Physical activity prevalence in Australian children and adolescents: Why do different surveys provide so different estimates, and what can we do about it?

To illustrate how the differences in measurement protocols affect physical activity (PA) monitoring among Australian children and adolescents aged ~5-17 years, this review aimed to summarize and critically assess the most recent findings from the national and state or territory health surveillance systems and population surveys. We compared methods and results of 21 population surveys identified in an extensive web-based search conducted using the entries ‘Physical Activity’, ‘Surveillance’, ‘Monitoring’, ‘Survey’, ‘Australia’ and the names of Australian states and territories as keywords. A large variability between PA prevalence rates from different Australian national- and state-level surveys was observed, both for selfreported and pedometer-based estimates. The prevalence estimates tended to be: [i] higher among children when compared with adolescents; [ii] higher for boys than for girls when assessed using self-reports; and [iii] higher for girls than for boys when assessed using pedometers. The true prevalence of compliance with PA guidelines among children and adolescents in Australia seems to be difficult to determine. To ensure comparability of prevalence estimates, key elements of data collection and processing protocols, such as PA questionnaires, survey administration modes, survey time frames, and definitions of a ‘sufficient’ PA level, should be standardised throughout all PA surveillance systems and population surveys in Australia

Becoming and staying physically active in adolescents with cerebral palsy: protocol of a qualitative study of facilitators and barriers to physical activity

<p>Abstract</p> <p>Background</p> <p>Adolescents with cerebral palsy (CP) show a reduced physical activity (PA). Currently there are no interventions for adolescents with CP in this critical life phase that optimise and maintain the individuals' physical activity in the long term. To develop such a program it is important to fully understand the factors that influence physical activity behaviours in adolescents with CP. The aim of this study is to explore what makes it easy or hard for adolescents with CP to be and to become physically active.</p> <p>Methods/Design</p> <p>A qualitative research method is chosen to allow adolescents to voice their own opinion. Because we will investigate the lived experiences this study has a phenomenological approach. Thirty ambulatory and non-ambulatory adolescents (aged 10-18 years) with CP, classified as level I to IV on the Gross Motor Function Classification System and 30 parents of adolescents with CP will be invited to participate in one of the 6 focus groups or an individual interview. Therapists from all Children's Treatment Centres in Ontario, Canada, will be asked to fill in a survey. Focus groups will be audio- and videotaped and will approximately take 1.5 hours. The focus groups will be conducted by a facilitator and an assistant. In preparation of the focus groups, participants will fill in a demographic form with additional questions on physical activity. The information gathered from these questions and recent research on barriers and facilitators to physical activity will be used as a starting point for the content of the focus groups. Recordings of the focus groups will be transcribed and a content analysis approach will be used to code the transcripts. A preliminary summary of the coded data will be shared with the participants before themes will be refined.</p> <p>Discussion</p> <p>This study will help us gain insight and understanding of the participants' experiences and perspectives in PA, which can be of great importance when planning programs aimed at helping them to stay or to become physically active.</p

Sedentary Time and Physical Activity Surveillance Through Accelerometer Pooling in Four European Countries.

OBJECTIVE: The objective of this study was to pool, harmonise and re-analyse national accelerometer data from adults in four European countries in order to describe population levels of sedentary time and physical inactivity. METHODS: Five cross-sectional studies were included from England, Portugal, Norway and Sweden. ActiGraph accelerometer count data were centrally processed using the same algorithms. Multivariable logistic regression analyses were conducted to study the associations of sedentary time and physical inactivity with sex, age, weight status and educational level, in both the pooled sample and the separate study samples. RESULTS: Data from 9509 participants were used. On average, participants were sedentary for 530 min/day, and accumulated 36 min/day of moderate to vigorous intensity physical activity. Twenty-three percent accumulated more than 10 h of sedentary time/day, and 72% did not meet the physical activity recommendations. Nine percent of all participants were classified as high sedentary and low active. Participants from Norway showed the highest levels of sedentary time, while participants from England were the least physically active. Age and weight status were positively associated with sedentary time and not meeting the physical activity recommendations. Men and higher-educated people were more likely to be highly sedentary, while women and lower-educated people were more likely to be inactive. CONCLUSIONS: We found high levels of sedentary time and physical inactivity in four European countries. Older people and obese people were most likely to display these behaviours and thus deserve special attention in interventions and policy planning. In order to monitor these behaviours, accelerometer-based cross-European surveillance is recommended.The original studies were funded by the Norwegian Directorate of Health and the Norwegian School of Sport Sciences; the Portuguese Institute of Sport; a grant from the Stockholm County Council; and grants from the Swedish Council for Working Life and Social Research, and The Swedish Research Council for Environment, Agricultural Sciences, and Spatial Planning. AL, JL, JB and HvdP were supported by the Netherlands Organisation for Health Research and Development (Grant no. 200.600.001). KS was supported by the National Heart, Lung, And Blood Institute of the National Institutes of Health (award no. R01HL116381). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. KW was supported by the British Heart Foundation (Grant FS/12/58/29709). KW and SB were supported by the UK Medical Research Council (Grant MC_UU_12015/3)

Modern modelling techniques are data hungry: a simulation study for predicting dichotomous endpoints

BACKGROUND: Modern modelling techniques may potentially provide more accurate predictions of binary outcomes than classical techniques. We aimed to study the predictive performance of different modelling techniques in relation to the effective sample size (“data hungriness”). METHODS: We performed simulation studies based on three clinical cohorts: 1282 patients with head and neck cancer (with 46.9% 5 year survival), 1731 patients with traumatic brain injury (22.3% 6 month mortality) and 3181 patients with minor head injury (7.6% with CT scan abnormalities). We compared three relatively modern modelling techniques: support vector machines (SVM), neural nets (NN), and random forests (RF) and two classical techniques: logistic regression (LR) and classification and regression trees (CART). We created three large artificial databases with 20 fold, 10 fold and 6 fold replication of subjects, where we generated dichotomous outcomes according to different underlying models. We applied each modelling technique to increasingly larger development parts (100 repetitions). The area under the ROC-curve (AUC) indicated the performance of each model in the development part and in an independent validation part. Data hungriness was defined by plateauing of AUC and small optimism (difference between the mean apparent AUC and the mean validated AUC <0.01). RESULTS: We found that a stable AUC was reached by LR at approximately 20 to 50 events per variable, followed by CART, SVM, NN and RF models. Optimism decreased with increasing sample sizes and the same ranking of techniques. The RF, SVM and NN models showed instability and a high optimism even with >200 events per variable. CONCLUSIONS: Modern modelling techniques such as SVM, NN and RF may need over 10 times as many events per variable to achieve a stable AUC and a small optimism than classical modelling techniques such as LR. This implies that such modern techniques should only be used in medical prediction problems if very large data sets are available. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/1471-2288-14-137) contains supplementary material, which is available to authorized users

Design of the Physical exercise during Adjuvant Chemotherapy Effectiveness Study (PACES):A randomized controlled trial to evaluate effectiveness and cost-effectiveness of physical exercise in improving physical fitness and reducing fatigue

<p>Abstract</p> <p>Background</p> <p>Cancer chemotherapy is frequently associated with a decline in general physical condition, exercise tolerance, and muscle strength and with an increase in fatigue. While accumulating evidence suggests that physical activity and exercise interventions during chemotherapy treatment may contribute to maintaining cardiorespiratory fitness and strength, the results of studies conducted to date have not been consistent. Additional research is needed to determine the optimal intensity of exercise training programs in general and in particular the relative effectiveness of supervised, outpatient (hospital- or physical therapy practice-based) versus home-based programs.</p> <p>Methods</p> <p>This multicenter, prospective, randomized trial will evaluate the effectiveness of a low to moderate intensity, home-based, self-management physical activity program, and a high intensity, structured, supervised exercise program, in maintaining or enhancing physical fitness (cardiorespiratory fitness and muscle strength), in minimizing fatigue and in enhancing the health-related quality of life (HRQoL). Patients receiving adjuvant chemotherapy for breast or colon cancer (n = 360) are being recruited from twelve hospitals in the Netherlands, and randomly allocated to one of the two treatment groups or to a 'usual care' control group. Performance-based and self-reported outcomes are assessed at baseline, at the end of chemotherapy and at six month follow-up.</p> <p>Discussion</p> <p>This large, multicenter, randomized clinical trial will provide additional empirical evidence regarding the effectiveness of physical exercise during adjuvant chemotherapy in enhancing physical fitness, minimizing fatigue, and maintaining or enhancing patients' quality of life. If demonstrated to be effective, exercise intervention programs will be a welcome addition to the standard program of care offered to patients with cancer receiving chemotherapy.</p> <p>Trial registration</p> <p>This study is registered at the Netherlands Trial Register (NTR 2159)</p

Attaching metabolic expenditures to standard occupational classification systems: perspectives from time-use research

Abstract Background Traditionally, time-use data have been used to inform a broad range of economic and sociological research topics. One of the new areas in time-use research is the study of physical activity (PA) and physical activity energy expenditure (PAEE). Time-use data can be used to study PAEE by assigning MET values to daily activities using the Ainsworth Compendium of Physical Activities. Although most diarists record their daily activities accurately and in detail, they are only required to record their paid working hours, not the job-specific tasks they undertake. This makes it difficult to assign MET values to paid work episodes. Methods In this methodological paper, we explain how we addressed this problem by using the detailed information about respondents’ occupational status included in time-use survey household and individual questionnaires. We used the 2008 ISCO manual, a lexicon of the International Labour Organization of occupational titles and their related job-specific tasks. We first assigned a MET value to job-specific tasks using the Ainsworth compendium (2011) then calculated MET values for each of the 436 occupations in the ISCO-08 manual by averaging all job-specific MET values for each occupation. Results The ISCO-08 Major Groups of ‘elementary occupations’ and ‘craft and related trades workers’ are associated with high PAEE variation in terms of their job-specific MET values and together represented 21.6% of the Belgian working population in 2013. We recommend that these occupational categories should be prioritised for further in-depth research into occupational activity (OA). Conclusions We developed a clear and replicable procedure to calculate occupational activity for all ISCO-08 occupations. All of our calculations are attached to this manuscript which other researchers may use, replicate and refine

Development of a questionnaire to assess sedentary time in older persons -- a comparative study using accelerometry

Background: There is currently no validated questionnaire available to assess total sedentary time in older adults. Most studies only used TV viewing time as an indicator of sedentary time. The first aim of our study was to investigate the self-reported time spent by older persons on a set of sedentary activities, and to compare this with objective sedentary time measured by accelerometry. The second aim was to determine what set of self-reported sedentary activities should be used to validly rank people's total sedentary time. Finally we tested the reliability of our newly developed questionnaire using the best performing set of sedentary activities. Methods. The study sample included 83 men and women aged 65-92 y, a random sample of Longitudinal Aging Study Amsterdam participants, who completed a questionnaire including ten sedentary activities and wore an Actigraph GT3X accelerometer for 8 days. Spearman correlation coefficients were calculated to examine the association between self-reported time and objective sedentary time. The test-retest reliability was calculated using the intraclass correlation coefficient (ICC). Results: Mean total self-reported sedentary time was 10.4 (SD 3.5) h/d and was not significantly different from mean total objective sedentary time (10.2 (1.2) h/d, p = 0.63). Total self-reported sedentary time on an average day (sum of ten activities) correlated moderately (Spearman's r = 0.35, p < 0.01) with total objective sedentary time. The correlation improved when using the sum of six activities (r = 0.46, p < 0.01), and was much higher than when using TV watching only (r = 0.22, p = 0.05). The test-retest reliability of the sum of six sedentary activities was 0.71 (95% CI 0.57-0.81). Conclusions: A questionnaire including six sedentary activities was moderately associated with accelerometry-derived sedentary time and can be used to reliably rank sedentary time in older persons. © 2013 Visser and Koster; licensee BioMed Central Ltd