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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.

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.

There has been growing interest in the use of smart wearable technology to promote physical activity (PA) behaviour change. However, little is known concerning PA patterns throughout an intervention or engagement with trackers. The objective of the study was to explore patterns of Fitbit-measured PA and wear-time over 24-weeks and their relationship to changes in Actigraph-derived moderate-to-vigorous PA (MVPA). Twenty-nine intervention participants (88%) from the wearable activity technology and action-planning (WATAAP) trial in colorectal and endometrial cancer survivors accepted a Fitbit friend request from the research team to permit monitoring of Fitbit activity. Daily steps and active minutes were recorded for each participant over the 12-week intervention and throughout the follow-up period to 24-weeks. Accelerometer (GT9X) derived MVPA was assessed at end of intervention (12-weeks) and end of follow-up (24-weeks). Fitbit wear-time over the 24-weeks of data was remarkably consistent, with median adherence score of 100% for all weeks. During the intervention, participants recorded a median 8006 steps/day. Daily step count was slightly increased through week-13 to week-24 with a median of 8191 steps/day (p = 0.039). Actigraph and Fitbit derived measures were highly correlated but demonstrated poor agreement overall. Fitbit measured activity was closest to MVPA measured using Freedson cut-points as no bias was observed. Step count was maintained throughout the trial displaying promise for the effectiveness of smart-wearable interventions to reduce sedentary behaviour beyond the intervention period. Further worthwhile work should compare more advanced smart-wearable technology with accelerometers in order to improve agreement and explore less resource-intensive methods to assess PA that could be scalable.

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

Actigraphs are wrist-worn devices that record tri-axial accelerometry data used clinically and in research studies. The expense of research-grade actigraphs, however, limit their widespread adoption, especially in clinical settings. Tri-axial accelerometer-based consumer wearable devices have gained worldwide popularity and hold potential for a cost-effective alternative. The lack of independent validation of minute-to-minute accelerometer data with polysomnographic data or even research-grade actigraphs, as well as access to raw data has hindered the utility and acceptance of consumer-grade actigraphs. Sleep clinic patients wore a consumer-grade wearable (Huami Arc) on their non-dominant wrist while undergoing an overnight polysomnography (PSG) study. The sample was split into two, 20 in a training group and 21 in a testing group. In addition to the Arc, the testing group also wore a research-grade actigraph (Philips Actiwatch Spectrum). Sleep was scored for each 60-s epoch on both devices using the Cole-Kripke algorithm. Based on analysis of our training group, Arc and PSG data were aligned best when a threshold of 10 units was used to examine the Arc data. Using this threshold value in our testing group, the Arc has an accuracy of 90.3%±4.3%, sleep sensitivity (or wake specificity) of 95.5%±3.5%, and sleep specificity (wake sensitivity) of 55.6%±22.7%. Compared to PSG, Actiwatch has an accuracy of 88.7%±4.5%, sleep sensitivity of 92.6%±5.2%, and sleep specificity of 60.5%±20.2%, comparable to that observed in the Arc. An optimized sleep/wake threshold value was identified for a consumer-grade wearable Arc trained by PSG data. By applying this sleep/wake threshold value for Arc generated accelerometer data, when compared to PSG, sleep and wake estimates were adequate and comparable to those generated by a clinical-grade actigraph. As with other actigraphs, sleep specificity plateaus due to limitations in distinguishing wake without movement from sleep. Further studies are needed to evaluate the Arc's ability to differentiate between sleep and wake using other sources of data available from the Arc, such as high resolution accelerometry and photoplethysmography.

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.

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.

Background Current knowledge about the promotion of long-term physical activity (PA) maintenance in cancer survivors is limited. The aims of this study were to 1) determine the effect of self-regulatory BCTs on long-term PA maintenance, and 2) identify predictors of long-term PA maintenance in cancer survivors 12 months after participating in a six-month exercise intervention during cancer treatment. Methods In a multicentre study with a 2 × 2 factorial design, the Phys-Can RCT, 577 participants with curable breast, colorectal or prostate cancer and starting their cancer treatment, were randomized to high intensity exercise with or without self-regulatory behaviour change techniques (BCTs; e.g. goal-setting and self-monitoring) or low-to-moderate intensity exercise with or without self-regulatory BCTs. Participants’ level of PA was assessed at the end of the exercise intervention and 12 months later (i.e. 12-month follow-up), using a PA monitor and a PA diary. Participants were categorized as either maintainers (change in minutes/week of aerobic PA ≥ 0 and/or change in number of sessions/week of resistance training ≥0) or non-maintainers. Data on potential predictors were collected at baseline and at the end of the exercise intervention. Multiple logistic regression analyses were performed to answer both research questions. Results A total of 301 participants (52%) completed the data assessments. A main effect of BCTs on PA maintenance was found (OR = 1.80, 95%CI [1.05–3.08]) at 12-month follow-up. Participants reporting higher health-related quality-of-life (HRQoL) (OR = 1.03, 95%CI [1.00–1.06] and higher exercise motivation (OR = 1.02, 95%CI [1.00–1.04]) at baseline were more likely to maintain PA levels at 12-month follow-up. Participants with higher exercise expectations (OR = 0.88, 95%CI [0.78–0.99]) and a history of tobacco use at baseline (OR = 0.43, 95%CI [0.21–0.86]) were less likely to maintain PA levels at 12-month follow-up. Finally, participants with greater BMI increases over the course of the exercise intervention (OR = 0.63, 95%CI [0.44–0.90]) were less likely to maintain their PA levels at 12-month follow-up. Conclusions Self-regulatory BCTs improved PA maintenance at 12-month follow-up and can be recommended to cancer survivors for long-term PA maintenance. Such support should be considered especially for patients with low HRQoL, low exercise motivation, high exercise expectations or with a history of tobacco use at the start of their cancer treatment, as well as for those gaining weight during their treatment. However, more experimental studies are needed to investigate the efficacy of individual or combinations of BCTs in broader clinical populations. Trial registration NCT02473003 (10/10/2014).

Objective To evaluate the reliability of the GeneActiv actigraphy device in measuring sleep parameters and compare its performance with polysomnography (PSG) in older adults with self-reported sleep disturbances. Methods This sub-study was part of a pilot double-blinded randomized controlled crossover trial (CleverLights Study, ANZCTR ID 12619000138189). Participants ( n  = 12, mean age 67.7 years) underwent two nights of sleep studies with simultaneous GeneActiv actigraphy and PSG, separated by a 2-week interval. Sleep parameters including time in bed (TIB), total sleep time (TST), wake after sleep onset (WASO), sleep onset latency (SOL), sleep efficiency (SE), and number of awakenings were assessed. Intraclass Correlation Coefficients (ICCs) and Bland-Altman plots were used to determine reliability and agreement between methods. Results GeneActiv actigraphy demonstrated strong correlations with PSG for TST (ICC = 0.79, p  = 0.001) and SE (ICC = 0.85, p  < 0.001), but tended to overestimate these parameters. Actigraphy also significantly underestimated the number of awakenings (ICC = 0.45, p  = 0.021). Correlations with observed TIB (ICC = 0.30, p  = 0.433), WASO (ICC = 0.33, p  = 0.386), and SOL (ICC = 0.32, p  = 0.056) were non-significant. Bland-Altman plots revealed proportional bias, especially in SOL and the number of awakenings. Conclusion Compared to PSG, the GeneActiv actigraphy device provides reliable measurements for total sleep time and sleep efficiency, but agreement was weaker for wake after sleep onset, sleep onset latency, and the number of awakenings. The device showed consistent performance across multiple nights, suggesting good reproducibility. However, it systematically overestimated total sleep time and underestimates wake-related parameters, hence it may not fully replace PSG for detailed sleep assessments.