Are we missing the sitting? Agreement between accelerometer non-wear time validation methods used with older adults' data.
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ABSTRACT:
SUBMITTER: Chudyk AM
PROVIDER: S-EPMC5756085 | biostudies-literature | 2017
REPOSITORIES: biostudies-literature
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Are we missing the sitting? Agreement between accelerometer non-wear time validation methods used with older adults' data.
Chudyk Anna M AM McAllister Megan M MM Cheung Hiu Kan HK McKay Heather A HA Ashe Maureen C MC
Cogent medicine 20170331
We used Bland Altman plots to compare agreement between a self-report diary and five different non-wear time algorithms [an algorithm that uses ≥60 min of consecutive zeroes (Troiano) and four variations of an algorithm that uses ≥90 min of consecutive zeroes to define a non-wear period] for estimating community-dwelling older adults' (<i>n</i> = 106) sedentary behaviour and wear time (min/day) as measured by accelerometry. We found that the Troiano algorithm may overestimate sedentary behaviour ...[more]
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Project description:the use of movement monitors (accelerometers) for measuring physical activity (PA) in intervention and population-based studies is becoming a standard methodology for the objective measurement of sedentary and active behaviors and for the validation of subjective PA self-reports. A vital step in PA measurement is the classification of daily time into accelerometer wear and nonwear intervals using its recordings (counts) and an accelerometer-specific algorithm.the purpose of this study was to validate and improve a commonly used algorithm for classifying accelerometer wear and nonwear time intervals using objective movement data obtained in the whole-room indirect calorimeter.we conducted a validation study of a wear or nonwear automatic algorithm using data obtained from 49 adults and 76 youth wearing accelerometers during a strictly monitored 24-h stay in a room calorimeter. The accelerometer wear and nonwear time classified by the algorithm was compared with actual wearing time. Potential improvements to the algorithm were examined using the minimum classification error as an optimization target.the recommended elements in the new algorithm are as follows: 1) zero-count threshold during a nonwear time interval, 2) 90-min time window for consecutive zero or nonzero counts, and 3) allowance of 2-min interval of nonzero counts with the upstream or downstream 30-min consecutive zero-count window for detection of artifactual movements. Compared with the true wearing status, improvements to the algorithm decreased nonwear time misclassification during the waking and the 24-h periods (all P values < 0.001).the accelerometer wear or nonwear time algorithm improvements may lead to more accurate estimation of time spent in sedentary and active behaviors.
| S-EPMC3184184 | biostudies-literature
Project description:BACKGROUND:Accelerometers are widely used to measure sedentary time and daily physical activity (PA). However, data collection and processing criteria, such as non-wear time rules might affect the assessment of total PA and sedentary time and the associations with health variables. The study aimed to investigate whether the choice of different non-wear time definitions would affect the outcomes of PA levels in youth. METHODS:Seventy-seven healthy youngsters (44 boys), aged 10-17?years, wore an accelerometer and kept a non-wear log diary during 4 consecutives days. We compared 7 published algorithms (10, 15, 20, 30, 60?min of continuous zeros, Choi, and Troiano algorithms). Agreements of each algorithm with the log diary method were assessed using Bland-Altmans plots and by calculating the concordance correlation coefficient for repeated measures. RESULTS:Variations in time spent in sedentary and moderate to vigorous PA (MVPA) were 30 and 3.7%. Compared with the log diary method, greater discrepancies were found for the algorithm 10?min (p?<?0.001). For the time assessed in sedentary, the agreement with diary was excellent for the 4 algorithms (Choi, r?=?0.79; Troiano, r?=?0.81; 30?min, r?=?0.79; 60?min, r?=?0.81). Concordance for each method was excellent for the assessment of time spent in MVPA (>?0.86). The agreement for the wear time assessment was excellent for 5 algorithms (Choi r?=?0.79; Troiano r?=?0.79; 20?min r?=?0.77; 30?min r?=?0.80; 60?min r?=?0.80). CONCLUSIONS:The choice of non-wear time rules may considerably affect the sedentary time assessment in youth. Using of appropriate data reduction decision in youth is needed to limit differences in associations between health outcomes and sedentary behaviors and may improve comparability for future studies. Based on our results, we recommend the use of the algorithm of 30?min of continuous zeros for defining non-wear time to improve the accuracy in assessing PA levels in youth. TRIAL REGISTRATION:NCT02844101 (retrospectively registered at July 13th 2016).
| S-EPMC6444637 | biostudies-literature
Project description:BackgroundThere are many unresolved issues regarding data reduction algorithms for accelerometry. The choice of criterion for removal of non-wear time might have a profound influence on physical activity (PA) and sedentary time (SED) estimates. The aim of the present study was to compare 10 different non-wear criteria and a log of non-wear periods in 11-year-old children.MethodsChildren from the Active Smarter Kids study performed 7-days of hip-worn accelerometer monitoring (Actigraph GT3X+) and logged the number of non-wear periods each day, along with the approximate duration and reason for non-wear. Accelerometers were analyzed using 10 different non-wear criteria: ≥ 10, 20, 30, 45, 60, and 90 min of consecutive zero counts without allowance for interruptions, and ≥60 and 90 min with allowance for 1 and 2 min of interruptions.Results891 children provided 5203 measurement days, and reported 1232 non-wear periods ranging from 0 to 3 periods per day: on most days children reported no non-wear periods (77.1% of days). The maximum number of non-wear periods per day was 2 for the 90-min criterion, 3 to 5 for most criteria, 7 for the 20-min criterion, and 20 for the 10-min criterion. The non-wear criteria influenced overall PA (mean values across all criteria: 591 to 649 cpm; 10% difference) and SED time (461 to 539 min/day; 17% difference) estimates, especially for the most prolonged SED bouts. Estimates were similar for time spent in intensity-specific (light, moderate, vigorous, and moderate-to-vigorous) PA, but varied 6-9% among the non-wear criteria for proportions of time spent in intensity-specific PA (% of total wear time).ConclusionsPopulation level estimates of PA and SED differed between different accelerometer non-wear criteria, meaning that non-wear time algorithms should be standardized across studies to reduce confusion and improve comparability of children's PA level. Based on the numbers and reasons for non-wear periods, we suggest a 45 or 60-min consecutive zero count-criterion not allowing any interruptions to be applied in future pediatric studies, at least for children older than 10 years.Trial registrationThe study is registered in Clinicaltrials.gov with identification number NCT02132494 . Registered 7 April 2014.
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Project description:To date, non-wear detection algorithms commonly employ a 30, 60, or even 90 mins interval or window in which acceleration values need to be below a threshold value. A major drawback of such intervals is that they need to be long enough to prevent false positives (type I errors), while short enough to prevent false negatives (type II errors), which limits detecting both short and longer episodes of non-wear time. In this paper, we propose a novel non-wear detection algorithm that eliminates the need for an interval. Rather than inspecting acceleration within intervals, we explore acceleration right before and right after an episode of non-wear time. We trained a deep convolutional neural network that was able to infer non-wear time by detecting when the accelerometer was removed and when it was placed back on again. We evaluate our algorithm against several baseline and existing non-wear algorithms, and our algorithm achieves a perfect precision, a recall of 0.9962, and an F1 score of 0.9981, outperforming all evaluated algorithms. Although our algorithm was developed using patterns learned from a hip-worn accelerometer, we propose algorithmic steps that can easily be applied to a wrist-worn accelerometer and a retrained classification model.
| S-EPMC8065130 | biostudies-literature
Project description:BackgroundLow levels of physical activity are implicated in low life expectancies of people receiving maintenance haemodialysis. Accelerometers are increasingly being used to quantify activity behaviours of this population but guidance to quality-assure such data is lacking. The objective of this study was to provide data processing and reduction recommendations to ensure accelerometer-derived outcomes are sufficiently reliable for interpretative analysis.MethodsSeventy people receiving maintenance haemodialysis (age 55.9 ± 15.7 years, 34% women, 23% diabetic) from a single outpatient renal unit volunteered for the study. Participants wore Actigraph GT3x and ActivPAL monitors during waking hours over seven days. Reliability of accelerometer output (normalised to wear-time) was assessed via intraclass correlation coefficient (ICC). The Spearman-Brown prophecy formula was subsequently applied to the ICCs to derive the minimum required accelerometer wear-time for each behavioural outcome.ResultsMonitor wear compliance was greater on dialysis compared to non-dialysis days (90% v 77%). Participants were significantly more active on non-dialysis days compared to dialysis days but there were no significant differences in estimated behaviours between days within the same condition. Average measure ICCs for all accelerometer outcomes were high (range 0.76-0.96). Computations indicated that habitual physical activity and sedentary behaviour could be estimated with a minimum reliability level of 0.80 from one dialysis day and two non-dialysis days, and at least eight hours monitor wear per day. Applying this rubric allowed 90% of participant data to be retained for further analysis.ConclusionsRegardless of accelerometer, one dialysis and two non-dialysis days data with a minimum of eight hours wear each day should enable habitual activity of people receiving maintenance haemodialysis to be characterised with acceptable reliability. These recommendations reconcile the tension between wear-time criteria stringency and retention of an adequately representative sample.
| S-EPMC7296937 | biostudies-literature
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Project description:ObjectiveTo investigate characteristics associated with wearing an accelerometer for the required and requested time among 8-year-old to 10-year-old children.DesignCross-sectional.Setting60 Bristol and North Somerset primary schools taking part in the 'Active for Life Year 5' randomised controlled trial (RCT) in 2011.Participants2048 children, aged 8-10 years, invited to wear an accelerometer for 5 days of recording.Primary outcome measureNumbers meeting required wear-time for inclusion in main RCT analysis (?8 h/day ?3 days) and numbers meeting requested wear-time (?8 h/day for all 5 days).Results817 (40%) of the children wore the accelerometer for the requested time and 1629 (80%) for the required time. In adjusted multivariable analyses the odds of wearing the accelerometer for the required time were greater in females as compared with males (OR 1.76 (1.42-2.18)), those with higher scores for reporting their mother restricted time on sedentary behaviours (1.26 (1.04-1.52) per increase of 1 on a 1-4 scale) and in children from schools with larger year group sizes (1.01 (1.00-1.02) per additional child). Living in a neighbourhood with higher levels of deprivation (0.49 (0.33-0.72) comparing highest to lowest third of the deprivation score) or reporting higher levels of weekday outdoor play (0.97 (0.94, 1.00) per 30 min more) were associated with reduced odds of meeting required time. Results were essentially the same for requested wear-time. Other characteristics, including child body mass index, were not associated with required or requested wear-time.ConclusionsOnly 40% of children wore the accelerometer for the requested time but 80% fulfilled the required criteria to be included in the main study analyses. Knowing which characteristics are associated with accelerometer wear could help target interventions to increase wear-time.
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Project description:ImportancePractical health promotion strategies for improving cardiometabolic health in older adults are needed.ObjectiveTo examine the efficacy of a sedentary behavior reduction intervention for reducing sitting time and improving blood pressure in older adults.Design, setting, and participantsThis parallel-group randomized clinical trial was conducted in adults aged 60 to 89 years with high sitting time and body mass index of 30 to 50 from January 1, 2019, to November 31, 2022, at a health care system in Washington State.InterventionParticipants were randomized 1:1 to the sitting reduction intervention or a healthy living attention control condition for 6 months. Intervention participants received 10 health coaching contacts, sitting reduction goals, and a standing desk and fitness tracker to prompt sitting breaks. The attention control group received 10 health coaching contacts to set general healthy living goals, excluding physical activity or sedentary behavior.Main outcomes and measuresThe primary outcome, measured at baseline, 3 months, and 6 months, was sitting time assessed using accelerometers worn for 7 days at each time point. Coprimary outcomes were systolic and diastolic blood pressure measured at baseline and 6 months.ResultsA total of 283 participants (140 intervention and 143 control) were randomized (baseline mean [SD] age, 68.8 [6.2] years; 186 [65.7%] female; mean [SD] body mass index, 34.9 [4.7]). At baseline, 147 (51.9%) had a hypertension diagnosis and 97 (69.3%) took at least 1 antihypertensive medication. Sitting time was reduced, favoring the intervention arm, with a difference in the mean change of -31.44 min/d at 3 months (95% CI, -48.69 to -14.19 min/d; P < .001) and -31.85 min/d at 6 months (95% CI, -52.91 to -10.79 min/d; P = .003). Systolic blood pressure change was lower by 3.48 mm Hg, favoring the intervention arm at 6 months (95% CI, -6.68 to -0.28 mm Hg; P = .03). There were 6 serious adverse events in each arm and none were study related.Conclusions and relevanceIn this study of a 6-month sitting reduction intervention, older adults in the intervention reduced sedentary time by more than 30 min/d and reduced systolic blood pressure. Sitting reduction could be a promising approach to improve health in older adults.Trial registrationClinicalTrials.gov Identifier: NCT03739762.
| S-EPMC10973891 | biostudies-literature
Project description:Missing data due to non-wear are common in accelerometer studies measuring physical activity and sedentary behavior. Accelerometer output are high-dimensional time-series data that are episodic and often highly skewed, presenting unique challenges for handling missing data. Common methods for missing accelerometry either are ad-hoc, require restrictive parametric assumptions, or do not appropriately impute bouts. This study developed a flexible hot deck multiple imputation (MI; i.e., "replacing" missing data with observed values) procedure to handle missing accelerometry. For each missing segment of accelerometry, "donor pools" contained observed segments from either the same or different participants, and 10 imputed segments were randomly drawn from the donor pool according to selection weights, where the donor pool and selection weight depended on variables associated with non-wear and/or accelerometer-based measures. A simulation study of 2,550 women compared hot deck MI to two standard methods in the field: available case (AC) analysis (i.e., analyzing all observed accelerometry with no restriction on wear time or number of days) and complete case (CC) analysis (i.e., analyzing only participants that wore the accelerometer for ?10 hours for 4-7 days). This was repeated using accelerometry from the entire 24-hour day and daytime (10am- 8pm) only, and data were missing at random. For the entire 24-hour day, MI produced less bias and better 95% confidence interval (CI) coverage than AC and CC. For the daytime only, MI produced less bias and better 95% CI coverage than AC; CC produced similar bias and 95% CI coverage, but longer 95% CIs than MI.
| S-EPMC6707749 | biostudies-literature
Project description:BackgroundAccelerometers objectively assess physical activity (PA) and are currently used in several large-scale epidemiological studies, but there is no consensus for processing the data. This study compared the impact of wear-time assessment methods and using either vertical (V)-axis or vector magnitude (VM) cut-points on accelerometer output.MethodsParticipants (7,650 women, mean age 71.4 y) were mailed an accelerometer (ActiGraph GT3X+), instructed to wear it for 7 days, record dates and times the monitor was worn on a log, and return the monitor and log via mail. Data were processed using three wear-time methods (logs, Troiano or Choi algorithms) and V-axis or VM cut-points.ResultsUsing algorithms alone resulted in "mail-days" incorrectly identified as "wear-days" (27-79% of subjects had >7-days of valid data). Using only dates from the log and the Choi algorithm yielded: 1) larger samples with valid data than using log dates and times, 2) similar wear-times as using log dates and times, 3) more wear-time (V, 48.1 min more; VM, 29.5 min more) than only log dates and Troiano algorithm. Wear-time algorithm impacted sedentary time (~30-60 min lower for Troiano vs. Choi) but not moderate-to-vigorous (MV) PA time. Using V-axis cut-points yielded ~60 min more sedentary time and ~10 min less MVPA time than using VM cut-points.ConclusionsCombining log-dates and the Choi algorithm was optimal, minimizing missing data and researcher burden. Estimates of time in physical activity and sedentary behavior are not directly comparable between V-axis and VM cut-points. These findings will inform consensus development for accelerometer data processing in ongoing epidemiologic studies.
| S-EPMC4247661 | biostudies-literature