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Accelerometers are commonly used to assess physical activity. Consumer activity trackers have become increasingly popular today, such as the Fitbit. This study aimed to compare the average number of steps per day using the wrist-worn Fitbit Flex and waist-worn ActiGraph (wGT3X-BT) in free-living conditions. 104 adult participants (n = 35 males; n = 69 females) were asked to wear a Fitbit Flex and an ActiGraph concurrently for 7 days. Daily step counts were used to classify inactive (<10,000 steps) and active (≥10,000 steps) days, which is one of the commonly used physical activity guidelines to maintain health. Proportion of agreement between physical activity categorizations from ActiGraph and Fitbit Flex was assessed. Statistical analyses included Spearman's rho, intraclass correlation (ICC), median absolute percentage error (MAPE), Kappa statistics, and Bland-Altman plots. Analyses were performed among all participants, by each step-defined daily physical activity category and gender. The median average steps/day recorded by Fitbit Flex and ActiGraph were 10193 and 8812, respectively. Strong positive correlations and agreement were found for all participants, both genders, as well as daily physical activity categories (Spearman's rho: 0.76-0.91; ICC: 0.73-0.87). The MAPE was: 15.5% (95% confidence interval [CI]: 5.8-28.1%) for overall steps, 16.9% (6.8-30.3%) vs. 15.1% (4.5-27.3%) in males and females, and 20.4% (8.7-35.9%) vs. 9.6% (1.0-18.4%) during inactive days and active days. Bland-Altman plot indicated a median overestimation of 1300 steps/day by the Fitbit Flex in all participants. Fitbit Flex and ActiGraph respectively classified 51.5% and 37.5% of the days as active (Kappa: 0.66). There were high correlations and agreement in steps between Fitbit Flex and ActiGraph. However, findings suggested discrepancies in steps between devices. This imposed a challenge that needs to be considered when using Fibit Flex in research and health promotion programs.

Abstract Rationale Increasing physical activity is a key therapeutic aim in chronic obstructive pulmonary disease (COPD). Pulmonary rehabilitation (PR) improves exercise capacity, but there is conflicting evidence regarding its ability to improve physical activity levels. Objectives To determine whether using pedometers as an adjunct to PR can enhance time spent in at least moderate-intensity physical activity (time expending ≥3 metabolic equivalents [METs]) by people with COPD. Methods In this single-blind randomized controlled trial, participants were assigned 1:1 to receive a control intervention (PR comprising 8 wk, two supervised sessions per week) or the trial intervention (PR plus pedometer-directed step targets, reviewed weekly for 8 wk). In the randomization process, we used minimization to balance groups for age, sex, FEV1 percent predicted, and baseline exercise capacity and physical activity levels. Outcome assessors and PR therapists were blinded to group allocation. The primary analysis was based on the intention-to-treat principle. Measurements and Main Results The primary outcome was change from baseline to 8 weeks in accelerometer-measured daily time expending at least 3 METs. A total of 152 participants (72% male; mean [SD] FEV1 percent predicted, 50.5% [21.2]; median [first quartile, third quartile] time expending ≥3 METs, 46 [21, 92] min) were enrolled and assigned to the intervention (n = 76) or control (n = 76) arm. There was no significant difference in change in time expending at least 3 METs between the intervention and control groups at 8 weeks (median [first quartile, third quartile] difference, 0.5 [−1.0, 31.0] min; P = 0.87) or at the 6-month follow-up (7.0 [−9, 27] min; P = 0.16). Conclusions Pedometer-directed step-count targets during an outpatient PR program did not enhance moderate-intensity physical activity levels in people with COPD. Clinical trial registered with www.clinicaltrials.gov (NCT01719822).

Introduction Physical inactivity is prevalent in pregnant women, and innovative strategies to promote physical activity are strongly needed. The purpose of the study was to test a 12-week mobile health (mHealth) physical activity intervention for feasibility and potential efficacy. Methods Participants were recruited between December 2012 and February 2014 using diverse recruitment methods. Thirty pregnant women between 10 and 20 weeks of gestation were randomized to an intervention (mobile phone app plus Fitbit) or a control (Fitbit) group. Both conditions targeted gradual increases in physical activity. The mHealth intervention included daily messages and a mobile phone activity diary with automated feedback and self-monitoring systems. Results On monthly average, 4 women were screened for initial eligibility by telephone and 2.5 were randomized. Intervention participants had a 1096 ± 1898 step increase in daily steps compared to an increase of 259 ± 1604 steps in control participants at 12 weeks. The change between groups in weekly mean steps per day during the 12-week study period was not statistically significant ( p  = 0.38). The intervention group reported lower perceived barrier to being active, lack of energy, than the control group at 12-week visit ( p  = 0.02). The rates of responding to daily messages and using the daily diary through the mobile app declined during the 12 week study period. Discussion It was difficult to recruit and randomize inactive women who wanted to increase physical activity during pregnancy. Pregnant women who were motivated to increase physical activity might find using mobile technologies in assessing and promoting PA acceptable. Possible reasons for the non-significant treatment effect of the mHealth intervention on physical activity are discussed. Public awareness of safety and benefits of physical activity during pregnancy should be promoted. Clinicaltrials.Gov Identifier NCT01461707.

Data are lacking from physical activity (PA) trials with long-term follow-up of both objectively measured PA levels and robust health outcomes. Two primary care 12-week pedometer-based walking interventions in adults and older adults (PACE-UP and PACE-Lift) found sustained objectively measured PA increases at 3 and 4 years, respectively. We aimed to evaluate trial intervention effects on long-term health outcomes relevant to walking interventions, using routine primary care data. Randomisation was from October 2012 to November 2013 for PACE-UP participants from seven general (family) practices and October 2011 to October 2012 for PACE-Lift participants from three practices. We downloaded primary care data, masked to intervention or control status, for 1,001 PACE-UP participants aged 45-75 years, 36% (361) male, and 296 PACE-Lift participants, aged 60-75 years, 46% (138) male, who gave written informed consent, for 4-year periods following randomisation. The following new events were counted for all participants, including those with preexisting diseases (apart from diabetes, for which existing cases were excluded): nonfatal cardiovascular, total cardiovascular (including fatal), incident diabetes, depression, fractures, and falls. Intervention effects on time to first event post-randomisation were modelled using Cox regression for all outcomes, except for falls, which used negative binomial regression to allow for multiple events, adjusting for age, sex, and study. Absolute risk reductions (ARRs) and numbers needed to treat (NNTs) were estimated. Data were downloaded for 1,297 (98%) of 1,321 trial participants. Event rates were low (<20 per group) for outcomes, apart from fractures and falls. Cox hazard ratios for time to first event post-randomisation for interventions versus controls were nonfatal cardiovascular 0.24 (95% confidence interval [CI] 0.07-0.77, p = 0.02), total cardiovascular 0.34 (95% CI 0.12-0.91, p = 0.03), diabetes 0.75 (95% CI 0.42-1.36, p = 0.34), depression 0.98 (95% CI 0.46-2.07, p = 0.96), and fractures 0.56 (95% CI 0.35-0.90, p = 0.02). Negative binomial incident rate ratio for falls was 1.07 (95% CI 0.78-1.46, p = 0.67). ARR and NNT for cardiovascular events were nonfatal 1.7% (95% CI 0.5%-2.1%), NNT = 59 (95% CI 48-194); total 1.6% (95% CI 0.2%-2.2%), NNT = 61 (95% CI 46-472); and for fractures 3.6% (95% CI 0.8%-5.4%), NNT = 28 (95% CI 19-125). Main limitations were that event rates were low and only events recorded in primary care records were counted; however, any underrecording would not have differed by intervention status and so should not have led to bias. Routine primary care data used to assess long-term trial outcomes demonstrated significantly fewer new cardiovascular events and fractures in intervention participants at 4 years. No statistically significant differences between intervention and control groups were demonstrated for other events. Short-term primary care pedometer-based walking interventions can produce long-term health benefits and should be more widely used to help address the public health inactivity challenge. PACE-UP isrctn.com ISRCTN98538934; PACE-Lift isrctn.com ISRCTN42122561.

Background Sedentary behavior (SB) is a recognized risk factor for many chronic diseases. ActiGraph and activPAL are two commonly used wearable accelerometers in SB research. The former measures body movement and the latter measures body posture. The goal of the current study is to quantify the pattern and variation of movement (by ActiGraph activity counts) during activPAL-identified sitting events, and examine associations between patterns and health-related outcomes, such as systolic and diastolic blood pressure (SBP and DBP). Methods The current study included 314 overweight postmenopausal women, who were instructed to wear an activPAL (at thigh) and ActiGraph (at waist) simultaneously for 24 hours a day for a week under free-living conditions. ActiGraph and activPAL data were processed to obtain minute-level time-series outputs. Multilevel functional principal component analysis (MFPCA) was applied to minute-level ActiGraph activity counts within activPAL-identified sitting bouts to investigate variation in movement while sitting across subjects and days. The multilevel approach accounted for the nesting of days within subjects. Results At least 90% of the overall variation of activity counts was explained by two subject-level principal components (PC) and six day-level PCs, hence dramatically reducing the dimensions from the original minute-level scale. The first subject-level PC captured patterns of fluctuation in movement during sitting, whereas the second subject-level PC delineated variation in movement during different lengths of sitting bouts: shorter (< 30 minutes), medium (30 -39 minutes) or longer (> 39 minute). The first subject-level PC scores showed positive association with DBP (standardized β ^ : 2.041, standard error: 0.607, adjusted p = 0.007), which implied that lower activity counts (during sitting) were associated with higher DBP. Conclusion In this work we implemented MFPCA to identify variation in movement patterns during sitting bouts, and showed that these patterns were associated with cardiovascular health. Unlike existing methods, MFPCA does not require pre-specified cut-points to define activity intensity, and thus offers a novel powerful statistical tool to elucidate variation in SB patterns and health. Trial registration ClinicalTrials.gov NCT03473145; Registered 22 March 2018; https://clinicaltrials.gov/ct2/show/NCT03473145 ; International Registered Report Identifier (IRRID): DERR1-10.2196/28684

Study Objectives: To compare two commercial sleep devices, an accelerometer worn as a wristband (UP by Jawbone) and a smartphone application (MotionX 24/7), against polysomnography (PSG) and actigraphy (Actiwatch2) in a clinical pediatric sample. Methods: Children and adolescents (n = 78, 65% male, mean age 8.4 ± 4.0 y) with suspected sleep disordered breathing (SDB), simultaneously wore an actiwatch, a commercial wrist-based device and had a smartphone with a sleep application activated placed near their right shoulder, during their diagnostic PSG. Outcome variables were sleep onset latency (SOL), total sleep time (TST), wake after sleep onset (WASO), and sleep efficiency (SE). Paired comparisons were made between PSG, actigraphy, UP, and MotionX 24/7. Epoch-by-epoch comparisons determined sensitivity, specificity, and accuracy between PSG, actigraphy, and UP. Bland-Altman plots determined level of agreement. Differences in bias between SDB severity and developmental age were assessed. Results: No differences in mean TST, WASO, or SE between PSG and actigraphy or PSG and UP were found. Actigraphy overestimated SOL (21 min). MotionX 24/7 underestimated SOL (12 min) and WASO (63 min), and overestimated TST (106 min) and SE (17%). UP showed good sensitivity (0.92) and accuracy (0.86) but poor specificity (0.66) when compared to PSG. Bland-Altman plots showed similar levels of bias in both actigraphy and UP. Bias did not differ by SDB severity, however was affected by age. Conclusions: When compared to PSG, UP was analogous to Actiwatch2 and may have some clinical utility in children with sleep disordered breathing. MotionX 24/7 did not accurately reflect sleep or wake and should be used with caution. Citation: Toon E, Davey MJ, Hollis SL, Nixon GM, Horne RS, Biggs SN. Comparison of commercial wrist-based and smartphone accelerometers, actigraphy, and PSG in a clinical cohort of children and adolescents. J Clin Sleep Med 2016;12(3):343–350.

Study Objectives: The purpose of the present study was to preliminarily evaluate whether knowing the dim light melatonin onset (DLMO) time is advantageous when treating delayed sleep-wake phase disorder with low-dose melatonin treatment plus behavioral interventions (ie, evening dim light and time in bed scheduling). Methods: In this randomized, controlled, double-blind trial, 40 adults with delayed sleep-wake phase disorder were randomly assigned to 4 weeks of 0.5 mg timed to be administered either 3 hours before the DLMO (measured DLMO group, n = 20) or 5 hours before sleep-onset time per actigraphy (estimated DLMO group, n = 20), in conjunction with behavioral interventions. The primary outcome was change in the DLMO (measured in-home). Secondary outcomes included sleep parameters per diary and actigraphy (sleep-onset and -offset times and total sleep time), Morningness-Eveningness Questionnaire, Multidimensional Fatigue Inventory, PROMIS–Sleep Disturbance, PROMIS–Sleep Related Impairment, and Pittsburgh Sleep Quality Index. Mixed-effects models tested for group differences in these outcome. Results: After applying the Bonferroni correction for multiple comparisons (significant P value set at < .004), there were significant main effects for visit on all outcomes except for the Pittsburgh Sleep Quality Index and total sleep time per wrist actigraphy and diary. There were no group-by-visit interactions for any of the outcomes ( P > .004). Conclusions: Scheduled low-dose melatonin plus behavioral interventions may improve many circadian and sleep parameters regardless of whether melatonin administration is scheduled based on estimated or measured DLMO. A larger-scale trial is needed to confirm these preliminary findings. Clinical Trial Registration: Registry: ClinicalTrials.gov; Name: The Clinical Utility of Measuring the Circadian Clock in Treatment of Delayed Sleep-Wake Phase Disorder; URL: https://clinicaltrials.gov/study/NCT03715465 ; Identifier: NCT03715465. Citation: Swanson LM, de Sibour T, DuBuc K, et al. Low-dose exogenous melatonin plus evening dim light and time in bed scheduling advances circadian phase irrespective of measured or estimated dim light melatonin onset time:preliminary findings. J Clin Sleep Med . 2024;20(7):1131–1140.

The majority of people do not achieve recommended levels of physical activity. There is a need for effective, scalable interventions to promote activity. Self-monitoring by pedometer is a potentially suitable strategy. We assessed the effectiveness and cost-effectiveness of a very brief (5-minute) pedometer-based intervention ('Step It Up') delivered as part of National Health Service (NHS) Health Checks in primary care. The Very Brief Intervention (VBI) Trial was a two parallel-group, randomised controlled trial (RCT) with 3-month follow-up, conducted in 23 primary care practices in the East of England. Participants were 1,007 healthy adults aged 40 to 74 years eligible for an NHS Health Check. They were randomly allocated (1:1) using a web-based tool between October 1, 2014, and December 31, 2015, to either intervention (505) or control group (502), stratified by primary care practice. Participants were aware of study group allocation. Control participants received the NHS Health Check only. Intervention participants additionally received Step It Up: a 5-minute face-to-face discussion, written materials, pedometer, and step chart. The primary outcome was accelerometer-based physical activity volume at 3-month follow-up adjusted for sex, 5-year age group, and general practice. Secondary outcomes included time spent in different intensities of physical activity, self-reported physical activity, and economic measures. We conducted an in-depth fidelity assessment on a subsample of Health Check consultations. Participants' mean age was 56 years, two-thirds were female, they were predominantly white, and two-thirds were in paid employment. The primary outcome was available in 859 (85.3%) participants. There was no significant between-group difference in activity volume at 3 months (adjusted intervention effect 8.8 counts per minute [cpm]; 95% CI -18.7 to 36.3; p = 0.53). We found no significant between-group differences in the secondary outcomes of step counts per day, time spent in moderate or vigorous activity, time spent in vigorous activity, and time spent in moderate-intensity activity (accelerometer-derived variables); as well as in total physical activity, home-based activity, work-based activity, leisure-based activity, commuting physical activity, and screen or TV time (self-reported physical activity variables). Of the 505 intervention participants, 491 (97%) received the Step it Up intervention. Analysis of 37 intervention consultations showed that 60% of Step it Up components were delivered faithfully. The intervention cost £18.04 per participant. Incremental cost to the NHS per 1,000-step increase per day was £96 and to society was £239. Adverse events were reported by 5 intervention participants (of which 2 were serious) and 5 control participants (of which 2 were serious). The study's limitations include a participation rate of 16% and low return of audiotapes by practices for fidelity assessment. In this large well-conducted trial, we found no evidence of effect of a plausible very brief pedometer intervention embedded in NHS Health Checks on objectively measured activity at 3-month follow-up. Current Controlled Trials (ISRCTN72691150).

Valid objective measurement is integral to increasing our understanding of physical activity and sedentary behaviours. However, no population-specific cut points have been calibrated for children with intellectual disabilities. Therefore, this study aimed to calibrate and cross-validate the first population-specific accelerometer intensity cut points for children with intellectual disabilities. Fifty children with intellectual disabilities were randomly assigned to the calibration (n = 36; boys = 28, 9.53±1.08yrs) or cross-validation (n = 14; boys = 9, 9.57±1.16yrs) group. Participants completed a semi-structured school-based activity session, which included various activities ranging from sedentary to vigorous intensity. Direct observation (SOFIT tool) was used to calibrate the ActiGraph wGT3X+, which participants wore on the right hip. Receiver Operating Characteristic curve analyses determined the optimal cut points for sedentary, moderate, and vigorous intensity activity for the vertical axis and vector magnitude. Classification agreement was investigated using sensitivity, specificity, total agreement, and Cohen's kappa scores against the criterion measure of SOFIT. The optimal (AUC = .87-.94) vertical axis cut points (cpm) were ≤507 (sedentary), 1008-2300 (moderate), and ≥2301 (vigorous), which demonstrated high sensitivity (81-88%) and specificity (81-85%). The optimal (AUC = .86-.92) vector magnitude cut points (cpm) of ≤1863 (sedentary), 2610-4214 (moderate), and ≥4215 (vigorous) demonstrated comparable, albeit marginally lower, accuracy than the vertical axis cut points (sensitivity = 80-86%; specificity = 77-82%). Classification agreement ranged from moderate to almost perfect (κ = .51-.85) with high sensitivity and specificity, and confirmed the trend that accuracy increased with intensity, and vertical axis cut points provide higher classification agreement than vector magnitude cut points. This study provides the first valid methods of interpreting accelerometer output in children with intellectual disabilities. The calibrated physical activity cut points are notably higher than existing cut points, thus raising questions on the validity of previous low physical activity estimates in children with intellectual disabilities that were based on typically developing cut points.

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 (light <3 METs, moderate 3–5.99 METs, vigorous ≥6 METs). Current prediction techniques have many limitations when translating accelerometer counts to EE.