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David R. Paul, Matthew Kramer, Kim S. Stote and David J. Baer

Background:

The number of days of data and number of subjects necessary to estimate total physical activity (TPA) and moderate-to-vigorous physical activity (MVPA) requires an understanding of within- and between-subject variances, and the influence of sex, body composition, and age.

Methods:

Seventy-one adults wore accelerometers for 7-day intervals over 6 consecutive months.

Results:

Body fat and sex influenced TPA and MVPA. The sources of subject-related variation for TPA and MVPA were within-subject (48.4% and 54.3%), between-subject (34.3% and 31.8%), and calendar effects (17.3% and 13.9%). Based on within-subject variances, the error associated with estimating TPA and MVPA by collecting 1 to 7 days of data ranged from 28.2% to 13.3% for TPA and 62.0% to 28.6% for MVPA. Based on between-subject variances, detecting a 10% difference between 2 groups at a power of 90% requires approximately 200 and 725 subjects per group for TPA and MVPA, respectively.

Conclusions:

Estimates of MVPA are more variable than TPA in overweight adults, therefore more days of data are required to estimate MVPA and larger sample sizes to detect treatment differences for MVPA. Log-transforming data reduces the need for additional days of data collection, thereby improving chances of detecting treatment effects.

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Robert Medairos, Vicky Kang, Carissa Aboubakare, Matthew Kramer and Sheila Ann Dugan

Background:

This study aims to identify patterns of use and preferences related to technology platforms that could support physical activity (PA) programs in an underserved population.

Methods:

A 29-item questionnaire was administered at 5 health and wellness sites targeting low income communities in Chicago. Frequency tables were generated for Internet, cell phone, and social media use and preferences. Chi-squared analysis was used to evaluate differences across age and income groups.

Results:

A total of 291 individuals participated and were predominantly female (69.0%). Majority reported incomes less than $30,000 (72.9%) and identified as African American/Black/Caribbean (49.3%) or Mexican/Mexican American (34.3%). Most participants regularly used smartphones (63.2%) and the Internet (75.9%). Respondents frequently used Facebook (84.8%), and less commonly used Instagram (43.6%), and Twitter (20.0%). Free Internet-based exercise programs were the most preferred method to increase PA levels (31.6%), while some respondents (21.0%) thought none of the surveyed technology applications would help.

Conclusion:

Cell phone, Internet, and social media use is common among the surveyed underserved population. Technology preferences to increase PA levels varied, with a considerable number of respondents not preferring the surveyed technology platforms. Creating educational opportunities to increase awareness may maximize the effectiveness of technology-based PA interventions.

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David R. Paul, Ryan McGrath, Chantal A. Vella, Matthew Kramer, David J. Baer and Alanna J. Moshfegh

Background: The National Health and Nutrition Examination Survey physical activity questionnaire (PAQ) is used to estimate activity energy expenditure (AEE) and moderate to vigorous physical activity (MVPA). Bias and variance in estimates of AEE and MVPA from the PAQ have not been described, nor the impact of measurement error when utilizing the PAQ to predict biomarkers and categorize individuals. Methods: The PAQ was administered to 385 adults to estimate AEE (AEE:PAQ) and MVPA (MVPA:PAQ), while simultaneously measuring AEE with doubly labeled water (DLW; AEE:DLW) and MVPA with an accelerometer (MVPA:A). Results: Although AEE:PAQ [3.4 (2.2) MJ·d−1] was not significantly different from AEE:DLW [3.6 (1.6) MJ·d−1; P > .14], MVPA:PAQ [36.2 (24.4) min·d−1] was significantly higher than MVPA:A [8.0 (10.4) min·d−1; P < .0001]. AEE:PAQ regressed on AEE:DLW and MVPA:PAQ regressed on MVPA:A yielded not only significant positive relationships but also large residual variances. The relationships between AEE and MVPA, and 10 of the 12 biomarkers were underestimated by the PAQ. When compared with accelerometers, the PAQ overestimated the number of participants who met the Physical Activity Guidelines for Americans. Conclusions: Group-level bias in AEE:PAQ was small, but large for MVPA:PAQ. Poor within-participant estimates of AEE:PAQ and MVPA:PAQ lead to attenuated relationships with biomarkers and misclassifications of participants who met or who did not meet the Physical Activity Guidelines for Americans.