BackgroundAge‐related endothelial dysfunction and vascular stiffening are associated with increased cardiovascular (CV) risk. Many groups have encouraged goals of ≥10 000 steps/day or ≥30 min/day of moderate intensity physical activity (MPA) to reduce age‐related CV risk. The impact of MPA on the vasculature of older adults remains unclear.Methods and ResultsWe randomized 114 sedentary older adults ages ≥50 to 12 weeks of either no intervention (group 1), a pedometer‐only intervention (group 2), or a pedometer with an interactive website employing strategies to increase the adoption of habitual physical activity (PA, group 3). Endothelial function by brachial flow‐mediated dilation (FMD%), vascular stiffness by tonometry, step‐count by pedometer, and PA intensity/distribution by accelerometer were measured. Step‐count increased in groups 2 (5136±1554 to 9596±3907, P<0.001) and 3 (5474±1512 to 8167±3111, P<0.001) but not in group 1 (4931±1667 to 5410±2410). Both groups 2 and 3 increased MPA ≥30 min/day. Only group 3 increased MPA in continuous bouts of ≥10 minutes (P<0.001) and improved FMD% (P=0.001). Neither achievement of ≥10 000 steps/day nor ≥30 min/day of MPA resulted in improved FMD%. However, achieving ≥20 min/day in MPA bouts resulted in improved FMD%. No changes in vascular stiffness were observed.ConclusionsMPA reverses age‐related endothelial dysfunction, but may require MPA to be performed in bouts of ≥10 minutes duration for ≥20 min/day to be effective. Commonly encouraged PA goals do not guarantee improved endothelial function and may not be as effective in reducing CV risk.Clinical Trial RegistrationURL: Clinicaltrials.gov. Unique identifier: NCT‐01212978.

This study examined the predictive validity of accelerometers (ACC) to estimate physical activity intensity (PAI) across age and differences in intensity predictions when expressed in relative and absolute PAI terms. Ninety adults categorized into 3 age groups (20-29, 40-49, and 60-69 yr) completed a treadmill calibration study with simultaneous ACC (7164 Actigraph) and oxygenconsumption assessment. Results revealed strong linear relations between ACC output and measured PAI (R 2 = .62-.89) across age and similar ACC cut-point ranges across age delineating absolute PAI ranges compared with previous findings. Comparing measured metabolic equivalents (METs) with estimated METs derived from previously published regression equations revealed that age did not affect predictive validity of ACC estimates of absolute PAI. Comparing ACC output expressed in relative vs. absolute terms across age revealed substantial differences in PAI ACC count ranges. Further work is warranted to increase the applicability of ACC use relative to PAI differences associated with physiological changes with age.Keywords older adults; motion sensor; validity; physical activity assessment; METs Statistics and trends describing relationships between physical activity and health and prevalence rates of physical activity have been based largely on self-report physical activity surveys and questionnaires because of their feasibility in large-scale studies. In the literature it has been established that self-report physical activity surveys and questionnaires can accurately account for vigorous activity Richardson, Ainsworth, Jacobs, & Leon, 2001;Strath, Bassett, & Swartz, 2004) but are unable to accurately quantify more ubiquitous activities typically classified as light-to moderate-intensity activities LaMonte & Ainsworth, 2001;Strath et al.) that occur throughout the day. As such, there is a strong need to explore other physical activity assessment devices that can accurately predict different levels of physical activity intensity (PAI). Numerous reviews have been published on the strengths and limitations of objective physical activity monitoring and, specifically, the use of accelerometers (Bassett, 2000;Chen & Bassett, 2005;Matthew, 2005;Westerterp, 1999). Overall, an accelerometer can assess the frequency, intensity, and duration of movement. An accelerometer "count" represents an intensity of movement over a user-specified period of time. Researchers have developed regression relationships between accelerometer counts and criterion assessments of physical activity to allow for estimates of physical-activity-related energy expenditure and time spent in various absolute intensities of physical activity. The regression "cut point" approach to delineate different absolute PAIs (i.e., <3, 3-6, and >6 metabolic equivalents [MET]; 1 MET = 3.5 ml O 2 · kg −1 · min −1 ) is perhaps the most commonly employed method for using accelerometer data, with other analytical approaches also gaining in popularity (Crouter, Clowers, & Bassett, 2006 Fr...

Older adult physical activity (PA) levels obtained from the International Physical Activity Questionnaire–Short Form (IPAQ) and accelerometry (ACC) were compared. Mean difference scores between accumulated or bout ACC PA and the IPAQ were computed. Spearman rank-order correlations were used to assess relations between time spent in PA measured from ACC and self-reported form of the IPAQ, and percentage agreement across measures was used to classify meeting or not meeting PA recommendations. The IPAQ significantly underestimated sitting and overestimated time spent in almost all PA intensities. Group associations across measures revealed significant relations in walking, total PA, and sitting for the whole group (r = .29–.36, p < .05). Significant relationships between bout ACC and IPAQ walking (r = .28–.39, p < .05) were found. There was 40–46% agreement between measures for meeting PA recommendations. The IPAQ appears not to be a good indicator of individual older adult PA behavior but is better suited for larger population-based samples.

Older adult physical activity (PA) levels obtained from the International Physical Activity Questionnaire-Short Form (IPAQ) and accelerometry (ACC) were compared. Mean difference scores between accumulated or bout ACC PA and the IPAQ were computed. Spearman rank-order correlations were used to assess relations between time spent in PA measured from ACC and self-reported form of the IPAQ, and percentage agreement across measures was used to classify meeting or not meeting PA recommendations. The IPAQ significantly underestimated sitting and overestimated time spent in almost all PA intensities. Group associations across measures revealed significant relations in walking, total PA, and sitting for the whole group (r = .29-.36, p < .05). Significant relationships between bout ACC and IPAQ walking (r = .28-.39, p < .05) were found. There was 40-46% agreement between measures for meeting PA recommendations. The IPAQ appears not to be a good indicator of individual older adult PA behavior but is better suited for larger population-based samples.

Individually tailored, Internet-mediated PA interventions are an effective way to significantly increase PA in older adults.

Background Increasing physical activity (PA) levels in older adults represents an important public health challenge. The purpose of this study was to evaluate the feasibility of combining individualized motivational messaging with pedometer walking step targets to increase PA in previously inactive and insufficiently active older adults. Methods In this 12-week intervention study older adults were randomized to 1 of 4 study arms: Group 1—control; Group 2—pedometer 10,000 step goal; Group 3—pedometer step goal plus individualized motivational feedback; or Group 4—everything in Group 3 augmented with biweekly telephone feedback. Results 81 participants were randomized into the study, 61 participants completed the study with an average age of 63.8 ± 6.0 years. Group 1 did not differ in accumulated steps/day following the 12-week intervention compared with participants in Group 2. Participants in Groups 3 and 4 took on average 2159 (P < .001) and 2488 (P < .001) more steps/day, respectively, than those in Group 1 after the 12-week intervention. Conclusion In this 12-week pilot randomized control trial, a pedometer feedback intervention partnered with individually matched motivational messaging was an effective intervention strategy to significantly increase PA behavior in previously inactive and insufficiently active older adults.

Replacing SB with LPA was linked to a significant improvement in the 400W, but not the other brief functional measures. Mixed doses of LPA and MVPA may add flexibility to interventions targeting reductions of SB in older adults for clinically relevant improvements in physical function.

A comparison of the validity of downloadable motion sensors, which use either a glass-enclosed magnetic reed proximity switch technology, a piezo-electric sensor accelerometer with a horizontal beam technology, or an internal pendulum based mechanism to determine energy expenditure (EE), across different body sizes does not exist. Therefore, the purpose of this study was to determine the validity of three different downloadable motion sensors to estimate EE during walking activity in normal weight, overweight and obese volunteers. Forty-eight participants completed this study. Each participant had their body height and mass measured and completed a treadmill walking protocol. Body mass index (BMI) was calculated. The treadmill walking protocol included six 5-minute stages starting at 1.5 mph and increasing by 0.5 mph, up to 4.0 mph while grade was constant at 0% for the duration of the test. The Kenz Life-Corder EX (LC), the Omron HJ-700IT (OM) and the Sportbrain iStep X1 (SB) were worn during the treadmill walking protocol. Heart rate, oxygen consumption, carbon dioxide production and EE estimated from the motion sensors were monitored throughout the walking protocol. Results showed the OM overestimated net EE in normal, overweight and obese participants. The LC underestimated gross EE in all groups. The SB overestimated net EE in normal BMI participants, was not significantly different from the criterion measure of net EE in overweight participants and underestimated net EE in obese individuals. This study demonstrates that these devices do not offer the accuracy needed to provide precise feedback on EE for individuals with varying BMI levels.