Search Results

You are looking at 1 - 3 of 3 items for

  • Author: Tamara B. Harris x
Clear All Modify Search
Restricted access

Anna Pulakka, Eric J. Shiroma, Tamara B. Harris, Jaana Pentti, Jussi Vahtera and Sari Stenholm

Background: An important step in accelerometer data analysis is the classification of continuous, 24-hour data into sleep, wake, and non-wear time. We compared classification times and physical activity metrics across different data processing and classification methods. Methods: Participants (n = 576) from the Finnish Retirement and Aging Study (FIREA) wore an accelerometer on their non-dominant wrist for seven days and nights and filled in daily logs with sleep and waking times. Accelerometer data were first classified as sleep or wake time by log, and Tudor-Locke, Tracy, and ActiGraph algorithms. Then, wake periods were classified as wear or non-wear by log, Choi algorithm, and wear sensor. We compared time classification (sleep, wake, and wake wear time) as well as physical activity measures (total activity volume and sedentary time) across these classification methods. Results: M (SD) nightly sleep time was 467 (49) minutes by log and 419 (88), 522 (86), and 453 (74) minutes by Tudor-Locke, Tracy, and ActiGraph algorithms, respectively. Wake wear time did not differ substantially when comparing Choi algorithm and the log. The wear sensor did not work properly in about 29% of the participants. Daily sedentary time varied by 8–81 minutes after excluding sleep by different methods and by 1–18 minutes after excluding non-wear time by different methods. Total activity volume did not substantially differ across the methods. Conclusion: The differences in wear and sedentary time were larger than differences in total activity volume. Methods for defining sleep periods had larger impact on outcomes than methods for defining wear time.

Restricted access

Jeremy A. Steeves, Catrine Tudor-Locke, Rachel A. Murphy, George A. King, Eugene C. Fitzhugh, David R. Bassett, Dane Van Domelen, John M. Schuna Jr and Tamara B. Harris

Background: Little is known about the daily physical activity (PA) levels of people employed in different occupational categories. Methods: Nine ActiGraph accelerometer-derived daily PA variables are presented and ranked for adults (N = 1465, 20–60 y) working in the 22 occupational categories assessed by NHANES 2005–2006. A composite score was generated for each occupational category by summing the rankings of 3 accelerometer-derived daily PA variables known to have strong associations with health outcomes (total activity counts [TAC], moderate to vigorous PA minutes per week in modified 10-minute bouts [MVPA 10], and percentage of time spent in sedentary activity [SB%]). Results: Classified as high-activity occupational categories, “farming, fishing, forestry,” and “building & grounds cleaning, maintenance” occupations had the greatest TAC (461 996 and 449 452), most MVPA 10 (149.6 and 97.8), most steps per day (10 464 and 11 602), and near the lowest SB% (45.2% and 45.4%). “Community, social services” occupations, classified as low-activity occupational categories, had the second lowest TAC (242 085), least MVPA 10 (12.1), fewest steps per day (5684), and near the highest SB% (64.2%). Conclusions: There is a strong association between occupational category and daily activity levels. Objectively measured daily PA permitted the classification of the 22 different occupational categories into 3 activity groupings.

Restricted access

Dane R. Van Domelen, Paolo Caserotti, Robert J. Brychta, Tamara B. Harris, Kushang V. Patel, Kong Y. Chen, Nanna Ýr Arnardóttir, Gudny Eirikdottir, Lenore J. Launer, Vilmundur Gudnason, Thórarinn Sveinsson, Erlingur Jóhannsson and Annemarie Koster

Background:

Accelerometers have emerged as a useful tool for measuring free-living physical activity in epidemiological studies. Validity of activity estimates depends on the assumption that measurements are equivalent for males and females while performing activities of the same intensity. The primary purpose of this study was to compare accelerometer count values in males and females undergoing a standardized 6-minute walk test.

Methods:

The study population was older adults (78.6 ± 4.1 years) from the AGES-Reykjavik Study (N = 319). Participants performed a 6-minute walk test at a self-selected fast pace while wearing an ActiGraph GT3X at the hip. Vertical axis counts·s−1 was the primary outcome. Covariates included walking speed, height, weight, BMI, waist circumference, femur length, and step length.

Results:

On average, males walked 7.2% faster than females (1.31 vs. 1.22 m·s−1, P < .001) and had 32.3% greater vertical axis counts·s−1 (54.6 vs. 39.4 counts·s−1, P < .001). Accounting for walking speed reduced the sex difference to 19.2% and accounting for step length further reduced the difference to 13.4% (P < .001).

Conclusion:

Vertical axis counts·s−1 were disproportionally greater in males even after adjustment for walking speed. This difference could confound free-living activity estimates.