| Title: | Comparing Cadence vs. Machine Learning Based Physical Activity Intensity Classifications: Variations in the Associations of Physical Activity With Mortality |
| Journal: | Scandinavian Journal of Medicine and Science in Sports |
| Published: | 9 Sep 2024 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/39252407/ |
| DOI: | https://doi.org/10.1111/sms.14719 |
Publication 13513
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Abstract
Step cadence-based and machine-learning (ML) methods have been used to classify physical activity (PA) intensity in health-related research. This study examined the association of intensity-specific PA duration with all-cause (ACM) and CVD mortality using the cadence-based and ML methods in 68 561 UK Biobank participants wearing wrist-worn accelerometers. The two-stage-ML method categorized activity type and then intensity. The one-level-cadence-method (1LC) derived intensity-specific duration using all detected steps (including standing utilitarian steps) and cadence thresholds of ≥100 steps/min (moderate intensity) and ≥130 steps/min (vigorous intensity). The two-level-cadence-method (2LC) detected ambulatory steps (i.e., walking and running) and then applied the same cadence thresholds. The 2LC exhibited the most pronounced association at the lower end of duration spectrum. For example, the 2LC showed the smallest minimum moderate-to-vigorous-PA (MVPA) duration (amount associated with 50% of optimal risk reduction) with similar corresponding ACM hazard ratio (HR) to other methods (2LC: 2.8 min/day [95% CI: 2.6, 2.8], HR: 0.83 [95% CI: 0.78, 0.88]; 1LC, 11.1[10.8, 11.4], 0.80 [0.76, 0.85]; ML, 14.9 [14.6, 15.2], 0.82 [0.76, 0.87]). The ML elicited the greatest mortality risk reduction. For example, the medians and corresponding HR in VPA-ACM association: 2LC, 2.0 min/day [95% CI: 2.0, 2.0], HR, 0.69 [95% CI: 0.61, 0.79]; 1LC, 6.9 [6.9, 7.0], 0.68 [0.60, 0.77]; ML, 3.2 [3.2, 3.2], 0.53 [0.44, 0.64]. After standardizing durations, the ML exhibited the most pronounced associations. For example, the standardized minimum durations in MPA-CVD mortality association were: 2LC, -0.77; 1LC, -0.85; ML, -0.94; with corresponding HR of 0.82 [0.72, 0.92], 0.79 [0.69, 0.90], and 0.77 [0.69, 0.85], respectively. The 2LC exhibited the most pronounced association with all-cause and CVD mortality at the lower end of the duration spectrum. The ML method provided the most pronounced association with all-cause and CVD mortality, thus might be appropriate for estimating health benefits of moderate and vigorous intensity PA in observational studies.</p>
13 Keywords
- Accelerometry
- Adult
- Aged
- Cardiovascular Diseases
- Exercise
- Female
- Humans
- Machine Learning
- Male
- Middle Aged
- Mortality
- United Kingdom
- Walking
5 Authors
- Le Wei
- Matthew N. Ahmadi
- Raaj Kishore Biswas
- Stewart G. Trost
- Emmanuel Stamatakis
1 Application
| Application ID | Title |
|---|---|
| 25813 | Integrated paradigms to investigate the interplay between lifestyle behaviours, the social environment, and genetic risk in the development of cardio- metabolic disease and mental health. |
Enabling scientific discoveries that improve human health