Short term e-bicycle riding results in favorable cardiometabolic shifts in moderately active adults.

PURPOSE: Electric bikes (EB) are a form of active transportation with demonstrated health benefits. The purpose of this study was to determine the influence of riding an EB for one week on indices of cardiometabolic health in middle-aged adults. METHODS: Adults (n = 22; age = 57.1 ± 11.3 year; BMI = 27.7 ± 4.9) participated in a 2 week study. During Week 1, participants were instructed to continue regular activities. Starting Week 2 participants were provided an EB to ride at least 3 days for a minimum of 30 min·day-1. Physical activity (PA) and glucose were measured continuously. Body composition, blood lipids, glucose, insulin, hemoglobin A1c (HbA1c), plasma endothelin-1 (ET-1), and carotid-femoral pulse wave velocity (cf-PWV) were measured on days 1 and 14.Data and Statistical analyses or Statistics. Each participant served as their own control. Paired t-tests compared dependent variables between week 1 (without EB) and week 2 (with EB). RESULTS: When provided an EB for one week, moderate to vigorous PA increased by 6-9 min·day-1 (P < 0.05) and sedentary time decreased by ~ 77 min·day-1 (P < 0.05). Data from 24 h continuous glucose monitoring showed the percentage of time in healthy range (70-120 mg·dl-1 glucose) increased (P < 0.05) from week 1 to week 2. Compared to day 1, cf-PWV was lower at day 14 (P < 0.05) following one week of riding an EB. CONCLUSION: Moderately-active, middleaged adults showed improved continuous glucose regulation and lower central arterial stiffness following one week of riding an EB.

Understanding lower limb blood flow occlusion parameters for use in field-based settings.

Reduction of blood flow to the limb using cuffs before or during exercise has become increasingly popular for training and rehabilitation. Our study tested the effects of cuff brand/width on pressures required to reach limb occlusion pressure (LOP) and developed, cross-validated, and compared accuracy of two LOP prediction equations to previously created methods. Supine LOP was determined in the distal popliteal artery using four different cuff brands/widths in 23 adult participants. Participants then had demographic and resting variables assessed, and two LOP prediction equations were developed from these variables and were compared to five previously developed models and a method using posterior tibial artery palpation for LOP assessment in an independent sample (n = 14 adult runners). For cuff comparison, the widest two cuffs had significantly lower LOP (mean ~149 mmHg) than the narrowest cuffs (mean ~176 mmHg), with the narrowest cuff unable to reach LOP. The eight methods used to predict LOP ranged in accuracy (mean absolute percent errors 3.9-23.0%), with highest accuracy in equations using mean arterial pressure (MAP) and BMI. Practitioners using blood flow reduction methods should be consistent with cuff use due to demonstrated differences across brands/widths. Equations using MAP and BMI appear best for prediction of leg LOP.

Validation of the SmartPlate for detecting food weight and type.

This study determined accuracy (comparing to criterion), inter-plate reliability (comparing measures between two plates), and intra-plate reliability (comparing successive measures on one plate) of the SmartPlate for food weight and type. Food weight validation included comparing SmartPlate weights to criterion [reference] scale weights (1,980 measures) and weights of 188 foods (2,256 measures). Food type validation included assessing SmartPlate accuracy for 188 foods. For weight, mean absolute percent errors for accuracy, inter-plate reliability, and intra-plate reliability were 6.2, 7.4, and 4.9%, respectively. For food type, foods were correctly identified/listed or searchable 67.0 or 98.9% of the time, respectively, with 76.0% inter-plate reliability and 86.3% intra-plate reliability. The SmartPlate had acceptable accuracy and reliability for assessing food weight and type and may be appealing for monitoring dietary surveillance or intervention. Due to high intra-plate reliability, the SmartPlate may be especially useful for one-on-one interventions and assessing change over time.

Self-recorded heart rate variability profiles are associated with health and lifestyle markers in young adults.

PURPOSE: To quantify associations between self-recorded heart rate variability (HRV) profiles and various health and lifestyle markers in young adults. METHODS: Otherwise healthy volunteers (n = 40, 50% male) recorded 60-s, post-waking HRV with a cost-free mobile application in supine and standing positions for 7 days. The 7-day average and coefficient of variation (CV, reflects daily fluctuation) for the mean RR interval and root mean square of successive differences (LnRMSSD) were assessed. 7-day sleep duration and physical activity profiles were characterized via wrist-worn accelerometer. Subsequent laboratory assessments included aerobic fitness ([Formula: see text]O2peak) and markers of cardiovascular, metabolic, and psychoemotional health. Associations were evaluated before and after [Formula: see text]O2peak adjustment. RESULTS: Bivariate correlations (P < 0.05) demonstrated that higher 7-day averages and/or lower CV values were associated with higher activity levels and superior cardiovascular (lower systolic and diastolic blood pressure [BP] and aortic stiffness [cf-PWV]), metabolic (lower body fat percentage, fasting glucose, and low-density lipoprotein cholesterol [LDL-C]), and psychoemotional health (lower perceived stress) markers, with some variation between sexes and recording position. In males, associations between HRV parameters and cf-PWV remained significant following [Formula: see text]O2peak adjustment (P < 0.05). In females, HRV parameters were associated (P < 0.05) with numerous cardiovascular (systolic and diastolic BP, cf-PWV) and metabolic (fasting glucose and LDL-C) parameters following [Formula: see text]O2peak adjustment. CONCLUSIONS: Higher or more stable supine and standing HRV were generally associated with superior health and lifestyle markers in males and females. These findings lay groundwork for future investigation into the usefulness of self-recorded ultra-short HRV as a health-promoting behavior-modification tool in young adults.

Comparison of Wrist- and Hip-Worn Activity Monitors When Meeting Step Guidelines.

INTRODUCTION: Physical activity (PA) guidelines aimed at accumulating 10,000 steps per day have become increasingly common with the advent of wristband PA monitors. However, accumulated steps measured with wristband PA monitors may not be equal to steps measured with validated, hip-worn pedometers. Consequently, evaluating and developing guidelines for step counts using wristband PA monitors for the general population is needed. We compared step counts accumulated with hip-worn pedometers with those accumulated with wrist-worn activity monitors during 1) treadmill exercise, 2) treadmill walking, and 3) activities of daily living (ADL) to determine their accuracy in meeting step count guidelines (ie, 10,000 steps/d). METHODS: Eighty-six adults (aged 18-65 y; body mass index, 19-45 kg/m2) completed 30 minutes of treadmill exercise while simultaneously using a hip-worn pedometer and wrist-worn PA monitor. Remaining steps needed to reach 10,000 steps (ie, 10,000 steps minus the number of pedometer steps recorded from treadmill exercise = remainder) were completed via treadmill walking or ADL. Steps were recorded for both devices after treadmill exercise, treadmill walking, and ADL for both devices. RESULTS: Fewer steps were accumulated via wrist-worn PA monitors than via hip-worn pedometers during treadmill exercise (3,552 [SD, 63] steps vs 3,790 [SD, 55] steps, P < .01) and treadmill walking (5,877 [SD, 83] steps vs 6,243 [SD, 49] steps, P < .01). More steps were accumulated via wrist-worn PA monitors than hip-worn pedometers during ADL (7,695 [SD, 207] steps vs 6,309 [SD, 57] steps, P < .01). Consequently, total steps were significantly higher for wristband PA monitors than hip-worn pedometers (11,247 [SD, 210] steps vs 10,099 [SD, 39] steps; P < .01). CONCLUSION: The widely used 10,000-step recommendation may not be accurate for all users of all activity monitors, given the discrepancy in daily step count among wrist-worn and hip-worn devices. Having a more accurate indication of number of steps taken per day based on the device used could have positive effects on health.

A consensus method for estimating physical activity levels in adults using accelerometry.

Identifying the best analytical approach for capturing moderate-to-vigorous physical activity (MVPA) using accelerometry is complex but inconsistent approaches employed in research and surveillance limits comparability. We illustrate the use of a consensus method that pools estimates from multiple approaches for characterising MVPA using accelerometry. Participants (n = 30) wore an accelerometer on their right hip during two laboratory visits. Ten individual classification methods estimated minutes of MVPA, including cut-point, two-regression, and machine learning approaches, using open-source count and raw inputs and several epoch lengths. Results were averaged to derive the consensus estimate. Mean MVPA ranged from 33.9-50.4 min across individual methods, but only one (38.9 min) was statistically equivalent to the criterion of direct observation (38.2 min). The consensus estimate (39.2 min) was equivalent to the criterion (even after removal of the one individual method that was equivalent to the criterion), had a smaller mean absolute error (4.2 min) compared to individual methods (4.9-12.3 min), and enabled the estimation of participant-level variance (mean standard deviation: 7.7 min). The consensus method allows for addition/removal of methods depending on data availability or field progression and may improve accuracy and comparability of device-based MVPA estimates while limiting variability due to convergence between estimates.

Cross-generational comparability of hip- and wrist-worn ActiGraph GT3X+, wGT3X-BT, and GT9X accelerometers during free-living in adults.

ActiGraph accelerometers are frequently used to characterize physical activity, but free-living cross-generational comparability of newer models has not been verified. Participants (N = 70) wore GT9X and wGT3X-BT accelerometers at the hip and a sub-sample (n = 54) wore GT9X and either wGT3X-BT or GT3X+ monitors at each wrist for 4 days. Vector magnitude (VM) counts, VM acceleration, Mean Amplitude Deviation (MAD), and Euclidean Norm Minus One (ENMO) were calculated (60-s epoch), and cut-points were used to determine percent of time spent in each intensity (sedentary/light, moderate, vigorous). Epoch-level correlation coefficients (r) were ≥0.73, and weighted kappa for intensity classifications ranged from 0.71 (ENMO, hip) to 0.98 (VM counts, non-dominant wrist). Monitors were equivalent for all outcomes, except ENMO (all locations/monitors), percent of time spent in sedentary/light (hip) and moderate (hip and non-dominant wrist) activity as classified by ENMO-based cut-points, and vigorous activity as classified by VM count cut-points (non-dominant wrist; p > 0.05). While epoch-level data were not identical, most outcomes were strongly related between models (e.g., MAD, VM) and equivalent once reduced to percent of time spent in each intensity. However, monitor output was not equivalent for the acceleration-based metric ENMO, suggesting that caution should be exercised when comparing this outcome among ActiGraph models.

Development of cut-points for determining activity intensity from a wrist-worn ActiGraph accelerometer in free-living adults.

Despite recent popularity of wrist-worn accelerometers for assessing free-living physical behaviours, there is a lack of user-friendly methods to characterize physical activity from a wrist-worn ActiGraph accelerometer. Participants in this study completed a laboratory protocol and/or 3-8 hours of directly observed free-living (criterion measure of activity intensity) while wearing ActiGraph GT9X Link accelerometers on the right hip and non-dominant wrist. All laboratory data (n = 36) and 11 participants' free-living data were used to develop vector magnitude count cut-points (counts/min) for activity intensity for the wrist-worn accelerometer, and 12 participants' free-living data were used to cross-validate cut-point accuracy. The cut-points were: <2,860 counts/min (sedentary); 2,860-3,940 counts/min (light); and ≥3,941counts/min (moderate-to-vigorous (MVPA)). These cut-points had an accuracy of 70.8% for assessing free-living activity intensity, whereas Sasaki/Freedson cut-points for the hip accelerometer had an accuracy of 77.1%, and Hildebrand Euclidean Norm Minus One (ENMO) cut-points for the wrist accelerometer had an accuracy of 75.2%. While accuracy was higher for a hip-worn accelerometer and for ENMO wrist cut-points, the high wear compliance of wrist accelerometers shown in past work and the ease of use of count-based analysis methods may justify use of these developed cut-points until more accurate, equally usable methods can be developed.

Reporting accelerometer methods in physical activity intervention studies: a systematic review and recommendations for authors.

OBJECTIVE: This systematic review assessed the completeness of accelerometer reporting in physical activity (PA) intervention studies and assessed factors related to accelerometer reporting. DESIGN: The PubMed database was used to identify manuscripts for inclusion. Included studies were PA interventions that used accelerometers, were written in English and were conducted between 1 January 1998 and 31 July 2014. 195 manuscripts from PA interventions that used accelerometers to measure PA were included. Manuscript completeness was scored using 12 questions focused on 3 accelerometer reporting areas: accelerometer information, data processing and interpretation and protocol non-compliance. Variables, including publication year, journal focus and impact factor, and population studied were evaluated to assess trends in reporting completeness. RESULTS: The number of manuscripts using accelerometers to assess PA in interventions increased from 1 in 2002 to 29 in the first 7 months of 2014. Accelerometer reporting completeness correlated weakly with publication year (r=0.24, p<0.001). Correlations were greater when we assessed improvements over time in reporting data processing in manuscripts published in PA-focused journals (r=0.43, p=0.002) compared to manuscripts published in non-PA-focused journals (r=0.19, p=0.021). Only 7 of 195 (4%) manuscripts reported all components of accelerometer use, and only 132 (68%) reported more than half of the components. CONCLUSIONS: Accelerometer reporting of PA in intervention studies has been poor and improved only minimally over time. We provide recommendations to improve accelerometer reporting and include a template to standardise reports.

Validation of a wireless accelerometer network for energy expenditure measurement.

The purpose of this study was to validate a wireless network of accelerometers and compare it to a hip-mounted accelerometer for predicting energy expenditure in a semi-structured environment. Adults (n = 25) aged 18-30 engaged in 14 sedentary, ambulatory, exercise, and lifestyle activities over a 60-min protocol while wearing a portable metabolic analyser, hip-mounted accelerometer, and wireless network of three accelerometers worn on the right wrist, thigh, and ankle. Participants chose the order and duration of activities. Artificial neural networks were created separately for the wireless network and hip accelerometer for energy expenditure prediction. The wireless network had higher correlations (r = 0.79 vs. r = 0.72, P < 0.01) but similar root mean square error (2.16 vs. 2.09 METs, P > 0.05) to the hip accelerometer. Measured (from metabolic analyser) and predicted energy expenditure from the hip accelerometer were significantly different for the 3 of the 14 activities (lying down, sweeping, and cycle fast); conversely, measured and predicted energy expenditure from the wireless network were not significantly different for any activity. In conclusion, the wireless network yielded a small improvement over the hip accelerometer, providing evidence that the wireless network can produce accurate estimates of energy expenditure in adults participating in a range of activities.

Comparison of the Omron HeartGuide to the Welch Allyn ProBP 3400 blood pressure monitor.

Hypertension affects approximately 100 million U.S. adults and is the leading single contributing risk factor to all-cause mortality. Accurate blood pressure (BP) measurement is essential in the treatment of BP, and a number of devices exist for monitoring. Recently, a new watch-type design was released, the Omron HeartGuide (BP8000), with claims to provide clinically accurate BP measurement while also tracking activity and sleep similar to smart watches. The aim of this research was done in two studies: (1) evaluation of the HeartGuide device for measurement of resting BP and heart rate (HR); and (2) assessment of the HeartGuide for BP, HR, step-counting and sleep monitoring during activities of daily living. Study 1 compared the Omron HeartGuide to the previously validated Welch Allyn ProBP 3400 following a modified version of the Universal Standard for validation of BP measuring devices set by the AAMI/ESH/ISO. While resting HR measured by the HeartGuide was similar to Welch Allyn measures, both systolic and diastolic BP were significantly lower ( P ≤0.001), with differences of 10.4 (11.1) and 3.2 (10.0) mmHg, respectively. Study 2 compared HeartGuide measures to Welch Allyn measures for BP, HR, steps and sleep during various body positions (supine, seated, standing), physiological stressors (cold pressor test, lower body submersion, exercise), and free-living. The HeartGuide significantly underestimated BP though provided accurate HR during most conditions. It also significantly underestimated steps, but reported sleep measures similar to those subjectively reported. Based on the significant differences between the HeartGuide and Welch Allyn, our data indicate the HeartGuide is not a suitable replacement for existing BP monitors.

Location, Location, Location: Accelerometer Placement Affects Steps-Based Physical Activity Outcomes During Pregnancy and Postpartum.

As pregnancy progresses, physical changes may affect physical activity (PA) measurement validity. n = 11 pregnant women (30.1 ± 3.8 years) wore ActiGraph GT3X+ accelerometers on the right hip, right ankle, and non-dominant wrist for 3-7 days during the second and third trimesters (21 and 32 weeks, respectively) and 12 weeks postpartum. Data were downloaded into 60-second epochs from which stepping cadence was calculated; repeated-measures analysis of variance was used to determine significant differences among placements. At all time points, the wrist accelerometer measured significantly more daily steps (9930-10 452 steps/d) and faster average stepping cadence (14.5-14.6 steps/min) than either the hip (4972-5944 steps/d, 7.1-8.6 steps/min) or ankle (7161-8205 steps/d, 10.3-11.9 steps/min) placement, while moderate- to vigorous-intensity activity at the wrist (1.2-1.7 min/d) was significantly less than either hip (3.0-5.9 min/d) or ankle (6.1-7.3 min/d). Steps, cadence, and counts were significantly lower for the hip than the ankle at all time points. Kappa calculated for agreement in intensity classification between the various pairwise comparisons ranged from .06 to .41, with Kappa for hip-ankle agreement (.34-.41) significantly higher than for wrist-ankle (.09-.11) or wrist-hip (.06-.16). These data indicate that wrist accelerometer placement during pregnancy likely results in over counting of PA parameters and should be used with caution.

A Pilot, Virtual Exercise Intervention Improves Health and Fitness during the COVID-19 Pandemic.

Physical activity levels are low in individuals with chronic disease (e.g., obesity) and have worsened during the COVID-19 pandemic. PURPOSE: Our pilot study tested a virtual exercise intervention for rural-dwelling adults with chronic disease from January-April 2021 for changes in mental health, physical fitness, and physical activity and for intervention fidelity. METHODS: Participants (n = 8 [7 female]; age = 57.5 ± 13.8 years, body mass index = 38.2 ± 8.0 kg/m2) completed an exercise intervention led virtually by collegiate health science majors. Participants attended two 60-minute sessions/week for 12 weeks, completing individually-tailored and progressed aerobic and muscle-strengthening training. A non-randomized control group matched on gender and age continued normal activity during the 12 weeks. Changes in mental health, physical fitness, and physical activity measures were evaluated using a 2×2 (group × time) analysis of covariance. RESULTS: Both groups improved mental health, but only intervention participants lost weight (3.1 ± 1.0 kg; no change in controls). Step test, arm curls, and chair stands improved by 16.1-20.6% in the intervention and 7.8-12.1% in the control groups. Intervention participants did not increase overall physical activity during or after the intervention. Intervention fidelity was high; participants attended ~73% of sessions and rated the sessions 4.7 ± 0.6 (out of 5). Researcher observations rated exercise sessions as meeting 12.7 ± 0.6 of 16 goals. CONCLUSIONS: Our virtual exercise program was associated with positive mental health and physical fitness changes. Such programs may provide a method, even beyond the pandemic, to improve fitness in adults with chronic disease.

Methods for estimating physical activity and energy expenditure using raw accelerometry data or novel analytical approaches: a repository, framework, and reporting guidelines.

The proliferation of approaches for analyzing accelerometer data using raw acceleration or novel analytic approaches like machine learning ('novel methods') outpaces their implementation in practice. This may be due to lack of accessibility, either because authors do not provide their developed models or because these models are difficult to find when included as supplementary material. Additionally, when access to a model is provided, authors may not include example data or instructions on how to use the model. This further hinders use by other researchers, particularly those who are not experts in statistics or writing computer code.Objective: We created a repository of novel methods of analyzing accelerometer data for the estimation of energy expenditure and/or physical activity intensity and a framework and reporting guidelines to guide future work.Approach: Methods were identified from a recent scoping review. Available code, models, sample data, and instructions were compiled or created.Main Results: Sixty-three methods are hosted in the repository, in preschoolers (n = 6), children/adolescents (n = 20), and adults (n = 42), using hip (n = 45), wrist (n = 25), thigh (n = 4), chest (n = 4), ankle (n = 6), other (n = 4), or a combination of monitor wear locations (n = 9). Fifteen models are implemented in R, while 48 are provided as cut-points, equations, or decision trees.Significance: The developed tools should facilitate the use and development of novel methods for analyzing accelerometer data, thus improving data harmonization and consistency across studies. Future advances may involve including models that authors did not link to the original published article or those which identify activity type.

Accessibility and use of novel methods for predicting physical activity and energy expenditure using accelerometry: a scoping review.

Use of raw acceleration data and/or 'novel' analytic approaches like machine learning for physical activity measurement will not be widely implemented if methods are not accessible to researchers.Objective: This scoping review characterizes the validation approach, accessibility and use of novel analytic techniques for classifying energy expenditure and/or physical activity intensity using raw or count-based accelerometer data.Approach: Three databases were searched for articles published between January 2000 and February 2021. Use of each method was coded from a list of citing articles compiled from Google Scholar. Authors' provision of access to the model (e.g., by request, sample code) was recorded.Main Results: Studies (N = 168) included adults (n = 143), and/or children (n = 38). Model use ranged from 0 to 27 uses/year (average 0.83) with 101 models that have never been used. Approximately half of uses occurred in a free-living setting (52%) and/or by other authors (56%). Over half of included articles (n = 107) did not provide complete access to their model. Sixty-one articles provided access to their method by including equations, coefficients, cut-points, or decision trees in the paper (n = 48) and/or by providing access to code (n = 13).Significance: The proliferation of approaches for analyzing accelerometer data outpaces the use of these models in practice. As less than half of the developed models are made accessible, it is unsurprising that so many models are not used by other researchers. We encourage researchers to make their models available and accessible for better harmonization of methods and improved capabilities for device-based physical activity measurement.

Impact of ActiGraph sampling rate on free-living physical activity measurement in youth.

ActiGraph sampling frequencies of more than 30 Hz may result in overestimation of activity counts in both children and adults, but research on free-living individuals has not included the range of sampling frequencies used by researchers.Objective.We compared count- and raw-acceleration-based metrics from free-living children and adolescents across a range of sampling frequencies.Approach.Participants (n = 445; 10-15 years of age) wore an ActiGraph accelerometer for at least one 10 h day. Vector magnitude counts, mean amplitude deviation, monitor-independent movement summary units, and activity intensity classified using six methods (four cut-points, two-regression model, and artificial neural network) were compared between 30 Hz and 60, 80, 90, and 100 Hz sampling frequencies using mean absolute differences, correlations, and equivalence testing.Main results.All outcomes were statistically equivalent, and correlation coefficients were ≥0.970. Absolute differences were largest for the 30 versus 80 and 30 versus 100 Hz count comparisons. For comparisons of 30 with 60, 80, 90, or 100 Hz, mean (and maximum) absolute differences in minutes of moderate-to-vigorous physical activity per day ranged from 0.1 to 0.3 (0.4 to 1.5), 0.3 to 1.3 (1.6 to 8.6), 0.1 to 0.3 (1.1 to 2.5), and 0.3 to 2.5 (1.6 to 14.3) across the six classification methods.Significance.Acceleration-based outcomes are comparable across the full range of sampling rates and therefore recommended for future research. If using counts, we recommend a multiple of 30 Hz because using a 100 Hz sampling rate resulted in large maximum individual differences and epoch-level differences, and increasing differences with activity level.

Differences in Physical Activity During Walking and Two Pokémon Go Playing Styles.

Objective: Two self-selected playing styles of Pokémon Go (PG) have been observed: intermittent and continuous. However, it is unknown how playing style impacts volume and pattern of physical activity (PA) compared with a traditional walking bout. Materials and Methods: Twenty experienced PG players followed a 1.77 km walking loop during two randomly ordered, 30-minute walking conditions: PG and traditional walking. Objective measurement tools (pedometer, accelerometer, heart rate monitor, GPS watch) were used to measure steps, caloric expenditure, intensity, heart rate, and distance traveled, and direct observation was used to determine number of stops and playing style. Results: Most PG players used the intermittent playing style (60%). Traditional walking resulted in significantly greater PA than PG. There was a significant interaction between the PG playing style (continuous, intermittent) and condition (PG, traditional walking). There was no difference in PA variables between continuous and intermittent PG during traditional walking; however, total steps (3394.3 ± 255.8 vs. 2779.1 ± 317.5), aerobic steps (3314.3 ± 318.9 vs. 2387.5 ± 771.8), caloric expenditure (146.00 [130.0-201.3] vs. 110.0 [89.3-132.3 Kcals]), distance (2.4 [2.3-2.6] vs. 2.0 [1.8-2.1 km]), and moderate minutes (29.7 [27.5-29.8] vs. 25.8 [23.1-27.1]) were significantly greater, while sedentary minutes (0.2 [0-1.0] vs. 2.7 [1.7-4.3]) and number of stops (0 [0-0.8] vs. 4.5 [2.3-7.0]) were less, for continuous compared with intermittent PG. There was a main effect of condition on PA for intermittent PG players, but not continuous PG players. Conclusion: Continuous PG produced similar PA to traditional walking, while intermittent PG reduced PA, nevertheless PG may be a strategy for increasing PA participation.

The Effect of Compression Socks on Maximal Exercise Performance and Recovery in Insufficiently Active Adults.

In athletic populations, compression socks (CS) may improve exercise performance recovery. However, their potential to improve performance and/or recovery following exercise in non-athletic populations is unknown. Our study evaluated the effects of CS on exercise performance and recovery from a graded maximal treadmill test. Insufficiently active adults (n = 10, 60% female, average physical activity ~60 minutes/week) performed two graded maximal exercise tests; one while wearing below-knee CS, and the other trial with regular socks (CON). Order of trials was randomized. For both trials, heart rate, lactate, and rating of perceived exertion were measured at each stage and at one, five, and ten-minutes post-exercise. Additionally, recovery variables (soreness, tightness, annoyingness, tenderness, pulling) were measured at 24 and 48 hours post-exercise using a visual analog scale. Paired-samples t-tests were used to compare exercise and recovery variables between CS and CON trials. Heart rate, lactate, and rating of perceived exertion were not different between trials for any stage during the exercise test or immediate recovery. Most 24-and 48-hour recovery variables were significantly improved after the CS trial, with values 34.6 - 42.3% lower at 24 hours and 40.3 - 61.4% lower at 48 hours compared to CON. Compression socks provided a significant and meaningful improvement in recovery variables 24-48 hours following maximal exercise. Therefore, CS may remove a common barrier to exercise adherence and facilitate more effective training recovery for insufficiently active adults.

Individual versus Group Calibration of Machine Learning Models for Physical Activity Assessment Using Body-Worn Accelerometers.

PURPOSE: We sought to determine if individually calibrated machine learning models yielded higher accuracy than a group calibration approach for physical activity intensity assessment. METHODS: Participants (n = 48) wore accelerometers on the right hip and nondominant wrist while performing activities of daily living in a semistructured laboratory and/or free-living setting. Criterion measures of activity intensity (sedentary, light, moderate, vigorous) were determined using direct observation. Data were reintegrated into 30-s epochs, and eight random forest models were created to determine physical activity intensity by using all possible conditions of training data (individual vs group), protocol (laboratory vs free-living), and placement (hip vs wrist). A 2 × 2 × 2 repeated-measures analysis of variance was used to compare epoch-level accuracy statistics (% accuracy, kappa [κ]) of the models when used to determine activity intensity in an independent sample of free-living participants. RESULTS: Main effects were significant for the type of training data (group: accuracy = 80%, κ = 0.59; individual: accuracy = 74% [P = 0.02], κ = 0.50 [P = 0.01]) and protocol (free-living: accuracy = 81%, κ = 0.63; laboratory: accuracy = 74% [P = 0.04], κ = 0.47 [P < 0.01]). Main effects were not significant for placement (hip: accuracy = 79%, κ = 0.58; wrist: accuracy = 75% [P = 0.18]; κ = 0.52 [P = 0.18]). Point estimates for mean absolute error were generally lowest for the group training, free-living protocol, and hip placement. CONCLUSIONS: Contrary to expectations, individually calibrated machine learning models yielded poorer accuracy than a traditional group approach. In addition, models should be developed in free-living settings when possible to optimize predictive accuracy.

Validity and Reliability of the VO2 Master Pro for Oxygen Consumption and Ventilation Assessment.

This study assessed validity and reliability of the VO2 Master Pro portable metabolic analyzer for assessment of oxygen consumption (VO2) and minute ventilation (VE). In Protocol 1, eight male participants (height: 182.6 ± 5.8 cm, weight: 79.6 ± 8.3 kg, age: 41.0 ± 12.3 years) with previous competitive cycling experience completed an hour-long stationary cycling protocol twice, progressing from 100-300 Watts every 10 minutes while wearing the VO2 Master and a criterion measure (Parvomedics) for five minutes each, at each stage. In Protocol 2, 16 recreationally active male participants (height: 168.2 ± 8.4 cm, weight: 76.5 ± 13.3 kg, age: 23.0 ± 9.4 years) completed three incremental, maximal stationary cycling tests wearing one of three analyzers for each test (VO2 Master version 1.1.1, VO2 Master version 1.2.1, Parvomedics). For Protocol 1 and convergent validity, the VO2 Master had mean absolute differences from the Parvomedics of <0.3 L/min for absolute VO2 and <5 L/min for VE overall and at each exercise stage. Mean absolute percent differences (MAPD) for VO2 and VE were <9% overall and <12% at each stage. Test-retest reliability of the VO2 Master (MAPD: 8.9-10.9%) was somewhat poorer than the Parvomedics (MAPD: 5.3-7.6%). For Protocol 2, validity was similar for both VO2 Master models (MAPD ~12% overall) compared to the Parvomedics for VO2 and VE. The VO2 Master had an acceptable validity and test-retest reliability for most intensities tested and may be an appealing option for field-based VO2 and VE analysis.