Impact of accelerometer wear time on physical activity data: a NHANES semisimulation data approach

Herrmann, Stephen D.; Barreira, Tiago V.; Kang, Minsoo; Ainsworth, Barbara E.

doi:10.1136/bjsports-2012-091410

Cited by 104 publications

(84 citation statements)

References 37 publications

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“…I found that non-wear time had the greatest impact on estimates of time spent in SB. This is consistent with previous research that demonstrated that shorter wear times resulted in lower estimates of time spent in PA and SB, with the greatest difference seen for SB 320 .…”

Section: Discussionsupporting

confidence: 92%

“…Despite their wide use, inconsistencies in data reduction procedures have been identified that may reduce comparability between studies [317][318][319][320] , and compromise the validity of accelerometer estimates [321][322][323][324][325][326] . It is important to identify and minimise sources of error and bias in data reduction procedures.…”

Section: Introductionmentioning

confidence: 99%

“…To reduce this potential source of error, recent suggestions have been to increase the valid-day criterion to 13 hours/day 320,322 . This may not be appropriate for population groups that commonly exhibit long sleep durations, such as people with depression or low socioeconomic status 332 , due to the exclusion of participants with short waking hours.…”

Section: Introductionmentioning

confidence: 99%

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Measuring and positively influencing physical activity and sedentary behaviour in adults with mental illness

Chapman¹

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Background: Mental illness has a high global prevalence, and is associated with higher risk of chronic physical conditions, and reduced psychosocial functioning and life expectancy. Physical activity (PA) protects against chronic disease and mortality risk, and reduces depression and anxiety. Prolonged sedentary behaviour (SB) can have deleterious metabolic consequences, and may be associated with poor mental health.Accurate measurement of PA and SB is important for assessing health risk, and evaluating behaviour change interventions. Objective measures (e.g. accelerometers) allow for unbiased measurement, but do not assess the context of participation, which may be important for intervention planning. Questionnaires can assess context, however, they are prone to reporting errors. The feasibility of using these measures, and the impact of standard data management procedures, has not been assessed in adults with mental illness.PA interventions may positively influence PA levels of adults with mental illness. It is important to understand PA attitudes when designing interventions, as well as the acceptability and efficacy of different exercise approaches. Aims and objectives:The overarching aim of this PhD was to measure, understand, and positively influence the PA patterns of non-institutionalised adults with mental illness. Specific objectives were to: i) assess the utility of questionnaires and accelerometers for measuring PA and SB; ii) investigate questionnaire and accelerometer data management procedures; iii) measure PA and SB levels; iv) assess PA attitudes and opinions; and v) assess the feasibility and acceptability of two exercise protocols. Data from two research studies are presented in six thesis chapters. Study 1Design: Cross-sectional.Method: Participants were adult clients of mental health services. Participants completed self-administered questionnaires on the time spent in PA and SB in different contexts, wore an ActiGraph GT3x+ accelerometer on the hip for 7-days, and rated the ease/difficulty of completing the questionnaires and accelerometry. Participants also completed self-administered questionnaires on PA attitudes and opinions.Results: 142 participants completed the questionnaires (mean age=40yrs, female=43%); of these, 99 (70%) completed the accelerometry (mean age=40yrs, female=47%).ii Participants found it easier to report PA than SB, and to complete accelerometry than questionnaires. Defining a valid day of accelerometry using standard absolute criteria biased the sample by excluding participants with short waking hours; defining a valid day relative to waking hours prevented this bias. The median self-reported time spent in moderate-to-vigorous physical activity (MVPA) and SB was 4.5 hours/week and 10.7 hours/day respectively. Walking for transport, and sitting to watch TV, contributed most to these self-report estimates. The median accelerometer-derived time spent in MVPA and SB was 26 minutes/day and 9.2 hours/day respectively; 7% of MVPA time was in bouts of 10 minutes or ...

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Section: Discussionsupporting

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Measuring and positively influencing physical activity and sedentary behaviour in adults with mental illness

Chapman¹

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“…It has further been shown that assessments of an individual's physical activity are fairly accurate when monitored using an activity monitor (pedometer or accelerometer) for approximately 1 week [25]. It has also been reported that wearing an accelerometer leads to increased number of steps [26]. Therefore, we believe that the fact that participants of this study wore accelerometers generated motivation, making their physical activity visible, and self-monitoring led to increase in steps and activity calories.…”

Section: Discussionmentioning

confidence: 54%

The Effectiveness of Hyperlipidemia Prevention Intervention Designed to Affect Behavior Change: Focus on Changes in Health Behavior

Hayashi

Mastubara²,

Saeki³

et al. 2014

OJN

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Hyperlipidemia prevention intervention for behavior change was conducted on subjects in their 30s and 40s to evaluate changes in health behavior and investigate the effectiveness of these changes, with respect to early prevention and improvement of disease. Subjects were middle-aged individuals aged 30 -49, suffering from serum lipid abnormalities. Intervention designed to affect behavior change was implemented for 3 weeks in 11 employees (nine males, two females) at five workplaces. Intervention mainly consisted of improving self-efficacy. The study's purpose was explained and the written consent was obtained from subjects. Subjects filled questionnaires (characteristics, history of present illness, health behavior, self-efficacy scale for prevention of hyperlipidemia, etc.) both before and after intervention. Subjects recorded their physical activity using accelerometers and dietary intake photographically. Each subject created realistic behavior goals and recorded their progress using self-monitoring charts. Data were analyzed using descriptive statistics; therefore, pre-and post-intervention data were compared. Physical activity and achievement rate of goals during the first week and over the next 2 -3 weeks were compared. Health behavior scores showed modification of behavior in a desirable direction after interven-* Corresponding author. H. Izumi et al. 963tion compared with those before intervention. No significant difference in self-efficacy before and after intervention was observed. However, the subscale for diet was significantly higher after intervention compared with that before intervention. Steps, total calories, and activity calories were increased in the second and third weeks compared with those in the first week. Three weeks of intervention resulted in health behavior changing in a desirable direction. We infer that intervention in the form of self-monitoring using accelerometers that make physical activity visible was effective in leading to behavior change.

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“…Despite the importance of high compliance rates, wear time per day usually remains below 24 h in studies measuring PA in children. This leads to arbitrary decisions regarding wear time validation criteria and inclusion criteria for a valid day, which can significantly affect the compliance rates (12), wear time (7), time spent in different PA intensities (13), and differential bias by exclusion of participants with certain characteristics (27). Furthermore, a review by Cain et al (5) highlighted the great diversity of methods and criteria used to process accelerometer-based data in studies investigating PA in children published between 2005 and 2010, which makes comparisons between studies difficult.…”

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confidence: 99%

Measuring Children's Physical Activity

Schneller

Bentsen

Nielsen

et al. 2017

Medicine &Amp; Science in Sports &Amp; Exercise

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Impact of accelerometer wear time on physical activity data: a NHANES semisimulation data approach

Abstract: These data suggest that using accelerometer wear time criteria of 12 h/day or less may underestimate step count and time spent in various PA levels.

Cited by 104 publications

References 37 publications

Measuring and positively influencing physical activity and sedentary behaviour in adults with mental illness

Measuring and positively influencing physical activity and sedentary behaviour in adults with mental illness

The Effectiveness of Hyperlipidemia Prevention Intervention Designed to Affect Behavior Change: Focus on Changes in Health Behavior

Measuring Children's Physical Activity

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