Refining the accelerometric measurement of physical activity

Advances in electronic sensor technologies have led to the increased use of accelerometers for measuring physical activity and sedentary behaviours. Accelerometers overcome many of the inherent limitations of other measurement methods; for example, unlike self-reported instruments, accelerometers are free from random and systematic errors introduced by respondents and interviewers, cultural tradition, and language. However, accelerometers have their own set of limitations; for example, not all accelerometers are created equal and raw accelerometer data require significant data mining procedures in order to yield meaningful outcome variables. Therefore the overall purpose of this three study dissertation was to determine the impact accelerometer model has on the development of a comprehensive physical activity and sedentary behaviour profile and to design and apply novel profiling methods in an order to gain new insights into children’s physical activity. Study One Purpose: To determine which of the three most commonly used accelerometer models has the best intra- and inter-instrument reliability using a mechanical laboratory setup. Secondly, to determine the effect acceleration and frequency have on these reliability measures. Methods: Three experiments were performed. In the first, five each of the Actical, Actigraph, and RT3 accelerometers were placed on a hydraulic shaker plate and simultaneously accelerated in the vertical plane at varying accelerations and frequencies. Six different conditions of varying intensity were used to produce a range of accelerometer counts. Reliability was calculated using standard deviation, standard error of the measurement, coefficient of variation, and intraclass correlation coefficients. In the second and third experiment, 39 Actical and 50 Actigraph accelerometers were put through the same six conditions. Results: Experiment One showed poor reliability in the RT3 (intra- and inter-instrument CV > 40%). Experiments Two and Three clearly indicated that the Actical (CVintra = 0.5%; CVinter = 5.4%) was more reliable than the Actigraph (CVintra = 3.2%; CVinter = 8.6%). Variability in the Actical was negatively related to the acceleration of the condition while no relationship was found between acceleration and reliability in the Actigraph. Variability in the Actigraph was negatively related to the frequency of the condition while no relationship was found between frequency and reliability in the Actical. Conclusion: Of the three accelerometer models measured in this study, the Actical had the best intra- and inter-instrument reliability. However, discrepant trends in the variability of Actical and Actigraph counts across accelerations and frequencies preclude the selection of a ‘superior’ model. More work is needed to understand why accelerometers designed to measure the same thing, behave so differently. Study Two The accurate measurement of habitual physical activity is fundamental to the study of the relationship between physical activity and health. However, many physical activity measurement techniques produce variables accurate to only the day level, such as total energy expenditure via self-report questionnaire, pedometer step counts or accelerometer measurements of minutes of moderate to vigorous physical activity. Monitoring technologies providing more detailed information on physical activity/sedentary behaviour can now be used to explore the relationships between health and movement frequency, intensity, and duration more comprehensively. This paper explores the activity and sedentary profile that can be acquired through objective monitoring, with a focus on accelerometry. Using previously collected objective data, a detailed physical activity profile is presented and case study examples of data utilization and interpretation are provided. The rich detail captured through comprehensive profiling creates new surveillance and study possibilities and could inform new physical activity guidelines. Data are presented in various formats to demonstrate the dangers of misinterpretation when monitoring population adherence to Canada’s Physical Activity Guidelines. Recommendations for physical activity and sedentary profiling are provided and future research needs identified. Study Three Purpose: This study explored the influence of modernity on the physical activity behaviours (e.g. intensity and timing) of children. Methods: Children aged 8-13 years living a traditional lifestyle (Old Order Amish; OOA n=68, Old Order Mennonite; OOM n=120) were compared with children living a contemporary lifestyle (rural Saskatchewan; RSK n=132 and urban Saskatchewan; USK n=93). Physical activity was objectively assessed for seven consecutive days using Actigraph 7164 accelerometers. Custom software was used to reduce the raw accelerometer data into standardized outcome variables. Results: On weekdays there were group differences in moderate physical activity between all lifestyle groups (OOA > OOM > USK > RSK). On the weekend, the group differences in moderate physical activity persisted between, but not within, lifestyle groups (OOA = OOM > USK = RSK). During school hours, all groups had similar activity and sedentary timings; however, they differed in magnitude with the OOA and OOM being both more sedentary and more active. Compared to in school, the OOA and OOM children had 44% lower sedentary time out of school compared to only 15% lower for RSK and USK children. Conclusions: Though cross-sectional, these data suggest that contemporary/modern living is associated with lower levels of moderate and vigorous intensity physical activity compared to lifestyles representative of earlier generations. Analyzing the physical activity and sedentary patterns of traditional lifestyle groups such as the OOA and OOM can provide valuable insight into the quantity and quality of physical activity necessary to promote health. General Conclusions: Together, these three studies will help contribute to the generation of best practices in the accelerometric profiling of both physical activity and sedentary behaviours

Validation of the ADAMO Care Watch for step counting in older adults

Background: Accurate measurement devices are required to objectively quantify physical activity. Wearable activity monitors, such as pedometers, may serve as affordable and feasible instruments for measuring physical activity levels in older adults during their normal activities of daily living. Currently few available accelerometer-based steps counting devices have been shown to be accurate at slow walking speeds, therefore there is still lacking appropriate devices tailored for slow speed ambulation, typical of older adults. This study aimed to assess the validity of step counting using the pedometer function of the ADAMO Care Watch, containing an embedded algorithm for measuring physical activity in older adults. Methods: Twenty older adults aged ≥ 65 years (mean ± SD, 75±7 years; range, 68–91) and 20 young adults (25±5 years, range 20–40), wore a care watch on each wrist and performed a number of randomly ordered tasks: walking at slow, normal and fast self-paced speeds; a Timed Up and Go test (TUG); a step test and ascending/descending stairs. The criterion measure was the actual number of steps observed, counted with a manual tally counter. Absolute percentage error scores, Intraclass Correlation Coefficients (ICC), and Bland–Altman plots were used to assess validity. Results: ADAMO Care Watch demonstrated high validity during slow and normal speeds (range 0.5–1.5 m/s) showing an absolute error from 1.3% to 1.9% in the older adult group and from 0.7% to 2.7% in the young adult group. The percentage error for the 30-metre walking tasks increased with faster pace in both young adult (17%) and older adult groups (6%). In the TUG test, there was less error in the steps recorded for older adults (1.3% to 2.2%) than the young adults (6.6% to 7.2%). For the total sample, the ICCs for the ADAMO Care Watch for the 30-metre walking tasks at each speed and for the TUG test were ranged between 0.931 to 0.985. Conclusion: These findings provide evidence that the ADAMO Care Watch demonstrated highly accurate measurements of the steps count in all activities, particularly walking at normal and slow speeds. Therefore, these data support the inclusion of the ADAMO Care Watch in clinical applications for measuring the number of steps taken by older adults at normal, slow walking speeds

Regression model: Association of speed on reliability of ADAMO Care Watch of the whole sample.

<p>Regression model: Association of speed on reliability of ADAMO Care Watch of the whole sample.</p

Bland–Altman plots between criterion and ADAMO Care Watch during walk test.

<p>a) Normal speed (<i>a1</i> Older adults and <i>a2</i> Young adults); b) Slow speed (<i>b1</i> Older adults and <i>b2</i> Young adults); c) Fast speed (<i>c1</i> Older adults and <i>c2</i> Young adults). Dashed lines represent mean bias differences; Solid lines represent 95% prediction intervals.</p

Percent error scores calculated for each side and each task.

<p>Percent error scores calculated for each side and each task.</p

30-meters walking tasks (Normal, Slow, Fast) and TUG performances in times and speeds.

<p>30-meters walking tasks (Normal, Slow, Fast) and TUG performances in times and speeds.</p

ICC scores calculated for each ADAMO Care Watch across tasks.

<p>ICC scores calculated for each ADAMO Care Watch across tasks.</p