School- and Leisure Time Factors Are Associated With Sitting Time of German and Irish Children and Adolescents During School: Results of a DEDIPAC Feasibility Study.

Objective: The study aims to investigate to what extent school- and leisure time-related factors are associated with sedentary behavior during school in German and Irish children and adolescents. Methods: The study based on a sample of 198 children and adolescents surveyed in 2015. Sedentary and activity behavior were measured using the activPAL physical activity monitor. Information on socio-economic status, school- and leisure-time related factors were provided by questionnaires. Associations between school- and leisure time-related factors and sedentary time during school were estimated using linear multi-level models. Results: Access to play equipment in school was associated with reduced sitting time (hours/day) of children (ß = 0.78; 95%CI = 0.06-1.48). Media devices in bedroom and assessing the neighborhood as activity friendly was associated with increased sitting time of children (ß = 0.92; 95%CI = 0.12-1.72 and ß = 0.30; 95%CI = 0.01-0.60, respectively). The permission to use media devices during breaks was associated with increased sitting time (hours/day) of adolescents (ß = 0.37; 95% CI = 0.06-0.69). A less safe traffic surrounding at school was associated with reduced sitting time of adolescents (ß = -0.42; 95% CI = -0.80 to -0.03). Conclusion: Results suggest that school- and leisure time-related factors are associated to the sedentary behavior during school. We suggest that future strategies to reduce sedentary time should consider both contexts.

Modeling physical activity data using L0 -penalized expectile regression.

In recent years accelerometers have become widely used to objectively assess physical activity. Usually intensity ranges are assigned to the measured accelerometer counts by simple cut points, disregarding the underlying activity pattern. Under the assumption that physical activity can be seen as distinct sequence of distinguishable activities, the use of hidden Markov models (HMM) has been proposed to improve the modeling of accelerometer data. As further improvement we propose to use expectile regression utilizing a Whittaker smoother with an L0 -penalty to better capture the intensity levels underlying the observed counts. Different expectile asymmetries beyond the mean allow the distinction of monotonous and more variable activities as expectiles effectively model the complete distribution of the counts. This new approach is investigated in a simulation study, where we simulated 1,000 days of accelerometer data with 1 and 5 s epochs, based on collected labeled data to resemble real-life data as closely as possible. The expectile regression is compared to HMMs and the commonly used cut point method with regard to misclassification rate, number of identified bouts and identified levels as well as the proportion of the estimate being in the range of ±10% of the true activity level. In summary, expectile regression utilizing a Whittaker smoother with an L0 -penalty outperforms HMMs and the cut point method and is hence a promising approach to model accelerometer data.

Sports Contribute to Total Moderate to Vigorous Physical Activity in School Children.

INTRODUCTION: A third of all children spend at least 60 min·d in moderate to vigorous physical activity (MVPA) as recommended by the World Health Organization. We investigated the distribution of objectively measured physical activity (PA) intensities achieved by schoolchildren during physical education (PE) and organized youth sports (OYS) and the contribution of PE and OYS to the achievement of the PA recommendations. METHODS: In two surveys, PA of 396 children was measured using accelerometry during typical school weeks. Proportions (%) of sedentary behavior, light PA, and MVPA per day and particularly during sports were assessed in children 6-17 yr old. Adjusted odds ratios (AOR) were calculated to analyze the association between frequency (d·wk) and duration (min·wk) of PE and OYS and meeting the PA recommendations. RESULTS: Average proportions of MVPA during PE and OYS ranged between 15% and 31%. Between 68% and 74% of children (6-10 yr) and between 27% and 37% of adolescents (11-17 yr) met the PA recommendations. In both age-groups, PE and OYS on at least 2 d·wk were both significantly associated with meeting the PA recommendations (AOR for PE = 3.60, 95% confidence interval [CI] = 2.05-6.31; AOR for OYS = 3.83, 95% CI = 2.09-6.99), compared with no PE or no OYS in that week. Likewise, significant AOR values were found for >90 min of PE per week (AOR = 3.48, 95% CI = 2.03-5.98) and >120 min of OYS per week (AOR = 3.72, 95% CI = 2.01-6.89), compared with no PE or no OYS in that week, respectively. CONCLUSION: Schoolchildren spent less than one-third of PE and OYS in MVPA. PE and OYS were found to support children and adolescents to engage sufficiently in MVPA as recommended by the World Health Organization.

Impact of a tailored activity counselling intervention during inpatient rehabilitation after knee and hip arthroplasty - an explorative RCT.

BACKGROUND: The aim of the study was to improve physical activity (PA), well-being and clinical outcome after total knee and hip arthroplasty through tailored activity counselling during inpatient rehabilitation. METHODS: 65 patients (aged 70.4 ± 7.3 years, BMI 28.5 ± 4.3) starting inpatient rehabilitation after primary knee or hip arthroplasty due to osteoarthritis were recruited and pseudo-randomized into an intervention (IG) and a control group (CG). Twice a week, the IG was encouraged to increase their daily step count by 5%. PA, e. g. number of steps, step frequency, or active minutes, was measured by step activity monitoring. Well-being and clinical outcome were assessed using the SF-36, Oxford Knee/Hip Score and Global rating of Change. Procedures were conducted at the onset of inpatient rehabilitation, and repeated one and 6 months after inpatient rehabilitation. RESULTS: Data sets were obtained from 49 patients (IG: n = 23, CG: n = 26). Both groups significantly increased their number of daily steps from the 1 month to the 6 months follow up after rehabilitation: CG: 9019 (95%CI: 7812, 10,226), IG: 9280 (7972, 10,588) and CG: 10921 (9571, 12,271), IG: 11326 (9862, 12,791) respectively. Additionally, well-being and clinical outcome improved significantly in both groups. No significant differences in physical activity, clinical outcome and well-being were found between the groups. CONCLUSIONS: PA counselling during inpatient rehabilitation does not improve PA, well-being and clinical outcome in patients with primary knee or hip arthroplasty in addition to the rehabilitation program. PA interventions may be more effective after the completion of the inpatient rehabilitation phase. TRIAL REGISTRATION: DRKS DRKS00012682 . Registered retrospectively on 03-07- 2017.

Energy Cost of Common Physical Activities in Preschoolers.

BACKGROUND: To determine the energy cost of common physical activities in preschoolers and to compare it with the Compendium of Energy Expenditure for Youth (CEEY). METHODS: In total, 42 children [age: 4.8 (0.8) y; body mass index: 15.3 (2.0) kg/m2; 22 boys] completed 13 common physical activities covering sedentary to vigorous intensities, while energy expenditure (EE) was measured continuously by indirect calorimetry. Activity-specific metabolic equivalents (AME) were calculated as the EE observed during each single activity divided by the EE during observed rest. Independent t tests were applied to analyze differences between boys and girls and between AME and CEEY. RESULTS: No significant differences in AME were observed between girls and boys. Except for playing hide-and-seek, all indoor activities revealed significantly higher energy costs compared with those stated in the compendium. Significant differences in outdoor activities were found for riding a tricycle [5.67 (95% confidence interval, 4.94-6.4) AME vs 6.2 metabolic equivalents, riding a bike, P < .05] and for fast walking [5.42 (95% confidence interval, 4.84-6.0) AME vs 4.6 metabolic equivalents, P < .05]. CONCLUSIONS: Applying the CEEY to preschoolers will lead to a substantial underestimation of EE. Therefore, we recommend that a CEEY for preschool children be developed if measurement of EE is not feasible.

Domain-Specific Self-Reported and Objectively Measured Physical Activity in Children.

Little is known about the extent that different domains contribute to total sedentary (SED), light (LPA) and moderate-to-vigorous physical activity (MVPA). We aimed to identify domain-specific physical activity (PA) patterns in school-aged children who were assessed by questionnaire and accelerometry. For the study, 298 German school children and adolescents aged 6-17 years wore an accelerometer for one week and completed a PA recall-questionnaire for the same period. Spearman coefficients (r) were used to evaluate the agreement between self-reported and objectively measured PA in five domains (transport, school hours, physical education, leisure-time, organized sports activities). School hours mainly contributed to the total objectively measured SED, LPA and MVPA (55%, 53% and 46%, respectively), whilst sports activities contributed only 24% to total MVPA. Compared to accelerometry, the proportion of self-reported LPA and MVPA during school hours was substantially underestimated but overestimated during leisure-time. The agreement of self-reported and objectively measured PA was low for total LPA (r = 0.09, 95% CI (confidence interval): -0.03-0.20) and total MVPA (r = 0.21, 95% CI: 0.10-0.32), while moderate agreement was only found for total SED (r = 0.44, 95% CI: 0.34-0.53), LPA during transport (r = 0.59; 95% CI: 0.49-0.67) and MVPA during organized sports activities (r = 0.54; 95% CI: 0.38-0.67). Since school hours mainly contribute to total SED, LPA and MVPA and self-reported LPA and MVPA during school were importantly underestimated compared to objectively measured LPA and MVPA, the application of objective measurements is compulsory to characterize the entire activity pattern of school-aged children.

Incidence of high blood pressure in children - effects of physical activity and sedentary behaviors: the IDEFICS study: High blood pressure, lifestyle and children.

BACKGROUND/OBJECTIVES: High blood pressure (HBP) is one of the most important risk factors for cardiovascular diseases and it has a high prevalence in pediatric populations. However, the determinants of the incidence of Pre-HBP and HBP in children are not well known. i) To describe the incidence of HBP in European children; and ii) to evaluate the effect of physical activity (PA) and sedentary behavior (SB) on the Pre-HBP and HBP. METHODS: The IDEFICS cohort study. A total of 16,228 children 2-9years at baseline were recruited by complex sampling population-based survey in eight European countries. At baseline (T0), 5221 children were selected for accelerometer measurements; 5061 children were re-examined 2years later (T1). We estimated the incidence of Pre-HBP and HBP and evaluate the effect of PA and SB on the Pre-HBP and HBP, by computing relative risks and the corresponding 95% confidence intervals (RR, 95% CI). RESULTS: Incidences of Pre-HBP and HBP per year were: 121/1000 children and 110/1000 children, respectively. We found that children maintaining SB>2h/d during the two year follow-up showed a RR of having HBP of 1.28 (1.03-1.60). Children in T1 not performing the recommended amount of PA (<60min/d) have a RR of HBP of 1.53 (1.12 to 2.09). We found no association between pre-HBP and the behaviors. CONCLUSION: The incidence of pre-HBP and HBP is high in European children. Maintaining sedentary behaviors during childhood increases the risk of developing HBP after two years of follow-up.

Using hidden markov models to improve quantifying physical activity in accelerometer data - a simulation study.

INTRODUCTION: The use of accelerometers to objectively measure physical activity (PA) has become the most preferred method of choice in recent years. Traditionally, cutpoints are used to assign impulse counts recorded by the devices to sedentary and activity ranges. Here, hidden Markov models (HMM) are used to improve the cutpoint method to achieve a more accurate identification of the sequence of modes of PA. METHODS: 1,000 days of labeled accelerometer data have been simulated. For the simulated data the actual sedentary behavior and activity range of each count is known. The cutpoint method is compared with HMMs based on the Poisson distribution (HMM[Pois]), the generalized Poisson distribution (HMM[GenPois]) and the Gaussian distribution (HMM[Gauss]) with regard to misclassification rate (MCR), bout detection, detection of the number of activities performed during the day and runtime. RESULTS: The cutpoint method had a misclassification rate (MCR) of 11% followed by HMM[Pois] with 8%, HMM[GenPois] with 3% and HMM[Gauss] having the best MCR with less than 2%. HMM[Gauss] detected the correct number of bouts in 12.8% of the days, HMM[GenPois] in 16.1%, HMM[Pois] and the cutpoint method in none. HMM[GenPois] identified the correct number of activities in 61.3% of the days, whereas HMM[Gauss] only in 26.8%. HMM[Pois] did not identify the correct number at all and seemed to overestimate the number of activities. Runtime varied between 0.01 seconds (cutpoint), 2.0 minutes (HMM[Gauss]) and 14.2 minutes (HMM[GenPois]). CONCLUSIONS: Using simulated data, HMM-based methods were superior in activity classification when compared to the traditional cutpoint method and seem to be appropriate to model accelerometer data. Of the HMM-based methods, HMM[Gauss] seemed to be the most appropriate choice to assess real-life accelerometer data.

Citizen participation in patient prioritization policy decisions: an empirical and experimental study on patients' characteristics.

Health systems worldwide are grappling with the need to control costs to maintain system viability. With the combination of worsening economic conditions, an aging population and reductions in tax revenues, the pressures to make structural changes are expected to continue growing. Common cost control mechanisms, e.g. curtailment of patient access and treatment prioritization, are likely to be adversely viewed by citizens. It seems therefore wise to include them in the decision making processes that lead up to policy changes. In the context of a multilevel iterative mixed-method design a quantitative survey representative of the German population (N = 2031) was conducted to probe the acceptance of priority setting in medicine and to explore the practicability of direct public involvement. Here we focus on preferences for patients' characteristics (medical aspects, lifestyle and socio-economic status) as possible criteria for prioritizing medical services. A questionnaire with closed response options was fielded to gain insight into attitudes toward broad prioritization criteria of patient groups. Furthermore, a discrete choice experiment was used as a rigorous approach to investigate citizens' preferences toward specific criteria level in context of other criteria. Both the questionnaire and the discrete choice experiment were performed with the same sample. The citizens' own health and social situation are included as explanatory variables. Data were evaluated using corresponding analysis, contingency analysis, logistic regression and a multinomial exploded logit model. The results show that some medical criteria are highly accepted for prioritizing patients whereas socio-economic criteria are rejected.

Age as a criterion for setting priorities in health care? A survey of the German public view.

Although the German health care system has budget constraints similar to many other countries worldwide, a discussion on prioritization has not gained the attention of the public yet. To probe the acceptance of priority setting in medicine, a quantitative survey representative for the German public (n = 2031) was conducted. Here we focus on the results for age, a highly disputed criterion for prioritizing medical services. This criterion was investigated using different types of questionnaire items, from abstract age-related questions to health care scenarios, and discrete choice settings, all performed within the same sample. Several explanatory variables were included to account for differences in preference; in particular, interviewee's own age but also his or her sex, socioeconomic status, and health status. There is little evidence that the German public accepts age as a criterion to prioritize health care services.