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.

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.

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.

Adaptive empirical pattern transformation (ADEPT) with application to walking stride segmentation.

Quantifying gait parameters and ambulatory monitoring of changes in these parameters have become increasingly important in epidemiological and clinical studies. Using high-density accelerometry measurements, we propose adaptive empirical pattern transformation (ADEPT), a fast, scalable, and accurate method for segmentation of individual walking strides. ADEPT computes the covariance between a scaled and translated pattern function and the data, an idea similar to the continuous wavelet transform. The difference is that ADEPT uses a data-based pattern function, allows multiple pattern functions, can use other distances instead of the covariance, and the pattern function is not required to satisfy the wavelet admissibility condition. Compared to many existing approaches, ADEPT is designed to work with data collected at various body locations and is invariant to the direction of accelerometer axes relative to body orientation. The method is applied to and validated on accelerometry data collected during a $450$-m outdoor walk of $32$ study participants wearing accelerometers on the wrist, hip, and both ankles. Additionally, all scripts and data needed to reproduce presented results are included in supplementary material available at Biostatistics online.

The effects of vitamin D supplementation on frailty in older adults at risk for falls.

BACKGROUND: Low serum 25-hydroxyvitamin D [25(OH)D] level is associated with a greater risk of frailty, but the effects of daily vitamin D supplementation on frailty are uncertain. This secondary analysis aimed to examine the effects of vitamin D supplementation on frailty using data from the Study To Understand Fall Reduction and Vitamin D in You (STURDY). METHODS: The STURDY trial, a two-stage Bayesian, response-adaptive, randomized controlled trial, enrolled 688 community-dwelling adults aged ≥ 70 years with a low serum 25(OH)D level (10-29 ng/mL) and elevated fall risk. Participants were initially randomized to 200 IU/d (control dose; n = 339) or a higher dose (1000 IU/d, 2000 IU/d, or 4000 IU/d; n = 349) of vitamin D3. Once the 1000 IU/d was selected as the best higher dose, other higher dose groups were reassigned to the 1000 IU/d group and new enrollees were randomized 1:1 to 1000 IU/d or control group. Data were collected at baseline, 3, 12, and 24 months. Frailty phenotype was based on number of the following conditions: unintentional weight loss, exhaustion, slowness, low activity, and weakness (≥ 3 conditions as frail, 1 or 2 as pre-frail, and 0 as robust). Cox proportional hazard models estimated the risk of developing frailty, or improving or worsening frailty status at follow-up. All models were adjusted for demographics, health conditions, and further stratified by baseline serum 25(OH)D level (insufficiency (20-29 ng/mL) vs. deficiency (10-19 ng/mL)). RESULTS: Among 687 participants (mean age 77.1 ± 5.4, 44% women) with frailty assessment at baseline, 208 (30%) were robust, 402 (59%) were pre-frail, and 77 (11%) were frail. Overall, there was no significant difference in risk of frailty outcomes comparing the pooled higher doses (PHD; ≥ 1000 IU/d) vs. 200 IU/d. When comparing each higher dose vs. 200 IU/d, the 2000 IU/d group had nearly double the risk of worsening frailty status (HR = 1.89, 95% CI: 1.13-3.16), while the 4000 IU/d group had a lower risk of developing frailty (HR = 0.22, 95% CI: 0.05-0.97). There were no significant associations between vitamin D doses and frailty status in the analyses stratified by baseline serum 25(OH)D level. CONCLUSIONS: High dose vitamin D supplementation did not prevent frailty. Significant subgroup findings might be the results of type 1 error. TRIAL REGISTRATION: ClinicalTrials.gov: NCT02166333 .

HIV Infection Is Associated With Variability in Ventricular Repolarization: The Multicenter AIDS Cohort Study (MACS).

BACKGROUND: People living with human immunodeficiency virus (HIV+) have greater risk for sudden arrhythmic death than HIV-uninfected (HIV-) individuals. HIV-associated abnormal cardiac repolarization may contribute to this risk. We investigated whether HIV serostatus is associated with ventricular repolarization lability by using the QT variability index (QTVI), defined as a log measure of QT-interval variance indexed to heart rate variance. METHODS: We studied 1123 men (589 HIV+ and 534 HIV-) from MACS (Multicenter AIDS Cohort Study), using the ZioXT ambulatory electrocardiography patch. Beat-to-beat analysis of up to 4 full days of electrocardiographic data per participant was performed using an automated algorithm (median analyzed duration [quartile 1-quartile 3]: 78.3 [66.3-83.0] hours/person). QTVI was modeled using linear mixed-effects models adjusted for demographics, cardiac risk factors, and HIV-related and inflammatory biomarkers. RESULTS: Mean (SD) age was 60.1 (11.9) years among HIV- and 54.2 (11.2) years among HIV+ participants (P<0.001), 83% of whom had undetectable (<20 copies/mL) HIV-1 viral load (VL). In comparison with HIV- men, HIV+ men had higher QTVI (adjusted difference of +0.077 [95% CI, +0.032 to +0.123]). The magnitude of this association depended on the degree of viremia, such that in HIV+ men with undetectable VL, adjusted QTVI was +0.064 (95% CI, +0.017 to +0.111) higher than in HIV- men, whereas, in HIV+ men with detectable VL, adjusted QTVI was higher by +0.150 (95% CI, 0.072-0.228) than in HIV- referents. Analysis of QTVI subcomponents showed that HIV+ men had: (1) lower heart rate variability irrespective of VL status, and (2) higher QT variability if they had detectable, but not with undetectable, VL, in comparison with HIV- men. Higher levels of C-reactive protein, interleukin-6, intercellular adhesion molecule-1, soluble tumor necrosis factor receptor 2, and soluble cluster of differentiation-163 (borderline), were associated with higher QTVI and partially attenuated the association with HIV serostatus. CONCLUSIONS: HIV+ men have greater beat-to-beat variability in QT interval (QTVI) than HIV- men, especially in the setting of HIV viremia and heightened inflammation. Among HIV+ men, higher QTVI suggests ventricular repolarization lability, which can increase susceptibility to arrhythmias, whereas lower heart rate variability signals a component of autonomic dysfunction.

Use of Functional Linear Models to Detect Associations between Characteristics of Walking and Continuous Responses Using Accelerometry Data.

Various methods exist to measure physical activity. Subjective methods, such as diaries and surveys, are relatively inexpensive ways of measuring one's physical activity; however, they are prone to measurement error and bias due to self-reporting. Wearable accelerometers offer a non-invasive and objective measure of one's physical activity and are now widely used in observational studies. Accelerometers record high frequency data and each produce an unlabeled time series at the sub-second level. An important activity to identify from the data collected is walking, since it is often the only form of activity for certain populations. Currently, most methods use an activity summary which ignores the nuances of walking data. We propose methodology to model specific continuous responses with a functional linear model utilizing spectra obtained from the local fast Fourier transform (FFT) of walking as a predictor. Utilizing prior knowledge of the mechanics of walking, we incorporate this as additional information for the structure of our transformed walking spectra. The methods were applied to the in-the-laboratory data obtained from the Developmental Epidemiologic Cohort Study (DECOS).

Re-evaluating the effect of age on physical activity over the lifespan.

Advancements in accelerometer analytic and visualization techniques allow researchers to more precisely identify and compare critical periods of physical activity (PA) decline by age across the lifespan, and describe how daily PA patterns may vary across age groups. We used accelerometer data from the 2003-2006 cohorts of the National Health and Nutrition Examination Survey (NHANES) (n=12,529) to quantify total PA as well as PA by intensity across the lifespan using sex-stratified, age specific percentile curves constructed using generalized additive models. We additionally estimated minute-to-minute diurnal PA using smoothed bivariate surfaces. We found that from childhood to adolescence (ages 6-19) across sex, PA is sharply lower by age partially due to a later initiation of morning PA. Total PA levels, at age 19 are comparable to levels at age 60. Contrary to prior evidence, during young adulthood (ages 20-30) total and light intensity PA increases by age and then stabilizes during midlife (ages 31-59) partially due to an earlier initiation of morning PA. We additionally found that males compared to females have an earlier lowering in PA by age at midlife and lower total PA, higher sedentary behavior, and lower light intensity PA in older adulthood; these trends seem to be driven by lower PA in the afternoon compared to females. Our results suggest a re-evaluation of how emerging adulthood may affect PA levels and the importance of considering time of day and sex differences when developing PA interventions.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.