Traditional Physical Activity Indexes Derived from the Harvard Alumni Activity Survey Have Low Construct Validity in a Lower Income, Urban Population

Rundle, Andrew; Hagins, Marshall; Orjuela, Manuela; Mooney, LaVerne A.; Kim, Marty; Perera, Frederica

doi:10.1007/s11524-007-9212-4

Cited by 11 publications

(7 citation statements)

References 31 publications

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“…Past studies have suggested that physical activity questionnaires might be less valid among some lower-income urban [46] and black populations [47]. These population socio-demographics are important because few self-report measures, including the IPAQ-S, effectively capture physical activity accumulated through routine, non-leisure activities (e.g.…”

Section: Discussionmentioning

confidence: 99%

Differential Accuracy of Physical Activity Self-Report by Body Mass Index

et al. 2012

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Objectives To examine whether agreement between self-reported and accelerometer measured physical activity varies by BMI category in a low-income black sample. Methods Participants completed a questionnaire and wore an accelerometer for 4–6 days. Using one- and ten-minute bouts, accelerometers measured light, moderate, and vigorous physical activity time. Results Correlations varied by obesity (non-obese: one-minute r = 0.41; ten-minute r = 0.47; obese: one-minute r = 0.21; ten-minute r =0.14). Agreement was highest among non-obese persons (one-minute kappa = 0.48, ten-minute kappa = 0.023; obese: one-minute kappa = −0.024, ten-minute kappa = −0.020). Conclusions We found compromised questionnaire performance among obese participants.

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Section: Discussionmentioning

confidence: 99%

Differential Accuracy of Physical Activity Self-Report by Body Mass Index

et al. 2012

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“…20 Responses to items related to walking frequency and duration, flights of stairs climbed, and the mode, frequency, and duration of sporting or recreational activities were incorporated into a single estimate of energy expenditure through physical activity in kcal/week, as in previous studies. 3,21–23 …”

Section: Methodsmentioning

confidence: 99%

Association Between Energy Availability and Physical Activity in Older Adults

Schrager

Schrack

Simonsick

et al. 2014

American Journal of Physical Medicine &Amp; Rehabilitation

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Objective Age-related declines in physical activity are commonly observed in human and animal populations, but their physiological bases are not fully understood. We hypothesize that a lack of available energy contributes to low levels of activity in older persons. Design Cross-sectional analyses of relationships between physical activity level and energy availability were performed in 602 community-dwelling volunteers aged 45 to 91 yrs from the Baltimore Longitudinal Study of Aging (BLSA). Energy expenditure was measured at rest and during a maximal 400-meter walk for calculation of “available energy.” Overall and vigorous physical activity levels were assessed using standardized questionnaires. General linear regression models were used to assess the relationships between available energy and general and vigorous physical activity, and stratified analyses were used to analyze the possible differential association between available energy and physical activity across high and low (peak sustained walking VO2 <18.3 ml O2/kg/min) levels of aerobic fitness. Results Low available energy was associated with low levels of total physical activity (β = 64.678, p = .015) and vigorous activity (β = 9.123, p < .0001). The direct relationship between available energy and physical activity was particularly strong in persons categorized as having low aerobic fitness between available energy and physical activity with both total (β = 119.783, p = .022) and vigorous activity (β = 10.246, p = .015) and was independent of body composition and age. Conclusions Findings from this study support the hypothesis that available energy promotes the maintenance of physical activity in older persons. The findings also run counter to the perception that age-related declines in physical activity are primarily societally or behaviorally driven.

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“…The R 2 for the overall model was 0.17, mainly due to associations with the demographic variables, and the overall model had >95% power to observe a R 2 of this magnitude. Past work with this study population and these activity measures has shown associations between activity and body mass index, blood pressure, and blood glutathione levels, suggesting that the measure has construct validity (10,20). It is possible that other, unknown, demographic, or life-style differences between the populations account for the differences in findings.…”

Section: Discussionmentioning

confidence: 80%