Associations of Physical Activity and Heart Rate Variability from a Two-Week ECG Monitor with Cognitive Function and Dementia: The ARIC Neurocognitive Study.

Low physical activity (PA) measured by accelerometers and low heart rate variability (HRV) measured from short-term ECG recordings are associated with worse cognitive function. Wearable long-term ECG monitors are now widely used, and some devices also include an accelerometer. The objective of this study was to evaluate whether PA or HRV measured from long-term ECG monitors was associated with cognitive function among older adults. A total of 1590 ARIC participants had free-living PA and HRV measured over 14 days using the Zio® XT Patch [aged 72-94 years, 58% female, 32% Black]. Cognitive function was measured by cognitive factor scores and adjudicated dementia or mild cognitive impairment (MCI) status. Adjusted linear or multinomial regression models examined whether higher PA or higher HRV was cross-sectionally associated with higher factor scores or lower odds of MCI/dementia. Each 1-unit increase in the total amount of PA was associated with higher global cognition (ß = 0.30, 95% CI: 0.16-0.44) and executive function scores (ß = 0.38, 95% CI: 0.22-0.53) and lower odds of MCI (OR = 0.38, 95% CI: 0.22-0.67) or dementia (OR = 0.25, 95% CI: 0.08-0.74). HRV (i.e., SDNN and rMSSD) was not associated with cognitive function. More research is needed to define the role of wearable ECG monitors as a tool for digital phenotyping of dementia.

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.

Sensory and motor deficits as contributors to early cognitive impairment.

INTRODUCTION: Age-related sensory and motor impairment are associated with risk of dementia. No study has examined the joint associations of multiple sensory and motor measures on prevalence of early cognitive impairment (ECI). METHODS: Six hundred fifty participants in the Baltimore Longitudinal Study of Aging completed sensory and motor function tests. The association between sensory and motor function and ECI was examined using structural equation modeling with three latent factors corresponding to multisensory, fine motor, and gross motor function. RESULTS: The multisensory, fine, and gross motor factors were all correlated (r = 0.74 to 0.81). The odds of ECI were lower for each additional unit improvement in the multisensory (32%), fine motor (30%), and gross motor factors (12%). DISCUSSION: The relationship between sensory and motor impairment and emerging cognitive impairment may guide future intervention studies aimed at preventing and/or treating ECI. HIGHLIGHTS: Sensorimotor function and early cognitive impairment (ECI) prevalence were assessed via structural equation modeling. The degree of fine and gross motor function is associated with indicators of ECI. The degree of multisensory impairment is also associated with indicators of ECI.

Application and validation of an algorithmic classification of early impairment in cognitive performance.

OBJECTIVE: Due to the long prodromal period for dementia pathology, approaches are needed to detect cases before clinically recognizable symptoms are apparent, by which time it is likely too late to intervene. This study contrasted two theoretically-based algorithms for classifying early cognitive impairment (ECI) in adults aged ≥50 enrolled in the Baltimore Longitudinal Study of Aging. METHOD: Two ECI algorithms were defined as poor performance (1 standard deviation [SD] below age-, sex-, race-, and education-specific means) in: (1) Card Rotations or California Verbal Learning Test (CVLT) immediate recall and (2) ≥1 (out of 2) memory or ≥3 (out of 6) non-memory tests. We evaluated concurrent criterion validity against consensus diagnoses of mild cognitive impairment (MCI) or dementia and global cognitive scores using receiver operating characteristic (ROC) curve analysis. Predictive criterion validity was evaluated using Cox proportional hazards models to examine the associations between algorithmic status and future adjudicated MCI/dementia. RESULTS: Among 1,851 participants (mean age = 65.2 ± 11.8 years, 50% women, 74% white), the two ECI algorithms yielded comparably moderate concurrent criterion validity with adjudicated MCI/dementia. For predictive criterion validity, the algorithm based on impairment in Card Rotations or CVLT immediate recall was the better predictor of MCI/dementia (HR = 3.53, 95%CI: 1.59-7.84) over 12.3 follow-up years. CONCLUSIONS: Impairment in visuospatial ability or memory may be capable of detecting early cognitive changes in the preclinical phase among cognitively normal individuals.

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.

Assessment of Physical Activity in Adults Using Wrist Accelerometers.

The health benefits of physical activity (PA) have been widely recognized, yet traditional measures of PA, including questionnaires and category-based assessments of volume and intensity, provide only broad estimates of daily activities. Accelerometers have advanced epidemiologic research on PA by providing objective and continuous measurement of PA in free-living conditions. Wrist-worn accelerometers have become especially popular because of low participant burden. However, the validity and reliability of wrist-worn devices for adults have yet to be summarized. Moreover, accelerometer data provide rich information on how PA is accumulated throughout the day, but only a small portion of these rich data have been used by researchers. Last, new methodological developments are emerging that aim to overcome some of the limitations of accelerometers. In this review, we provide an overview of accelerometry research, with a special focus on wrist-worn accelerometers. We describe briefly how accelerometers work; summarize the validity and reliability of wrist-worn accelerometers; discuss the benefits of accelerometers, including measuring light-intensity PA; and discuss pattern metrics of daily PA recently introduced in the literature. A summary of large-scale cohort studies and randomized trials that implemented wrist-worn accelerometry is provided. We conclude the review by discussing new developments and directions of research using accelerometers, with a focus on wrist-worn accelerometers.

Impact of Time in Motion on Blood Pressure Regulation Among Patients with Metabolic Syndrome.

PURPOSE OF REVIEW: This review assessed recent evidence on the association between objectively measured physical activity from wearable accelerometers and blood pressure (BP) in participants with metabolic syndrome (MetS). RECENT FINDINGS: Results directly related to BP were mixed, with some studies showing positive associations and others showing null results. Importantly, several studies noted that participants with MetS demonstrated greater improvements in components of MetS after engaging in higher amounts of daily physical activity. Although this suggests greater volume of physical activity may be a means to partially mitigate hypertension in those with MetS, it remains unclear whether physical activity or inactivity (i.e., sedentary behavior) is more strongly associated with MetS. Although there may be benefit to greater volumes of daily PA among hypertensive patients with MetS, more research is needed to quantify and define the amount of daily activity needed to improve health and refine clinical recommendations. Moreover, although the evidence for improving components of MetS through engaging in physical activity is high, the amount and type(s) of physical activity needed to achieve these benefits is unclear.

Association between walking energy utilisation and longitudinal cognitive performance in older adults.

BACKGROUND: Human motor function is optimised for energetic efficiency, however, age-related neurodegenerative changes affects neuromotor control of walking. Energy utilisation has been associated with motor performance, but its association with cognitive performance is unknown. METHODS: The study population included 979 Baltimore Longitudinal Study of Aging participants aged $\ge$50 years (52% female, mean age: 70$\pm$10.2 years) with a median follow-up time of 4.7 years. Energy utilisation for walking was operationalised as a ratio of the energy cost of slow walking to peak walking energy expenditure during standardised tasks ('cost-ratio'). Cognitive functioning was measured using the Trail Making Tests, California Verbal Learning Test, Wechsler Adult Intelligence Scale (WAIS), letter and category fluency and card rotation tests. Linear mixed models adjusted for demographics, education and co-morbidities assessed the association between baseline cost-ratio and cognitive functioning, cross-sectionally and longitudinally. To investigate the relationship among those with less efficient energy utilisation, subgroup analyses were performed. RESULTS: In fully adjusted models, a higher cost-ratio was cross-sectionally associated with poorer performance on all cognitive tests except WAIS (P < 0.05 for all). Among those with compromised energy utilisation, the baseline cost-ratio was also associated with a faster decline in memory (long-delay free recall: ß = -0.4, 95% confidence interval [CI] = [-0.8, -0.02]; immediate word recall: ß = -1.3, 95% CI = [-2.7, 0.1]). CONCLUSIONS: These findings suggest cross-sectional and longitudinal links between energy utilisation and cognitive performance, highlighting an intriguing link between brain function and the energy needed for ambulation. Future research should examine this association earlier in the life course to gauge the potential for interventive mechanisms.

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 .

Are Machine Learning Models on Wrist Accelerometry Robust against Differences in Physical Performance among Older Adults?

Sufficient physical activity (PA) reduces the risk of a myriad of diseases and preserves physical capabilities in later life. While there have been significant achievements in mapping accelerations to real-life movements using machine learning (ML), errors continue to be common, particularly for wrist-worn devices. It remains unknown whether ML models are robust for estimating age-related loss of physical function. In this study, we evaluated the performance of ML models (XGBoost and LASSO) to estimate the hallmark measures of PA in low physical performance (LPP) and high physical performance (HPP) groups. Our models were built to recognize PA types and intensities, identify each individual activity, and estimate energy expenditure (EE) using wrist-worn accelerometer data (33 activities per participant) from a large sample of participants (n = 247, 57% females, aged 60+ years). Results indicated that the ML models were accurate in recognizing PA by type and intensity while also estimating EE accurately. However, the models built to recognize individual activities were less robust. Across all tasks, XGBoost outperformed LASSO. XGBoost obtained F1-Scores for sedentary (0.932 ± 0.005), locomotion (0.946 ± 0.003), lifestyle (0.927 ± 0.006), and strength flexibility exercise (0.915 ± 0.017) activity type recognition tasks. The F1-Scores for recognizing low, light, and moderate activity intensity were (0.932 ± 0.005), (0.840 ± 0.004), and (0.869 ± 0.005), respectively. The root mean square error for EE estimation was 0.836 ± 0.059 METs. There was no evidence showing that splitting the participants into the LPP and HPP groups improved the models' performance on estimating the hallmark measures of physical activities. In conclusion, using features derived from wrist-worn accelerometer data, machine learning models can accurately recognize PA types and intensities and estimate EE for older adults with high and low physical function.

Association of Physical Activity With Maximal and Submaximal Tests of Exercise Capacity in Middle- and Older-Aged Adults.

Although physical activity (PA) is an important determinant of exercise capacity, the association between these constructs is modest. The authors investigated the associations of self-reported and objectively measured PA with maximal and submaximal tests of exercise capacity. Participants aged ≥40 years (N = 413; 49.6% female) completed a PA questionnaire, wore a uniaxial accelerometer (5.2 ± 1.1 days), and performed maximal (cardiopulmonary exercise test [CPET]) and submaximal (long-distance corridor walk) tests with indirect calorimetry (oxygen consumption, V˙O2). Linear regression models were fitted to assess the variation in exercise capacity explained (partial eta squared, η2) by PA variables. Accelerometer-measured vigorous (η2 = 22% female; η2 = 16% male) and total PA (η2 = 17% female; η2 = 13% male) explained the most variance in CPET V˙O2 (p < .001). All η2 values were lower for long-distance corridor walk V˙O2 (η2 ≤ 11%). Age contributed more to CPET V˙O2 than any PA variable in males (η2 = 32%), but not in females (η2 = 19%). Vigorous and total PA play important roles in CPET V˙O2 in mid to late life.

Patterns of Daily Physical Activity across the Spectrum of Visual Field Damage in Glaucoma Patients.

PURPOSE: To define and quantify patterns of objectively measured daily physical activity by level of visual field (VF) damage in glaucoma patients including: (1) activity fragmentation, a metric of health and physiologic decline, and (2) diurnal patterns of activity, a measure of rest and activity rhythms. DESIGN: Prospective cohort study. PARTICIPANTS: Older adults diagnosed with glaucoma or suspected glaucoma. METHODS: Degree of VF damage was defined by the average VF sensitivity within the integrated VF (IVF). Each participant wore a hip accelerometer for 1 week to measure daily minute-by-minute activity for 7 consecutive days. Activity fragmentation was calculated as the reciprocal of the average activity bout duration in minutes, with higher fragmentation indicating more transient, rather than sustained, activity. Multivariate linear regression was used to test for cross-sectional associations between VF damage and activity fragmentation. Multivariate linear mixed-effects models were used to assess the associations between VF damage and accumulation of activity across 6 3-hour intervals from 5 am to 11 pm. MAIN OUTCOME MEASURES: Activity fragmentation and amount of activity (steps) over the course of the day. RESULTS: Each 5-dB decrement in IVF sensitivity was associated with 16.3 fewer active minutes/day (P < 0.05) and 2% higher activity fragmentation (P < 0.05), but not with the number of active bouts per day (P = 0.30). In time-of-day analyses, lower IVF sensitivity was associated with fewer steps over the 11 am to 2 pm, 2 pm to 5 pm, and 5 pm to 8 pm periods (106.6, 93.1, and 89.2 fewer steps, respectively; P < 0.05 for all), but not over other periods. The activity midpoint (the time at which half of the daily activity is completed) did not vary across level of VF damage. CONCLUSIONS: At worse levels of VF damage, glaucoma patients demonstrate shorter, more fragmented bouts of physical activity throughout the day and lower activity levels during typical waking hours, reflecting low physiologic functioning. Further work is needed to establish the temporality of this association and whether glaucoma patients with such activity patterns are at a greater risk of adverse health outcomes associated with activity fragmentation.

Age Differences in Estimating Physical Activity by Wrist Accelerometry Using Machine Learning.

Accelerometer-based fitness trackers and smartwatches are proliferating with incessant attention towards health tracking. Despite their growing popularity, accurately measuring hallmark measures of physical activities has yet to be accomplished in adults of all ages. In this work, we evaluated the performance of four machine learning models: decision tree, random forest, extreme gradient boosting (XGBoost) and least absolute shrinkage and selection operator (LASSO), to estimate the hallmark measures of physical activities in young (20-50 years), middle-aged (50-70 years], and older adults (70-89 years]. Our models were built to recognize physical activity types, recognize physical activity intensities, estimate energy expenditure (EE) and recognize individual physical activities using wrist-worn tri-axial accelerometer data (33 activities per participant) from a large sample of participants (n = 253, 62% women, aged 20-89 years old). Results showed that the machine learning models were quite accurate at recognizing physical activity type and intensity and estimating energy expenditure. However, models performed less optimally when recognizing individual physical activities. F1-Scores derived from XGBoost's models were high for sedentary (0.955-0.973), locomotion (0.942-0.964) and lifestyle (0.913-0.949) activity types with no apparent difference across age groups. Low (0.919-0.947), light (0.813-0.828) and moderate (0.846-0.875) physical activity intensities were also recognized accurately. The root mean square error range for EE was approximately 1 equivalent of resting EE [0.835-1.009 METs]. Generally, random forest and XGBoost models outperformed other models. In conclusion, machine learning models to label physical activity types, activity intensity and energy expenditure are accurate and there are minimal differences in their performance across young, middle-aged and older adults.

A smartwatch-based framework for real-time and online assessment and mobility monitoring.

Smartphone and smartwatch technology is changing the transmission and monitoring landscape for patients and research participants to communicate their healthcare information in real time. Flexible, bidirectional and real-time control of communication allows development of a rich set of healthcare applications that can provide interactivity with the participant and adapt dynamically to their changing environment. Additionally, smartwatches have a variety of sensors suitable for collecting physical activity and location data. The combination of all these features makes it possible to transmit the collected data to a remote server, and thus, to monitor physical activity and potentially social activity in real time. As smartwatches exhibit high user acceptability and increasing popularity, they are ideal devices for monitoring activities for extended periods of time to investigate the physical activity patterns in free-living condition and their relationship with the seemingly random occurring illnesses, which have remained a challenge in the current literature. Therefore, the purpose of this study was to develop a smartwatch-based framework for real-time and online assessment and mobility monitoring (ROAMM). The proposed ROAMM framework will include a smartwatch application and server. The smartwatch application will be used to collect and preprocess data. The server will be used to store and retrieve data, remote monitor, and for other administrative purposes. With the integration of sensor-based and user-reported data collection, the ROAMM framework allows for data visualization and summary statistics in real-time.

Contrasting characteristics of daily physical activity in older adults by cancer history.

BACKGROUND: Using objectively collected physical activity (PA) data from the Baltimore Longitudinal Study of Aging, the authors tested whether patterns of daily activity and sedentary time differed by cancer survivorship in older adults. METHODS: In total, 659 participants (mean age ± standard deviation, 71 ± 10 years; 51% women) who had self-reported information on cancer history were instructed to wear an accelerometer for 7 consecutive days. Accelerometer data were summarized into: 1) PA volume and 2) activity fragmentation (interrupted activity), expressed as both continuous and as dichotomized (low and high) variables. Participants were categorized into 4 groups by cross-classification of dichotomous PA volume and fragmentation. Multiple regression models were used to estimate differences in PA patterns by cancer history. RESULTS: Cancer survivors averaged 0.12 fewer log-transformed activity counts per day (standard error, 0.05; P = .02) than individuals who reported no history of cancer after adjusting for demographics, behavioral factors, and comorbidities. Although fragmentation did not differ by cancer survivorship in the continuous model (P = .13), cancer survivorship was associated with 77% greater odds (odds ratio, 1.77; 95% confidence interval, 1.11-2.82) of having high (vs low) fragmentation and 94% greater odds (odds ratio, 1.94; 95% confidence interval, 1.13-3.33) of having combined low PA/high fragmentation (vs high PA/low fragmentation) relative to those with no cancer history. CONCLUSIONS: The current findings suggest that cancer survivors engage in lower total daily PA and that they perform this activity in a more fragmented manner compared with adults without a history of cancer. These results may reflect the onset and progression of a low-activity phenotype that is more vulnerable to heightened levels of fatigue and functional decline with aging.

Wrist accelerometer shape feature derivation methods for assessing activities of daily living.

BACKGROUND: There has been an increasing interest in understanding the usefulness of wrist-based accelerometer data for physical activity (PA) assessment due to the ease of use and higher user compliance than other body placements. PA assessment studies have relied on machine learning methods which take accelerometer data in forms of variables, or feature vectors. METHODS: In this work, we introduce automated shape feature derivation methods to transform epochs of accelerometer data into feature vectors. As the first step, recurring patterns in the collected data are identified and placed in a codebook. Similarities between epochs of accelerometer data and codebook's patterns are the basis of feature calculations. In this paper, we demonstrate supervised and unsupervised approaches to learn codebooks. We evaluated these methods and compared them with the standard statistical measures for PA assessment. The experiments were performed on 146 participants who wore an ActiGraph GT3X+ accelerometer on the right wrist and performed 33 activities of daily living. RESULTS: Our evaluations show that the shape feature derivation methods were able to perform comparably with the standard wrist model (F1-score: 0.89) for identifying sedentary PAs (F1-scores of 0.86 and 0.85 for supervised and unsupervised methods, respectively). This was also observed for identifying locomotion activities (F1-scores: 0.87, 0.83, and 0.81 for the standard wrist, supervised, unsupervised models, respectively). All the wrist models were able to estimate energy expenditure required for PAs with low error (rMSE: 0.90, 0.93, and 0.90 for the standard wrist, supervised, and unsupervised models, respectively). CONCLUSION: The automated shape feature derivation methods offer insights into the performed activities by providing a summary of repeating patterns in the accelerometer data. Furthermore, they could be used as efficient alternatives (or additions) for manually engineered features, especially important for cases where the latter fail to provide sufficient information to machine learning methods for PA assessment.

Physical impairment and body weight history in postmenopausal women: the Women's Health Initiative.

OBJECTIVE: To examine whether weight history and weight transitions over adult lifespan contribute to physical impairment among postmenopausal women. DESIGN: BMI categories were calculated among postmenopausal women who reported their weight and height at age 18 years. Multiple-variable logistic regression was used to determine the association between BMI at age 18 years and BMI transitions over adulthood on severe physical impairment (SPI), defined as scoring <60 on the Physical Functioning subscale of the Rand thirty-six-item Short-Form Health Survey. SETTING: Participants were part of the Women's Health Initiative Observational Study (WHI OS), where participants' health was followed over time via questionnaires and clinical assessments. SUBJECTS: Postmenopausal women (n 76 016; mean age 63·5 (sd 7·3) years). RESULTS: Women with overweight (BMI=25·0-29·9 kg/m2) or obesity (BMI≥30·0 kg/m2) at 18 years had greater odds (OR (95 % CI)) of SPI (1·51 (1·35, 1·69) and 2·14 (1·72, 2·65), respectively) than normal-weight (BMI=18·5-24·9 kg/m2) counterparts. Transitions from normal weight to overweight/obese or to underweight (BMI<18·5 kg/m2) were associated with greater odds of SPI (1·97 (1·84, 2·11) and 1·35 (1·06, 1·71), respectively) compared with weight stability. Shifting from underweight to overweight/obese also had increased odds of SPI (1·52 (1·11, 2·09)). Overweight/obese to normal BMI transitions resulted in a reduced SPI odds (0·52 (0·39, 0·71)). CONCLUSIONS: Higher weight history and transitions into higher weight classes were associated with higher likelihood of SPI, while transitioning into lower weight classes for those with overweight/obesity was protective among postmenopausal women.

Correlates of life course physical activity in participants of the Baltimore longitudinal study of aging.

Physical activity is consistently associated with better health and longer life spans. However, the extent to which length and intensity of exercise across the life course impact health outcomes relative to current activity is undefined. Participants of the Baltimore Longitudinal Study of Aging were asked to categorize their level of physical activity in each decade of life from adolescence to the current decade. In linear mixed effects models, self-reported past levels of physical activity were significantly associated with activity assessed at study visits in the corresponding decade of life either by questionnaire or accelerometry. A pattern of life course physical activity (LCPA) derived by ranking participants on reported activity intensity across multiple decades was consistent with the trajectories of activity estimated from standard physical activity questionnaires assessed at prior study visits. In multivariable linear regression models LCPA was associated with clinical characteristics, measures of body composition and indicators of physical performance independent of current physical activity. After adjustment for minutes of high intensity exercise, LCPA remained significantly associated with peak VO2, fasting glucose, thigh muscle area and density, abdominal subcutaneous fat, usual gait speed, lower extremity performance, and multimorbidity (all p < 0.01) at the index visit. The observed associations suggest that an estimate of physical activity across decades provides complementary information to information on current activity and reemphasizes the importance of consistently engaging in physical activity over the life course.

Comparison of sleep parameters from wrist-worn ActiGraph and Actiwatch devices.

Sleep and physical activity, two important health behaviors, are often studied independently using different accelerometer types and body locations. Understanding whether accelerometers designed for monitoring each behavior can provide similar sleep parameter estimates may help determine whether one device can be used to measure both behaviors. Three hundred and thirty one adults (70.7 ±â€…13.7 years) from the Baltimore Longitudinal Study of Aging wore the ActiGraph GT9X Link and the Actiwatch 2 simultaneously on the non-dominant wrist for 7.0 ±â€…1.6 nights. Total sleep time (TST), wake after sleep onset (WASO), sleep efficiency, number of wake bouts, mean wake bout length, and sleep fragmentation index (SFI) were extracted from ActiGraph using the Cole-Kripke algorithm and from Actiwatch using the software default algorithm. These parameters were compared using paired t-tests, Bland-Altman plots, and Deming regression models. Stratified analyses were performed by age, sex, and body mass index (BMI). Compared to the Actiwatch, the ActiGraph estimated comparable TST and sleep efficiency, but fewer wake bouts, longer WASO, longer wake bout length, and higher SFI (all p < .001). Both devices estimated similar 1-min and 1% differences between participants for TST and SFI (ß = 0.99, 95% CI: 0.95, 1.03, and 0.91, 1.13, respectively), but not for other parameters. These differences varied by age, sex, and/or BMI. The ActiGraph and the Actiwatch provide comparable absolute and relative estimates of TST, but not other parameters. The discrepancies could result from device differences in movement collection and/or sleep scoring algorithms. Further comparison and calibration is required before these devices can be used interchangeably.

Perceived Fatigability, Fatigue, and Mortality in Mid-to-Late Life in the Baltimore Longitudinal Study of Aging.

INTRODUCTION/PURPOSE: Fatigue is an established prognostic indicator of mortality risk. It remains unknown whether fatigability anchored to a physical task is a more sensitive prognostic indicator and whether sensitivity differs by prevalent chronic conditions. METHODS: A total of 1076 physically well-functioning participants 50 yr or older in the Baltimore Longitudinal Study of Aging self-reported fatigue (unusual tiredness or low energy) and had perceived fatigability assessed after a standardized treadmill walk. All-cause mortality was ascertained by proxy contact and National Death Index linkage. Cox proportional hazards models estimated associations of perceived fatigability and fatigue with all-cause mortality, adjusting for demographic and clinical covariates. Interactions by chronic conditions were also examined. RESULTS: Each 1 SD higher in perceived fatigability, unusual tiredness, or low energy was associated with a higher relative hazard of all-cause mortality after covariate adjustment (fatigability: hazard ratio (HR), 1.18 (95% confidence interval (CI), 1.03-1.36); unusual tiredness: HR, 1.25 (95% CI, 1.08-1.44); low energy: HR, 1.27 (95% CI, 1.10-1.46)). Models had similar discrimination ( P > 0.14 for all). Perceived fatigability was associated with mortality risk among participants free of arthritis or osteoarthritis who otherwise appeared healthy (no arthritis: HR, 1.45 (95% CI, 1.15-1.84); arthritis: HR, 1.09 (95% CI, 0.92-1.30); P -interaction = 0.031). Unusual tiredness was associated with mortality among those with a history of diabetes (no diabetes: HR, 1.16 (95% CI, 0.97-1.38); diabetes: HR, 1.65 (95% CI, 1.22-2.23); P -interaction = 0.045) or pulmonary disease (no pulmonary disease: HR, 1.22 (95% CI, 1.05-1.43); pulmonary disease: HR, 2.15 (95% CI, 1.15-4.03); P -interaction = 0.034). CONCLUSIONS: Higher perceived fatigability and fatigue symptoms were similarly associated with higher all-cause mortality, but utility differed by chronic condition. Perceived fatigability might be useful for health screening and long-term mortality risk assessment for well-functioning adults. Alternatively, self-reported fatigue seems more disease-specific with regard to mortality risk.