Bronwyn K. Clark

Measurement of adults' sedentary time in population-based studies.

Sedentary time (too much sitting) increasingly is being recognized as a distinct health risk behavior. This paper reviews the reliability and validity of self-reported and device-based sedentary time measures and provides recommendations for their use in population-based studies. The focus is on instruments that have been used in free-living, population-based research in adults. Data from the 2003-2006 National Health and Nutrition Examination Survey are utilized to compare the descriptive epidemiology of sedentary time that arises from the use of different sedentary time measures. A key recommendation from this review is that, wherever possible, population-based monitoring of sedentary time should incorporate both self-reported measures (to capture important domain- and behavior-specific sedentary time information) and device-based measures (to measure both total sedentary time and patterns of sedentary time accumulation).

Occupational sitting and health risks: a systematic review

CONTEXT Emerging evidence suggests that sedentary behavior (i.e., time spent sitting) may be negatively associated with health. The aim of this study was to systematically review the evidence on associations between occupational sitting and health risks. EVIDENCE ACQUISITION Studies were identified in March-April 2009 by literature searches in PubMed, PsycINFO, CENTRAL, CINAHL, EMBASE, and PEDro, with subsequent related-article searches in PubMed and citation searches in Web of Science. Identified studies were categorized by health outcome. Two independent reviewers assessed methodologic quality using a 15-item quality rating list (score range 0-15 points, higher score indicating better quality). Data on study design, study population, measures of occupational sitting, health risks, analyses, and results were extracted. EVIDENCE SYNTHESIS 43 papers met the inclusion criteria (21% cross-sectional, 14% case-control, 65% prospective); they examined the associations between occupational sitting and BMI (n=12); cancer (n=17); cardiovascular disease (CVD, n=8); diabetes mellitus (DM, n=4); and mortality (n=6). The median study-quality score was 12 points. Half the cross-sectional studies showed a positive association between occupational sitting and BMI, but prospective studies failed to confirm a causal relationship. There was some case-control evidence for a positive association between occupational sitting and cancer; however, this was generally not supported by prospective studies. The majority of prospective studies found that occupational sitting was associated with a higher risk of DM and mortality. CONCLUSIONS Limited evidence was found to support a positive relationship between occupational sitting and health risks. The heterogeneity of study designs, measures, and findings makes it difficult to draw definitive conclusions at this time.

Prolonged sedentary time and physical activity in workplace and non-work contexts: a cross-sectional study of office, customer service and call centre employees

BackgroundTo examine sedentary time, prolonged sedentary bouts and physical activity in Australian employees from different workplace settings, within work and non-work contexts.MethodsA convenience sample of 193 employees working in offices (131), call centres (36) and customer service (26) was recruited. Actigraph GT1M accelerometers were used to derive percentages of time spent sedentary (<100 counts per minute; cpm), in prolonged sedentary bouts (≥20 minutes or ≥30 minutes), light-intensity activity (100–1951 cpm) and moderate-to-vigorous physical activity (MVPA; ≥1952 cpm). Using mixed models adjusted for confounders, these were compared for: work days versus non-work days; work hours versus non-work hours (work days only); and, across workplace settings.ResultsWorking hours were mostly spent sedentary (77.0%, 95%CI: 76.3, 77.6), with approximately half of this time accumulated in prolonged bouts of 20 minutes or more. There were significant (p<0.05) differences in all outcomes between workdays and non-work days, and, on workdays, between work- versus non-work hours. Results consistently showed “work” was more sedentary and had less light-intensity activity, than “non-work”. The period immediately after work appeared important for MVPA. There were significant (p<0.05) differences in all sedentary and activity outcomes occurring during work hours across the workplace settings. Call-centre workers were generally the most sedentary and least physically active at work; customer service workers were typically the least sedentary and the most active at work.ConclusionThe workplace is a key setting for prolonged sedentary time, especially for some occupational groups, and the potential health risk burden attached requires investigation. Future workplace regulations and health promotion initiatives for sedentary occupations to reduce prolonged sitting time should be considered.

Associations between television viewing time and overall sitting time with the metabolic syndrome in older men and women: the Australian Diabetes, Obesity and Lifestyle study.

OBJECTIVES: To examine associations between self‐reported television (TV) viewing time and overall sitting time with the metabolic syndrome and its components.

Measuring older adults' sedentary time: Reliability, validity, and responsiveness

PURPOSE With evidence that prolonged sitting has deleterious health consequences, decreasing sedentary time is a potentially important preventive health target. High-quality measures, particularly for use with older adults, who are the most sedentary population group, are needed to evaluate the effect of sedentary behavior interventions. We examined the reliability, validity, and responsiveness to change of a self-report sedentary behavior questionnaire that assessed time spent in behaviors common among older adults: watching television, computer use, reading, socializing, transport and hobbies, and a summary measure (total sedentary time). METHODS In the context of a sedentary behavior intervention, nonworking older adults (n = 48, age = 73 ± 8 yr (mean ± SD)) completed the questionnaire on three occasions during a 2-wk period (7 d between administrations) and wore an accelerometer (ActiGraph model GT1M) for two periods of 6 d. Test-retest reliability (for the individual items and the summary measure) and validity (self-reported total sedentary time compared with accelerometer-derived sedentary time) were assessed during the 1-wk preintervention period, using Spearman (ρ) correlations and 95% confidence intervals (CI). Responsiveness to change after the intervention was assessed using the responsiveness statistic (RS). RESULTS Test-retest reliability was excellent for television viewing time (ρ (95% CI) = 0.78 (0.63-0.89)), computer use (ρ (95% CI) = 0.90 (0.83-0.94)), and reading (ρ (95% CI) = 0.77 (0.62-0.86)); acceptable for hobbies (ρ (95% CI) = 0.61 (0.39-0.76)); and poor for socializing and transport (ρ < 0.45). Total sedentary time had acceptable test-retest reliability (ρ (95% CI) = 0.52 (0.27-0.70)) and validity (ρ (95% CI) = 0.30 (0.02-0.54)). Self-report total sedentary time was similarly responsive to change (RS = 0.47) as accelerometer-derived sedentary time (RS = 0.39). CONCLUSIONS The summary measure of total sedentary time has good repeatability and modest validity and is sufficiently responsive to change suggesting that it is suitable for use in interventions with older adults.

Identifying sedentary time using automated estimates of accelerometer wear time

Purpose The authors evaluated the accuracy of three automated accelerometer wear-time estimation algorithms against self-report. Direct effects on sedentary time (<100 cpm) and indirect effects on moderate-to-vigorous physical activity (MVPA, ≥1952 cpm) time were examined. Methods A subsample from the 2004/2005 Australian Diabetes, Obesity and Lifestyle Study (n=148) completed activity logs and wore accelerometers for a total of 987 days. A published algorithm that allows movement within non-wear periods (Algorithm 1) was compared with one that allows less movement (Algorithm 2) or no movement (Algorithm 3). Implications for population estimates were examined using 2003/2004 US National Health and Nutrition Examination Survey data. Results Mean difference per day between the criterion and estimated wear time was negligible for all three algorithms (≤11 min), but 95% limits of agreement (LOA) were wide (±≥2 h). Respectively, the algorithms (1, 2 and 3) misclassified sedentary time as non-wear on 31.9%, 19.4% and 18% of days and misclassified non-wear time as sedentary on 42.8%, 43.7% and 51.3% of days. Use of Algorithm 2 (compared with Algorithm 1) affected population estimates of sedentary time (higher by 20 min/day) but not MVPA time. Agreement between Algorithms 1 and 2 was good for MVPA time (mean difference −0.08, LOA: −2.08, 1.91 min), but not for wear time or sedentary time. Conclusion Accelerometer wear time can be estimated accurately on average; however, misclassification can be substantial for individuals. Algorithm choice affects estimates of sedentary time. Allowing very limited movement within non-wear periods can improve accuracy.

The validity of the GENEActiv wrist-worn accelerometer for measuring adult sedentary time in free living

OBJECTIVES Based on self-reported measures, sedentary time has been associated with chronic disease and mortality. This study examined the validity of the wrist-worn GENEactiv accelerometer for measuring sedentary time (i.e. sitting and lying) by posture classification, during waking hours in free living adults. DESIGN Fifty-seven participants (age=18-55 years 52% male) were recruited using convenience sampling from a large metropolitan Australian university. METHODS Participants wore a GENEActiv accelerometer on their non-dominant wrist and an activPAL device attached to their right thigh for 24-h (00:00 to 23:59:59). Pearsons Correlation Coefficient was used to examine the convergent validity of the GENEActiv and the activPAL for estimating total sedentary time during waking hours. Agreement was illustrated using Bland and Altman plots, and intra-individual agreement for posture was assessed with the Kappa statistic. RESULTS Estimates of average total sedentary time over 24-h were 623 (SD 103) min/day from the GENEActiv, and 626 (SD 123) min/day from the activPAL, with an Intraclass Correlation Coefficient of 0.80 (95% confidence intervals 0.68-0.88). Bland and Altman plots showed slight underestimation of mean total sedentary time for GENEActiv relative to activPAL (mean difference: -3.44min/day), with moderate limits of agreement (-144 to 137min/day). Mean Kappa for posture was 0.53 (SD 0.12), indicating moderate agreement for this sample at the individual level. CONCLUSIONS The estimation of sedentary time by posture classification of the wrist-worn GENEActiv accelerometer was comparable to the activPAL. The GENEActiv may provide an alternative, easy to wear device based measure for descriptive estimates of sedentary time in population samples.

Nine year changes in sitting time in young and mid-aged Australian women: findings from the Australian Longitudinal Study for Women's Health.

OBJECTIVE To examine changes in sitting time (ST) in women over nine years and to identify associations between life events and these changes. METHODS Young (born 1973-78, n=5215) and mid-aged (born 1946-51, n=6973) women reported life events and ST in four surveys of the Australian Longitudinal Study on Womens Health between 2000 and 2010. Associations between life events and changes in ST between surveys (decreasers ≥2 h/day less, increasers ≥2 h/day more) were estimated using generalized estimating equations. RESULTS Against a background of complex changes there was an overall decrease in ST in young women (median change -0.48 h/day, interquartile range [IQR]=-2.54, 1.50) and an increase in ST in mid-aged women (median change 0.43 h/day; IQR=-1.29, 2.0) over nine years. In young women, returning to study and job loss were associated with increased ST, while having a baby, beginning work and decreased income were associated with decreased ST. In mid-aged women, changes at work were associated with increased ST, while retiring and decreased income were associated with decreased ST. CONCLUSIONS ST changed over nine years in young and mid-aged Australian women. The life events they experienced, particularly events related to work and family, were associated with these changes.

Field evaluation of a random forest activity classifier for wrist-worn accelerometer data

OBJECTIVES Wrist-worn accelerometers are convenient to wear and associated with greater wear-time compliance. Previous work has generally relied on choreographed activity trials to train and test classification models. However, validity in free-living contexts is starting to emerge. Study aims were: (1) train and test a random forest activity classifier for wrist accelerometer data; and (2) determine if models trained on laboratory data perform well under free-living conditions. DESIGN Twenty-one participants (mean age=27.6±6.2) completed seven lab-based activity trials and a 24h free-living trial (N=16). METHODS Participants wore a GENEActiv monitor on the non-dominant wrist. Classification models recognising four activity classes (sedentary, stationary+, walking, and running) were trained using time and frequency domain features extracted from 10-s non-overlapping windows. Model performance was evaluated using leave-one-out-cross-validation. Models were implemented using the randomForest package within R. Classifier accuracy during the 24h free living trial was evaluated by calculating agreement with concurrently worn activPAL monitors. RESULTS Overall classification accuracy for the random forest algorithm was 92.7%. Recognition accuracy for sedentary, stationary+, walking, and running was 80.1%, 95.7%, 91.7%, and 93.7%, respectively for the laboratory protocol. Agreement with the activPAL data (stepping vs. non-stepping) during the 24h free-living trial was excellent and, on average, exceeded 90%. The ICC for stepping time was 0.92 (95% CI=0.75-0.97). However, sensitivity and positive predictive values were modest. Mean bias was 10.3min/d (95% LOA=-46.0 to 25.4min/d). CONCLUSIONS The random forest classifier for wrist accelerometer data yielded accurate group-level predictions under controlled conditions, but was less accurate at identifying stepping verse non-stepping behaviour in free living conditions Future studies should conduct more rigorous field-based evaluations using observation as a criterion measure.

An Advanced Pharmacy Practice Framework for Australia

The need to develop An Advanced Pharmacy Practice Framework for Australia (the “APPF”) was identified during the 2010 review of the competency standards for Australian pharmacists. The Advanced Pharmacy Practice Framework Steering Committee, a collaborative profession-wide committee comprised of representatives of ten pharmacy organisations, examined and adapted existing advanced practice frameworks, all of which were found to have been based on the Competency Development and Evaluation Group (CoDEG) Advanced and Consultant Level Framework (the “CoDEG Framework”) from the United Kingdom. Its competency standards were also found to align well with the Domains of the National Competency Standards Framework for Pharmacists in Australia (the “National Framework”). Adaptation of the CoDEG Framework created an APPF that is complementary to the National Framework, sufficiently flexible to customise for recognising advanced practice in any area of professional practice and has been approved by the boards/councils of all participating organisations. The primary purpose of the APPF is to assist the development of the profession to meet the changing health care needs of the community. However, it is also a valuable tool for assuring members of the public of the competence of an advanced practice pharmacist and the quality and safety of the services they deliver.