Teresa L. Hart

How many days of monitoring predict physical activity and sedentary behaviour in older adults

BackgroundThe number of days of pedometer or accelerometer data needed to reliably assess physical activity (PA) is important for research that examines the relationship with health. While this important research has been completed in young to middle-aged adults, data is lacking in older adults. Further, data determining the number of days of self-reports PA data is also void. The purpose of this study was to examine the number of days needed to predict habitual PA and sedentary behaviour across pedometer, accelerometer, and physical activity log (PA log) data in older adults.MethodsParticipants (52 older men and women; age = 69.3 ± 7.4 years, range= 55-86 years) wore a Yamax Digiwalker SW-200 pedometer and an ActiGraph 7164 accelerometer while completing a PA log for 21 consecutive days. Mean differences each instrument and intensity between days of the week were examined using separate repeated measures analysis of variance for with pairwise comparisons. Spearman-Brown Prophecy Formulae based on Intraclass Correlations of .80, .85, .90 and .95 were used to predict the number of days of accelerometer or pedometer wear or PA log daily records needed to represent total PA, light PA, moderate-to-vigorous PA, and sedentary behaviour.ResultsResults of this study showed that three days of accelerometer data, four days of pedometer data, or four days of completing PA logs are needed to accurately predict PA levels in older adults. When examining time spent in specific intensities of PA, fewer days of data are needed for accurate prediction of time spent in that activity for ActiGraph but more for the PA log. To accurately predict average daily time spent in sedentary behaviour, five days of ActiGraph data are needed.ConclusionsThe number days of objective (pedometer and ActiGraph) and subjective (PA log) data needed to accurately estimate daily PA in older adults was relatively consistent. Despite no statistical differences between days for total PA by the pedometer and ActiGraph, the magnitude of differences between days suggests that day of the week cannot be completely ignored in the design and analysis of PA studies that involve < 7-day monitoring protocols for these instruments. More days of accelerometer data were needed to determine typical sedentary behaviour than PA level in this population of older adults.

Expected Values for Pedometer-Determined Physical Activity in Youth

This review assembles pedometry literature focused on youth, with particular attention to expected values for habitual, school day, physical education class, recess, lunch break, out-of-school, weekend, and vacation activity. From 31 studies published since 1999, we constructed a youth habitual activity step-curve that indicates: (a) from ages 6 to 18 years, boys typically take more steps per day than girls; (b) for both sexes the youngest age groups appear to take fewer steps per day than those immediately older; and (c) from a young age, boys decline more in steps per day to become more consistent with girls at older ages. Additional studies revealed that boys take approximately 42–49% of daily steps during the school day; girls take 41–47%. Steps taken during physical education class contribute to total steps per day by 8.7–23.7% in boys and 11.4–17.2% in girls. Recess represents 8–11% and lunch break represents 15–16% of total steps per day. After-school activity contributes approximately 47–56% of total steps per day for boys and 47–59% for girls. Weekdays range from approximately 12,000 to 16,000 steps per day in boys and 10,000 to 14,000 steps per day in girls. The corresponding values for weekend days are 12,000–13,000 steps per day in boys and 10,000–12,000 steps per day in girls.

Expected values for steps/day in special populations

OBJECTIVE To assemble expected values for free-living steps/day in special populations living with chronic illnesses and disabilities. METHOD Studies identified since 2000 were categorized into similar illnesses and disabilities, capturing the original reference, sample descriptions, descriptions of instruments used (i.e., pedometers, piezoelectric pedometers, accelerometers), number of days worn, and mean and standard deviation of steps/day. RESULTS Sixty unique studies represented: 1) heart and vascular diseases, 2) chronic obstructive lung disease, 3) diabetes and dialysis, 4) breast cancer, 5) neuromuscular diseases, 6) arthritis, joint replacement, and fibromyalgia, 7) disability (including mental retardation/intellectual difficulties), and 8) other special populations. A median steps/day was calculated for each category. Waist-mounted and ankle-mounted instruments were considered separately due to fundamental differences in assessment properties. For waist-mounted instruments, the lowest median values for steps/day are found in disabled older adults (1214 steps/day) followed by people living with COPD (2237 steps/day). The highest values were seen in individuals with Type 1 diabetes (8008 steps/day), mental retardation/intellectual disability (7787 steps/day), and HIV (7545 steps/day). CONCLUSION This review will be useful to researchers/practitioners who work with individuals living with chronic illness and disability and require such information for surveillance, screening, intervention, and program evaluation purposes.

Objective and subjective measures of sedentary behavior and physical activity.

PURPOSE To examine the convergent validity of the ActiGraph and activPAL accelerometers with the Bouchard Activity Record (BAR) in adults. Sedentary behavior and walking were evaluated in all instruments; standing and moderate-to-vigorous physical activity (MVPA) was evaluated only in those that detected such variables. METHODS Thirty-two participants wore the accelerometers and completed the BAR concurrently for 1 d. Descriptive statistics and delta values were reported for all instruments. Summary time spent in sedentary behavior and walking was compared between all instruments using repeated-measures ANOVA. Dependent t-tests were used to analyze summary time in 1) standing between activPAL and BAR and 2) MVPA between ActiGraph and BAR. Bland-Altman plots were interpreted for systematic bias. On a detailed level, concurrent time interval data were compared using mean percent agreement and κ statistics. RESULTS There was a significant difference found in summary time spent in sedentary behavior apparent between ActiGraph and activPAL as well as between ActiGraph and BAR. There was also a significant difference detected in time spent in walking, apparent between ActiGraph and activPAL, and between ActiGraph and BAR. In the time interval analysis, mean percent agreement ranged from 54.0% (for walking detected by ActiGraph and activPAL) to 86.7% (for MVPA by ActiGraph and BAR). κ values ranged from 0.25 (for walking by ActiGraph and activPAL) to 0.70 (for sedentary behavior between activPAL and BAR). Differences were also found in standing and MVPA. CONCLUSIONS The activPAL and BAR showed convergence on both summary and concurrent time interval levels in both sedentary behavior and walking. The comparative discordance between activPAL and BAR with ActiGraph was likely a function of different approaches used to distinguish sedentary behavior from walking.

Pedometry Methods for Assessing Free-Living Youth

The purpose of this review is to integrate and summarize specific measurement topics (instrument and metric choice, validity, reliability, how many and what types of days, reactivity, and data treatment) appropriate to the study of youth physical activity. Research quality pedometers are necessary to aid interpretation of steps per day collected in a range of young populations under a variety of circumstances. Steps per day is the most appropriate metric choice, but steps per minute can be used to interpret time-in-intensity in specifically delimited time periods (e.g., physical education class). Reported intraclass correlations (ICC) have ranged from .65 over 2 days (although higher values also have been reported for 2 days) to .87 over 8 days (although higher values have been reported for fewer days). Reported ICCs are lower on weekend days (.59) versus weekdays (.75) and lower over vacation days (.69) versus school days (.74). There is no objective evidence of reactivity at this time. Data treatment includes (a) identifying and addressing missing values, (b) identifying outliers and reducing data appropriately if necessary, and (c) transforming the data as required in preparation for inferential analysis. As more pedometry studies in young populations are published, these preliminary methodological recommendations should be modified and refined.

Measured and perceived environmental characteristics are related to accelerometer defined physical activity in older adults

BackgroundFew studies have investigated both the self-perceived and measured environment with objectively determined physical activity in older adults. Accordingly, the aim of this study was to examine measured and perceived environmental associations with physical activity of older adults residing across different neighborhood types.MethodsOne-hundred and forty-eight older individuals, mean age 64.3 ± 8.4, were randomly recruited from one of four neighborhoods that were pre-determined as either having high- or low walkable characteristics. Individual residences were geocoded and 200 m network buffers established. Both objective environment audit, and self-perceived environmental measures were collected, in conjunction with accelerometer derived physical activity behavior. Using both perceived and objective environment data, analysis consisted of a macro-level comparison of physical activity levels across neighborhood, and a micro-level analysis of individual environmental predictors of physical activity levels.ResultsIndividuals residing in high-walkable neighborhoods on average engaged in 11 min of moderate to vigorous physical activity per day more than individuals residing in low-walkable neighborhoods. Both measured access to non-residential destinations (b = .11, p < .001) and self-perceived access to non-residential uses (b = 2.89, p = .031) were significant predictors of time spent in moderate to vigorous physical activity. Other environmental variables significantly predicting components of physical activity behavior included presence of measured neighborhood crime signage (b = .4785, p = .031), measured street safety (b = 26.8, p = .006), and perceived neighborhood satisfaction (b = .5.8, p = .003).ConclusionsOlder adult residents who live in high-walkable neighborhoods, who have easy and close access to nonresidential destinations, have lower social dysfunction pertinent to crime, and generally perceive the neighborhood to a higher overall satisfaction are likely to engage in higher levels of physical activity behavior. Efforts aimed at promoting more walkable neighborhoods could influence activity levels in older adults.

Evaluation of Low-Cost, Objective Instruments for Assessing Physical Activity in 10–11-Year-Old Children

This study compared step counts detected by four, low-cost, objective, physical-activity-assessment instruments and evaluated their ability to detect moderate-to-vigorous physical activity (MVPA) compared to the ActiGraph accelerometer (AG). Thirty-six 10–11-year-old children wore the NL-1000, Yamax Digiwalker SW 200, Omron HJ-151, and Walk4Life MVP concurrently with the AG during school hours on a single day. AG MVPA was derived from activity count data using previously validated cut points. Two of the evaluated instruments provided similar group mean MVPA and step counts compared to AG (dependent on cut point). Low-cost instruments may be useful for measurement of both MVPA and steps in childrens physical activity interventions and program evaluation.

Prompts to disrupt sitting time and increase physical activity at work, 2011-2012.

Introduction The objective of this study was to assess change in sitting and physical activity behavior in response to a workplace intervention to disrupt prolonged sitting time. Methods Sixty office workers were randomized to either a Stand group (n = 29), which received hourly prompts (computer-based and wrist-worn) to stand up, or a Step group (n = 31), which received the same hourly prompts and an additional prompt to walk 100 steps or more upon standing. An ActivPAL monitor was used to assess sitting and physical activity behavior on the same 3 consecutive workdays during baseline and intervention periods. Mixed-effect models with random intercepts and random slopes for time were performed to assess change between groups and across time. Results Both groups significantly reduced duration of average sitting bouts (Stand group, by 16%; Step group, by 19%) and the number of sitting bouts of 60 minutes or more (Step group, by 36%; Stand group, by 54%). The Stand group significantly reduced total sitting time (by 6.6%), duration of the longest sitting bout (by 29%), and number of sitting bouts of 30 minutes or more (by 13%) and increased the number of sit-to-stand transitions (by 15%) and standing time (by 23%). Stepping time significantly increased in the Stand (by 14%) and Step (by 29%) groups, but only the Step group significantly increased (by 35%) the number of steps per workday. Differences in changes from baseline to intervention between groups were not significant for any outcome. Conclusion Interventions that focus on disrupting sitting time only in the workplace may result in less sitting. When sitting time disruptions are paired with a physical activity prompt, people may be more likely to increase their workday physical activity, but the effect on sitting time may be attenuated.

Evaluation of the MyWellness Key accelerometer

Objective To examine the concurrent validity of the Technogym MyWellness Key accelerometer against objective and subjective physical activity (PA) measures. Design Randomised, cross-sectional design with two phases. The laboratory phase compared the MyWellness Key with the ActiGraph GT1M and the Yamax SW200 Digiwalker pedometer during graded treadmill walking, increasing speed each minute. The free-living phase compared the MyWellness Key with the ActiGraph, Digiwalker, Bouchard Activity cord (BAR) and Global Physical Activity Questionnaire (GPAQ) for seven continuous days. Data were analysed using Spearman rank-order correlation coefficients for all comparisons. Setting Laboratory and free-living phases. Participants Sixteen participants randomly stratified from 41 eligible respondents by sex (n=8 men; n=8 women) and PA levels (n=4 low, n=8 middle and n=4 high active). Results There was a strong association between the MyWellness Key and the ActiGraph accelerometer during controlled graded treadmill walking (r=0.91, p<0.01) and in free-living settings (r=0.73–0.76 for light to vigorous PA, respectively, p<0.01). No associations were observed between the MyWellness Key and the BAR and GPAQ (p>0.05). Conclusions The MyWellness Key has a high concurrent validity with the ActiGraph accelerometer to detect PA in both controlled laboratory and free-living settings.

Effectiveness of long and short bout walking on increasing physical activity in women.

BACKGROUND The accumulation of physical activity (PA) throughout the day has been suggested as a means to increase PA behavior. It is not known, however, if accumulated PA results in equivalent increases in PA behavior compared with one continuous session. The purpose of this investigation was to compare changes in PA between participants assigned to walk daily in accumulated shorter bouts vs. one continuous session. METHODS In this 8-week randomized controlled trial, 60 inactive women were randomly assigned to one of the following: (1) control group, (2) 30 minutes a day of walking 5 days a week in one continuous long bout (LB), or (3) three short 10-minute bouts (SB) of walking a day, all at a prescribed heart rate intensity. Walking was assessed by pedometer and self-reported walking log. Before and after measures were taken of average steps/day, resting systolic and diastolic blood pressure (SBP, DBP), resting heart rate (RHR), six-minute walk test (6MWT) distance, height, weight, body mass index (BMI), and hip and waist circumference. RESULTS Both walking groups significantly increased PA measured as steps/day compared to controls (p < 0.001), and no significant differences were found between LB and SB groups. The LB group demonstrated significant decreases in hip circumference and significant increases in 6MWT distance compared to the control group. CONCLUSIONS Both walking groups significantly increased PA participation. LB group participants completed more walking at a higher intensity than the SB and control groups, which resulted in significant increases in health benefits.