Introduction: Understanding determinants of community mobility disability is critical for developing interventions aimed at preventing or delaying disability in older adults. In an effort to understand these determinants, capturing and measuring community mobility has become a key factor. The objectives of this paper are to present and illustrate the signal processing workflow and outcomes that can be extracted from an activity and community mobility measurement approach based on GPS and accelerometer sensor data and 2) to explore the construct validity of the proposed measurement approach using data collected from healthy older adults in free-living conditions.Methods: Personal, functional impairment and environmental variables were obtained by self-report questionnaires in 75 healthy community-living older adults (mean age = 66 ± 7 years old) living on the island of Montreal, QC, Canada. Participants wore, for 14 days during waking hours on the hip, a data logger incorporating a GPS receiver with a 3-axis accelerometer. Time at home ratio (THR), Trips out (TO), Destinations (D), Maximal distance of destinations (MDD), Active time ratio (ATR), Steps (S), Distance in a vehicle (DV), Time in a vehicle (TV), Distance on foot (DF), Time on foot (TF), Ellipse area (EA), and Ellipse maximum distance (EMD) were extracted from the recordings.Results: After applying quality control criteria, the original data set was reduced from 75 to 54 participants (28% attrition). Results from the remaining sample show that under free-living conditions in healthy older adults, location, activity and community mobility outcomes vary across individuals and certain personal variables (age, income, living situation, professional status, vehicle access) have potential mitigating effects on these outcomes. There was a significant (yet small) relationship (rho < 0.40) between self-reported life space and MDD, DV, EA, and EMD.Conclusion: Wearability and usability of the devices used to capture free-living community mobility impact participant compliance and the quality of the data. The construct validity of the proposed approach appears promising but requires further studies directed at populations with mobility impairments.

BackgroundRecently, much attention has been given to the use of inertial sensors for remote monitoring of individuals with limited mobility. However, the focus has been mostly on the detection of symptoms, not specific activities. The objective of the present study was to develop an automated recognition and segmentation algorithm based on inertial sensor data to identify common gross motor patterns during activity of daily living.MethodA modified Time-Up-And-Go (TUG) task was used since it is comprised of four common daily living activities; Standing, Walking, Turning, and Sitting, all performed in a continuous fashion resulting in six different segments during the task. Sixteen healthy older adults performed two trials of a 5 and 10 meter TUG task. They were outfitted with 17 inertial motion sensors covering each body segment. Data from the 10 meter TUG were used to identify pertinent sensors on the trunk, head, hip, knee, and thigh that provided suitable data for detecting and segmenting activities associated with the TUG. Raw data from sensors were detrended to remove sensor drift, normalized, and band pass filtered with optimal frequencies to reveal kinematic peaks that corresponded to different activities. Segmentation was accomplished by identifying the time stamps of the first minimum or maximum to the right and the left of these peaks. Segmentation time stamps were compared to results from two examiners visually segmenting the activities of the TUG.ResultsWe were able to detect these activities in a TUG with 100% sensitivity and specificity (n = 192) during the 10 meter TUG. The rate of success was subsequently confirmed in the 5 meter TUG (n = 192) without altering the parameters of the algorithm. When applying the segmentation algorithms to the 10 meter TUG, we were able to parse 100% of the transition points (n = 224) between different segments that were as reliable and less variable than visual segmentation performed by two independent examiners.ConclusionsThe present study lays the foundation for the development of a comprehensive algorithm to detect and segment naturalistic activities using inertial sensors, in hope of evaluating automatically motor performance within the detected tasks.

Introduction: High energy expenditure by healthy older individuals has numerous benefits, and housework and exercises done at home are among the most common physical activities. However, there is little knowledge about how characteristics of the urban built environment could impact energy expenditure for moderate and vigorous daily activities. This study characterizes accessibility and a number of physical barriers, investigates the relationship between home environmental press and energy expenditure at home, and identifies the environmental characteristics that could explain variability in energy expenditure. Method: The home energy expenditure of 35 healthy older women was determined from retrospective geolocation data and a multi-sensor device measuring energy expenditure (SenseWear Armband V R). Barriers at home were identified with the Housing Enabler. Results: The median was 51 environmental barriers with only 7.5 barriers between the 1st and 3rd quartile, on a total of 161 possible environmental barriers of the Housing Enabler. The number of home environmental barriers was positively and moderately correlated with energy expenditure at home (r s ¼ 0.47, p ¼ 0.01). No characteristic of the home built environment was identified that could explain the variability in energy expenditure. Conclusion: Future research should identify the characteristics of the home associated with a lower or higher energy expenditure according to the characteristics of the person. This could be carried out by occupational therapists for the purpose of preventing deconditioning, energy management, promotion of social participation, recommendations for home adaptations or relocation.

Aims Absolute grip strength (aGS) measures are not only used to detect dynapenia, but can also provide a robust indicator of functional impairments such as mobility limitations. Mobility limitations can impact community mobility. The main objective of this study was to investigate whether dynapenia status measured with aGS can be used as a predictor of the level of community mobility measured by Global Positioning System (GPS) and the Life-Space Assessment questionnaire (LSA) in healthy older adults. It has been shown that body weight related grip strength (GS/BW) is also a clinical predictor of functional limitation. The secondary objective of the study was to assess the relationship between the community mobility and the GS/BW. Method and resultsThe population studied (n=62) was composed of a dynapenic group of women (aged 66.4 ± 4.8) according to an aGS threshold of ≤ 19.9kg and an age-matched group of women (aged 66.1 ± 5.2) with no detectable dynapenia. Clinical and laboratory evaluations were conducted to measure functional capacity tests, body composition and respiratory capacity. Body weigth related to grip strength (GS/BW) was computed. During 12 days, each participant wore a GPS receiver unit with a data logging =-.67) compared system during waking hours. Transit distance in vehicle per day, Transit distance on foot per day and Ellipse area were extracted from the time series of GPS data (longitude, latitude) collected at 1 Hz. The Life space was assessed using a questionnaire. A Wilcoxon test was used to compare the 2 groups for the community mobility measures. Then, data of the 2 groups were pooled to assess the relationship between GS/BW and community mobility measures. A Spearman correlation was used. The dynapenic group had indeed lower aGS (z=-5.3, p≤.05, r=-.67) and GS/BW (z=-5.3, p≤.05, r=-.67)compared to the non-dynapenic group. Furthermore, we found a lower performance to the step test (z=-2.5, p=.011, r=-.32) and lower walking speed (z=-2.1, p=033, r=-.27) for the dynapenic group. However, no significant differences (Wilcoxon signed-ranks test) were found for community mobility measures with the GPS and the LSA between the two groups. There were significant positive relationships between the GS/BW and one leg stand test (r=.353, p=0.005), step test (r=.409, p=0.001) and walking speed (r=.428, p=0.001). No significant relationship (Spearman correlation test) was found for the GS/BW and community mobility measures with the GPS and the LSA. ConclusionsThis study confirms that aGS and GS/BW are good indicators of mobility limitations measured with clinical and laboratory evaluations. However, grip strength alone should not be considered as an indicator of community mobility restriction in an older adult dynapenic population.