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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2301.08280 |
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| _version_ | 1866909088652197888 |
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| author | Wu, Yinxiang Rosenberg, Dori E. Greenwood-Hickman, Mikael Anne McCurry, Susan M. Proust-Lima, Cecile Nelson, Jennifer C. Crane, Paul K. LaCroix, Andrea Z. Larson, Eric B. Shaw, Pamela A. |
| author_facet | Wu, Yinxiang Rosenberg, Dori E. Greenwood-Hickman, Mikael Anne McCurry, Susan M. Proust-Lima, Cecile Nelson, Jennifer C. Crane, Paul K. LaCroix, Andrea Z. Larson, Eric B. Shaw, Pamela A. |
| contents | The 24-hour activity cycle (24HAC) is a new paradigm for studying activity behaviors in relation to health outcomes. This approach captures the interrelatedness of the daily time spent in physical activity (PA), sedentary behavior (SB), and sleep. We illustrate and compare the use of three popular approaches, namely isotemporal substitution model (ISM), compositional data analysis (CoDA), and latent profile analysis (LPA) for modeling outcome associations with the 24HAC. We apply these approaches to assess an association with a cognitive outcome, measured by CASI item response theory (IRT) score, in a cohort of 1034 older adults (mean [range] age = 77 [65-100]; 55.8% female; 90% White) who were part of the Adult Changes in Thought (ACT) Activity Monitoring (ACT-AM) sub-study. PA and SB were assessed with thigh-worn activPAL accelerometers for 7 days. We highlight differences in assumptions between the three approaches, discuss statistical challenges, and provide guidance on interpretation and selecting an appropriate approach. ISM is easiest to apply and interpret; however, the typical ISM model assumes a linear association. CoDA specifies a non-linear association through isometric logratio transformations that are more challenging to apply and interpret. LPA can classify individuals into groups with similar time-use patterns. Inference on associations of latent profiles with health outcomes need to account for the uncertainty of the LPA classifications which is often ignored. The selection of the most appropriate method should be guided by the scientific questions of interest and the applicability of each model's assumptions. The analytic results did not suggest that less time spent on SB and more in PA was associated with better cognitive function. Further research is needed into the health implications of the distinct 24HAC patterns identified in this cohort. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2301_08280 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Analysis of the 24-Hour Activity Cycle: An illustration examining the association with cognitive function in the Adult Changes in Thought (ACT) Study Wu, Yinxiang Rosenberg, Dori E. Greenwood-Hickman, Mikael Anne McCurry, Susan M. Proust-Lima, Cecile Nelson, Jennifer C. Crane, Paul K. LaCroix, Andrea Z. Larson, Eric B. Shaw, Pamela A. Applications The 24-hour activity cycle (24HAC) is a new paradigm for studying activity behaviors in relation to health outcomes. This approach captures the interrelatedness of the daily time spent in physical activity (PA), sedentary behavior (SB), and sleep. We illustrate and compare the use of three popular approaches, namely isotemporal substitution model (ISM), compositional data analysis (CoDA), and latent profile analysis (LPA) for modeling outcome associations with the 24HAC. We apply these approaches to assess an association with a cognitive outcome, measured by CASI item response theory (IRT) score, in a cohort of 1034 older adults (mean [range] age = 77 [65-100]; 55.8% female; 90% White) who were part of the Adult Changes in Thought (ACT) Activity Monitoring (ACT-AM) sub-study. PA and SB were assessed with thigh-worn activPAL accelerometers for 7 days. We highlight differences in assumptions between the three approaches, discuss statistical challenges, and provide guidance on interpretation and selecting an appropriate approach. ISM is easiest to apply and interpret; however, the typical ISM model assumes a linear association. CoDA specifies a non-linear association through isometric logratio transformations that are more challenging to apply and interpret. LPA can classify individuals into groups with similar time-use patterns. Inference on associations of latent profiles with health outcomes need to account for the uncertainty of the LPA classifications which is often ignored. The selection of the most appropriate method should be guided by the scientific questions of interest and the applicability of each model's assumptions. The analytic results did not suggest that less time spent on SB and more in PA was associated with better cognitive function. Further research is needed into the health implications of the distinct 24HAC patterns identified in this cohort. |
| title | Analysis of the 24-Hour Activity Cycle: An illustration examining the association with cognitive function in the Adult Changes in Thought (ACT) Study |
| topic | Applications |
| url | https://arxiv.org/abs/2301.08280 |