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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

OBJECTIVE To evaluate the diagnostic performance of perfusion pressure (PP) thresholds for fasciotomy. DESIGN Prospective observational study. SETTING Seven Level-1 trauma centers. PATIENTS/PARTICIPANTS One hundred fifty adults with severe leg injuries and ≥2 hours of continuous PP data who had been enrolled in a multicenter observational trial designed to develop a clinical prediction rule for acute compartment syndrome (ACS). MAIN OUTCOME MEASUREMENTS For each patient, a given PP criterion was positive if it was below the specified threshold for at least 2 consecutive hours. The diagnostic performance of PP thresholds between 10 and 30 mm Hg was determined using 2 reference standards for comparison: (1) the likelihood of ACS as determined by an expert panel who reviewed each patient's data portfolio or (2) whether the patient underwent fasciotomy. RESULTS Using the likelihood of ACS as the diagnostic standard (ACS considered present if median likelihood ≥70%, absent if

Digital health technologies (DHTs) enable us to measure human physiology and behavior remotely, objectively and continuously. With the accelerated adoption of DHTs in clinical trials, there is an unmet need to identify statistical approaches to address missing data to ensure that the derived endpoints are valid, accurate, and reliable. It is not obvious how commonly used statistical methods to handle missing data in clinical trials can be directly applied to the complex data collected by DHTs. Meanwhile, current approaches used to address missing data from DHTs are of limited sophistication and focus on the exclusion of data where the quantity of missing data exceeds a given threshold. High-frequency time series data collected by DHTs are often summarized to derive epoch-level data, which are then processed to compute daily summary measures. In this article, we discuss characteristics of missing data collected by DHT, review emerging statistical approaches for addressing missingness in epoch-level data including within-patient imputations across common time periods, functional data analysis, and deep learning methods, as well as imputation approaches and robust modeling appropriate for handling missing data in daily summary measures. We discuss strategies for minimizing missing data by optimizing DHT deployment and by including the patients' perspectives in the study design. We believe that these approaches provide more insight into preventing missing data when deriving digital endpoints. We hope this article can serve as a starting point for further discussion among clinical trial stakeholders.

Developments in wearable technology have enabled researchers to continuously and objectively monitor various aspects and physiological domains of real-life including levels of physical activity, quality of sleep, and strength of circadian rhythm in many epidemiological and clinical studies. Current analytical practice is to summarize each of these three domains individually via a standard inventory of interpretable features, and explore individual associations between the features and clinical variables. However, the features often exhibit significant interaction and correlation both within and between domains. Integration of features across multiple domains remains methodologically challenging. To address this problem, we propose to use joint and individual variation explained (JIVE), a dimension reduction technique that efficiently deals with multivariate data representing multiple domains. In this paper, we review the most frequently used features to characterize the domains of physical activity, sleep, and circadian rhythniicity and illustrate the approach using wrist-worn actigraphy data from 198 participants of the Baltimore Longitudinal Study of Aging.

Background Rest-activity rhythm (RAR), a manifestation of circadian rhythms, has been associated with morbidity and mortality risk. However, RAR patterns in the general population and specifically the role of demographic characteristics in RAR pattern have not been comprehensively assessed. Therefore, we aimed to describe RAR patterns among non-institutionalized US adults and age, sex, and race/ethnicity variation using accelerometry data from a nationally representative population. Methods This cross-sectional study was conducted using the US National Health and Nutrition Examination Survey (NHANES) 2011–2014. Participants aged ≥20 years who were enrolled in the physical activity monitoring examination and had at least four 24-h periods of valid wrist accelerometer data were included in the present analysis. 24-h RAR metrics were generated using both extended cosinor model (amplitude, mesor, acrophase and pseudo-F statistic) and nonparametric methods (interdaily stability [IS] and intradaily variability [IV]). Multivariable linear regression was used to assess the association between RAR and age, sex, and race/ethnicity. Results Eight thousand two hundred participants (mean [SE] age, 49.1 [0.5] years) were included, of whom 52.2% were women and 67.3% Whites. Women had higher RAR amplitude and mesor, and also more robust (pseudo-F statistic), more stable (higher IS) and less fragmented (lower IV) RAR (all Ptrend Ptrend Ptrend Ptrend Conclusions In the general adult population, RAR patterns vary significantly according to sex, age and race/ethnicity. These results may reflect demographic-dependent differences in intrinsic circadian rhythms and may have important implications for understanding racial, ethnic, sex and other disparities in morbidity and mortality risk.

Key Points Question Is there an association between hearing loss and physical activity? Findings In this cross-sectional study that included 291 adults aged 60 to 69 years, hearing loss was associated with a worse physical activity profile, including less moderate-to-vigorous physical activity, less light-intensity activity, and a more fragmented physical activity pattern. Meaning These findings suggest that promoting physical activity among older adults with hearing loss is important, and further research is needed to investigate whether hearing loss interventions could improve physical health profiles., This cross-sectional study examines the association of hearing loss with objectively measured physical activity, including moderate-to-vigorous physical activity, light-intensity physical activity, sedentary behavior, and physical activity fragmentation., Importance Hearing loss may be a modifiable factor associated with decreased physical activity in older adults. Objective To examine the association of hearing loss with objectively measured physical activity, including moderate-to-vigorous physical activity, light-intensity physical activity, sedentary behavior, and pattern of physical activity (physical activity fragmentation). Design, Setting, and Participants This population-based cross-sectional study used National Health and Nutrition Examination Survey (NHANES) data collected in the 2003 to 2004 cycle and analyzed in 2017 to 2020. Participants aged 60 to 69 years with complete audiometry, physical activity, and comorbidity data were included in the analysis. Data analysis was performed from January 2017 to December 2020. Exposures Hearing defined by the pure tone average (PTA; range, 0.5-4 kHz) in the better ear, with normal PTA defined as less than 25 dB hearing loss, mild hearing loss defined as PTA 25 to less than 40 dB hearing loss, and moderate or greater hearing loss defined as a PTA greater than or equal to 40 dB hearing loss. Main Outcomes and Measures The primary outcomes were comprehensive metrics of objectively measured physical activity, including time spent in moderate-to-vigorous physical activity, light-intensity physical activity, and sedentary behavior, and physical activity fragmentation. Linear regression was used to model the association between hearing loss and physical activity. Results Of the 291 participants (mean [SD] age, 64.53 [2.96] years), 139 (47.8%) were male, 48 (16.5%) had mild hearing loss, and 22 (7.6%) had moderate or greater hearing loss. After adjusting for age, sex, education, race/ethnicity, and comorbidities, hearing loss (vs normal hearing) was significantly associated with less time spent in moderate-to-vigorous physical activity by 5.53 minutes per day (95% CI, −10.15 to −0.90 minutes per day), less time spent in light-intensity physical activity by 28.55 minutes per day (95% CI, −53.07 to −4.02 minutes per day), more time spent in sedentary behaviors by 34.07 minutes per day (95% CI, 8.32 to 59.82 minutes per day), and more fragmented physical activity pattern by 0.38 SD higher in active-to-sedentary transition probability (95% CI, to 0.10 to 0.65). The magnitude of the association of hearing loss (vs normal hearing) with physical activity metrics was equivalent to 7.28 years (95% CI, 3.19 to 11.37 years) of accelerated age for moderate-to-vigorous physical activity, 5.84 years (95% CI, 1.45 to 10.23 years) of accelerated age for light-intensity physical activity, and 10.53 years (95% CI, 2.89 to 18.16 years) of accelerated age for degree of physical activity fragmentation. Conclusions and Relevance These findings suggest that hearing loss is associated with a worse physical activity profile. Whether interventions to address hearing loss in adults could improve physical activity profiles will require further study.

Patients with atopic dermatitis experience increased nocturnal pruritus which leads to scratching and sleep disturbances that significantly contribute to poor quality of life. Objective measurements of nighttime scratching and sleep quantity can help assess the efficacy of an intervention. Wearable sensors can provide novel, objective measures of nighttime scratching and sleep; however, many current approaches were not designed for passive, unsupervised monitoring during daily life. In this work, we present the development and analytical validation of a method that sequentially processes epochs of sample-level accelerometer data from a wrist-worn device to provide continuous digital measures of nighttime scratching and sleep quantity. This approach uses heuristic and machine learning algorithms in a hierarchical paradigm by first determining when the patient intends to sleep, then detecting sleep–wake states along with scratching episodes, and lastly deriving objective measures of both sleep and scratch. Leveraging reference data collected in a sleep laboratory (NCT ID: NCT03490877), results show that sensor-derived measures of total sleep opportunity (TSO; time when patient intends to sleep) and total sleep time (TST) correlate well with reference polysomnography data (TSO: r = 0.72, p r = 0.76, p N = 32). Log transformed sensor derived measures of total scratching duration achieve strong agreement with reference annotated video recordings (r = 0.82, p N = 25). These results support the use of wearable sensors for objective, continuous measurement of nighttime scratching and sleep during daily life.

Study Objectives To examine in a subsample at the screening phase of a clinical trial of a β-amyloid (Aβ) antibody whether disturbed sleep and altered 24-hour rest/activity rhythms (RARs) may serve as markers of preclinical Alzheimer’s disease (AD). Methods Overall, 26 Aβ-positive (Aβ+) and 33 Aβ-negative (Aβ−) cognitively unimpaired participants (mean age = 71.3 ± 4.6 years, 59% women) from the Anti-Amyloid Treatment in Asymptomatic Alzheimer’s (A4) and the Longitudinal Evaluation of Amyloid Risk and Neurodegeneration (LEARN) studies, respectively, wore actigraphs for 5.66 ± 0.88 24-hour periods. We computed standard sleep parameters, standard RAR metrics (mean estimating statistic of rhythm, amplitude, acrophase, interdaily stability, intradaily variability, relative amplitude), and performed a novel RAR analysis (function-on-scalar regression [FOSR]). Results We were unable to detect any differences between Aβ+ and Aβ− participants in standard sleep parameters or RAR metrics with our sample size. When we used novel FOSR methods, however, Aβ+ participants had lower activity levels than Aβ− participants in the late night through early morning (11:30 pm to 3:00 am), and higher levels in the early morning (4:30 am to 8:30 am) and from midday through late afternoon (12:30 pm to 5:30 pm; all p < .05). Aβ+ participants also had higher variability in activity across days from 9:30 pm to 1:00 am and 4:30 am to 8:30 am, and lower variability from 2:30 am to 3:30 am (all p < .05). Conclusions Although we found no association of preclinical AD with standard actigraphic sleep or RAR metrics, a novel data-driven analytic method identified temporally “local” RAR alterations in preclinical AD.

Along the pathway from behavioral symptoms to the development of psychotic disorders sits the multivariate mediating brain. The functional organization and structural topography of large-scale neural mediators among patients with brain disorders, however, are not well understood. Here, we design a high-dimensional brain-wide functional mediation framework to investigate brain regions that intermediate between baseline behavioral symptoms and future conversion to full psychosis among individuals at clinical high risk (CHR). Using resting-state functional magnetic resonance imaging (fMRI) data from 263 CHR subjects, we extract an α brain atlas and a β brain atlas: the former underlines brain areas associated with prodromal symptoms and the latter highlights brain areas associated with disease onset. In parallel, we identify the P mediators and the N mediators that respectively facilitate or protect against developing brain disorders among subjects with more severe behavioral symptoms and quantify the effect of each neural mediator on disease development. Taken together, the α-β atlases and the P-N mediators paint a brain-wide picture of neural markers that are potentially regulating behavioral symptoms and the development of psychotic disorders and highlight a statistical framework that is useful to uncover large-scale intermediating variables in a regulatory biological organization.

We recorded measurements of muscle perfusion using near-infrared spectroscopy (NIRS) and intramuscular pressure (IMP) in a study designed to develop a decision rule for predicting acute compartment syndrome (ACS). The purpose of this study was to report our experience measuring NIRS data in the context of this broader investigation and to explore factors related to variations in data capture.One hundred and eighty-five patients with lower-leg injuries had data consisting of continuous NIRS measurement of the O2 saturation in the anterior compartment of the injured limb and the contralateral (control) limb, and continuous IMP recording in the anterior and deep posterior compartments of the injured leg as part of their participation in an institutional review board-approved multicenter trial. All monitoring was done for a prescribed period of time. For both types of data, the percentage of valid data capture was defined as the ratio of the minutes of observed data points within a physiological range to the total minutes of expected data points. Clinically useful NIRS data required simultaneous data from the injured and control limbs to calculate the ratio. Statistical tests were used to compare the 2 methods as well as factors associated with the percent of valid NIRS data capture.For the original cohort, clinically useful NIRS data were available a median of 9.1% of the expected time, while IMP data were captured a median of 87.6% of the expected time (p0.001). Excluding 46 patients who had erroneous NIRS data recorded, the median percentage was 31.6% for NIRS compared with 87.4% for IMP data (p0.00001). Fractures with an associated hematoma were less likely to have valid data points (odds ratio [OR], 0.53; p = 0.04). Gustilo types-I and II open fractures were more likely than Tscherne grades C0 and C1 closed fractures to have valid data points (OR, 1.97; p = 0.03).In this study, NIRS data were not collected reliably. In contrast, IMP measurements were collected during85% of the expected monitoring period. These data raise questions about the utility of current NIRS data capture technology for monitoring oxygenation in patients at risk of ACS.

Introduction Chronotype is a potentially modifiable contributor to human well-being and longevity, with eveningness commonly linked to poorer outcomes. We examined the relationship between actigraphy-measured chronotype and all-cause mortality in a nationally representative sample of US adults. We also examined the association between social jetlag, a measure of circadian misalignment, and all-cause mortality. Methods Data were from 2,256 participants ≥50 from the National Health and Nutrition Examination Survey 2003-2006 cohorts. Participants were asked to wear a hip-worn Actigraph 7164 uni-axial activity monitor for 7 days, and to remove the device for sleep. Objectively-measured bedtime (OBT) was computed as the start of the non-wear period with the longest duration within each 24h period. Duration of the in-bed period (OBT-D) was computed as the hours from OBT to the end of the in-bed period. Midpoint of OBT (OBT-M) was computed as the midpoint between OBT and the end of the in-bed period. Chronotype was estimated using the average OBT-M separately for weekdays, weekends (Friday and Saturday nights), and all days combined. A weekend OBT-M corrected for sleep debt for participants with weekend OBT-D>weekday OBT-D was also computed. The following formula was applied to correct for sleep debt: weekend OBT-M minus ((weekend OBT-D minus weekday OBT-D)/2). Consistent with previous research, OBT-Ms were categorized into intermediate (≥3:30am & ≤4:30am), morningness (4:30am) chronotypes. Social jetlag was defined as the difference between weekend and weekday OBT-Ms and expressed in hours. Survey-weighted Cox proportional hazard models were used to examine the relationship between circadian factors and all-cause mortality. There were 642 deaths, excluding accidental deaths. Results Adjusted for age, sex, race, SES, BMI, smoking and drinking status, comorbidities, and average OBT-D, an eveningness chronotype (i.e., weekend OBT-M corrected for sleep debt) was associated with a greater hazard of death compared to an intermediate chronotype (HR=1.68, 95% CI=1.25, 2.26). There were no other significant associations. Conclusion Evening-oriented chronotype is associated with greater mortality risk in adults aged ≥50. To our knowledge, this is the first study to report the link between chronotype, estimated objectively via actigraphy, and all-cause mortality in a nationally representative sample. Support (if any) NIH grant 5T32MH014592-39.

We thank Wolff-Hughes and colleagues for their thoughtful response (Wolff-Hughes et al., 2017) to our recently published findings (Varma et al., 2017). Wolff-Hughes et al. correctly indicate that our findings, which model physical activity (PA) over the lifespan using total log-transformed activity counts (TLAC) as a proxy measure for total volume of PA, are inconsistent with their findings (Wolff-Hughes et al., 2014; Wolff-Hughes et al., 2015) that use total (non-transformed) activity counts (TAC). The main issue is whether TLAC or TAC is the most appropriate measure of total volume of PA. We argue that 1) TAC most closely reflects moderate-to-vigorous PA (MVPA) while TLAC most closely reflects light-intensity PA (LiPA); and propose 2) TAC(λ) as a unifying measure to appropriately capture total volume of PA.

Key Points Question Is the manner in which older adults accumulate physical activity throughout the day, beyond the total amount of physical activity, associated with their mortality risk? Findings In this cohort study of 548 well-functioning adults aged 65 years and older, more fragmentated physical activity, but not total daily activity, was associated with increased mortality risk. Meaning Fragmented physical activity of bouts lasting less than 5 minutes may reveal compensatory changes resulting from impaired physical function and may be associated with increased mortality risk., This cohort study of adults aged 65 years and older examines whether more fragmentated physical activity, rather than total daily activity, is associated with increased mortality risk., Importance Fragmented daily physical activity may be a sign of physiological decline that provides more powerful insight into impending mortality than total daily activity. Objective To compare and contrast the association between total daily activity and activity fragmentation, which encompasses activity bouts and duration, and mortality risk. Design, Setting, and Participants In this cohort study, accelerometer data from 2007 through 2015 and mortality data from 2007 through 2017 were collected from 548 adults aged 65 years and older participating in the Baltimore Longitudinal Study of Aging. The dates of analysis were November 2016 to June 2019, with data collected through December 31, 2017. Using Cox proportional hazards regression, the association between accelerometer-derived patterns of physical activity and mortality was estimated after adjusting for demographic characteristics, lifestyle factors, and comorbidities. Exposures Minute-by-minute physical activity data were collected over a 24-hour, 7-day period (excluding times between 11:00 pm and 4:59 am) using an accelerometer. Each minute was labeled either active or sedentary, and 5 features of accelerometer data were extracted: total daily activity (defined as any activity performed throughout the day), activity fragmentation (defined as an active-to-sedentary transition probability), and 3 measures of activity bouts (

Electronic diary data, such as that acquired through Ecological Momentary Assessments (EMA), has historically provided novel insights into diverse psychological processes. Analyses of these data typically focus on modeling participant-specific means, variability, and stability. We propose a novel statistical framework to determine participant stability by quantifying fragmentation of standardized trajectories using the following 2-step approach: (1) participant-level EMA scores are normalized, and (2) normalized scores are dichotomized into 2 states, inside and outside a range of 1 standard deviation. Within-participant fragmentation measures were calculated from dichotomized scores and modeled with various covariates. We used this method to study patterns of emotional states and showed that the proposed fragmentation measures differentiate mood disorder subtypes, including Bipolar I (BPI), Bipolar II, and major depressive disorder (MDD) compared with unaffected controls. Fragmentation measures were regressed on the mood disorder subtype, adjusting for age, sex, body mass index, and mean squared successive difference. The analyses revealed decreased stability (more fragmentation) among those with BPI when inside the participant-specific standard range of attention (β = 0.09, p = .004) and decreased stability among those with MDD inside the standard range of mood (β = 0.04, p = .039) and attention (β = 0.05, p = .017). This work provides an illustration of the clinical significance of EMA in characterizing the stability of mood, attention, or other psychological states that may underlie psychological disorders and phenomena. The application of fragmentation provides a novel statistical approach that can characterize within-participant stability beyond currently available traditional approaches. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Deteriorating physical activity (PA) accumulation may be an indicator of premature mortality. In 698 older adults, Cox proportional hazard models were fitted to determine the interaction effect between age and three different PA metrics (total PA minutes; fragmentation defined as the # contiguous PA minutes (bouts)/total PA minutes; and proportion of time spent in

BACKGROUND: With aging, daily physical activity (PA) becomes less frequent and more fragmented. Accumulation patterns of daily PA—including transitions from active-to-sedentary behaviors—may provide important insights into functional status in older, less active populations. METHODS: Participants of the Baltimore Longitudinal Study of Aging (n = 680, 50% male, aged 27–94 years) completed a clinical assessment and wore an Actiheart accelerometer. Transitions between active and sedentary states were modeled as a probability (Active-to-Sedentary Transition Probability [ASTP]) defined as the reciprocal of the average PA bout duration. Cross-sectional associations between ASTP and gait speed (m/s), fatigability (rating-of-perceived-exertion [RPE]), 400 m time (seconds), and expanded short physical performance battery score were modeled using linear and logistic regression, adjusted for chronic conditions. Further analyses explored the utility of ASTP over-and-above total daily PA. RESULTS: In continuous models, each 0.10-unit higher ASTP was associated slower gait (β = −0.06 m/s, SE = 0.01), higher fatigability (β = 0.60 RPE, SE = 0.12), slower 400 m time (β = 16.31 s, SE = 2.70), and lower functioning (β = −0.13 expanded short physical performance battery score, SE = 0.03; p < .001). In categorical analyses, those in the highest tertile of ASTP were >2 times more likely to have high fatigability (rating of perceived exertion ≥10), slow 400 m time (>300 seconds) and reduced functional performance (expanded short physical performance battery score < 3.07) than those in the lowest tertile (p < .01). Further analyses demonstrated ASTP provided additional insight into functional outcomes beyond total daily PA. CONCLUSION: Fragmented daily PA—as measured by ASTP—is strongly linked with measures of health and functional status and may identify those at risk of high fatigability and reduced functional performance over and above traditional PA metrics.

BACKGROUND: We propose a method for estimating the timing of in-bed intervals using objective data in a large representative US sample, and quantify the association between these intervals and age, sex, and day of the week. METHODS: The study included 11,951 participants 6 years and older from the National Health and Nutrition Examination Survey (NHANES) 2003–2006, who wore accelerometers to measure physical activity for seven consecutive days. Participants were instructed to remove the device just before the nighttime sleep period and put it back on immediately after. This nighttime period of non-wear was defined in this paper as the objective bedtime (OBT), an objectively estimated record of the in-bed interval. For each night of the week, we estimated two measures: the duration of the OBT (OBT-D) and, as a measure of the chronotype, the midpoint of the OBT (OBT-M). We estimated day-of-the-week-specific OBT-D and OBT-M using gender-specific population percentile curves. Differences in OBT-M (chronotype) and OBT-D (the amount of time spent in bed) by age and sex were estimated using regression models. RESULTS: The estimates of OBT-M and their differences among age groups were consistent with the estimates of chronotype obtained via self-report in European populations. The average OBT-M varied significantly by age, while OBT-D was less variable with age. In the reference group (females, aged 17–22 years), the average OBT-M across 7 days was 4:19 AM (SD = 30 min) and the average OBT-D was 9 h 19 min (SD = 12 min). In the same age group the average OBT-D was 18 minutes shorter for males than for females, while the average OBT-M was not significantly different between males and females. The most pronounced differences were observed between OBT-M of weekday and weekend nights. In the reference group, compared to the average OBT-M of 3:50 am on Monday through Thursday nights, there was a 57-minute delay in OBT-M on Friday nights (entering the weekend), a 69-minute delay on Saturday nights (staying in the weekend), and a 23-minute delay on Sunday night (leaving the weekend). For both OBT-M and OBT-D, in most age groups and for most days of the week, there were no statistically significant differences between males and females, except for OBT-D on Wednesdays and Thursdays, with males having 31 (p-value < 0.05) and 45 (p-value < 0.05) minutes shorter OBT-D, respectively. CONCLUSIONS: The proposed measures, OBT-D and OBT-M, provide useful information of time in bed and chronotype in NHANES 2003–2006. They identify within-week patterns of bedtime and can be used to study associations between the bedtime and the large number of health outcomes collected in NHANES 2003–2006.

PurposeSedentary behavior has become a public health pandemic and has been associated with a variety of comorbidities including cardiovascular disease, type 2 diabetes, and some cancers. Previous studies have also shown that excessive amount of sedentary behavior is associated with all-cause mortality. However, no studies investigated whether patterns of sedentary and active time accumulation are associated with mortality independently of total sedentary and total active times. This study addresses this question by i) comparing several analytical ways to quantify patterns of both sedentary and active time accumulation through metrics of fragmentation of objectively-measured physical activity and ii) exploring the association of these metrics with all-cause mortality in a nationally representative US sample of elderly adults.MethodsThe accelerometry data of 3400 participants aged 50 to 84 in the National Health and Nutrition Examination Survey 2003-2006 cohorts were analyzed. Ten fragmentation metrics were calculated to quantify the duration of sedentary and active bouts: average bout duration, Gini index, average hazard, between-state transition probability, and the parameter of power law distribution. The association of these fragmentation metrics with all-cause mortality followed through December 31, 2011 was assessed with survey-weighted Cox proportional hazard models.ResultsIn models adjusted for age, sex, race/ethnicity, education, body mass index, common comorbidities, and total sedentary/active time, four fragmentation metrics were associated with lower mortality risk: average active bout duration (HR=0.72 for 1SD increase, 95% CI = 0.590.88), Gini index for active bouts (HR = 0.75, 95% CI = 0.64-0.86), the parameter of power law distribution for sedentary bouts (HR = 0.75, 95% CI = 0.63-0.90), and sedentary-to-active transition probability (HR = 0.77, 95% CI = 0.61-0.96), and four fragmentation metrics were associated with higher mortality risk: the active-to-sedentary transition probability (HR = 1.40, 95% CI=1.23-1.58), the parameter of power law distribution for active bouts (HR = 1.33, 95% CI = 1.16-1.52), average hazard for durations of active bouts (HR = 1.32, 95% CI = 1.18-1.48), and average sedentary bout duration (HR =1.07, 95% CI = 1.01-1.13). After sensitivity analysis, average sedentary bout duration and sedentary-to-active transition probability became insignificant.ConclusionLonger average duration of active bouts, a lower probability of transitioning from active to sedentary behavior, and a higher normalized variability of active bout durations were strongly negatively associated with all-cause mortality independently of total active time. A larger proportion of longer sedentary bouts were positively associated with all-cause mortality independently of total sedentary time. The results also suggested a nonlinear association of average active bout duration with mortality that corresponded to the largest risk increase in subjects with average active bout duration less than 3 minutes.

We review existing methods for quantifying fragmentation of accelerometry-measured physical activity and develop a unifying statistical framework that connects all methods by separately modeling switching between and duration of sedentary and active bouts. We analyzed the association of these metrics with mortality in National Health and Nutrition Examination Survey 2003–2006. We have identified that “Intradaily Variability” (1SD HR=1.32, 95%CI 1.18–1.48), “Active Bouts Fragmentation Index” (1SD HR=1.43, 95%CI 1.25–1.64), “Sedentary Bouts Gini Index” (1SD HR=1.15, 95%CI 1.06–1.25), and hazard rates of distribution function of durations of active and sedentary bouts (1SD HR=1.93, 1.40, 95% CI 1.59–2.34 and 1.23–1.59) independently predict mortality after adjusting for total sedentary time, total daily activity, age, sex, education, medical comorbidities, smoking status, alcohol consumption, and mobility limitations. Future studies should concentrate on developing metrics that jointly model the frequency of switching between the sedentary and active bouts as well as the bout durations.

Low daily physical activity has been linked to poor health and functional performance with aging. However, the health implications of the daily patterns/temporal accumulation of physical activity remain unknown. We created an activity fragmentation index (AFI) to account for the number and duration of daily active bouts using 7-day accelerometry data from the Baltimore Longitudinal Study of Aging (n = 734, mean age 67.8 ± 13.2 years, 50% female). Minute-epoch activity counts were dichotomized into active or sedentary states, and summarized according to the distribution of active bout durations. AFI was calculated as the reciprocal of the average duration of the active bouts. Total daily volume of physical activity was summarized using total log-transformed activity counts (TLAC). Using multiple linear regression models adjusted for age, sex, height, weight, TLAC, and disease conditions, AFI was negatively associated with gait speed (β = -0.06 m/s, p < 0.004), and positively associated with laboratory assessments of high fatigability (p < 0.001). TLAC was a significant predictor of gait speed and fatigability until AFI was added to the model, suggesting that the duration and number of active bouts throughout the day mediates the associations among TLAC, gait speed, and fatigability. Together, these results imply that accounting for fragmented daily physical activity may be more important than total volume of physical activity when assessing functional performance in older adults. Further research is needed to validate this measure and determine the longitudinal value of activity fragmentation in predicting adverse outcomes in older adults.

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 reevaluation of how emerging adulthood may affect PA levels and the importance of considering time of day and sex differences when developing PA interventions.

Time to finish the 400-meter (T400m) walk is a powerful measure of fitness and function. However, patterns of slowing or speeding up during the test, while typically ignored, may provide additional information. Lap split times were collected during a fast-paced10-lap 400-meter walk in 1299 participants of the Baltimore Longitudinal Study of Aging. For each lap, average gait speed (m/s) was calculated, resulting in 10-measures per participant. K-means clustering were used to identify 5 prevailing patterns. Clusters were modeled as a categorical covariate in linear regression models with endurance, aerobic capacity, and fatigability outcomes adjusted for age, sex, BMI and T400m. Patterns were differentially associated with endurance (p

Background: Survey satisficing occurs when participants respond to survey questions rapidly without carefully reading or comprehending them. Studies have demonstrated the occurrence of survey satisficing, which can degrade survey quality, particularly in longitudinal studies. Objective: The aim of this study is to use a group-based trajectory analysis method to identify satisficers when similar survey questions were asked periodically in a long-standing cohort, and to examine factors associated with satisficing in the surveys having sensitive human immunodeficiency virus (HIV)-related behavioral questions. Methods: Behavioral data were collected semiannually online at all four sites of the Multicenter AIDS Cohort Study (MACS) from October 2008 through March 2013. Based on the start and end times, and the word counts per variable, response speed (word counts per second) for each participant visit was calculated. Two-step group-based trajectory analyses of the response speed across 9 study visits were performed to identify potential survey satisficing. Generalized linear models with repeated measures were used to investigate the factors associated with satisficing on HIV-related behavioral surveys. Results: Among the total 2138 male participants, the median baseline age was 51 years (interquartile range, 45-58); most of the participants were non-Hispanic white (62.72%, 1341/2138) and college graduates (46.59%, 996/2138), and half were HIV seropositive (50.00%, 1069/2138). A total of 543 men (25.40%, 543/2138) were considered potential satisficers with respect to their increased trajectory tendency of response speed. In the multivariate analysis, being 10 years older at the baseline visit increased the odds of satisficing by 44% (OR 1.44, 95% CI 1.27-1.62, P

We examine whether the distribution of physical activity during the day acts as an independent predictor of mortality in National Health and Nutrition Examination Survey 2003–2006. Time of day analyses were conducted using 12 time windows of 2 hours represented by the percentage of the total log-transformed activity count (pTLAC) spent in each. In models adjusted for age, sex, race/ethnicity, education, body mass index, comorbidities, and the total log-transformed activity count, a higher percentage of total physical activity between 10am-12pm (HR: 0.96, p-value = 0.003) and a lower percentage of total physical activity between 8pm-4am (HR: 1.03, p-value < 0.01) were significantly associated with lower risk of mortality. These findings help define patterns of physical activity that are beneficial to health, and hold significant implications for future interventions aimed at increasing physical activity and enhancing sleep quality.