Maija Korpisaari

Maija Korpisaari
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Maija verified their affiliation via an institutional email.
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Maija verified their affiliation via an institutional email.
  • Master of Science
  • PhD Student at University of Oulu

About

4
Publications
378
Reads
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14
Citations
Introduction
In my doctoral research, I study the associations between features of residential environment, physical activity and cardiovascular health. Additionally, I examine how the physiological cumulative load of chronic stress, known as allostatic load, impacts health and the role of physical activity in its accumulation. I am also interested in how features of the residential environment, physical activity, and allostatic load interact with one another and jointly influence overall health.
Current institution
University of Oulu
Current position
  • PhD Student

Publications

Publications (4)
Article
Full-text available
Background Studies investigating the associations of 24-h movement behaviours (including moderate-to-vigorous-intensity physical activity (MVPA), light-intensity PA (LPA), sedentary time (ST) and sleep) with depressive symptoms are scarce. It is also unclear whether possible associations differ between urban and rural residents. Hence, we aimed to...
Article
Full-text available
Purpose This population‐based study examines the associations between physical activity (PA), residential environmental greenness, and cardiac health measured by resting short‐term heart rate variability (HRV). Methods Residential greenness of a birth cohort sample (n = 5433) at 46 years was measured with normalized difference vegetation index (ND...
Article
Full-text available
This study aimed to examine the associations of sedentary time, and substituting sedentary time with physical activity and sleep, with cardiometabolic health markers while accounting for a full 24 hours of movement and non‐movement behaviors, cardiorespiratory fitness (CRF), and other potential confounders. The participants were 4,585 members of th...
Article
Full-text available
Arctic charr is one of the fish species most sensitive to climate change but studies on their freshwater habitat preferences are limited, especially in the fluvial environment. Machine learning methods offer automatic and objective models for ecohydrological processes based on observed data. However, i) the number of ecological records is often muc...

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