A Primary Care Nurse-Delivered Walking Intervention in Older Adults: PACE (Pedometer Accelerometer Consultation Evaluation)-Lift Cluster Randomised Controlled Trial.

Background: Brisk walking in older people can increase step-counts and moderate to vigorous intensity physical activity (MVPA) in ≥10-minute bouts, as advised in World Health Organization guidelines. Previous interventions have reported step-count increases, but not change in objectively measured MVPA in older people. We assessed whether a primary care nurse-delivered complex intervention increased objectively measured step-counts and MVPA. Methods and Findings: A total of 988 60–75 year olds, able to increase walking and randomly selected from three UK family practices, were invited to participate in a parallel two-arm cluster randomised trial; randomisation was by household. Two-hundred-ninety-eight people from 250 households were randomised between 2011 and 2012; 150 individuals to the intervention group, 148 to the usual care control group. Intervention participants received four primary care nurse physical activity (PA) consultations over 3 months, incorporating behaviour change techniques, pedometer step-count and accelerometer PA intensity feedback, and an individual PA diary and plan. Assessors were not blinded to group status, but statistical analyses were conducted blind. The primary outcome was change in accelerometry assessed average daily step-counts between baseline and 3 months, with change at 12 months a secondary outcome. Other secondary outcomes were change from baseline in time in MVPA weekly in ≥10-minute bouts, accelerometer counts, and counts/minute at 3 months and 12 months. Other outcomes were adverse events, anthropometric measures, mood, and pain. Qualitative evaluations of intervention participants and practice nurses assessed the intervention’s acceptability. At 3 months, eight participants had withdrawn or were lost to follow-up, 280 (94%) individuals provided primary outcome data. At 3 months changes in both average daily step-counts and weekly MVPA in ≥10-minute bouts were significantly higher in the intervention than control group: by 1,037 (95% CI 513–1,560) steps/day and 63 (95% CI 40–87) minutes/week, respectively. At 12 months corresponding differences were 609 (95% CI 104–1,115) steps/day and 40 (95% CI 17–63) minutes/week. Counts and counts/minute showed similar effects to steps and MVPA. Adverse events, anthropometry, mood, and pain were similar in the two groups. Participants and practice nurses found the intervention acceptable and enjoyable. Conclusions : The PACE-Lift trial increased both step-counts and objectively measured MVPA in ≥10-minute bouts in 60–75 year olds at 3 and 12 months, with no effect on adverse events. To our knowledge, this is the first trial in this age group to demonstrate objective MVPA increases and highlights the value of individualised support incorporating objective PA assessment in a primary care setting. Trial Registration: Controlled-Trials.com ISRCTN4212256

Patterns of impact resulting from a 'sit less, move more' web-based program in sedentary office employees.

PURPOSE: Encouraging office workers to 'sit less and move more' encompasses two public health priorities. However, there is little evidence on the effectiveness of workplace interventions for reducing sitting, even less about the longer term effects of such interventions and still less on dual-focused interventions. This study assessed the short and mid-term impacts of a workplace web-based intervention (Walk@WorkSpain, W@WS; 2010-11) on self-reported sitting time, step counts and physical risk factors (waist circumference, BMI, blood pressure) for chronic disease. METHODS: Employees at six Spanish university campuses (n=264; 42±10 years; 171 female) were randomly assigned by worksite and campus to an Intervention (used W@WS; n=129; 87 female) or a Comparison group (maintained normal behavior; n=135; 84 female). This phased, 19-week program aimed to decrease occupational sitting time through increased incidental movement and short walks. A linear mixed model assessed changes in outcome measures between the baseline, ramping (8 weeks), maintenance (11 weeks) and follow-up (two months) phases for Intervention versus Comparison groups. RESULTS: A significant 2 (group) × 2 (program phases) interaction was found for self-reported occupational sitting (F[3]=7.97, p=0.046), daily step counts (F[3]=15.68, p=0.0013) and waist circumference (F[3]=11.67, p=0.0086). The Intervention group decreased minutes of daily occupational sitting while also increasing step counts from baseline (446±126; 8,862±2,475) through ramping (+425±120; 9,345±2,435), maintenance (+422±123; 9,638±3,131) and follow-up (+414±129; 9,786±3,205). In the Comparison group, compared to baseline (404±106), sitting time remained unchanged through ramping and maintenance, but decreased at follow-up (-388±120), while step counts diminished across all phases. The Intervention group significantly reduced waist circumference by 2.1cms from baseline to follow-up while the Comparison group reduced waist circumference by 1.3cms over the same period. CONCLUSIONS: W@WS is a feasible and effective evidence-based intervention that can be successfully deployed with sedentary employees to elicit sustained changes on "sitting less and moving more"

Feasibility of using wearable devices for collecting pedestrian travel data

Information on the travel characteristics of pedestrians is needed in the planning and design of pedestrian facilities. Desired information includes route selected, travel speed, trip origin and destination, and delay. Conventional methods of acquiring pedestrian travel data such as trip diaries suffer from a number of limitations.;Pedometers are simple wearable devices that are receiving considerable attention in the health promotion and physical activity fields. In recent years, there have been significant developments in global positioning system (GPS) technology. User-friendly devices are now available for under {dollar}100. At the same time, more expensive wearable GPS data loggers are available in the market that are capable of collecting more extensive data. While the technology offers great potential in terms of data collection capabilities, questions about accuracy, reliability, user acceptability, and post-processing requirements must be addressed.;A formal assessment was conducted of pedometers, a hand-held GPS unit and a wearable data logger to determine their feasibility in collecting pedestrian travel data. Experiments were devised and conducted to assess the accuracy and reliability of the devices in a variety of conditions including heavy precipitation, dense vegetative cover and between tall buildings. In addition, devices were given to a number of subjects who used them outdoors for a 24-hour period. Each subject also completed a brief questionnaire intended to assess user acceptability of these devices. Results indicated that the pedometer is not suitable for collecting pedestrian travel data. The GPS devices hold promise as data collection devices as long as their limitations are taken into account. The paper presents recommendations about the suitability of each device for collecting pedestrian travel data

Machine Learning and Pedometers: An Integration-Based Convolutional Neural Network for Step Counting and Detection

This thesis explores a machine learning-based approach to step detection and counting for a pedometer. Our novelty is to analyze a window of time containing an arbitrary number of steps, and integrate the detected count using a sliding window technique. We compare the effectiveness of this approach against classic deterministic algorithms. While classic algorithms perform well during regular gait (e.g. walking or running), they can perform significantly worse during semi-regular and irregular gaits that still contribute to a person’s overall step count. These non-regular gaits can make up a significant portion of the daily step count for people, and an improvement to measuring these gaits can drastically improve the performance of the overall pedometer. Using data collected by 30 participants performing 3 different activities to simulate regular, semi-regular, and irregular gaits, a training and testing strategy was implemented using a sliding window algorithm of pedometer accelerometer data. Data was cut in rows representative of the sliding window, normalized according to the minimum and maximum values of the corresponding sensor-axis combination, and finally collated in specific training and holdout groups for validation purposes. Nine models were trained to predict a continuous count of steps within a given window, for each fold of our five-fold validation process. These nine models correspond to each gait and sensor combination from the collected data set. Once models are trained, they are evaluated against the holdout validation set to test for both run count accuracy (RCA), a measure of the pedometers detected step to actual step count, and step detection accuracy (SDA), a measure of how well the algorithm can predict the time of an actual step. These are obtained through an additional post-processing step that integrates the predicted steps per window over time in order to find the total count of steps within a given training data set. Additionally, an algorithm estimates the times when predicted steps occur by using the running count of total steps. Once testing is performed on all nine models, the process is repeated across all five folds to verify model architecture consistency throughout the entire data set. A window size test was implemented to vary the window size of the sliding window algorithm between 1 and 10 seconds to discover the effect of the sliding window size on the convolutional neural network\u27s step count and detection performance. Again, these tests were run across five different folds to ensure an accurate average measure of each model\u27s performance. By comparing the metrics of RCA and SDA between the machine-learning approach and other algorithms, we see that the method introduced in this thesis performs similarly or better than both a consumer pedometer device, as well as the three classic algorithms of peak detection, thresholding, and autocorrelation. It was found that with a window size of two seconds, this novel approach can detect steps with an overall average RCA of 0.99 and SDA of 0.88, better than any individual classic algorithm

New speedometer for runners

Thesis (S.B. and M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1999.Includes bibliographical references (leaves 41-42).by Ravindra Vadali Sastry.S.B.and M.Eng

A loyalty scheme to encourage physical activity in office workers: a cluster RCT

Background: Increasing physical activity in the workplace can provide physical and mental health benefits for employees and economic benefits for the employer through reduced absenteeism and increased productivity. However, there is limited evidence on effective behaviour change interventions in workplace settings that led to maintained physical activity. This study aimed to address this gap and contribute to the evidence base for effective and cost-effective workplace interventions. Objectives: To determine the effectiveness and cost-effectiveness of the Physical Activity Loyalty scheme, a multicomponent intervention based on concepts similar to those that underpin a high-street loyalty card, which was aimed at encouraging habitual physical activity behaviour and maintaining increases in mean number of steps per day. Design: A cluster randomised controlled trial with an embedded economic evaluation, behavioural economic experiments, mediation analyses and process evaluation. Setting: Office-based employees from public sector organisations in Belfast and Lisburn city centres in Northern Ireland. Participants: A total of 853 participants [mean age 43.6 years (standard deviation 9.6 years); 71% of participants were female] were randomly allocated by cluster to either the intervention group or the (waiting list) control group. Intervention: The 6-month intervention consisted of financial incentives (retail vouchers), feedback and other evidence-based behaviour change techniques. Sensors situated in the vicinity of the workplaces allowed participants to monitor their accumulated minutes of physical activity. Main outcome measures: The primary outcome was mean number of steps per day recorded using a sealed pedometer (Yamax Digiwalker CW-701; Yamax, Tasley, UK) worn on the waist for 7 consecutive days and at 6 and 12 months post intervention. Secondary outcomes included health, mental well-being, quality of life, work absenteeism and presenteeism, and the use of health-care resources. Results: The mean number of steps per day were significantly lower for the intervention group than the control group [6990 mean number of steps per day (standard deviation 3078) vs. 7576 mean number of steps per day (standard deviation 3345), respectively], with an adjusted mean difference of –336 steps (95% confidence interval –612 to –60 steps; p = 0.02) at 6 months post baseline, but not significantly lower at 12 months post baseline. There was a small but significant enhancement of mental well-being in the intervention group (difference between groups for the Warwick–Edinburgh Mental Wellbeing Scale of 1.34 points, 95% confidence interval 0.48 to 2.20 points), but not for the other secondary outcomes. An economic evaluation suggested that, overall, the scheme was not cost-effective compared with no intervention. The intervention was £25.85 (95% confidence interval –£29.89 to £81.60) more costly per participant than no intervention and had no effect on quality-adjusted life-years (incremental quality-adjusted life-years –0.0000891, 95% confidence interval –0.008 to 0.008). Limitations: Significant restructuring of participating organisations during the study resulted in lower than anticipated recruitment and retention rates. Technical issues affected intervention fidelity. Conclusions: Overall, assignment to the intervention group resulted in a small but significant decline in the mean pedometer-measured steps per day at 6 months relative to baseline, compared with the waiting list control group. The Physical Activity Loyalty scheme was deemed not to be cost-effective compared with no intervention, primarily because no additional quality-adjusted life-years were gained through the intervention. Research to better understand the mechanisms of physical activity behaviour change maintenance will help the design of future interventions. Trial registration: Current Controlled Trials ISRCTN17975376. Funding: This project was funded by the National Institute for Health Research (NIHR) Public Health Research programme and will be published in full in Public Health Research; Vol. 7, No. 15. See the NIHR Journals Library website for further project information

A Short-Term Physical Activity Randomized Trial in the Lower Mississippi Delta

Background: The purpose of this study was to determine if a short-term pedometer-based intervention results in immediate increases in time spent in moderate-to-vigorous physical activity (MVPA) compared to a minimal educational intervention. Methods: A sample of 43 overweight adults 35 to 64 years of age participated in a one week pedometer-based feasibility trial monitored by accelerometry. Participants were randomized into a one-week education-only group or a group that also wore a pedometer. Accelerometer-measured MVPA was measured over 7 days at baseline and again for 7 days immediately post-intervention. Results: Minutes of MVPA increased significantly in the overall sample (p = 0.02); however, the effect of adding the pedometer to the education program was not significant (p = 0.89). Mean (6SE) MVPA increased from 12.762.4 min/day to 16.263.6 min/day in the education-only group and from 13.263.3 min/day to 16.363.9 min/day in the education+pedometer group. The correlation between change in steps/day and change in MVPA was 0.69 (p,0.0001). Conclusions: The results of this study suggest that the addition of a pedometer to a short-term education program doe

Self-Rewards and Cash (Dis)Incentives: Consequences for Effort, Integrity, and Habit Formation

Incentives are fundamental and often powerful motivators of human behavior. Considerable research has focused on financial rewards as a tool to encourage “good” decisions. This dissertation examines the psychology and efficacy of monetary incentives—compared to multiple nonmonetary incentives—with respect to individuals’ choices, performance, and habits. I document and explore a variety of interrelated effects that cash, relative to noncash, incentives can incur in four major areas of behavior: habit formation, choice (specifically, tradeoffs involving risk and delay), goal setting, and integrity. In three longitudinal field experiments, I devise and empirically test a novel incentive program based on self-reward, where individuals defined and administered their own rewards for reaching a goal. I find that this system outperforms cash on several consequential metrics, including task engagement and longer-term persistence. I further place these behaviors in the context of a greater focus on compensation when incentivized with cash: People become fixated on attaining the reward over the process of expending effort. Although this mentality fuels efficient goal attainment, it can also lead to—as I show using a series of online studies—distortionary effects on other aspects of goal pursuit, such as the tendency to choose easier effort streams and the willingness to forgo a reward’s magnitude for its certainty or immediacy. Combined, these findings suggest that practitioners seeking to motivate their constituents may do well to reconsider the use of cash incentives

Social, environmental and psychological factors associated with objective physical activity levels in the over 65s

Objective: To assess physical activity levels objectively using accelerometers in community dwelling over 65 s and to examine associations with health, social, environmental and psychological factors. Design: Cross sectional survey. Setting: 17 general practices in Scotland, United Kingdom. Participants: Random sampling of over 65 s registered with the practices in four strata young-old (65–80 years), old-old (over 80 years), more affluent and less affluent groups. Main Outcome Measures: Accelerometry counts of activity per day. Associations between activity and Theory of Planned Behaviour variables, the physical environment, health, wellbeing and demographic variables were examined with multiple regression analysis and multilevel modelling. Results: 547 older people (mean (SD) age 79(8) years, 54% female) were analysed representing 94% of those surveyed. Accelerometry counts were highest in the affluent younger group, followed by the deprived younger group, with lowest levels in the deprived over 80 s group. Multiple regression analysis showed that lower age, higher perceived behavioural control, the physical function subscale of SF-36, and having someone nearby to turn to were all independently associated with higher physical activity levels (R2 = 0.32). In addition, hours of sunshine were independently significantly associated with greater physical activity in a multilevel model. Conclusions: Other than age and hours of sunlight, the variables identified are modifiable, and provide a strong basis for the future development of novel multidimensional interventions aimed at increasing activity participation in later life.Publisher PDFPeer reviewe