Assessing the criterion validity of the activPAL CREA v1.3 algorithm and ActiPASS 2023.12 software for detecting steps during a progressive treadmill-based laboratory protocol.

Thigh-worn accelerometry is commonly implemented to measure step cadence. The default activPAL CREA algorithm is a valid measure of cadence during walking, but its validity during running is unknown. The ActiPASS software is designed to analyse tri-axial accelerometry data from various brands. We tested the validity of CREA v1.3 and ActiPASS 2023.12 to measure step cadence against manually-counted steps. Forty-five participants (26♀, 23.4 ± 4.6 years) completed 5 walking (6 min each, 2-4 mph) and 5 running treadmill (5-7 mph) stages (423 total stages completed). Based on equivalence testing, walking cadence (stages 1-5: 92-124 steps/min) from CREA was statistically equivalent (zone: <±2.2% of the manually-counted mean) to manual counts (92-125 steps/min). However, CREA underpredicted cadence during running stages (stages 6-10: 143-135 steps/min) by ~ 11-20 steps/min (p < 0.001). The ActiPASS-derived cadences were equivalent (zone: <±3.3%) to manual counts for all walking stages (99-127 steps/min) except Stage 1 (zone: ±10.5%). ActiPASS underpredicted cadences during running (stages 6-10: 137-153 steps/min) by ~ 10-16 steps/min (p < 0.001) compared to manual counts (stages 6-10: 154-164 steps/min). The CREA v1.3 algorithm is a valid measure of cadence during walking while ActiPASS 2023.12 is a valid measure of cadence during medium-fast walking. Further research is required to improve step cadence estimation across ambulation speeds.

Changes in 24-h Movement Behaviors During Relationship and Parenthood Transitions: A Compositional Data Analysis.

There is scarcity of studies using device-based measures to examine how relationship and parenthood transitions modify 24-h movement behaviors. This study examined how the composition of 24-h movement behaviors changes during these life transitions. Young adults (n = 170, mean age 25.6 years, SD 0.6) from the Special Turku Coronary Risk Factor Intervention Project (STRIP) wore wrist-worn accelerometers for 1 week and completed questionnaire at ages 26 and 31 years. Participants were categorized by relationship status into single (16%), those transitioning from single to partnered (31%), partnered (47%), and separated (7%), and by parenthood status into non-parents (73%), new parents (19%), and parents (8%). Changes in daily movement behaviors, including sleep, sedentary behavior (SED), light physical activity (LPA), and moderate-to-vigorous physical activity (MVPA), were examined using compositional linear mixed models. In general, LPA and MVPA decreased relative to sleep and SED (p = 0.007). Differences emerged between LPA and MVPA in relationship and parenthood groups (p for group × time interaction 0.008 and 0.001). Those transitioning to partnership decreased MVPA by 17 min/day, while partnered and separated individuals showed no notable MVPA change but decreased LPA by 14 and 43 min/day. Single individuals and non-parents decreased LPA and MVPA in similar proportions. New parents decreased MVPA by 20 min/day, while parents increased it by 19 min/day. Becoming first-time parent and starting relationship was associated with decline of MVPA. Our results suggest the importance of considering these life transitions and providing guidance for maintaining physical activity despite changes in life situations.

Evaluation of the ActiMotus Software to Accurately Classify Postures and Movements in Children Aged 3-14.

BACKGROUND: ActiMotus, a thigh-accelerometer-based software used for the classification of postures and movements (PaMs), has shown high accuracy among adults and school-aged children; however, its accuracy among younger children and potential differences between sexes are unknown. This study aimed to evaluate the accuracy of ActiMotus to measure PaMs among children between 3 and 14 years and to assess if this was influenced by the sex or age of children. METHOD: Forty-eight children attended a structured ~1-hour data collection session at a laboratory. Thigh acceleration was measured using a SENS accelerometer, which was classified into nine PaMs using the ActiMotus software. Human-coded video recordings of the session provided the ground truth. RESULTS: Based on both F1 scores and balanced accuracy, the highest levels of accuracy were found for lying, sitting, and standing (63.2-88.2%). For walking and running, accuracy measures ranged from 48.0 to 85.8%. The lowest accuracy was observed for classifying stair climbing. We found a higher accuracy for stair climbing among girls compared to boys and for older compared to younger age groups for walking, running, and stair climbing. CONCLUSIONS: ActiMotus could accurately detect lying, sitting, and standing among children. The software could be improved for classifying walking, running, and stair climbing, particularly among younger children.

Dose-response association of an accelerometer-measured physical activity with all-cause mortality and cardiovascular disease incidence: Prospective cohort with 76,074 participants.

OBJECTIVE: To investigate the prospective dose-response association of accelerometer-measured moderate-to-vigorous physical activity (PA;MVPA) with all-cause mortality and cardiovascular disease (CVD) incidence. METHODS: This prospective cohort of 76,074 participants from the UK Biobank study contained one week of individual accelerometer-based PA data collected between June 1, 2013 and December 23, 2015. Using restricted cubic splines to allow for potential non-linearity, we examined dose-response associations of MVPA with all-cause mortality and incident CVD, respectively. RESULTS: The median follow-up time was 8.0 years (IQR 7.5-8.5). The dose-response association of MVPA with all-cause mortality and CVD showed a similar L-shaped association, with significant risk reductions already from 10 min of MVPA per week for all-cause mortality (hazard ratio [HR], 0.98 [95 % CI,0.98-0.99]) and 15 min per week for CVD incidence (HR, 0.99 [95 % CI,0.98-0.99]). Doing more MVPA was associated with further risk reduction, but beyond around 500 min per week the benefits levelled off at HR's around 0.6 to 0.7. The highest additional benefit of adding more minutes per week for all-cause mortality and CVD incidence were observed between 100 and 250 weekly minutes of MVPA. From this point forward, the mean risk reduction rates decreased and were close to 0 beyond 500 weekly minutes. CONCLUSIONS: Significant, but small, risk reductions in all-cause mortality and CVD incidence can be achieved with as little as 10 and 15 min of MVPA per week, respectively. However, public health organizations should promote the attainment of 250 min of MVPA per week (with 100 min as a possible first target for inactive individuals), as these thresholds are associated with the greatest efficiency. Beyond that, less pronounced risk reductions can be achieved by accumulating additional MVPA, with hardly any additional benefits beyond 500 weekly minutes.

Smartphone-Based Sit-to-Stand Analysis for Mobility Assessment in Middle Age.

Background and Objectives: Mobility can decline in middle age and growing evidence highlights the importance of assessing mobility at this stage of life. Smartphone-based accelerometry during sit-to-stand has been shown to identify mobility impairments, but its utility in detecting subtle mobility deterioration in middle age has not been tested. This study aimed to examine whether smartphone-based accelerometry data measured during sit-to-stand tests performed on a regular chair and a cushioned sofa could be useful for detecting subtle changes in mobility in middle age. Research Design and Methods: Twenty-three young (25.0 ± 2.5 years), 25 middle-aged (52.0 ± 5.2 years), and 17 older adults (70.0 ± 4.1 years) performed the 5-times sit-to-stand test on both a standard chair and a sofa. A smartphone attached to the participants' lower back was used to measure lower-limb muscle power, maximal vertical velocity (MVV) during rising, the duration of the total task and the subphase of transition from sitting to standing (SiToSt), and repetition variability using the dynamic time warping method. Results: Middle-aged adults had reduced lower-limb muscle power compared to young adults (5.25 ±â€…1.08 vs 6.19 ±â€…1.38 W/kg, p = .034), being more pronounced on the sofa (6.23 ±â€…1.61 vs 8.08 ±â€…2.17 W/kg, p = .004). Differences between middle-aged and young adults in terms of MVV (p = .011) and SiToSt duration (p = .038) were only detected on the sofa, and the middle-aged adults showed less variability compared to the older adults on the chair (p = .018). There was no difference in total task duration between the middle-aged group and the young or older adults in either condition. Discussion and Implications: Most common tests are limited in their ability to detect early mobility deterioration in midlife due to a ceiling effect. Our results, which show the potential of smartphone-based sit-to-stand assessment in detecting subtle mobility decline in midlife, could serve as a screening tool for this purpose.

Instrumented measures of sedentary behavior and physical activity are associated with depression among children and adolescents: a systematic review and dose-response meta-analysis of observational studies.

Background: Higher sedentary behavior (SB) and lower physical activity (PA) are associated with negative physical and mental health outcomes. SB and PA can be objectively assessed using inertial sensors to evaluate body movements. This study aimed to quantify the association between instrumented measures of SB (i-SB) and PA (i-PA) and depression among children and adolescents using a systematic review and meta-analysis of observational studies. Methods: An electronic search was conducted on six databases up to May 12, 2024. A dose-response meta-analysis was conducted to determine the association between i-SB and i-PA and depression, expressed as odds ratios (ORs) and 95% confidence intervals (CIs). Results: Five cross-sectional and 11 longitudinal studies comprising 26,109 participants met the inclusion criteria. Comparing the most sedentary with the least sedentary groups of participants resulted in a pooled ORs of 1.05 (95% CI 0.94-1.16). Comparing the least active with the most active groups of participants resulted in pooled ORs of 0.93 (95% CI 0.84-1.07), 0.89 (95% CI 0.79-1.00), 0.83 (95% CI 0.66-0.99), and 0.73 (95% CI 0.58-0.89) for light, moderate-to-vigorous (MV), vigorous, and total PA, respectively. Robust error meta-regression analyses showed clear dose-response relationships between i-SB and i-MVPA and depression. Conclusion: Both i-SB and i-PA were significantly associated with risk of depression in children and adolescents, which may become non-significant after mutual adjustment for i-PA and i-SB. Systematic review registration: [https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=546666], identifier [CRD42024546666].

Association Between Accelerometer-Assessed Physical Activity and Cognitive Function in Older Adults: A Cross-Sectional Study.

OBJECTIVE: Research suggests that physical activity (PA) improves cognitive function across various domains. However, the specific role of different PA measures, including step count, remains to be explored. Our aim was to assess the correlation between objectively measured PA and cognitive function. METHODS: We included 663 adults, aged ≥66 years, from the Swedish SNAC-K study (2016-2019). Global cognition and three cognitive domains (processing speed, executive function, and episodic memory) were assessed with validated tests. PA was measured through ActivPAL3 accelerometers. We applied age-stratified (<70 vs. ≥80 years), multi-adjusted, quantile regression to examine the cross-sectional associations between cognitive function and PA, considering steps/day and time spent in moderate-to-vigorous PA (MVPA). RESULTS: Each 1000-step increment (ß = 0.04; 95% CI: 0.01, 0.07) and each additional hour of MVPA per day (ß = 0.28; 95% CI: 0.02, 0.54) were correlated with better processing speed in the youngest-old, but not in the oldest-old. When further stratifying by MVPA (<60 min vs. ≥60 min/week), each 1000-step increment was associated with better processing speed in the youngest-old, regardless of their MVPA levels. CONCLUSION: Our study links accelerometer-assessed PA (steps and MVPA) with better processing speed in the youngest-old adults. Step count correlated with processing speed regardless of intensity. Further research is needed to determine the directionality of these associations.

Validation of a data logger for measuring standing time and seat angles for community-based standing devices.

CONTEXT: Little is understood about community-based standing device use and the impact of standing on health outcomes (e.g. pressure injury) in those living with spinal cord injury (SCI). This project reports on the accuracy of a commercially available data logger for measuring standing time and seat angle. METHODS: A standing frame and a standing manual wheelchair were each instrumented with a commercially available data logger and each was tested by an non-disabled participant. Standing time in the standing frame was calculated from the data logger and compared to a user-recorded standing log over a two-month period in a laboratory environment. The standing wheelchair's seat angle was calculated using motion capture and compared to the calculated seat angle from the data logger. Average seat interface pressures were also captured during the testing of the standing wheelchair. RESULTS: The data logger demonstrated high accuracy (99.99999%) in classifying sitting and standing in the standing frame when compared to a user-recorded standing log. The wheelchair seat angle calculated from the data logger demonstrated a high level of agreement with the motion lab calculations of seat angle (ICC = 0.96 (0.95, 0.97)). The data logger seat angle results also demonstrated strong relationships to average seat pressure and rear dispersion index, measures relevant to pressure injuries. CONCLUSIONS: The data logger appears to be an appropriate tool for determining standing time and seat angle in standing devices, which may aid clinicians and researchers to better understand the use and impact of standing technologies on health outcomes.

Machine learning model identifies patient gait speed throughout the episode of care, generating notifications for clinician evaluation.

INTRODUCTION: The advent of digital and mobile health innovations, especially use of wearables for passive data collection, allows remote monitoring and creates an abundance of data. For this information to be interpretable, machine learning (ML) processes are necessary. RESEARCH QUESTION: Can a machine learning model successfully identify patients expected to have low gait speed in the early recovery period following joint replacement surgery? METHODS: A commercial database from a smartphone-based care management platform passively collecting mobility data pre- and post-lower limb arthroplasty was used. We sought to create a ML model to predict gait speed recovery curves and identify patients at risk of poor gait speed outcome, a measure associated with range of motion and patient-reported outcomes. Model performance including sensitivity, specificity, precision, and accuracy were determined. Receiver operator curve (ROC) analysis was used to compare true and false positive rates. To benchmark our model, we compared threshold-based notifications based on the patient's current gait speed. RESULTS: The performance of the predictive model was significantly improved compared to baseline of threshold-based exceptions using current gait speed. The ML model currently provides 53 % precision, 88 % accuracy, 36 % sensitivity, and 95 % specificity on the held-out test set. The ROC analysis suggests good clinical performance (AUC=0.81). SIGNIFICANCE: Utilization of ML to predict gait recovery following total joint replacement is feasible and provides results with excellent specificity. This model will allow inclusion of additional data for retraining as patient populations evolve. Clinician feedback regarding notifications, including resulting actions and outcomes, can be used to further inform the model and improve clinical utility.

Longitudinal Associations Between Physical Activity and Sleep Duration in Infants and Toddlers.

PURPOSE: This study examined longitudinal associations between average physical activity (PA) levels in children and their sleep duration, and whether changes in PA levels are associated with their sleep duration. METHODS: Data were collected on 108 children at 4 time points: when children were 6, 12, 18, and 24 months of age (44% female, 50% Non-Hispanic White). PA was assessed using accelerometry. Children's daytime, nighttime, and 24-hour sleep duration were measured with actigraphy. Linear mixed model analyses estimated the associations between average PA levels over time and changes in PA over time, treating each sleep duration variable as an outcome in separate linear mixed model analyses. RESULTS: Children with higher total PA levels slept less during the day compared with children with lower total PA levels over the 2-year period. The strength of the relationship between a child's PA levels and their 24-hour sleep duration decreased as they approached 24 months of age. CONCLUSIONS: The results suggest that while PA may be developmentally beneficial overall, it appears that its relationship with sleep duration is not clinically relevant in very young children.

Intensity or volume: the role of physical activity in longevity.

AIMS: To investigate how physical activity (PA) volume, intensity, duration, and fragmentation are associated with the risk of all-cause and cardiovascular disease mortality. To produce centile curves for PA volume and intensity representative of US adults. METHODS: This study is based on the observational 2011-2014 National Health and Nutrition Examination Survey (NHANES). Adults (age ≥20) with valid accelerometer, covariate, and mortality data were included. Average acceleration (AvAcc), intensity gradient (IG), and total PA served as proxies for volume, intensity, and duration of PA, respectively. Weighted Cox proportional hazard models estimated associations between outcome and PA metrics. RESULTS: In 7518 participants (52.0% women, weighted median age 49), there were curvilinear inverse dose-response relationships of all-cause mortality risk (81-month follow-up) with both AvAcc (-14.4% [95% CI -8.3 to -20.1%] risk reduction from 25th to 50th percentile) and IG (-37.1% [95% CI -30.0 to -43.4%] risk reduction from 25th to 50th percentile), but for cardiovascular disease mortality risk (N=7016, 82-month follow-up) only with IG (-41.0% [95% CI -26.7 to -52.4%] risk reduction from the 25th to 50th percentile). These relationships plateau at AvAcc: ∼35-45 mg and IG: -2.7 to -2.5. Associations of PA with all-cause and cardiovascular disease mortality are primarily driven by intensity and secondary by volume. Centile curves for volume and intensity were generated. CONCLUSION: Intensity is a main driver of reduced mortality risk suggesting that the intensity of PA rather than the quantity matters for longevity. The centile curves offer guidance for achieving desirable PA levels for longevity.

This study shows that the distribution of the intensity of physical activity accumulated across the day may be more important for mortality reduction than the quantity (volume), underscoring the relevance of integrating physical activity of higher intensity into daily routines for health optimisation.Higher physical activity intensity is more closely associated with reduced mortality risk than physical activity volume, particularly for cardiovascular disease mortality.We provide initial evidence suggesting health benefits when accumulating intense physical activity in continuous bouts rather than sporadically across the day.

Dynamic soaring decouples dynamic body acceleration and energetics in albatrosses.

Estimates of movement costs are essential for understanding energetic and life-history trade-offs. Although overall dynamic body acceleration (ODBA) derived from accelerometer data is widely used as a proxy for energy expenditure (EE) in free-ranging animals, its utility has not been tested in species that predominately use body rotations or exploit environmental energy for movement. We tested a suite of sensor-derived movement metrics as proxies for EE in two species of albatrosses, which routinely use dynamic soaring to extract energy from the wind to reduce movement costs. Birds were fitted with a combined heart-rate, accelerometer, magnetometer and GPS logger, and relationships between movement metrics and heart rate-derived V̇O2, an indirect measure of EE, were analyzed during different flight and activity modes. When birds were exclusively soaring, a metric derived from angular velocity on the yaw axis provided a useful proxy of EE. Thus, body rotations involved in dynamic soaring have clear energetic costs, albeit considerably lower than those of the muscle contractions required for flapping flight. We found that ODBA was not a useful proxy for EE in albatrosses when birds were exclusively soaring. As albatrosses spend much of their foraging trips soaring, ODBA alone was a poor predictor of EE in albatrosses. Despite the lower percentage of time flapping, the number of flaps was a useful metric when comparing EE across foraging trips. Our findings highlight that alternative metrics, beyond ODBA, may be required to estimate energy expenditure from inertial sensors in animals whose movements involve extensive body rotations.

Variation in Accelerometer-Derived Instantaneous Acceleration Distribution Curves of Elite Male Soccer Players According to Playing Position: A Pilot Study.

The incorporation of triaxial accelerometers into Global Positioning Systems (GPS) has significantly advanced our understanding of accelerations in sports. However, inter-positional differences are unknown. This study aimed to explore the variability of acceleration and deceleration (Acc) distribution curves according to players' positions during soccer matches. Thirty-seven male players from a national-level Portuguese club were monitored using 10 Hz GPS with an embedded accelerometer during the 2021/2022 season. Resultant Acc was obtained from the x (lateral), y (frontal/back), and z (vertical) axes and expressed in gravitational units (g). Statistical Parametric Mapping was employed to compare playing positions: central defenders (CD), fullbacks (FB), central midfielders (CM), wide midfielders (WM), and strikers (ST). All positions exhibited a decreasing Acc distribution curve, very similar in shape, with a high frequency of events in the lower ranges (i.e., 0 to 1 g) and a lower frequency of events in the higher values (2 to 10 g). Post hoc comparisons revealed significant differences between all positions, except between FB and WM. Out of 1000 points in the curve, CD had 540, 535, 414, and 264 different points compared to FB, CM, WM, and ST, respectively. These findings indicate that players in different positions face distinct demands during matches, emphasizing the need for position-specific Acc analysis and training programming. By analyzing Acc as a continuous variable, this study highlights the importance of individualized monitoring to ensure the comprehensive and precise tracking of all player activities, without overlooking or omitting critical information.

Correspondence between the Simple Physical Activity Questionnaire (SIMPAQ) and accelerometer-based physical activity in inpatients treated for major depressive disorders in comparison to non-depressed controls.

Introduction: Major depressive disorders (MDD) are a leading health concern worldwide. While first line medication treatments may fall short of desired therapeutic outcomes, physical activity (PA) interventions appear to be a promising and cost-effective add-on to improve symptoms of depression. This study aimed to address challenges in the assessment of PA in inpatients treated for MDD by examining the correspondence of self-reported and accelerometer-based PA. Methods: In 178 inpatients treated for MDD (mean age: M = 41.11 years, SD = 12.84; 45.5% female) and 97 non-depressed controls (mean age: M = 35.24 years, SD = 13.40; 36.1% female), we assessed self-reported PA via the Simple Physical Activity Questionnaire (SIMPAQ) for one week, followed by a week where PA was monitored using an accelerometer device (Actigraph wGT3x-BT). Additionally, we examined correlations between PA levels assessed with the SIMPAQ and exercise determinants in both groups. Results: Descriptively, inpatients treated for MDD showed lower levels of light PA on accelerometer-based measures, whereas they self-reported increased levels of certain types of PA on the SIMPAQ. More importantly, there was only a small degree of correspondence between self-reported and actigraphy-based PA levels in both in patients (r = 0.15, p < 0.05) and controls (r = 0.03, ns). Only few significant correlations were found for self-reported PA (SIMPAQ subscores) and perceived fitness, whereas self-reported PA and estimated VO2max were unrelated. Furthermore, only weak (and mostly statistically non-significant) correlations were found between exercise determinants and SIMPAQ-based exercise behavior in both populations. Discussion: Our findings emphasize the intricate challenges in the assessment of PA, not only in inpatients treated for MDD, but also in non-depressed controls. Our findings also underline the necessity for a diversified data assessment. Further efforts are needed to refine and improve PA questionnaires for a more accurate data assessment in psychiatric patients and healthy controls.

Characterizing objective and self-report habitual physical activity and sedentary time in outpatients with an acquired brain injury.

Outpatients with an acquired brain injury (ABI) experience physical, mental, and social deficits. ABI can be classified into two subgroups based on mechanism of injury: mild traumatic brain injury (mTBI; e.g., concussion) and other ABI (e.g., stroke, brain aneurysm, encephalitis). Our understanding of habitual activity levels within ABI populations is limited because they are often collected using self-report measures. The purpose of this study was to, 1) describe the habitual activity levels of outpatients with ABI using objective and self-report monitoring, and 2) compare the activity levels of outpatients with mTBI vs. other ABI. Sixteen outpatients with other ABI (mean â€‹± â€‹standard deviation: [58 â€‹± â€‹13] years, 9 females) and 12 outpatients with mTBI ([48 â€‹± â€‹11] years, 9 females) wore a thigh-worn activPAL 24 â€‹h/day (h/day) for 7-days. Outpatients with ABI averaged (6.0 â€‹± â€‹2.3) h/day of upright time, (10.6 â€‹± â€‹2.2) h/day of sedentary time, (5.6 â€‹± â€‹2.7) h/day in prolonged sedentary bouts > 1 â€‹h, (5 960 â€‹± â€‹3 037) steps/day, and (11 â€‹± â€‹13) minutes/day (min/day) of moderate-vigorous physical activity (MVPA). There were no differences between activPAL-derived upright, sedentary, prolonged sedentary time, and physical activity between the mTBI and other ABI groups (all, p â€‹> â€‹0.31). Outpatients with ABI overestimated their MVPA levels (+138 â€‹min/week) and underestimated sedentary time (-4.3 â€‹h/day) compared to self-report (all, p â€‹< â€‹0.001). Despite self-reporting high activity levels, outpatients with ABI objectively exhibit highly inactive and sedentary lifestyles. The habitual movement behaviours of our sample did not differ by mechanism of injury (i.e., mTBI versus other ABI). Targeting reductions in objectively measured sedentary time are needed to progressively improve the habitual movement behaviours of outpatients with ABI.

Characterizing daily physical activity patterns with unsupervised learning via functional mixture models.

Physical inactivity is a significant public health concern. Consideration of inter-individual variations in physical activity (PA) trends can provide additional information about the groups under study to aid intervention design. This study aims to identify latent profiles ("phenotypes") based on daily PA trends among adults living in. This was a secondary analysis of 724 person-level days of accelerometry data from 133 urban-dwelling adults (89% Latinx, age = 19-77 years). We used Actigraph accelerometers and the Actilife software to collect and process 24-hour PA data. We implemented a probabilistic clustering technique based on functional mixture models. Multiple days of data per person were averaged for entry into the models. We evaluated step counts, moderate-intensity PA (MOD), total activity and sedentary minutes as potential model variables. Bayesian Information Criterion (BIC) index was used to select the model that provided the best fit for the data. A 4-cluster resolution provided the best fit for the data (i.e., BIC=-3257, improvements of Δ = 13 and Δ = 7 from 3- and 5-cluster models, respectively). MOD provided the greatest between-cluster discrimination. Phenotype 1 (N = 61) was characterized by a morning peak in PA that declined until bedtime. Later bedtimes and the highest daily PA volume were distinct for phenotype 2 (N = 18), along with a similar peak pattern. Phenotype 3 (N = 29) membership was associated with the lowest PA levels throughout the day. Phenotype 4 was characterized by a more evenly distributed PA during the day, and later waking/bedtimes. Our findings point to distinct, interpretable PA phenotypes based on temporal patterns. Functional clustering of PA data could provide additional actionable points for tailoring behavioral interventions.

Associations of "Weekend Warrior" Physical Activity With Incident Disease and Cardiometabolic Health.

BACKGROUND: Achievement of guideline-recommended levels of physical activity (≥150 minutes of moderate-to-vigorous physical activity per week) is associated with lower risk of adverse cardiovascular events and represents an important public health priority. Although physical activity commonly follows a "weekend warrior" pattern, in which most moderate-to-vigorous physical activity is concentrated in 1 or 2 days rather than spread more evenly across the week (regular), the effects of physical activity pattern across a range of incident diseases, including cardiometabolic conditions, are unknown. METHODS: We tested associations between physical activity pattern and incidence of 678 conditions in 89 573 participants (62±8 years of age; 56% women) of the UK Biobank prospective cohort study who wore an accelerometer for 1 week between June 2013 and December 2015. Models were adjusted for multiple baseline clinical factors, and P value thresholds were corrected for multiplicity. RESULTS: When compared to inactive (<150 minutes moderate-to-vigorous physical activity/week), both weekend warrior (267 total associations; 264 [99%] with lower disease risk; hazard ratio [HR] range, 0.35-0.89) and regular activity (209 associations; 205 [98%] with lower disease risk; HR range, 0.41-0.88) were broadly associated with lower risk of incident disease. The strongest associations were observed for cardiometabolic conditions such as incident hypertension (weekend warrior: HR, 0.77 [95% CI, 0.73-0.80]; P=1.2×10-27; regular: HR, 0.72 [95% CI, 0.68-0.77]; P=4.5×10-28), diabetes (weekend warrior: HR, 0.57 [95% CI, 0.51-0.62]; P=3.9×10-32; regular: HR, 0.54 [95% CI, 0.48-0.60]; P=8.7×10-26), obesity (weekend warrior: HR, 0.55 [95% CI, 0.50-0.60]; P=2.4×10-43, regular: HR, 0.44 [95% CI, 0.40-0.50]; P=9.6×10-47), and sleep apnea (weekend warrior: HR, 0.57 [95% CI, 0.48-0.69]; P=1.6×10-9; regular: HR, 0.49 [95% CI, 0.39-0.62]; P=7.4×10-10). When weekend warrior and regular activity were compared directly, there were no conditions for which effects differed significantly. Observations were similar when activity was thresholded at the sample median (≥230.4 minutes of moderate-to-vigorous physical activity/week). CONCLUSIONS: Achievement of measured physical activity volumes consistent with guideline recommendations is associated with lower risk for >200 diseases, with prominent effects on cardiometabolic conditions. Associations appear similar whether physical activity follows a weekend warrior pattern or is spread more evenly throughout the week.

Raw Photoplethysmography as an Enhancement for Research-Grade Wearable Activity Monitors.

Wearable monitors continue to play a critical role in scientific assessments of physical activity. Recently, research-grade monitors have begun providing raw data from photoplethysmography (PPG) alongside standard raw data from inertial sensors (accelerometers and gyroscopes). Raw PPG enables granular and transparent estimation of cardiovascular parameters such as heart rate, thus presenting a valuable alternative to standard PPG methodologies (most of which rely on consumer-grade monitors that provide only coarse output from proprietary algorithms). The implications for physical activity assessment are tremendous, since it is now feasible to monitor granular and concurrent trends in both movement and cardiovascular physiology using a single noninvasive device. However, new users must also be aware of challenges and limitations that accompany the use of raw PPG data. This viewpoint paper therefore orients new users to the opportunities and challenges of raw PPG data by presenting its mechanics, pitfalls, and availability, as well as its parallels and synergies with inertial sensors. This includes discussion of specific applications to the prediction of energy expenditure, activity type, and 24-hour movement behaviors, with an emphasis on areas in which raw PPG data may help resolve known issues with inertial sensing (eg, measurement during cycling activities). We also discuss how the impact of raw PPG data can be maximized through the use of open-source tools when developing and disseminating new methods, similar to current standards for raw accelerometer and gyroscope data. Collectively, our comments show the strong potential of raw PPG data to enhance the use of research-grade wearable activity monitors in science over the coming years.

Optimizing Detection and Prediction of Cognitive Function in Multiple Sclerosis With Ambulatory Cognitive Tests: Protocol for the Longitudinal Observational CogDetect-MS Study.

BACKGROUND: Cognitive dysfunction is a common problem in multiple sclerosis (MS). Progress toward understanding and treating cognitive dysfunction is thwarted by the limitations of traditional cognitive tests, which demonstrate poor sensitivity and ecological validity. Ambulatory methods of assessing cognitive function in the lived environment may improve the detection of subtle changes in cognitive function and the identification of predictors of cognitive changes and downstream effects of cognitive change on other functional domains. OBJECTIVE: This paper describes the study design and protocol for the Optimizing Detection and Prediction of Cognitive Function in Multiple Sclerosis (CogDetect-MS) study, a 2-year longitudinal observational study designed to examine short- and long-term changes in cognition, predictors of cognitive change, and effects of cognitive change on social and physical function in MS. METHODS: Participants-ambulatory adults with medically documented MS-are assessed over the course of 2 years on an annual basis (3 assessments: T1, T2, and T3). A comprehensive survey battery, in-laboratory cognitive and physical performance tests, and 14 days of ambulatory data collection are completed at each annual assessment. The 14-day ambulatory data collection includes continuous wrist-worn accelerometry (to measure daytime activity and sleep); ecological momentary assessments (real-time self-report) of somatic symptoms, mood, and contextual factors; and 2 brief, validated cognitive tests, administered by smartphone app 4 times per day. Our aim was to recruit 250 participants. To ensure standard test protocol administration, all examiners passed a rigorous examiner certification process. Planned analyses include (1) nonparametric 2-tailed t tests to compare in-person to ambulatory cognitive test scores; (2) mixed effects models to examine cognitive changes over time; (3) mixed effects multilevel models to evaluate whether ambulatory measures of physical activity, sleep, fatigue, pain, mood, and stress predict changes in objective or subjective measures of cognitive functioning; and (4) mixed effects multilevel models to examine whether ambulatory measures of cognitive functioning predict social and physical functioning over short (within-day) and long (over years) time frames. RESULTS: The study was funded in August 2021 and approved by the University of Michigan Medical Institutional Review Board on January 27, 2022. A total of 274 adults with MS (first participant enrolled on May 12, 2022) have been recruited and provided T1 data. Follow-up data collection will continue through March 2026. CONCLUSIONS: Results from the CogDetect-MS study will shed new light on the temporal dynamics of cognitive function, somatic and mood symptoms, sleep, physical activity, and physical and social function. These insights have the potential to improve our understanding of changes in cognitive function in MS and enable us to generate new interventions to maintain or improve cognitive function in those with MS. TRIAL REGISTRATION: ClinicalTrials.gov NCT05252195; https://clinicaltrials.gov/study/NCT05252195. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/59876.

Diurnal timing of physical activity and risk of colorectal cancer in the UK Biobank.

BACKGROUND: Physical activity reduces colorectal cancer risk, yet the diurnal timing of physical activity in colorectal cancer etiology remains unclear. METHODS: This study used 24-h accelerometry time series from UK Biobank participants aged 42 to 79 years to derive circadian physical activity patterns using functional principal component analysis. Multivariable Cox proportional hazard models were used to examine associations with colorectal cancer risk. RESULTS: Among 86,252 participants (56% women), 529 colorectal cancer cases occurred during a median 5.3-year follow-up. We identified four physical activity patterns that explained almost 100% of the data variability during the day. A pattern of continuous day-long activity was inversely associated with colorectal cancer risk (hazard ratio (HR) = 0.94, 95% confidence interval (CI) = 0.89-0.99). A second pattern of late-day activity was suggestively inversely related to risk (HR = 0.93, 95% CI = 0.85-1.02). A third pattern of early- plus late-day activity was associated with decreased risk (HR = 0.89, 95% CI = 0.80-0.99). A fourth pattern of mid-day plus night-time activity showed no relation (HR = 1.02, 95% CI = 0.88-1.19). Our results were consistent across various sensitivity analyses, including the restriction to never smokers, the exclusion of the first 2 years of follow-up, and the adjustment for shift work. CONCLUSIONS: A pattern of early- plus late-day activity is related to reduced colorectal cancer risk, beyond the benefits of overall activity. Further research is needed to confirm the role of activity timing in colorectal cancer prevention.