Abstract
Time-activity patterns are an essential part of personal exposure assessment to various environmental factors. People move through different environments during the day and they have different daily activity patterns which are significantly influenced by individual characteristics and the residential environment. In this study, time spent in different microenvironments (MEs) were assessed for 125 participants for 7 consecutive days to evaluate the impact of individual characteristics on time-activity patterns in Kaunas, Lithuania. The data were collected with personal questionnaires and diaries. The global positioning system (GPS) sensor integrated into a smartphone was used to track daily movements and to assess time-activity patterns. The study results showed that behavioral and residential greenness have a statistically significant impact on time spent indoors. These results underline the high influence of the individual characteristics and environmental factors on time spent indoors, which is an important determinant for exposure assessment and health impact assessment studies.
Highlights
Exposure assessment is essential for determining the relationship between various environmental factors and health effects [1,2]
The impact of individual characteristics and environmental factors on the daily time-activity patterns and time spent indoors was assessed in an urban population in Kaunas, Lithuania
The study results showed that age, occupational status, and gender were the most significant individual factors influencing time-activity patterns
Summary
Exposure assessment is essential for determining the relationship between various environmental factors and health effects [1,2]. Individuals constantly move in time and space and a better understanding of time-activity patterns according to different demographic, socioeconomic and environmental variables is relevant to improve health impact assessment [4,5]. Several studies comparing time-activity patterns and the role of individual-level characteristics on these patterns have revealed significant variations between demographic and socioeconomic factors such as age, gender, employment status, income, education, and race/ethnicity [6,7]. In order to avoid exposure measurement errors and more accurately determine the relationship between environmental factors and health effects, it is necessary to take into account time-activity patterns
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