Changes in sedentary time and physical activity in response to an exercise training and/or lifestyle intervention.

BACKGROUND: Individuals may compensate for exercise training by modifying nonexercise behavior (ie, increase sedentary time (ST) and decrease nonexercise physical activity [NEPA]). PURPOSE: To compare ST and NEPA during a 12-week exercise training and/or lifestyle intervention. METHODS: Fifty-seven overweight/obese participants (19 M/39 F) completed the study (mean ± SD; age 43.6 ± 9.9 y, BMI 35.1 ± 4.6 kg/m2). There were no between-group differences in activity levels at baseline. Four-arm quasi-experimental intervention study 1) EX: exercise 5 days per week at a moderate intensity (40% to 65% VO2peak) 2) rST: reduce ST and increase NEPA, 3) EX-rST: combination of EX and rST and 4) CON: maintain habitual behavior. RESULTS: For the EX group, ST did not decrease significantly (mean ((95% confidence interval) 0.48 (-2.2 to 3.1)% and there was no changes in NEPA at week-12 compared with baseline. The changes were variable, with approximately 50% of participants increasing ST and decreasing NEPA. The rST group decreased ST (-4.8 (0.8 to 7.9)% and increased NEPA. EX-rST significantly decreased ST (-5.1 (-2.2 to 7.9)% and increased time in NEPA at week-12 compared with baseline. The control group increased ST by 4.3 (0.8 to 7.9)%. CONCLUSIONS: Changes in nonexercise ST and NEPA are variable among participants in an exercise-training program, with nearly half decreasing NEPA compared with baseline. Interventions targeting multiple behaviors (ST and NEPA) may effectively reduce compensation and increase daily activity.

Validation of a previous-day recall measure of active and sedentary behaviors.

PURPOSE: A previous-day recall (PDR) may be a less error-prone alternative to traditional questionnaire-based estimates of physical activity and sedentary behavior (e.g., past year), but the validity of the method is not established. We evaluated the validity of an interviewer administered PDR in adolescents (12-17 yr) and adults (18-71 yr). METHODS: In a 7-d study, participants completed three PDR, wore two activity monitors, and completed measures of social desirability and body mass index. PDR measures of active and sedentary time was contrasted against an accelerometer (ActiGraph) by comparing both to a valid reference measure (activPAL) using measurement error modeling and traditional validation approaches. RESULTS: Age- and sex-specific mixed models comparing PDR to activPAL indicated the following: 1) there was a strong linear relationship between measures for sedentary (regression slope, ß1 = 0.80-1.13) and active time (ß1 = 0.64-1.09), 2) person-specific bias was lower than random error, and 3) correlations were high (sedentary: r = 0.60-0.81; active: r = 0.52-0.80). Reporting errors were not associated with body mass index or social desirability. Models comparing ActiGraph to activPAL indicated the following: 1) there was a weaker linear relationship between measures for sedentary (ß1 = 0.63-0.73) and active time (ß1 = 0.61-0.72), (2) person-specific bias was slightly larger than random error, and (3) correlations were high (sedentary: r = 0.68-0.77; active: r = 0.57-0.79). CONCLUSIONS: Correlations between the PDR and the activPAL were high, systematic reporting errors were low, and the validity of the PDR was comparable with the ActiGraph. PDR may have value in studies of physical activity and health, particularly those interested in measuring the specific type, location, and purpose of activity-related behaviors.

The Feasibility of Reducing and Measuring Sedentary Time among Overweight, Non-Exercising Office Workers.

This study examined the feasibility of reducing free-living sedentary time (ST) and the convergent validity of various tools to measure ST. Twenty overweight/obese participants wore the activPAL (AP) (criterion measure) and ActiGraph (AG; 100 and 150 count/minute cut-points) for a 7-day baseline period. Next, they received a simple intervention targeting free-living ST reductions (7-day intervention period). ST was measured using two questionnaires following each period. ST significantly decreased from 67% of wear time (baseline period) to 62.7% of wear time (intervention period) according to AP (n = 14, P < 0.01). No other measurement tool detected a reduction in ST. The AG measures were more accurate (lower bias) and more precise (smaller confidence intervals) than the questionnaires. Participants reduced ST by ~5%, which is equivalent to a 48_min reduction over a 16-hour waking day. These data describe ST measurement properties from wearable monitors and self-report tools to inform sample-size estimates for future ST interventions.

Validation of wearable monitors for assessing sedentary behavior.

PURPOSE: A primary barrier to elucidating the association between sedentary behavior (SB) and health outcomes is the lack of valid monitors to assess SB in a free-living environment. The purpose of this study was to examine the validity of commercially available monitors to assess SB. METHODS: Twenty overweight (mean ± SD: body mass index = 33.7 ± 5.7 kg·m(-2)) inactive, office workers age 46.5 ± 10.7 yr were directly observed for two 6-h periods while wearing an activPAL (AP) and an ActiGraph GT3X (AG). During the second observation, participants were instructed to reduce sitting time. We assessed the validity of the commonly used cut point of 100 counts per minute (AG100) and several additional AG cut points for defining SB. We used direct observation (DO) using focal sampling with duration coding to record either sedentary (sitting/lying) or nonsedentary behavior. The accuracy and precision of the monitors and the sensitivity of the monitors to detect reductions in sitting time were assessed using mixed-model repeated-measures analyses. RESULTS: On average, the AP and the AG100 underestimated sitting time by 2.8% and 4.9%, respectively. The correlation between the AP and DO was R2 = 0.94, and the AG100 and DO sedentary minutes was R2 = 0.39. Only the AP was able to detect reductions in sitting time. The AG 150-counts-per-minute threshold demonstrated the lowest bias (1.8%) of the AG cut points. CONCLUSIONS: The AP was more precise and more sensitive to reductions in sitting time than the AG, and thus, studies designed to assess SB should consider using the AP. When the AG monitor is used, 150 counts per minute may be the most appropriate cut point to define SB.