Original articleDevelopment and Validation of a Movement and Activity in Physical Space Score as a Functional Outcome Measure
Section snippets
Participants
A total of 18 (n=9 postsurgical, n=9 control) adult participants (mean age ± SD, 25.6±9.8y; height, 172.2±7.1cm; weight, 76.7±16.3kg) volunteered in this study. Prior to data collection, all participants gave informed consent as approved by the university institutional review board. A power analysis was performed based on previous physical activity data27, 28 (power=.80, α=.05, effect size=1.5), which determined the necessary minimum sample size of N equal to 14.
The postsurgical group
Results
The descriptive statistics are provided in table 2 (T=0 and T+2) for the outcome measures: 5 KOOS subscales, step count, and MAPS scores.
Discussion
Physical activity monitoring has successfully demonstrated decreases and improvements in health status and function in various illness/disease populations.23, 25, 38, 42, 43 However, these studies have focused solely on physical activity and have not included environment interaction in their assessment of function. Thus, MAPS is different because it addresses environmental interaction and is not limited to a single component of function (ie, movement or physical activity). Through integrating
Conclusions
While traditional functional assessments have been clinic based, this new measure provides a more comprehensive assessment of a person's activity and interaction within their environment in real-life situations to assess function. It is important to assess the entirety of function and disability by including an assessment of physical activity and environmental interaction. This study showed that GPS and accelerometer data could be combined to provide a meaningful outcome score that evaluates
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An overview of data fusion techniques for Internet of Things enabled physical activity recognition and measure
2020, Information FusionCitation Excerpt :The results indicate that most wrist-worn devices adequately measure HR in laboratory-based activities but poorly estimated EE [78]. Also, in the work [81], the MAPS formula was created to incorporate measures of activity, time, and location to produce a single composite score: Movement and Activity in Physical Space (MAPS) score. We also extended this MAPS score as DAPS score [33] into our early lifelogging PA analysis model.
Multiple density maps information fusion for effectively assessing intensity pattern of lifelogging physical activity
2017, NeurocomputingCitation Excerpt :Here a walking speed related score is defined to represent a person’s physical fitness, named as Daily Activity in Physical Space (DAPS). This score takes inspiration from the work [40] in which Herrmann et al. proposes a Movement and Activity in Physical Space score as a functional outcome measurement for encompassing both PA and environmental interaction. h : Average daily walking speed.
Life-space characterization from cellular telephone collected GPS data
2013, Computers, Environment and Urban SystemsCitation Excerpt :For example, methods for travel mode detection based on vehicle-collected GPS data are unable to identify out-of-vehicle activities such as neighborhood walking; most methods were based on the assumption of complete GPS data and cannot be directly applied to person-based GPS data, where data loss is common (Krenn et al., 2011). In physical activity studies, GPS points were used to confirm individuals’ presence in a specific place or zone during a specific time (Almanza, Jerret, Dunton, Seto, & Pentz, 2012; Duncan, Badland, & Mummery, 2009; Elgethun, Fenske, Yost, & Palcisko, 2003; Herrmann et al., 2011; Troped, Wilson, Matthews, et al., 2010; Wheeler, Cooper, Page, & Jago, 2010). With the complementary information from participants’ activity diaries or from interviews, or from accelerometer data, these studies do not solely rely on GPS data to classify activities.
Lifelogging data validation model for internet of things enabled personalized healthcare
2018, IEEE Transactions on Systems, Man, and Cybernetics: SystemsAddressing location uncertainties in GPS-based activity monitoring: A methodological framework
2017, Transactions in GIS
Supported in part by a grant from the National Athletic Trainers' Association Research and Education Foundation and the Roy J. Carver Charitable Foundation to the University of Northern Iowa Graduate College.
No commercial party having a direct financial interest in the results of the research supporting this article has or will confer a benefit on the authors or on any organization with which the authors are associated.