cadence was significantly related to fall rates; every 10 steps per minute higher cadence was associated with a 13.2% lower fall rate (p = .036). Clinic-based measures of mobility were not related to falls (p > .05). Among higher-functioning participants (cadence ≥100 steps/minute), every 10 steps per minute higher free-living cadence was associated with a 27.7% lower fall rate (p = .01). In participants with slow baseline gait (gait speed <0.8 m/s), all metrics were significantly associated with fall rates. CONCLUSION: Data collected from biosensors in the free-living environment may provide a more sensitive indicator of fall risk than in-clinic tests, especially among higher-functioning older adults who may be more responsive to intervention. CLINICAL TRIAL REGISTRATION: NCT02166333.

Wrist-Worn Accelerometry, Aging, and Gait Speed in the Baltimore Longitudinal Study of Aging.

Wrist-worn accelerometry metrics are not well defined in older adults. Accelerometry data from 720 participants (mean age 70 years, 55% women) were summarized into (a) total activity counts per day, (b) active minutes per day, (c) active bouts per day, and (d) activity fragmentation (the reciprocal of the mean active bout length). Linear regression and mixed-effects models were utilized to estimate associations between age and gait speed with wrist accelerometry. Activity counts per day, daily active minutes per day, and active bouts per day were negatively associated with age among all participants, while positive associations with activity fragmentation were only observed among those ≥65 years. More activity counts, more daily active minutes, and lower activity fragmentation were associated with faster gait speed. There were baseline age interactions with annual changes in total activity counts per day, active minutes per day, and activity fragmentation (Baseline age × Time, p < .01 for all). These results help define and characterize changes in wrist-based physical activity patterns among older adults.

Distributional data analysis via quantile functions and its application to modeling digital biomarkers of gait in Alzheimer's Disease.

With the advent of continuous health monitoring with wearable devices, users now generate their unique streams of continuous data such as minute-level step counts or heartbeats. Summarizing these streams via scalar summaries often ignores the distributional nature of wearable data and almost unavoidably leads to the loss of critical information. We propose to capture the distributional nature of wearable data via user-specific quantile functions (QF) and use these QFs as predictors in scalar-on-quantile-function-regression (SOQFR). As an alternative approach, we also propose to represent QFs via user-specific L-moments, robust rank-based analogs of traditional moments, and use L-moments as predictors in SOQFR (SOQFR-L). These two approaches provide two mutually consistent interpretations: in terms of quantile levels by SOQFR and in terms of L-moments by SOQFR-L. We also demonstrate how to deal with multi-modal distributional data via Joint and Individual Variation Explained using L-moments. The proposed methods are illustrated in a study of association of digital gait biomarkers with cognitive function in Alzheimers disease. Our analysis shows that the proposed methods demonstrate higher predictive performance and attain much stronger associations with clinical cognitive scales compared to simple distributional summaries.

Patterns of Daily Physical Movement, Chronic Inflammation, and Frailty Incidence.

INTRODUCTION: Low physical activity is a criterion of phenotypic frailty defined as an increased state of vulnerability to adverse health outcomes. Whether disengagement from daily all-purpose physical activity is prospectively associated with frailty and possibly modified by chronic inflammation-a pathway often underlying frailty-remains unexplored. METHODS: Using the Study to Understand Fall Reduction and Vitamin D in You data from 477 robust/prefrail adults (mean age = 76 ± 5 yr; 42% women), we examined whether accelerometer patterns (activity counts per day, active minutes per day, and activity fragmentation [broken accumulation]) were associated with incident frailty using Cox proportional hazard regression. Baseline interactions between each accelerometer metric and markers of inflammation that include interleukin-6, C-reactive protein, and tumor necrosis factor-alpha receptor 1 were also examined. RESULTS: Over an average of 1.3 yr, 42 participants (9%) developed frailty. In Cox regression models adjusted for demographics, medical conditions, and device wear days, every 30 min·d -1 higher baseline active time, 100,000 more activity counts per day, and 1% lower activity fragmentation was associated with a 16% ( P = 0.003), 13% ( P = 0.001), and 8% ( P < 0.001) lower risk of frailty, respectively. No interactions between accelerometer metrics and baseline interleukin-6, C-reactive protein, or tumor necrosis factor-alpha receptor 1 were detected (interaction P > 0.06 for all). CONCLUSIONS: Among older adults who are either robust or prefrail, constricted patterns of daily physical activity (i.e., lower total activity minutes and counts, and higher activity fragmentation) were prospectively associated with higher risk of frailty but not modified by frailty-related chronic inflammation. Additional studies, particularly trials, are needed to understand if this association is causal.

Improving patient selection for use of consumer grade physical activity monitors in the hospital.

BACKGROUND: Bedrest is toxic for inpatients and consumer grade physical activity monitors offer an economical solution to monitor patient ambulation. But these devices may not be accurate in debilitated hospitalized patients who frequently ambulate very slowly. OBJECTIVE: To determine whether measures of physical capacity can help identify inpatients for whom wearable physical activity monitors may accurately measure step count. METHODS: Prospective observational study of 54 adult inpatients with acute neurological diagnoses. Patients were assessed using 2 physical capacity assessments (Activity Measure for Post-Acute Care Inpatient Mobility Short Form [AM-PAC IMSF] and Katz Activities of Daily Living [ADL] scale). They also completed a 2-minute walk test (2MWT) wearing a consumer grade physical activity monitor. RESULTS: The wearable activity monitor recorded steps (initiated) in 33 (61%) of the inpatients, and for 94% of inpatients with gait speeds >0.43 m/s. Physical capacity assessments correlated well with gait speed, AM-PAC IMSF r = 0.7, and Katz ADL r = 0.6, p < 0.05. When the physical activity monitor initiated, the mean absolute percent error (SD) comparing device calculated steps to observed steps, was 10% (13). AM-PAC IMSF (T-score >45) and Katz ADL (>5) cutoff scores identified inpatients for whom physical activity monitors initiated with a sensitivity of 94 and 91%, respectively. CONCLUSIONS: Physical capacity assessments, such as AM-PAC, and Katz ADL, may be a useful and feasible screening strategy to help identify inpatients where wearable physical activity monitors can measure their mobility.

Smartphone-Based Gait Cadence to Identify Older Adults with Decreased Functional Capacity.

Background: Functional capacity assessment is a critical step in the preoperative evaluation to identify patients at increased risk of cardiac complications and disability after major noncardiac surgery. Smartphones offer the potential to objectively measure functional capacity but are limited by inaccuracy in patients with poor functional capacity. Open-source methods exist to analyze accelerometer data to estimate gait cadence (steps/min), which is directly associated with activity intensity. Here, we used an updated Step Test smartphone application with an open-source method to analyze accelerometer data to estimate gait cadence and functional capacity in older adults. Methods: We performed a prospective observational cohort study within the Frailty, Activity, Body Composition and Energy Expenditure in Aging study at the University of Chicago. Participants completed the Duke Activity Status Index (DASI) and performed an in-clinic 6-min walk test (6MWT) while using the Step Test application on a study smartphone. Gait cadence was measured from the raw accelerometer data using an adaptive empirical pattern transformation method, which has been previously validated. A 6MWT distance of 370 m was used as an objective threshold to identify patients at high risk. We performed multivariable logistic regression to predict walking distance using a priori explanatory variables. Results: Sixty patients were enrolled in the study. Thirty-seven patients completed the protocol and were included in the final data analysis. The median (IQR) age of the overall cohort was 71 (69-74) years, with a body mass index of 31 (27-32). There were no differences in any clinical characteristics or functional measures between participants that were able to walk 370 m during the 6MWT and those that could not walk that distance. Median (IQR) gait cadence for the entire cohort was 110 (102-114) steps/min during the 6MWT. Median (IQR) gait cadence was higher in participants that walked more than 370 m during the 6MWT 112 (108-118) versus 106 (96-114) steps/min; p = 0.0157). The final multivariable model to identify participants that could not walk 370 m included only median gait cadence. The Youden's index cut-point was 107 steps/min with a sensitivity of 0.81 (95% CI: 0.77, 0.85) and a specificity of 0.57 (95% CI: 0.55, 0.59) and an AUCROC of 0.69 (95% CI: 0.51, 0.87). Conclusions: Our pilot study demonstrates the feasibility of using gait cadence as a measure to estimate functional capacity. Our study was limited by a smaller than expected sample size due to COVID-19, and thus, a prospective study with preoperative patients that measures outcomes is necessary to validate our findings.

Patterns of objectively measured physical activity differ between men living with and without HIV.

OBJECTIVE: To use accelerometers to quantify differences in physical activity (PA) by HIV serostatus and HIV viral load (VL) in the Multicenter AIDS Cohort Study (MACS). METHODS: MACS participants living with (PLWH, n = 631) and without (PWOH, n = 578) HIV wore an ambulatory electrocardiogram monitor containing an accelerometer for 1-14 days. PA was summarized as cumulative mean absolute deviation (MAD) during the 10 most active consecutive hours (M10), cumulative MAD during the six least active consecutive hours (L6), and daily time recumbent (DTR). PA summaries were compared by HIV serostatus and by detectability of VL (>20 vs. ≤20 copies/ml) using linear mixed models adjusted for sociodemographics, weight, height, substance use, physical function, and clinical factors. RESULTS: In sociodemographic-adjusted models, PLWH with a detectable VL had higher L6 (ß = 0.58 mg, P = 0.027) and spent more time recumbent (ß = 53 min/day, P = 0.003) than PWOH. PLWH had lower M10 than PWOH (undetectable VL ß = -1.62 mg, P = 0.027; detectable VL ß = -1.93 mg, P = 0.12). A joint test indicated differences in average PA measurements by HIV serostatus and VL (P = 0.001). However, differences by HIV serostatus in M10 and DTR were attenuated and no longer significant after adjustment for renal function, serum lipids, and depressive symptoms. CONCLUSIONS: Physical activity measures differed significantly by HIV serostatus and VL. Higher L6 among PLWH with detectable VL may indicate reduced amount or quality of sleep compared to PLWH without detectable VL and PWOH. Lower M10 among PLWH indicates lower amounts of physical activity compared to PWOH.

Free-living wrist and hip accelerometry forecast cognitive decline among older adults without dementia over 1- or 5-years in two distinct observational cohorts.

The prevalence of major neurocognitive disorders is expected to rise over the next 3 decades as the number of adults ≥65 years old increases. Noninvasive screening capable of flagging individuals most at risk of subsequent cognitive decline could trigger closer monitoring and preventive strategies. In this study, we used free-living accelerometry data to forecast cognitive decline within 1- or 5-years in older adults without dementia using two cohorts. The first cohort, recruited in the south side of Chicago, wore hip accelerometers for 7 continuous days. The second cohort, nationally recruited, wore wrist accelerometers continuously for 72 h. Separate classifier models forecasted 1-year cognitive decline with over 85% accuracy using hip data and forecasted 5-year cognitive decline with nearly 70% accuracy using wrist data, significant improvements compared to demographics and comorbidities alone. The proposed models are readily translatable to clinical practices serving ageing populations.

Scalar on time-by-distribution regression and its application for modelling associations between daily-living physical activity and cognitive functions in Alzheimer's Disease.

Wearable data is a rich source of information that can provide a deeper understanding of links between human behaviors and human health. Existing modelling approaches use wearable data summarized at subject level via scalar summaries in regression, temporal (time-of-day) curves in functional data analysis (FDA), and distributions in distributional data analysis (DDA). We propose to capture temporally local distributional information in wearable data using subject-specific time-by-distribution (TD) data objects. Specifically, we develop scalar on time-by-distribution regression (SOTDR) to model associations between scalar response of interest such as health outcomes or disease status and TD predictors. Additionally, we show that TD data objects can be parsimoniously represented via a collection of time-varying L-moments that capture distributional changes over the time-of-day. The proposed method is applied to the accelerometry study of mild Alzheimer's disease (AD). We found that mild AD is significantly associated with reduced upper quantile levels of physical activity, particularly during morning hours. In-sample cross validation demonstrated that TD predictors attain much stronger associations with clinical cognitive scales of attention, verbal memory, and executive function when compared to predictors summarized via scalar total activity counts, temporal functional curves, and quantile functions. Taken together, the present results suggest that SOTDR analysis provides novel insights into cognitive function and AD.

Comparison of Accelerometry-Based Measures of Physical Activity: Retrospective Observational Data Analysis Study.

BACKGROUND: Given the evolution of processing and analysis methods for accelerometry data over the past decade, it is important to understand how newer summary measures of physical activity compare with established measures. OBJECTIVE: We aimed to compare objective measures of physical activity to increase the generalizability and translation of findings of studies that use accelerometry-based data. METHODS: High-resolution accelerometry data from the Baltimore Longitudinal Study on Aging were retrospectively analyzed. Data from 655 participants who used a wrist-worn ActiGraph GT9X device continuously for a week were summarized at the minute level as ActiGraph activity count, monitor-independent movement summary, Euclidean norm minus one, mean amplitude deviation, and activity intensity. We calculated these measures using open-source packages in R. Pearson correlations between activity count and each measure were quantified both marginally and conditionally on age, sex, and BMI. Each measures pair was harmonized using nonparametric regression of minute-level data. RESULTS: Data were from a sample (N=655; male: n=298, 45.5%; female: n=357, 54.5%) with a mean age of 69.8 years (SD 14.2) and mean BMI of 27.3 kg/m2 (SD 5.0). The mean marginal participant-specific correlations between activity count and monitor-independent movement summary, Euclidean norm minus one, mean amplitude deviation, and activity were r=0.988 (SE 0.0002324), r=0.867 (SE 0.001841), r=0.913 (SE 0.00132), and r=0.970 (SE 0.0006868), respectively. After harmonization, mean absolute percentage errors of predicting total activity count from monitor-independent movement summary, Euclidean norm minus one, mean amplitude deviation, and activity intensity were 2.5, 14.3, 11.3, and 6.3, respectively. The accuracies for predicting sedentary minutes for an activity count cut-off of 1853 using monitor-independent movement summary, Euclidean norm minus one, mean amplitude deviation, and activity intensity were 0.981, 0.928, 0.904, and 0.960, respectively. An R software package called SummarizedActigraphy, with a unified interface for computation of the measures from raw accelerometry data, was developed and published. CONCLUSIONS: The findings from this comparison of accelerometry-based measures of physical activity can be used by researchers and facilitate the extension of knowledge from existing literature by demonstrating the high correlation between activity count and monitor-independent movement summary (and other measures) and by providing harmonization mapping.

Development of a Novel Accelerometry-Based Performance Fatigability Measure for Older Adults.

INTRODUCTION: Efforts to study performance fatigability have been limited because of measurement constrains. Accelerometry and advanced statistical methods may enable us to quantify performance fatigability more granularly via objective detection of performance decline. Thus, we developed the Pittsburgh Performance Fatigability Index (PPFI) using triaxial raw accelerations from wrist-worn accelerometer from two in-laboratory 400-m walks. METHODS: Sixty-three older adults from our cross-sectional study (mean age, 78 yr; 56% women; 88% White) completed fast-paced ( n = 59) and/or usual-paced 400-m walks ( n = 56) with valid accelerometer data. Participants wore ActiGraph GT3X+ accelerometers (The ActiGraph LLC, Pensacola, FL) on nondominant wrist during the walking task. Triaxial raw accelerations from accelerometers were used to compute PPFI, which quantifies percentage of area under the observed gait cadence-versus-time trajectory during a 400-m walk to a hypothetical area that would be produced if the participant sustained maximal cadence throughout the entire walk. RESULTS: Higher PPFI scores (higher score = greater fatigability) correlated with worse physical function, slower chair stands speed and gait speed, worse cardiorespiratory fitness and mobility, and lower leg peak power (| ρ | = 0.36-0.61 from fast-paced and | ρ | = 0.28-0.67 from usual-paced walks, all P < 0.05). PPFI scores from both walks remained associated with chair stands speed, gait speed, fitness, and mobility, after adjustment for sex, age, race, weight, height, and smoking status; PPFI scores from the fast-paced walk were associated with leg peak power. CONCLUSIONS: Our findings revealed that the objective PPFI is a sensitive measure of performance fatigability for older adults and can serve as a risk assessment tool or outcome measure in future studies and clinical practice.

Daily Physical Activity Patterns as a Window on Cognitive Diagnosis in the Baltimore Longitudinal Study of Aging (BLSA).

BACKGROUND: Gradual disengagement from daily physical activity (PA) could signal present or emerging mild cognitive impairment (MCI) or Alzheimer's disease (AD). OBJECTIVE: This study examined whether accelerometry-derived patterns of everyday movement differ by cognitive diagnosis in participants of the Baltimore Longitudinal Study of Aging (BLSA). METHODS: Activity patterns, overall and by time-of-day, were cross-sectionally compared between participants with adjudicated normal cognition (n = 549) and MCI/AD diagnoses (n = 36; 5 participants [14%] living with AD) using covariate-adjusted regression models. RESULTS: Compared to those with normal cognition, those with MCI/AD had 2.1% higher activity fragmentation (SE = 1.0%, p = 0.036) but similar mean total activity counts/day (p = 0.075) and minutes/day spent active (p = 0.174). Time-of-day analyses show MCI/AD participants had lower activity counts and minutes spent active during waking hours (6:00 am-5:59 pm; p < 0.01 for all). Also, they had lower activity fragmentation from 12:00-5:59 am (p < 0.001), but higher fragmentation from 12:00-5:59 pm (p = 0.026). CONCLUSION: Differences in the timing and patterns of physical activity throughout the day linked to MCI/AD diagnoses warrant further investigation into potential clinical utility.