BACKGROUND: The amount and intensity of people's activities are related to latent chronic diseases and the aging process. Accurate information about people's patterns of activity in their natural environments would go a long way toward understanding the relationship between types/levels of activity and health.
Unlike the commercially available activity monitors, an algorithm developed at Boston University utilizes frequency (cycles/second) to identify overground gait and pedaling. These studies evaluate the validity of this system in a real-life environment and then investigate people's locomotive behavior across weekdays of the same week.
METHODS: Wearing the monitoring system developed at BU on their right ankles, 16 older adults performed a battery of functional locomotive activities continuously in a residential setting, while being video recorded for reference. For the validity algorithm output regarding gait and pedaling variables was statistically compared to the video analysis of the same using the intraclass correlation coefficient (ICC).
To investigate the consistency of locomotor behavior across weekdays of the same week, 227 older adults wore the monitoring system under study on their right ankles continuously for a week. Daily gait and pedaling values were correlated across weekdays of the same week also using ICCs.
An investigation into the differences in gait variability for the average of 3 weekdays according to the subgroups; age, gender, and BMI was conducted on this sample using the Wilcoxon Signed Ranks test.
RESULTS: Three of the four gait validity ICCs were significant (p ≤ 0.019) ranging from 0.267 to 0.778. All pedaling validity variables had ICCs ≥ 0.993
The locomotive consistency study found all 6 daily gait variables significantly (p < 0.001) correlated across 3 weekdays, ranging from 0.534 to 0.914. Three of four ICCs for pedaling consistency variables were significant (p ≤ 0.029) ranging from 0.277 to 0.838.
CONCLUSIONS: This study's validity results support this monitoring system's gait and pedaling identification approach. There is also evidence to suggest how the system could improve its real-life locomotive detection validity and potentially diversify its applications.
Additionally, based on this dissertation's results, some of people's daily locomotive behaviors remain relatively constant over weekdays during the same week.
| Identifer | oai:union.ndltd.org:bu.edu/oai:open.bu.edu:2144/13683 |
| Date | 28 October 2015 |
| Creators | Vartanian, Richard Keith |
| Source Sets | Boston University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis/Dissertation |
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