A Practical Guide to Analyzing Time-Varying Associations between Physical Activity and Affect Using Multilevel Modeling
(2018) In Computational and Mathematical Methods in Medicine 2018.- Abstract
There is growing interest in within-person associations of objectively measured physical and physiological variables with psychological states in daily life. Here we provide a practical guide with SAS code of multilevel modeling for analyzing physical activity data obtained by accelerometer and self-report data from intensive and repeated measures using ecological momentary assessments (EMA). We review previous applications of EMA in research and clinical settings and the analytical tools that are useful for EMA research. We exemplify the analyses of EMA data with cases on physical activity data and affect and discuss the future challenges in the field.
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/1d966ccc-aa9a-48fd-9aa1-dcd5740e9d7f
- author
- Kim, Jinhyuk ; Marcusson-Clavertz, David LU ; Togo, Fumiharu and Park, Hyuntae
- organization
- publishing date
- 2018
- type
- Contribution to journal
- publication status
- published
- subject
- in
- Computational and Mathematical Methods in Medicine
- volume
- 2018
- article number
- 8652034
- publisher
- Hindawi Limited
- external identifiers
-
- scopus:85050820542
- pmid:30105083
- ISSN
- 1748-670X
- DOI
- 10.1155/2018/8652034
- language
- English
- LU publication?
- yes
- id
- 1d966ccc-aa9a-48fd-9aa1-dcd5740e9d7f
- date added to LUP
- 2018-10-01 11:13:28
- date last changed
- 2024-08-06 23:41:27
@article{1d966ccc-aa9a-48fd-9aa1-dcd5740e9d7f, abstract = {{<p>There is growing interest in within-person associations of objectively measured physical and physiological variables with psychological states in daily life. Here we provide a practical guide with SAS code of multilevel modeling for analyzing physical activity data obtained by accelerometer and self-report data from intensive and repeated measures using ecological momentary assessments (EMA). We review previous applications of EMA in research and clinical settings and the analytical tools that are useful for EMA research. We exemplify the analyses of EMA data with cases on physical activity data and affect and discuss the future challenges in the field.</p>}}, author = {{Kim, Jinhyuk and Marcusson-Clavertz, David and Togo, Fumiharu and Park, Hyuntae}}, issn = {{1748-670X}}, language = {{eng}}, publisher = {{Hindawi Limited}}, series = {{Computational and Mathematical Methods in Medicine}}, title = {{A Practical Guide to Analyzing Time-Varying Associations between Physical Activity and Affect Using Multilevel Modeling}}, url = {{http://dx.doi.org/10.1155/2018/8652034}}, doi = {{10.1155/2018/8652034}}, volume = {{2018}}, year = {{2018}}, }