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

Impact of accelerometer data processing decisions on the sample size, wear time and physical activity level of a large cohort study

Background: Accelerometers objectively assess physical activity (PA) and are currently used in several large-scale epidemiological studies, but there is no consensus for processing the data. This study compared the impact of wear-time assessment methods and using either vertical (V)-axis or vector magnitude (VM) cut-points on accelerometer output. Methods: Participants (7,650 women, mean age 71.4 y) were mailed an accelerometer (ActiGraph GT3X+), instructed to wear it for 7 days, record dates and times the monitor was worn on a log, and return the monitor and log via mail. Data were processed using three wear-time methods (logs, Troiano or Choi algorithms) and V-axis or VM cut-points. Results: Using algorithms alone resulted in "mail-days" incorrectly identified as "wear-days" (27-79% of subjects had >7-days of valid data). Using only dates from the log and the Choi algorithm yielded: 1) larger samples with valid data than using log dates and times, 2) similar wear-times as using log dates and times, 3) more wear-time (V, 48.1 min more; VM, 29.5 min more) than only log dates and Troiano algorithm. Wear-time algorithm impacted sedentary time (~30-60 min lower for Troiano vs. Choi) but not moderate-to-vigorous (MV) PA time. Using V-axis cut-points yielded ~60 min more sedentary time and ~10 min less MVPA time than using VM cut-points. Conclusions: Combining log-dates and the Choi algorithm was optimal, minimizing missing data and researcher burden. Estimates of time in physical activity and sedentary behavior are not directly comparable between V-axis and VM cut-points. These findings will inform consensus development for accelerometer data processing in ongoing epidemiologic studies. Electronic supplementary material The online version of this article (doi:10.1186/1471-2458-14-1210) contains supplementary material, which is available to authorized users

Resistance to exercise-induced weight loss: compensatory behavioral adaptations

This is not the published version.In many interventions that are based on an exercise program intended to induce weight loss, the mean weight loss observed is modest and sometimes far less than the individual expected. The individual responses are also widely variable, with some individuals losing a substantial amount of weight, others maintaining weight, and a few actually gaining weight. The media have focused on the sub-population that loses little weight, contributing to a public perception that exercise has limited utility to cause weight loss. The purpose of the symposium was to present recent, novel data that help explain how compensatory behaviors contribute to a wide discrepancy in exercise-induced weight loss. The presentations provide evidence that some individuals adopt compensatory behaviors, i.e. increased energy intake and/or reduced activity, that offset the exercise energy expenditure and limit weight loss. The challenge for both scientists and clinicians is to develop effective tools to identify which individuals are susceptible to such behaviors, and to develop strategies to minimize their impact

Response

This is not the published version.See the article "Resistance to exercise-induced weight loss: compensatory behavioral adaptations" here at http://hdl.handle.net/1808/24571

Impact and process evaluation of a co-designed ‘Move More, Sit Less’ intervention in a public sector workplace

BACKGROUND:High levels of sitting are associated with increased risk of adverse health outcomes, including chronic disease. Extensive sitting at work is common, hence organisations should provide options to employees to reduce prolonged sitting. OBJECTIVE:To assess the efficacy and acceptability of a co-designed intervention to increase standing and reduce sitting in a public-sector office. METHODS:Forty-six adults participated in the quasi-experimental study (30 intervention; 16 control). The intervention involved providing sit-stand desks, prompts, workshops, and information emails to assist behavior change. Participants wore a thigh-mounted Actigraph GT3X+ for five working days and responded to an online questionnaire at baseline (BL), 6 (T1) and 13 weeks (T2) post intervention. RESULTS:Inclinometer-measured proportion of time standing increased in the intervention group from 14% (baseline) to 28% (T1) and 27% (T2) (67 minutes more standing over an 8-hour workday). Intervention participants reduced sitting time from 79% (BL) to 63% (T1 and T2), (80 minutes less sitting over an 8-hour workday). The control group showed no changes. The program was highly recommended (94%), and perceived to support behavior change (81%). CONCLUSIONS:This Move More, Sit Less intervention appears to be efficacious and acceptable. Future interventions should be co-designed to ensure culturally appropriate components and higher acceptability

The influence of dog ownership on objective measures of free-living physical activity and sedentary behaviour in community-dwelling older adults : a longitudinal case-controlled study

Background: There is some evidence to suggest that dog ownership may improve physical activity (PA) among older adults, but to date, studies examining this, have either depended on self-report or incomplete datasets due to the type of activity monitor used to record physical activity. Additionally, the effect of dog ownership on sedentary behaviour (SB) has not been explored. The aim of the current study was to address these issues by using activPAL monitors to evaluate the influence of dog ownership on health enhancing PA and SB in a longitudinal study of independently-mobile, community-dwelling older adults. Methods: Study participants (43 pairs of dog owners and non-dog owners, matched on a range of demographic variables) wore an activPAL monitor continuously for three, one-week data collection periods over the course of a year. Participants also reported information about their own and their dog demographics, caring responsibilities, and completed a diary of wake times. Diary data was used to isolate waking times, and outcome measures of time spent walking, time spent walking at a moderate cadence (>100 steps/min), time spent standing, time spent sitting, number of sitting events (continuous periods of sitting), and the number of and of time spent sitting in prolonged events (>30 min). For each measure, a linear mixed effects model with dog ownership as a fixed effect, and a random effects structure of measurement point nested in participant nested in pair was used to assess the effect of dog ownership. Results: Owning a dog indicated a large, potentially health improving, average effect of 22 min additional time spent walking, 95%CI (12, 34), and 2760 additional steps per day, 95%CI (1667, 3991), with this additional walking undertaken at a moderate intensity cadence. Dog owners had significantly fewer sitting events. However, there were no significant differences between the groups for either the total time spent sitting, or the number or duration of prolonged sedentary events. Conclusions: The scale of the influence of dog ownership on PA found in this study, indicates that future research regarding PA in older adults should assess and report dog ownership and/or dog walking statu

The influence of free-living activity and inactivity on health outcomes and responsiveness to exercise training

On average, starting an exercise training program decreases one’s risk for chronic disease. However, there is remarkable individual variability in physiologic responses to exercise training. The activity and inactivity during the remaining 95% of the day (when the individual is not training) is rarely considered. The overall objective of this dissertation was to apply validated sedentary behavior (SB) and physical activity (PA) measurement techniques during an exercise training study to determine if time spent in SB and PA outside of training influences the physiological response to training. Twenty subjects participated in a pilot study to determine the feasibility of reducing SB and the validity of PA monitors for measuring SB compared to direct observation (DO). Participants completed a 1-week baseline period and a 1-week intervention period, where they were instructed to decrease SB. The correlation between the AP and DO was R2=0.94 and the AG100 and DO sedentary minutes was R2=0.39. SB significantly decreased from 67% of wear time (baseline period) to 62.7% of wear time (intervention period) according to AP. Only the AP was able to detect reductions in SB and was more precise than the AG. Study Two was a 12-week randomized controlled study. There were 4-groups that were instructed to: 1) CON: maintain habitual PA and SB 2) rST: reduce and break-up SB and increase daily steps 3) EX: exercise 5-days per week for 40-minutes per session at moderate intensity 4) EX-rST: combination of EX and rST. Cardiovascular disease risk factors were assessed pre-and post-intervention. The AP was used to verify AP between-group differences in activity at four time-points. EX-rST had improvements in insulin action variables that EX did not. All other physiologic responses to training were similar between EX groups and rST has less robust changes than either EX group. These data provide validation of activity monitors for measuring SB and present preliminary evidence that activity outside of exercise training may influence the metabolic response to training. This dissertation shows that what is done outside of exercise training can and should be quantified using objective monitors that assess daily exposure to activity and inactivity behavior

Can we "open" the black box and convince health researchers to use machine learning

Non UBCUnreviewedAuthor affiliation: California Polytechnic State UniversityResearche

A Method to Estimate Free-Living Active and Sedentary Behavior from an Accelerometer

Methods to estimate physical activity (PA) and sedentary behavior (SB) from wearable monitors need to be validated in free-living settings. PURPOSE: The purpose of this study was to develop and validate two novel machine-learning methods (soj-1x and soj-3x) in a free-living setting. METHODS: Participants were directly observed in their natural environment for ten consecutive hours on three separate occasions. PA and SB estimated from soj-1x, soj-3x and a neural network previously calibrated in the laboratory (lab-nnet) were compared to direct observation. RESULTS: Compared to the lab-nnet, soj-1x and soj-3x improved estimates of MET-hours (lab-nnet: % bias (95% CI) = 33.1 (25.9, 40.4), rMSE = 5.4 (4.6, 6.2), soj-1x: % bias = 1.9 (−2.0, 5.9), rMSE = 1.0 (0.6, 1.3), soj-3x: % bias = 3.4 (0.0, 6.7), rMSE = 1.0 (0.6, 1.5)) and minutes in different intensity categories (lab-nnet: % bias = −8.2 (sedentary), −8.2 (light) and 72.8 (MVPA), soj-1x: % bias = 8.8 (sedentary), −18.5 (light) and −1.0 (MVPA), soj-3x: % bias = 0.5 (sedentary), −0.8 (light) and −1.0 (MVPA)). Soj-1x and soj-3x also produced accurate estimates of guideline minutes and breaks from sedentary time. CONCLUSION: Compared to the lab-nnet algorithm, soj-1x and soj-3x improved the accuracy and precision in estimating free-living MET-hours, sedentary time, and time spent in light intensity activity and MVPA. Additionally, soj-3x is superior to soj-1x in differentiating sedentary behavior from light intensity activity

Evaluation of Artificial Neural Network Algorithms for Predicting Mets and Activity Type from Accelerometer Data: Validation on an Independent Sample

Previous work from our laboratory provided a “proof of concept” for use of artificial neural networks (nnets) to estimate metabolic equivalents (METs) and identify activity type from accelerometer data (Staudenmayer J, Pober D, Crouter S, Bassett D, Freedson P, J Appl Physiol 107: 1330–1307, 2009). The purpose of this study was to develop new nnets based on a larger, more diverse, training data set and apply these nnet prediction models to an independent sample to evaluate the robustness and flexibility of this machine-learning modeling technique. The nnet training data set (University of Massachusetts) included 277 participants who each completed 11 activities. The independent validation sample (n = 65) (University of Tennessee) completed one of three activity routines. Criterion measures were 1) measured METs assessed using open-circuit indirect calorimetry; and 2) observed activity to identify activity type. The nnet input variables included five accelerometer count distribution features and the lag-1 autocorrelation. The bias and root mean square errors for the nnet MET trained on University of Massachusetts and applied to University of Tennessee were +0.32 and 1.90 METs, respectively. Seventy-seven percent of the activities were correctly classified as sedentary/light, moderate, or vigorous intensity. For activity type, household and locomotion activities were correctly classified by the nnet activity type 98.1 and 89.5% of the time, respectively, and sport was correctly classified 23.7% of the time. Use of this machine-learning technique operates reasonably well when applied to an independent sample. We propose the creation of an open-access activity dictionary, including accelerometer data from a broad array of activities, leading to further improvements in prediction accuracy for METs, activity intensity, and activity type