Compositional data analysis applied to a study of movement behaviours of recent retirees

Studies of movement behaviours have numerous applications. A recent approach involves studying the time spent on different activity types using compositional data analysis. In compositional data analysis, several variables are constrained to an arbitrary sum and the primary interest is their proportions of the whole. This thesis explores the mathematical foundations of the study of compositional data and their practical applications. First, mathematical operations are defined for compositions using Aitchison geometry. Methods are presented for transforming compositions into real-valued coordinates and back. Various statistical methods are also defined for compositions and compositional data. Some of the techniques presented are demonstrated by applying them to a study of movement behaviours. REACT is a randomized controlled trial study focusing on whether commercial activity trackers affect movement behaviours among the recently retired. By using compositional data analysis, the proportions of time spent on different activity types can be studied. Based on the results, it would appear that those who used activity trackers spent a slightly higher portion of their day on physical activity than those who did not

Changes in the 24-h movement behaviors during the transition to retirement : compositional data analysis

Background Transition to retirement is shown to affect sleep, sedentary time and physical activity, but no previous studies have examined how retirement changes the distribution of time spent daily in these movement behaviors. The aim of this study was to examine longitudinally how the composition of 24-h movement behaviors changes during the transition to retirement using compositional data analysis (CoDA). Methods We included 551 retiring public sector workers (mean age 63.2 years, standard deviation 1.1) from the Finnish Retirement and Aging study. The study participants wore a wrist-worn ActiGraph accelerometer for one week 24 h per day before and after retirement, with one year between the measurements. The daily proportions to time spent sleeping, in sedentary behavior (SED), light physical activity (LPA) and moderate-to-vigorous physical activity (MVPA) were estimated using the GGIR package. Changes in the daily proportions of movement behaviors were examined using Compositional Data Analysis version of linear mixed models. Results In general, the proportion of time spent in active behaviors decreased relative to time spent in passive behaviors after retirement (p < .001). This change depended on occupation (occupation*time interaction p < .001). After retirement manual workers increased the proportions of both sleep and SED in relation to active behaviors, whereas non-manual workers increased the proportion of sleep in relation to active behaviors and SED. The proportion of MVPA decreased relatively more than the proportion of LPA (p = 0.01), independently of gender and occupation. Conclusions Retirement induced a decrease in the proportion of time spent in active behaviors, especially time spent in MVPA. Future studies are needed to find ways to maintain or increase daily physical activity levels at the cost of sedentary behaviors among retirees.Peer reviewe

Changes in the 24-h movement behaviors during the transition to retirement : compositional data analysis

Background Transition to retirement is shown to affect sleep, sedentary time and physical activity, but no previous studies have examined how retirement changes the distribution of time spent daily in these movement behaviors. The aim of this study was to examine longitudinally how the composition of 24-h movement behaviors changes during the transition to retirement using compositional data analysis (CoDA). Methods We included 551 retiring public sector workers (mean age 63.2 years, standard deviation 1.1) from the Finnish Retirement and Aging study. The study participants wore a wrist-worn ActiGraph accelerometer for one week 24 h per day before and after retirement, with one year between the measurements. The daily proportions to time spent sleeping, in sedentary behavior (SED), light physical activity (LPA) and moderate-to-vigorous physical activity (MVPA) were estimated using the GGIR package. Changes in the daily proportions of movement behaviors were examined using Compositional Data Analysis version of linear mixed models. Results In general, the proportion of time spent in active behaviors decreased relative to time spent in passive behaviors after retirement (p < .001). This change depended on occupation (occupation*time interaction p < .001). After retirement manual workers increased the proportions of both sleep and SED in relation to active behaviors, whereas non-manual workers increased the proportion of sleep in relation to active behaviors and SED. The proportion of MVPA decreased relatively more than the proportion of LPA (p = 0.01), independently of gender and occupation. Conclusions Retirement induced a decrease in the proportion of time spent in active behaviors, especially time spent in MVPA. Future studies are needed to find ways to maintain or increase daily physical activity levels at the cost of sedentary behaviors among retirees.Peer reviewe

Effects of physical activity intervention on 24-h movement behaviors: a compositional data analysis

We utilized compositional data analysis (CoDA) to study changes in the composition of the 24-h movement behaviors during an activity tracker based physical activity intervention. A total of 231 recently retired Finnish retirees were randomized into intervention and control groups. The intervention participants were requested to use a commercial activity tracker bracelet with daily activity goal and inactivity alerts for 12 months. The controls received no intervention. The 24-h movement behaviors, i.e., sleep, sedentary time (SED), light physical activity (LPA), and moderate-to-vigorous physical activity (MVPA) were estimated from wrist-worn ActiGraph data using the GGIR R-package. Three balance coordinates describing the composition of movement behaviors were applied: ratio of active vs. passive behaviors, LPA vs. MVPA, and sleep vs. SED. A linear mixed model was used to study changes between the baseline and 6-month time point. Overall, the changes in the 24-h movement behaviors were small and did not differ between the groups. Only the ratio of LPA to MVPA tended to change differently between the groups (group*time interaction p = 0.08) as the intervention group increased LPA similarly to controls but decreased their MVPA. In conclusion, the use of a commercial activity tracker may not be enough to induce changes in the 24-h movement behaviors among retirees

Changes in the 24-h movement behaviors during the transition to retirement: compositional data analysis

Background Transition to retirement is shown to affect sleep, sedentary time and physical activity, but no previous studies have examined how retirement changes the distribution of time spent daily in these movement behaviors. The aim of this study was to examine longitudinally how the composition of 24-h movement behaviors changes during the transition to retirement using compositional data analysis (CoDA). Methods We included 551 retiring public sector workers (mean age 63.2 years, standard deviation 1.1) from the Finnish Retirement and Aging study. The study participants wore a wrist-worn ActiGraph accelerometer for one week 24 h per day before and after retirement, with one year between the measurements. The daily proportions to time spent sleeping, in sedentary behavior (SED), light physical activity (LPA) and moderate-to-vigorous physical activity (MVPA) were estimated using the GGIR package. Changes in the daily proportions of movement behaviors were examined using Compositional Data Analysis version of linear mixed models. Results In general, the proportion of time spent in active behaviors decreased relative to time spent in passive behaviors after retirement (p Conclusions Retirement induced a decrease in the proportion of time spent in active behaviors, especially time spent in MVPA. Future studies are needed to find ways to maintain or increase daily physical activity levels at the cost of sedentary behaviors among retirees.</p

Is carrier mobility a limiting factor for charge transfer in TiO2/Si devices? A study by transient reflectance spectroscopy

TiO2 coatings are often deposited over silicon-based devices for surface passivation and corrosion protection. However, the charge transfer (CT) across the TiO2/Si interface is critical as it may instigate potential losses and recombination of charge carriers in optoelectronic devices. Therefore, to investigate the CT across the TiO2/Si interface, transient reflectance (TR) spectroscopy was employed as a contact-free method to evaluate the impact of interfacial SiOx, heat-treatments, and other phenomena on the CT. Thin-film interference model was adapted to separate signals for Si and TiO2 and to estimate the number of transferred carriers. Charge transfer velocity was found to be 5.2 × 104 cm s−1 for TiO2 heat-treated at 300 °C, and even faster for amorphous TiO2 if the interfacial SiOx layer was removed using HF before TiO2 deposition. However, the interface is easily oversaturated because of slow carrier diffusion in TiO2 away from the TiO2/Si interface. This inhibits CT, which could become an issue for heavily concentrated solar devices. Also, increasing the heat-treatment temperature from 300 °C to 550 °C has only little impact on the CT time but leads to reduced carrier lifetime of ¡3 ns in TiO2 due to back recombination via the interfacial SiOx, which is detrimental to TiO2/Si device performance.publishedVersionPeer reviewe

Effects of physical activity intervention on 24-h movement behaviors:a compositional data analysis

Abstract We utilized compositional data analysis (CoDA) to study changes in the composition of the 24-h movement behaviors during an activity tracker based physical activity intervention. A total of 231 recently retired Finnish retirees were randomized into intervention and control groups. The intervention participants were requested to use a commercial activity tracker bracelet with daily activity goal and inactivity alerts for 12 months. The controls received no intervention. The 24-h movement behaviors, i.e., sleep, sedentary time (SED), light physical activity (LPA), and moderate-to-vigorous physical activity (MVPA) were estimated from wrist-worn ActiGraph data using the GGIR R-package. Three balance coordinates describing the composition of movement behaviors were applied: ratio of active vs. passive behaviors, LPA vs. MVPA, and sleep vs. SED. A linear mixed model was used to study changes between the baseline and 6-month time point. Overall, the changes in the 24-h movement behaviors were small and did not differ between the groups. Only the ratio of LPA to MVPA tended to change differently between the groups (group*time interaction p = 0.08) as the intervention group increased LPA similarly to controls but decreased their MVPA. In conclusion, the use of a commercial activity tracker may not be enough to induce changes in the 24-h movement behaviors among retirees