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Purpose Quality of Life (QOL) is an important psychosocial outcome in cancer survivors (CS). Physical activity (PA), reducing sedentary time (ST), and sleep can help CS improve QOL, however these behaviors are commonly studied in isolation, despite their interdependence during the 24-hour day (i.e., time in one activity cannot increase without time decreasing in another activity). This study examined the effects of reallocating time between moderate to vigorous PA (MVPA), light PA, ST, and sleep on QOL in a mixed sample of CS. Methods A cross-sectional sample of CS (N = 73) diagnosed with breast (29.7%), colorectal (33.8%), or other (36.5%) cancer. MVPA, light PA, and ST were measured using the activPAL™ accelerometer, and sleep duration using the Actiwatch™ accelerometer. Both were worn for 7-days, 24-hours per day. QOL was self-reported using the Functional Assessment of Cancer Therapy-General (FACT-G) questionnaire. Isotemporal substitution models were used to reallocate 30-minutes between activities. Statistical significance was set at p M = 24.0 ± 18.9 minutes/day of MVPA, 291.7 ± 100.4 minutes/day of light PA, 593.1 ± 108.3 minutes/day of ST, and 486.6 ± 57.6 minutes/night of sleep. Isotemporal substitution effects for reallocating time between MVPA, light PA, ST and sleep were not significantly associated with QOL. Conclusions Findings from this study suggest that among CS who are achieving adequate levels of PA and sleep, changes in 30-minutes of these activities may not impact QOL. Future studies should aim to recruit a larger, more representative sample and explore the role of bouted activity time.

Introduction: Patterns of physical activity (PA) and time in bed (TIB) across the 24-hr cycle have important implications for many health outcomes; therefore, wearable accelerometers are often implemented in behavioral research to measure free-living PA and TIB. Two accelerometers, the activPAL and Actiwatch, are common accelerometers for measuring PA (activPAL) and TIB (Actiwatch), respectively. Both accelerometers have the capacity to measure TIB, but the degree to which these accelerometers agree is not clear. Therefore, this study compared estimates of TIB between activPAL and the Actiwatch accelerometers. Methods: Participants (mean ± SDage = 39.8 ± 7.6 years) with overweight or obesity (N = 83) wore an activPAL and Actiwatch continuously for 7 days, 24 hr per day. TIB was assessed using manufacturer-specific algorithms. Repeated-measures mixed-effect models and Bland–Altman plots were used to compare the activPAL and Actiwatch TIB estimates. Results: Statistical differences between TIB assessed by activPAL versus Actiwatch (p p = .87). The difference in TIB between accelerometers ranged from −72.9 ± 15.7 min (Day 7) to −98.6 ± 14.5 min (Day 3), with the Actiwatch consistently estimating longer TIB compared with the activPAL. Conclusion: Data generated by the activPAL and Actiwatch accelerometers resulted in divergent estimates of TIB. Future studies should continue to explore the validity of activity monitoring accelerometers for estimating TIB.

BackgroundDespite the recent proliferation of digital health technologies (DHTs), there is a lack of formal, industry-wide standards to evaluate the performance of the product’s algorithm in terms of its ability to measure, detect, or predict a clinical state. The advancement and successful use of DHTs in medicine requires that all stakeholders – clinicians, patients, payers, regulators, pharmaceutical companies, and the medical products industry – have a common understanding of what it means when a DHT has been analytically validated.ObjectiveWe conducted a systematic review to assess the state of the science on analytical validation for DHTs, using the criteria established by the V3 Framework and EVIDENCE checklist.MethodsThe systematic review was conducted according to the PRISMA guidelines. A multi-tier PubMed search identified studies published between April 15, 2020, and April 15, 2023, on analytical validation of DHTs; thereafter, each paper was assessed against the EVIDENCE checklist items specific to analytical validation. All studies were required to report quantitative data collected from a connected, mobile sensor product for the measurement, diagnosis, and/or treatment of a behavioral or physiological function, and compare the outcome measures to an established reference standard.ResultsOf the 1201 papers identified in the literature search, we identified 303 reporting the results of a DHT analytical validation study. The most prevalent therapeutic areas of focus were neurological (26%), cardiovascular (18%), and sleep conditions (17%). Health outcome categories most frequently captured by DHTs were gait (15%), heart rate/rhythm (15%), blood pressure and/or arterial stiffness (11%), sleep staging (10%), and mobility (9%). Only 208 papers (69%) reported all components of the EVIDENCE checklist focused on analytical validation, with the exception of software version and race/ethnicity, thereby meeting our definition of high-quality evidence reporting.ConclusionWe are encouraged by the emerging literature evaluating whether outcome measures assessed by DHTs adequately reflect the physiological or behavioral parameter of interest; however, the quality of reporting is not yet sufficient to ensure the advancement of digital clinical measures that are fit-for-purpose for all members of a defined population and eliminate the need for redundant studies. We recommend that journals publishing analytical validation studies require the use of the EVIDENCE checklist as a reporting standard for these manuscripts.

Background: Accelerometers are frequently used to measure free-living physical activity and sleep in cancer survivors. To obtain valid data, participants must adhere to wear-time guidelines; therefore, understanding survivor’s preference may be critical when selecting an accelerometer. This study compared cancer survivors’ reported discomfort and interference, and wear-time compliance between a wrist-worn accelerometer and a thigh-worn accelerometer. Methods: This was a secondary data analysis. Cancer survivors (N = 52, mean age = 51.8 [13.0], 82.3% female) wore the Actiwatch-2 (wrist) and the activPAL (thigh) for 7 days, 24 hours per day. On day 7, participants completed a questionnaire to evaluate each accelerometer using a 1 to 5 Likert scale and open-ended questions. The Kolmogorov–Smirnov test evaluated differences in discomfort and interference. Paired samples t test evaluated differences in wear-time compliance. Open-ended responses were analyzed using thematic analysis methods. Results: No differences were observed in discomfort, interference, or wear-time compliance (P = .08). Qualitative analysis resulted in 2 themes: discomfort and ease of use and interference and adverse reaction. Interferences were primarily reported with the Actiwatch-2, whereas discomfort and ease were primarily reported with the activPAL. Conclusion: No significant differences were observed regarding discomfort, interference, and compliance. Results of this study can prepare researchers for common issues regarding accelerometer compliance, allowing researchers to offer resources to alleviate discomforts or interferences that may affect wear-time compliance.

Millions of consumer sport and fitness wearables (CSFWs) are used worldwide, and millions of datapoints are generated by each device. Moreover, these numbers are rapidly growing, and they contain a heterogeneity of devices, data types, and contexts for data collection. Companies and consumers would benefit from guiding standards on device quality and data formats. To address this growing need, we convened a virtual panel of industry and academic stakeholders, and this manuscript summarizes the outcomes of the discussion. Our objectives were to identify (1) key facilitators of and barriers to participation by CSFW manufacturers in guiding standards and (2) stakeholder priorities. The venues were the Yale Center for Biomedical Data Science Digital Health Monthly Seminar Series (62 participants) and the New England Chapter of the American College of Sports Medicine Annual Meeting (59 participants). In the discussion, stakeholders outlined both facilitators of (e.g., commercial return on investment in device quality, lucrative research partnerships, and transparent and multilevel evaluation of device quality) and barriers (e.g., competitive advantage conflict, lack of flexibility in previously developed devices) to participation in guiding standards. There was general agreement to adopt Keadle et al.'s standard pathway for testing devices (i.e., benchtop, laboratory, field-based, implementation) without consensus on the prioritization of these steps. Overall, there was enthusiasm not to add prescriptive or regulatory steps, but instead create a networking hub that connects companies to consumers and researchers for flexible guidance navigating the heterogeneity, multi-tiered development, dynamicity, and nebulousness of the CSFW field.

To compare estimates of moderate-vigorous physical activity (MVPA) duration derived from accelerometers calibrated only to walking and running activities to estimates from calibrations based on a broader range of lifestyle and ambulatory activities.In a study of 932 older (50-74 yr) adults we compared MVPA estimates from accelerometer counts based on three ambulatory calibration methods (Freedson 1952 counts per minute; Sasaki 2690 counts per minute; activPAL 3+ METs) to estimates based on calibrations from lifestyle and ambulatory activities combined (Matthews 760 counts per minute; Crouter 3+ METs; Sojourn3x 3+ METs). We also examined data from up to 6 previous-day recalls describing the MVPA in this population.The MVPA duration values derived from ambulatory calibration methods were significantly lower than methods designed to capture a broader range of both lifestyle and ambulatory activities (P0.05). The MVPA (h·d) estimates in all participants were: Freedson (median, 0.35; interquartile range, 0.17-0.58); Sasaki (median, 0.91; interquartile range, 0.59-1.32); and activPAL (median, 0.97; interquartile range, 0.71-1.26) compared with Matthews (median, 1.82; interquartile range, 1.37-2.34); Crouter (2.28 [1.72-2.82]); and Sojourn3x (median, 1.85; interquartile range, 1.42-2.34). Recall-based estimates in all participants were comparable (median, 1.61; interquartile range, 0.89-2.57) and indicated participation in a broad range of lifestyle and ambulatory MVPA.Accelerometer calibration studies that employ only ambulatory activities may produce MVPA duration estimates that are substantially lower than methods calibrated to a broader range of activities. These findings highlight the potential to reduce potentially large differences among device-based measures of MVPA due to variation in calibration study design by including a variety of lifestyle and ambulatory activities.

Higher insulin following sedentary behavior may be due to increased insulin secretion (IS), decreased hepatic insulin extraction (HIE), or a combination of both. Ten healthy adults completed glucose tolerance tests following 7 days of normal activity and 7 days of increased sitting. There were no differences in IS; however, HIE at 120 min after ingestion (85.4% ± 7.2% vs. 74.6% ± 6.6%, p < 0.05) and the area under the curve (73.6% ± 9.4% vs. 67.5% ± 11.3%, p < 0.05) were reduced following 7 days of increased sedentary time.

We evaluated the accuracy and precision of wearable activity trackers and a pedometer (ATPs) in estimating steps and sedentary time (ST) in free-living settings. Thirty-two healthy men and women (M...

Breaking up sitting with light physical activity (PA) is effective in reducing hyperglycemia in the laboratory. Whether the same effects are observed in the free-living environment remains unknown. We evaluated how daily and postprandial glycemia is impacted by 20, 40, or 60 min of activity performed as either breaks from sitting after each meal (BR) or as one continuous walk after breakfast (WALK). Thirty individuals with type 2 diabetes completed three experimental conditions [BR, WALK, and control (CON)] in a randomized crossover design. Conditions were performed in a free-living environment with strict dietary control over 7 days. Participants increased PA in BR and WALK by 20, 40, or 60 min ( n = 10 in each group) and maintained habitual levels of PA during CON. A continuous glucose monitor (iPro2) and activPAL activity monitor were worn to quantify glycemic control and PA. Using linear mixed models with repeated measures, we 1) compared postprandial glucose (PPG) across conditions and 2) assessed the relationship between activity volume and glucose responses. Whereas WALK tended to shorten the daily duration of hyperglycemia compared with CON ( P = 0.0875), BR was not different from CON. BR and WALK significantly attenuated the breakfast PPG versus CON ( P ≤ 0.05), but lunch and dinner PPG were unaffected by BR and WALK. In conclusion, continuous walking was more effective than breaks from sitting in lowering daily hyperglycemia for the group, but both conditions lowered breakfast PPG. In contrast to tightly controlled laboratory studies, breaks from sitting did not lower hyperglycemia in the free-living environment. NEW & NOTEWORTHY Our “ecolabical” approach is new and noteworthy. This approach combines the external validity of the free-living environment (ecological) with the control of key confounding variables in the laboratory and allows for highly translatable findings by minimizing confounding variables. We found that both postmeal continuous walking and short breaks from sitting similarly attenuated the postprandial glucose (PPG) response to breakfast. Unlike previous laboratory studies, neither condition (walk after breakfast or postmeal breaks) significantly impacted PPG at lunch or dinner.

Background: Adenomatous polyps are associated with an increased risk of developing colorectal cancer. Physical activity (PA) and spending less time sedentary may reduce risk of polyp recurrence and cancer incidence. This study examined associations between PA, sedentary time, and stool metabolites in adults at high risk for developing colorectal cancer. Methods: Participants were ≥18 years old with ≥1 adenomatous polyps removed in the previous 3 years. PA and sedentary time were assessed using an activPAL™ accelerometer. Stool samples were analyzed for short-chain fatty acids, and primary/secondary bile acid metabolites by mass spectrometry. Linear regression models examined associations between PA, sedentary time, and stool parameters, with dietary fiber as a covariate. Results: Participants (N = 21) were 59 (9) years old and had a body mass index of 28.1 (3.35 kg/m2). Light-intensity PA was associated with butyrate (β = 1.88; 95% confidence interval [CI], 0.477 to 3.291) and propionate (β = 1.79; 95% CI, 0.862 to 2.724). Moderate to vigorous PA was associated with deoxycholic acid (β = −6.13; 95% CI, −12.14 to −0.11) and ursodeoxycholic acid (β = −0.45; 95% CI, −0.80 to −0.12) abundance. Conclusions: Both light and moderate to vigorous PA were associated with gut microbial metabolite production. These findings suggest the importance of examining PA intensity alongside stool metabolites for colorectal cancer prevention.

BACKGROUND/OBJECTIVES: Individuals successful at weight loss maintenance engage in high amounts of physical activity (PA). Understanding how and when weight loss maintainers accumulate PA within a day and across the week may inform PA promotion strategies and recommendations for weight management. METHODS: We compared patterns of PA in a cohort of weight loss maintainers (WLM, n=28, maintaining ≥13.6 kg weight loss for ≥1 year, BMI 23.6 ± 2.3 kg/m(2)), controls without obesity (NC, n=30, BMI similar to current BMI of WLM, BMI 22.8 ± 1.9 kg/m(2)), and controls with overweight/obesity (OC, n=26, BMI similar to pre-weight loss BMI of WLM, 33.6 ± 5.1 kg/m(2)). PA was assessed during 7 consecutive days using the activPAL(™) activity monitor. The following variables were quantified; sleep duration, sedentary time (SED), light-intensity PA (LPA), moderate-to-vigorous intensity PA (MVPA), and steps. Data were examined to determine differences in patterns of PA across the week and across the day using mixed effect models. RESULTS: Across the week, WLM engaged in ≥60 minutes of MVPA on 73% of days, significantly more than OC (36%, p

Background and objectives We tested the feasibility of reducing sedentary behavior common in CKD. Design, setting, participants, & measurements We carried out a Sit Less, Interact, Move More intervention in a 24-week parallel-group, randomized controlled trial in patients with stages 2–5 CKD. In the intervention group (n=54), accelerometry performed at baseline and repeated every 4 weeks was used to develop and monitor adherence to individualized plans targeting sedentary and stepping durations. The control group (n=52) was provided national physical activity recommendations; accelerometry was performed at baseline and every 8 weeks. Between-groups changes from baseline to the average follow-up values at weeks 8, 16, and 24 of the sedentary and stepping durations were the coprimary end points. Results The mean age was 69±13 years. Fourteen percent were on dialysis or received a kidney transplant. Eight percent of the control group and 17% of the intervention group were lost to follow-up. Sedentary and stepping durations did not change in the control group. Within the intervention group, the maximum decrease in sedentary duration (−43; 95% confidence interval, −69 to −17 min/d) and increase in stepping duration (16; 95% confidence interval, 7 to 24 min/d) and the number of steps per day (1265; 95% confidence interval, 518 to 2012) were seen at week 20. These attenuated at week 24. In mixed effects models, overall treatment effects between groups on sedentary (−17; 95% confidence interval, −43 to 8 min/d) and stepping (6; 95% confidence interval, −3 to 15 min/d) durations and the number of steps per day, a secondary end point (652; 95% confidence interval, −146 to 1449), were not significantly different. The intervention significantly reduced secondary end points of body mass index (−1.1; 95% confidence interval, −1.9 to −0.3 kg/m2) and body fat percentage (−2.1%; 95% confidence interval, −4.4% to −0.2%). Conclusions It is feasible to reduce sedentary duration and increase stepping duration in patients with CKD, but these were not sustained. Clinical Trial registry name and registration number: National Health and Nutrition Examination Survey (NHANES), NCT02970123

PURPOSE: To determine the effects of 5 months of ovarian hormone suppression in pre-menopausal women on objectively measured physical activity (PA). METHODS: Participants (age = 35±8 yr; body mass index = 27±6 kg·m(−2)) received monthly intramuscular injections of gonadotropin releasing hormone agonist therapy (GnRH(AG)) which suppresses pituitary gonadotropins and results in suppression of ovarian sex hormones. Women were randomized to receive concurrent transdermal E(2) (GnRH(AG)+E(2); n=30) or placebo (GnRH(AG)+PL, n=31). PA was assessed for 1 week before and during each month of the 5-month intervention using a hip-worn accelerometer (Actical, Mini Mitter Co., Inc., Bend, OR). Estimates of time spent in sedentary, light, and moderate-to-vigorous physical activity (MVPA) were derived using a previously published equation. Subsets of participants in each group were also randomized to a supervised progressive resistance exercise training program. RESULTS: Total MVPA tended towards being higher (p=0.08) in the GnRH(AG)+E(2) group at month 4. There were no significant effects of intervention or time in sedentary or light PA. In the subset of women who did not participate in structured exercise training for which Actical data were obtained (N=16 in each group), total MVPA was higher at month 4 (p=0.01). CONCLUSION: Physical activity levels appear to be maintained at a higher level in women undergoing pharmacological suppression of ovarian function with E(2) add back when compared with women treated with placebo. These data provide proof of concept data that E(2) contributes to the regulation of PA in humans. However, given the exploratory nature of this study, future confirmatory investigations will be necessary.

Objective The objective of this study was to compare patterns of objectively measured moderate-to-vigorous physical activity (MVPA, ≥ 3.00 metabolic equivalents [METs]), light-intensity physical activity (LPA, 1.50-2.99 METs), and sedentary behavior (SB

The activPAL (AP) monitor is well established for distinguishing sitting, standing, and stepping time. However, its validity in predicting time in physical activity intensity categories in a free-living environment has not been determined.This study aimed to determine the validity of the AP in estimating time spent in sedentary, light, and moderate-to-vigorous physical activity (MVPA) in a free-living environment.Thirteen participants (mean ± SD age = 24.8 ± 5.2 yr, BMI = 23.8 ± 1.9 kg·m) were directly observed for three 10-h periods wearing an AP. A custom R program was developed and used to summarize detailed active and sedentary behavior variables from the AP. AP estimates were compared with direct observation.The AP accurately and precisely estimated time in activity intensity categories (bias [95% confidence interval]; sedentary = 0.8 min [-2.9 to 4.5], light = 1.7 min [2.2-5.7], and -2.6 min [-5.8 to 0.7]). The overall accuracy rate for time in intensity categories was 96.2%. The AP also accurately estimated guideline minutes, guideline bouts, prolonged sitting minutes, and prolonged sitting bouts.The AP can be used to accurately capture individualized estimates of active and sedentary behavior variables in free-living settings.

Body-worn devices that estimate physical behavior have tremendous potential to address key research gaps. However, there is no consensus on how devices and processing methods should be developed and evaluated, resulting in large differences in summary estimates and confusion for end users. We propose a phase-based framework for developing and evaluating devices that emphasizes robust validation studies in naturalistic conditions.

e13584 Background: From objectively assessing performance status to recording simple vital signs, the adoption of high quality digital clinical measures offers enormous possibilities for transforming cancer care & research. However, the development & deployment of high quality digital measures in oncology lags behind adoption in other therapeutic areas. At the time of submission, the DiMe library of digital endpoints reports 166 unique digital endpoints being used in industry sponsored trials of new medical products, but none are being used in an oncology trial. There are seven digital clinical measures working their way through the FDA qualification program, but none in oncology. During the pandemic, care for patients with cardiac and other chronic conditions transitioned out of the clinic & into the home powered not only by virtual visits, but high quality information from remote monitoring. A study of oncology care facilities worldwide reported 89% finding difficulty in delivering usual care due to the challenges of safely bringing patients into the clinic during COVID. Methods: From March to Sep 2020, cross-industry stakeholders at the Digital Medicine Society (DiMe), Elektra Labs, Genentech, Koneksa, Myokardia, Sage Bionetworks, Scripps Research, and the US Food & Drug Administration came together to synthesize best practices from the digital health field, breaking down silos to create ‘ The Playbook’, the comprehensive and accessible “how-to” guide to support all stakeholders working to advance the safe, effective, ethical, and equitable use of digital clinical measures to improve lives. In October 2020, DiMe -- a 501c3 non-profit dedicated to advancing digital medicine to improve lives -- convenened a pre-competitive collaboration of 44 participants from 29 organizations to drive the adoption of the best practices articulated in The Playbook. Results: The Playbook describes trans-industry consensus for best practices for developing and deploying digital clinical measures across patient care, clinical research, and public health, including for oncology. Conclusions: The clinical, technical, and operational best practices necessary to develop and deploy high quality digital clinical measures into cancer care & research have been established. There is no reason for their successful adoption in oncology to lag behind other therapeutic areas. The Playbook and additional resources to support implementation due for launch on April 30 are critical tools for the field of oncology.

A tri-axial accelerometer worn on the thigh can provide information on the angle of rotation of the thigh. These data may be used to estimate periods of lying versus sitting. Purpose: To develop and test a classification algorithm to identify sedentary events as either lying or sitting events using a thigh-worn, tri-axial accelerometer. Methods: Seven day free-living activity from 14 sedentary workers was recorded using the activPAL3TM monitor. Participants recorded when they went to bed and when they got up in a diary. All “in bed” sedentary events were assumed to be lying and all “not in bed” sedentary events as sitting. An algorithm computed the angle of rotation of the y-axis, which was used to detect orientation of the thigh. Crossing a rotational threshold in the transverse plane of ±65was used to classify a sedentary event as lying. The classification accuracy of the algorithm was compared to self-reported classification from the diary. Results: The algorithm classified 96.7% of the sedentary time “in bed” (sensitivity) as lying and 92.9% of the time “not in bed” as not lying (specificity). Conclusion: Tri-axial accelerometer data recorded from a single site on the thigh can be used to classify sedentary events as sitting and lying. The automated method developed in this study will allow objective measurement of diurnal lying time and that while sleeping without relying on self-report. This will help advance the understanding of the impact of different types of sedentary behaviours on various health outcomes.

The objective of this study was to compare physical activity energy expenditure (PAEE) and total daily energy expenditure (TDEE) in successful weight loss maintainers (WLM) with normal weight controls (NC) and controls with overweight/obesity (OC).Participants were recruited in three groups: WLM (n = 25, BMI 24.1 ± 2.3 kg/mPAEE in WLM (812 ± 268 kcal/d, mean ± SD) was significantly higher compared with that in both NC (621 ± 285 kcal/d, P 0.01) and OC (637 ± 271 kcal/d, P = 0.02). As a result, TDEE in WLM (2,495 ± 366 kcal/d) was higher compared with that in NC (2,195 ± 521 kcal/d, P = 0.01) but was not significantly different from that in OC (2,573 ± 391 kcal/d).The high levels of PAEE and TDEE observed in individuals maintaining a substantial weight loss (-26.2 ± 9.8 kg maintained for 9.0 ± 10.2 years) suggest that this group relies on high levels of energy expended in physical activity to remain in energy balance (and avoid weight regain) at a reduced body weight.

(1) Background: This study compared manually-counted treadmill walking steps from the hip-worn DigiwalkerSW200 and OmronHJ720ITC, and hip and wrist-worn ActiGraph GT3X+ and GT9X; determined brand-specific acceleration amplitude (g) and/or frequency (Hz) step-detection thresholds; and quantified key features of the acceleration signal during walking. (2) Methods: Twenty participants (Age: 26.7 ± 4.9 years) performed treadmill walking between 0.89-to-1.79 m/s (2–4 mph) while wearing a hip-worn DigiwalkerSW200, OmronHJ720ITC, GT3X+ and GT9X, and a wrist-worn GT3X+ and GT9X. A DigiwalkerSW200 and OmronHJ720ITC underwent shaker testing to determine device-specific frequency and amplitude step-detection thresholds. Simulated signal testing was used to determine thresholds for the ActiGraph step algorithm. Steps during human testing were compared using bias and confidence intervals. (3) Results: The OmronHJ720ITC was most accurate during treadmill walking. Hip and wrist-worn ActiGraph outputs were significantly different from the criterion. The DigiwalkerSW200 records steps for movements with a total acceleration of ≥1.21 g. The OmronHJ720ITC detects a step when movement has an acceleration ≥0.10 g with a dominant frequency of ≥1 Hz. The step-threshold for the ActiLife algorithm is variable based on signal frequency. Acceleration signals at the hip and wrist have distinctive patterns during treadmill walking. (4) Conclusions: Three common research-grade physical activity monitors employ different step-detection strategies, which causes variability in step output.

Office workers are vulnerable to the adverse health effects of sedentary behavior (i.e., sitting time). Increasing physical activity and preventing time spent sitting is an occupational health priority. This randomized crossover design study compared the short-term (3-days) effects of hourly interruptions of sedentary time with 5-min micrrobouts of activity for 9 hours (MICRO) to a sedentary control condition (SED) and a duration-matched continuous single bout of physical activity (45-min/d, ONE) condition on inclinometer-derived sitting-time on work and non-work days in sedentary overweight/obese adults. Differences in sitting/lying, standing, stepping, number of sit/stand transitions, time spent in moderate and vigorous activity (MVPA), energy expenditure, self-perceived vigor and fatigue, and insulin sensitivity were also examined. Twenty-two participants (10M/12F, 31.7 &plusmn, 1.3 year old BMI 30.4 &plusmn, 0.5 kg/m2) completed all conditions. No between-condition effects were observed in sitting-time and sit/stand transitions. Both interventions increased daily steps, MVPA and energy expenditure with increases being greater in ONE than MICRO. Feelings of vigor and fasting insulin sensitivity were also improved. Participants reported less fatigue with MICRO than SED and ONE. Both interventions increase physical activity and energy expenditure in occupational and leisure-time contexts. The sustainability of these effects over the long term and on health outcomes will need to be tested in future studies.

Active workstations, such as treadmill and sit-to-stand workstations, enable office employees to break prolonged sitting with bouts of light-intensity walking and/or standing. Compared with sitting, walking and/or standing accumulated during the workday using these workstations will increase muscle contractions, which may influence blood flow, energy expenditure, metabolism, musculoskeletal health, and brain function. Physiological responses when using treadmill and sit-to-stand workstations may vary due to differences in muscle contraction type (dynamic vs. static) and may thus affect cardio-metabolic and musculoskeletal health and brain function in different ways.

The objective of this study was to compare patterns of objectively measured moderate-to-vigorous physical activity (MVPA, ≥ 3.00 metabolic equivalents [METs]), light-intensity physical activity (LPA, 1.50-2.99 METs), and sedentary behavior (SB, 1.50 METs) in successful weight loss maintainers (WLMs), normal weight controls (NC), and controls with overweight/obesity (OC).Participants (18-65 y) were recruited in three groups: WLM (maintaining ≥ 13.6-kg weight loss for ≥ 1 year, n = 30), NC (BMI matched to current BMI of WLM, n = 33), and OC (BMI matched to pre-weight loss BMI of WLM, n = 27). All participants wore the activPAL for 1 week.Compared with OC and NC, WLM spent more awake time in total MVPA (WLM: 9.6 ± 3.9%, NC: 7.1 ± 2.1%, OC: 5.9 ± 2.0%; P 0.01) and more time in sustained (≥ 10 min) bouts of MVPA (WLM: 39 ± 33, NC: 17 ± 14, OC: 9 ± 11 min/d; P 0.01). Compared with OC, WLM and NC spent more awake time in LPA (WLM: 29.6 ± 7.9%, NC: 29.1 ± 8.3%, OC: 24.8 ± 6.7%; P = 0.04) and less awake time sedentary (WLM: 60.8 ± 9.3%, NC: 63.8 ± 9.5%, OC: 69.3 ± 7.5%; P 0.01).Results provide additional data supporting the important role of MVPA in weight loss maintenance and suggest notable differences in LPA and SB between normal weight individuals and those with overweight/obesity. Increasing LPA and/or decreasing SB may be additional potential targets for weight management interventions.

Background:Individuals may compensate for exercise training by modifying nonexercise behavior (ie, increase sedentary time (ST) and decrease nonexercise physical activity [NEPA]).Purpose:To compare ST and NEPA during a 12-week exercise training and/or lifestyle intervention.Methods:Fifty-seven overweight/obese participants (19 M/39 F) completed the study (mean ± SD; age 43.6 ± 9.9 y, BMI 35.1 ± 4.6 kg/m2). There were no between-group differences in activity levels at baseline. Four-arm quasi-experimental intervention study 1) EX: exercise 5 days per week at a moderate intensity (40% to 65% VO2peak) 2) rST: reduce ST and increase NEPA, 3) EX-rST: combination of EX and rST and 4) CON: maintain habitual behavior.Results:For the EX group, ST did not decrease significantly (mean ((95% confidence interval) 0.48 (–2.2 to 3.1)% and there was no changes in NEPA at week-12 compared with baseline. The changes were variable, with approximately 50% of participants increasing ST and decreasing NEPA. The rST group decreased ST (–4.8 (0.8 to 7.9)% and increased NEPA. EX-rST significantly decreased ST (–5.1 (–2.2 to 7.9)% and increased time in NEPA at week-12 compared with baseline. The control group increased ST by 4.3 (0.8 to 7.9)%.Conclusions:Changes in nonexercise ST and NEPA are variable among participants in an exercise-training program, with nearly half decreasing NEPA compared with baseline. Interventions targeting multiple behaviors (ST and NEPA) may effectively reduce compensation and increase daily activity.

Background:Physical activity and sedentary behavior measurement tools need to be validated in free-living settings. Direct observation (DO) may be an appropriate criterion for these studies. However, it is not known if trained observers can correctly judge the absolute intensity of free-living activities.Purpose:To compare DO estimates of total MET-hours and time in activity intensity categories to a criterion measure from indirect calorimetry (IC).Methods:Fifteen participants were directly observed on three separate days for two hours each day. During this time participants wore an Oxycon Mobile indirect calorimeter and performed any activity of their choice within the reception area of the wireless metabolic equipment. Participants were provided with a desk for sedentary activities (writing, reading, computer use) and had access to exercise equipment (treadmill, bike).Results:DO accurately and precisely estimated MET-hours [% bias (95% CI) = –12.7% (–16.4, –7.3), ICC = 0.98], time in low intensity activity [% bias (95% CI) = 2.1% (1.1, 3.2), ICC = 1.00] and time in moderate to vigorous intensity activity [% bias (95% CI) –4.9% (–7.4, –2.5), ICC = 1.00].Conclusion:This study provides evidence that DO can be used as a criterion measure of absolute intensity in free-living validation studies.

AB Introduction: Methods to estimate physical activity (PA) and sedentary behavior (SB) from wearable monitors need to be validated in free-living settings. Purpose: The purpose of this study was to develop and validate two novel machine-learning methods (Sojourn-1 Axis [soj-1x] and Sojourn-3 Axis [soj-3x]) in a free-living setting. Methods: Participants were directly observed in their natural environment for 10 consecutive hours on three separate occasions. Physical activity and SB estimated from soj-1x, soj-3x, and a neural network previously calibrated in the laboratory (lab-nnet) were compared with direct observation. Results: Compared with lab-nnet, soj-1x and soj-3x improved estimates of MET-hours (lab-nnet: % bias [95% confidence interval] = 33.1 [25.9 to 40.4], root-mean-square error [RMSE] = 5.4 [4.6-6.2]; soj-1x: % bias = 1.9 [-2.0 to 5.9], RMSE = 1.0 [0.6 to 1.3]; soj-3x: % bias = 3.4 [0.0 to 6.7], RMSE = 1.0 [0.6 to 1.5]) and minutes in different intensity categories {lab-nnet: % bias = -8.2 (sedentary), -8.2 (light), and 72.8 (moderate-to-vigorous PA [MVPA]); soj-1x: % bias = 8.8 (sedentary), -18.5 (light), and -1.0 (MVPA); soj-3x: % bias = 0.5 (sedentary), -0.8 (light), and -1.0 (MVPA)}. Soj-1x and soj-3x also produced accurate estimates of guideline minutes and breaks from sedentary time. Conclusions: Compared with the lab-nnet algorithm, soj-1x and soj-3x improved the accuracy and precision in estimating free-living MET-hours, sedentary time, and time spent in light-intensity activity and MVPA. In addition, soj-3x is superior to soj-1x in differentiating SB from light-intensity activity.

© 2016 by the American College of Sports Medicine. Introduction The way physical activity (PA) and sedentary behavior (SB) are accumulated throughout the day (i.e., patterns) may be important for health, but identifying measurable and meaningful metrics of behavioral patterns is challenging. This study evaluated accelerometer-derived metrics to determine whether they predicted PA and SB patterns and were reliably measured. Methods We defined and measured 55 metrics that describe daily PA and SB using data collected by using the activPAL monitor in four studies. The first two studies were randomized crossover designs that included recreationally active participants. Study 1 experimentally manipulated time spent in moderate-to-vigorous-intensity PA and sedentary time, and study 2 held time in exercise constant and manipulated SB. Study 3 included inactive participants who increased exercise, decreased sedentary time, or both. The study conditions induced distinct behavioral patterns; thus, we tested whether the new metrics could improve the prediction of an individual's study condition after adjusting for the overall volume of PA or SB using conditional logistic regression. In study 4, we measured the 3-month reliability for the pattern metrics by calculating intraclass correlation coefficients in a community-dwelling sample who wore the activPAL monitor twice for 7 d. Results In each of the experimental studies, we identified new metrics that could improve the accuracy for predicting condition beyond SB and moderate-to-vigorous-intensity PA volume. In study 1, 23 metrics were predictive of a highly active condition, and in study 2, 24 metrics were predictive of a highly sedentary condition. In study 4, the median intraclass correlation coefficients (25-75th percentiles) of the metrics were 0.59 (0.46-0.65). Conclusions Several new metrics were predictive of patterns of SB, exercise, and nonexercise behavior and are moderately reliable for a 3-month period. Applying these metrics to determine whether daily behavioral patterns are associated with health-outcomes is an important area of future research.

Background:The Compendium of Energy Expenditures for Youth assigns MET values to a wide range of activities. However, only 35% of activity MET values were derived from energy cost data measured in youth; the remaining activities were estimated from adult values.Purpose:To determine the energy cost of common activities performed by children and adolescents and compare these data to similar activities reported in the compendium.Methods:Thirty-two children (8−11 years old) and 28 adolescents (12−16 years) completed 4 locomotion activities on a treadmill (TRD) and 5 age-specific activities of daily living (ADL). Oxygen consumption was measured using a portable metabolic analyzer.Results:In children, measured METs were significantly lower than compendium METs for 3 activities [basketball, bike riding, and Wii tennis (1.1−3.5 METs lower)]. In adolescents, measured METs were significantly lower than compendium METs for 4 ADLs [basketball, bike riding, board games, and Wii tennis (0.3−2.5 METs lower)] and 3 TRDs [2.24 m·s-1, 1.56 m·s-1, and 1.34 m·s-1 (0.4−0.8 METs lower)].Conclusion:The Compendium of Energy Expenditures for Youth is an invaluable resource to applied researchers. Inclusion of empirically derived data would improve the validity of the Compendium of Energy Expenditures for Youth.

The impact of additional structured outdoor playtime on preschoolers'; physical activity (PA) level is unclear. The purpose of this pilot study was to explore the effects of increasing structured outdoor playtime on preschoolers'; PA levels. Eight full-day classrooms (n=134 children) from two preschool programmes were randomised into a treatment (STRUCT, n=4) or control (CON, n=4) condition. Both groups received an additional 30 minutes of outdoor playtime three days per week for four weeks. The STRUCT intervention consisted of previously tested structured outdoor playtime activities/games. The CON intervention consisted of free outdoor playtime. Children were individually recruited (n=75) for the objective assessment of PA levels. The PA levels of 67 children (age, 4.1±0.8 years; STRUCT, n=38; CON, n=29) were assessed at baseline and during week four for seven consecutive days using Actigraph accelerometers. Data were analysed using mixed-model analysis of variance. Time spent in vigorous PA significantl...

Background:Thirty-five percent of the activities assigned MET values in the Compendium of Energy Expenditures for Youth were obtained from direct measurement of energy expenditure (EE). The aim of this study was to provide directly measured EE for several different activities in youth.Methods:Resting metabolic rate (RMR) of 178 youths (80 females, 98 males) was first measured. Participants then performed structured activity bouts while wearing a portable metabolic system to directly measure EE. Steady-state oxygen consumption data were used to compute activity METstandard (activity VO2/3.5) and METmeasured (activity VO2/measured RMR) for the different activities.Results:Rates of EE were measured for 70 different activities and ranged from 1.9 to 12.0 METstandard and 1.5 to 10.0 METmeasured.Conclusion:This study provides directly measured energy cost values for 70 activities in children and adolescents. It contributes empirical data to support the expansion of the Compendium of Energy Expenditures for Youth.

Previous work from our laboratory provided a “proof of concept” for use of artificial neural networks (nnets) to estimate metabolic equivalents (METs) and identify activity type from accelerometer data (Staudenmayer J, Pober D, Crouter S, Bassett D, Freedson P, J Appl Physiol 107: 1330–1307, 2009). The purpose of this study was to develop new nnets based on a larger, more diverse, training data set and apply these nnet prediction models to an independent sample to evaluate the robustness and flexibility of this machine-learning modeling technique. The nnet training data set (University of Massachusetts) included 277 participants who each completed 11 activities. The independent validation sample ( n = 65) (University of Tennessee) completed one of three activity routines. Criterion measures were 1) measured METs assessed using open-circuit indirect calorimetry; and 2) observed activity to identify activity type. The nnet input variables included five accelerometer count distribution features and the lag-1 autocorrelation. The bias and root mean square errors for the nnet MET trained on University of Massachusetts and applied to University of Tennessee were +0.32 and 1.90 METs, respectively. Seventy-seven percent of the activities were correctly classified as sedentary/light, moderate, or vigorous intensity. For activity type, household and locomotion activities were correctly classified by the nnet activity type 98.1 and 89.5% of the time, respectively, and sport was correctly classified 23.7% of the time. Use of this machine-learning technique operates reasonably well when applied to an independent sample. We propose the creation of an open-access activity dictionary, including accelerometer data from a broad array of activities, leading to further improvements in prediction accuracy for METs, activity intensity, and activity type.

Numerous accelerometers and prediction methods are used to estimate energy expenditure (EE). Validation studies have been limited to small sample sizes in which participants complete a narrow range of activities and typically validate only one or two prediction models for one particular accelerometer. The purpose of this study was to evaluate the validity of nine published and two proprietary EE prediction equations for three different accelerometers. Two hundred and seventy-seven participants completed an average of six treadmill (TRD) (1.34, 1.56, 2.23 ms(-1) each at 0 and 3% grade) and five self-paced activities of daily living (ADLs). EE estimates were compared with indirect calorimetry. Accelerometers were worn while EE was measured using a portable metabolic unit. To estimate EE, 4 ActiGraph prediction models were used, 5 Actical models, and 2 RT3 proprietary models. Across all activities, each equation underestimated EE (bias -0.1 to -1.4 METs and -0.5 to -1.3 kcal, respectively). For ADLs EE was underestimated by all prediction models (bias -0.2 to -2.0 and -0.2 to -2.8, respectively), while TRD activities were underestimated by seven equations, and overestimated by four equations (bias -0.8 to 0.2 METs and -0.4 to 0.5 kcal, respectively). Misclassification rates ranged from 21.7 (95% CI 20.4, 24.2%) to 34.3% (95% CI 32.3, 36.3%), with vigorous intensity activities being most often misclassified. Prediction equations did not yield accurate point estimates of EE across a broad range of activities nor were they accurate at classifying activities across a range of intensities (light3 METs, moderate 3-5.99 METs, vigorous ≥ 6 METs). Current prediction techniques have many limitations when translating accelerometer counts to EE.