Statistical modeling of physical activity based on accelerometer data

This thesis focuses on the objective measurement of physical activity (PA), recorded by accelerometers. Chapter 2 describes the objective measurement of PA using accelerometers in contrast to subjective measurements like PA questionnaires. Chapter 3 presents the basic assumption on PA. Contrary to the cutpoint method, it is more realistic to assume that human activity behavior consists of a sequence of non-overlapping, distinguishable activities that can be represented by a mean intensity level. The recorded accelerometer counts scatter around this mean level. In Chapter 4, two novel approaches to better capture PA are developed and implemented. The Hidden Markov models are stochastic models that allow fitting a Markov chain with a predefined number of activities to the data. Expectile regression utilizing the Whittaker smoother with an L0-penalty is introduced as a second innovative approach. Expectile regression is compared to HMMs and the cutpoint method in a simulation study. Chapter 5 presents the results of four studies on PA. Chapter 6 summarizes and discusses the findings of the previous chapters and ends with an outlook on future research

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

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

Statistische Modellierung von körperlicher Aktivität basierend auf Akzelerometerdaten

This thesis focuses on the objective measurement of physical activity (PA), recorded by accelerometers. Chapter 2 describes the objective measurement of PA using accelerometers in contrast to subjective measurements like PA questionnaires. Chapter 3 presents the basic assumption on PA. Contrary to the cutpoint method, it is more realistic to assume that human activity behavior consists of a sequence of non-overlapping, distinguishable activities that can be represented by a mean intensity level. The recorded accelerometer counts scatter around this mean level. In Chapter 4, two novel approaches to better capture PA are developed and implemented. The Hidden Markov models are stochastic models that allow fitting a Markov chain with a predefined number of activities to the data. Expectile regression utilizing the Whittaker smoother with an L0-penalty is introduced as a second innovative approach. Expectile regression is compared to HMMs and the cutpoint method in a simulation study. Chapter 5 presents the results of four studies on PA. Chapter 6 summarizes and discusses the findings of the previous chapters and ends with an outlook on future research

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

Accelerometry-Based Prediction of Energy Expenditure in Preschoolers

PURPOSE: Study purposes were to develop energy expenditure (EE) prediction models from raw accelerometer data and to investigate the performance of three different accelerometers on five different wear positions in preschoolers. METHODS: Fourty-one children (54% boys; 3–6.3 years) wore two Actigraph GT3X (left and right hip), three GENEActiv (right hip, left and right wrist), and one activPAL (right thigh) while completing a semi-structured protocol of 10 age-appropriate activities. Participants wore a portable indirect calorimeter to estimate EE. Utilized models to estimate EE included a linear model (LM), a mixed linear model (MLM), a random forest model (RF), and an artificial neural network model (ANN). For each accelerometer, model, and wear position, we assessed prediction accuracy via leave-one-out cross-validation and calculated the root-mean-squared-error (RMSE). RESULTS: Mean RMSE ranged from 2.56–2.76 kJ/min for the RF, 2.72–3.08 kJ/min for the ANN, 2.83–2.94 kJ/min for the LM, and 2.81–2.92 kJ/min for the MLM. The GENEActive obtained mean RMSE of 2.56 kJ/min (left and right wrist) and 2.73 kJ/min (right hip). Predicting EE using the GT3X on the left and right hip obtained mean RMSE of 2.60 and 2.74 kJ/min. The activPAL obtained a mean RMSE of 2.76 kJ/min. CONCLUSION: These results demonstrate good prediction accuracy for recent accelerometers on different wear positions in preschoolers. The RF and ANN were equally accurate in EE prediction compared with (mixed) linear models. The RF seems to be a viable alternative to linear and ANN models for EE prediction in young children in a semi-structured setting

Estimated Preferential Treatment Probabilities With Respect to Reference Patient.

<p>Estimated Preferential Treatment Probabilities With Respect to Reference Patient.</p

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

Partworth Utilities For Each Attribute Level And Relative Importance Of Attributes.

*<p>Estimation by maximum likelihood method, SAS PROC PHREG, option ties = breslow (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0036824#pone.0036824-Kuhfeld1" target="_blank">[32]</a>).</p

Illustrative Choice Set in Discrete Choice Experiment.

<p> <i>Above we introduce three patients with different characteristics. Which of the patients would you prefer be treated first and which last?</i></p