An orientation free adaptive step detection algorithm using a smart phone in physical activity monitoring

In this paper we present an Orientation Free Adaptive Step Detection (OFASD) algorithm for deployment in a smart phone for the purposes of physical activity monitoring. The OFASD algorithm detects individual steps and measures a user’s step counts using the smart phone’s in-built accelerometer. The algorithm considers both the variance of an individual’s walking pattern and the orientation of the smart phone. Experimental validation of the algorithm involved the collection of data from 10 participants using five phones (worn at five different body positions) whilst walking on a treadmill at a controlled speed for periods of 5 min. Results indicated that, for steps detected by the OFASD algorithm, there were no significant differences between where the phones were placed on the body (p > 0.05). The mean step detection accuracies ranged from 93.4 % to 96.4 %. Compared to measurements acquired using existing dedicated commercial devices, the results demonstrated that using a smart phone for monitoring physical activity is promising, as it adds value to an accepted everyday accessory, whilst imposing minimum interaction from the user. The algorithm can be used as the underlying component within an application deployed within a smart phone designed to promote self-management of chronic disease where activity measurement is a significant factor, as it provides a practical solution, with minimal requirements for user intervention and less constraints than current solutions.

Individual Characteristics Associated with Mismatches between Self-Reported and Accelerometer-Measured Physical Activity

Background: Accurate assessment tools are required for the surveillance of physical activity (PA) levels and the assessment of the effect of interventions. In addition, increasing awareness of PA is often used as the first step in pragmatic behavioural interventions, as discrepancies between the amount of activity an individual perceives they do and the amount actually undertaken may act as a barrier to change. Previous research has demonstrated differences in the amount of activity individuals report doing, compared to their level of physical activity when measured with an accelerometer. Understanding the characteristics of those whose PA level is ranked differently when measured with either self-report or accelerometry is important as it may inform the choice of instrument for future research. The aim of this project was to determine which individual characteristics are associated with differences between self-reported and accelerometer measured physical activity.

Methods: Participant data from the 2009 wave of the Commuting and Health in Cambridge study were used. Quartiles of self-reported and accelerometer measured PA were derived by ranking each measure from lowest to highest. These quartiles were compared to determine whether individuals’ physical activity was ranked higher by either method. Multinomial logistic regression models were used to investigate the individual characteristics associated with different categories of mismatch.

Results: Data from 486 participants (70% female) were included in the analysis. In adjusted analyses, the physical activity of overweight or obese individuals was significantly more likely to be ranked higher by self-report than by accelerometer than that of normal-weight individuals (OR = 2.07, 95%CI = 1.28–3.34), particularly among women (OR = 3.97, 95%CI = 2.11–7.47).

Conclusions: There was a greater likelihood of mismatch between self-reported and accelerometer measured physical activity levels in overweight or obese adults. Future studies in overweight or obese adults should consider employing both methods of measurement.

Using postal questionnaires to evaluate physical activity and diet behaviour change: case study exploring implications of valid responder characteristics in interpreting intervention outcomes.

Background: Patient reported outcome measures (PROMs) are used to evaluate lifestyle interventions but littleis known about differences between patients returning valid and invalid responses, or of potential for bias inevaluations. We aimed to examine the characteristics of patients who returned valid responses to lifestylequestionnaires compared to those whose responses were invalid for evaluating lifestyle change. 
Methods: We conducted a secondary data analysis from the SPHERE Study, a trial of an intervention to improveoutcomes for patients with coronary heart disease in primary care. Postal questionnaires were used to assessphysical activity (Godin) and diet (DINE) among study participants at baseline and 18 month follow-up. Three binaryresponse variables were generated for analysis: (1) valid Godin score; (2) valid DINE Fibre score; and (3) validDINE Total Fat score. Multivariate analysis comprised generalised estimating equation regression to examine theassociation of patients’ characteristics with their return of valid responses at both timepoints. 
Results: Overall, 92.1% of participants (832/903) returned questionnaires at both baseline and 18 months. Relativelyfewer valid Godin scores were returned by those who left school aged <15 years (36.5%) than aged 18 and over(50.5%), manual workers (39.5%) than non-manual (49.5%) and those with an elevated cholesterol (>5 mmol)(34.7%) than those with a lower cholesterol (44.4%) but multivariate analysis identified that only school leaving age(p = 0.047) was of statistical significance.Relatively fewer valid DINE scores were returned by manual than non-manual workers (fibre: 80.8% v 86.8%;fat: 71.2% v 80.0%), smokers (fibre: 72.6% v 84.7%; fat: 67.5% v 76.9%), patients with diabetes (fibre: 75.9% v 82.9%;fat: 66.9% v 75.8%) and those with cholesterol >5 mmol (fat: 68.2% v 76.2%) but multivariate analysis showedstatistical significance only for smoking (fibre: p = 0.013; fat: p = 0.045), diabetes (fibre: p = 0.039; fat: p = 0.047), andcholesterol (fat: p = 0.039). 
Conclusions: Our findings illustrate the importance of detailed reporting of research methods, with clearinformation about response rates, respondents and valid outcome data. Outcome measures which are relevant to astudy population should be chosen carefully. The impact of methods of outcome measurement and valid responserates in evaluating healthcare requires further study.

Physical activity assessment in cystic fibrosis: A position statement

Background

The aim of this position statement was to inform the choice of physical activity tools for use within CF research and clinical settings.

Methods

A systematic review of physical activity tools to explore evidence for reliability, validity, and responsiveness. Narrative answers to “four key questions” on motion sensors, questionnaires and diaries were drafted by the core writing team and then discussed at the Exercise Working Group in ECFS Lisbon 2013.

Results and summary

Our current position is that activity monitors such as SenseWear or ActiGraph offer informed choices to facilitate a comprehensive assessment of physical activity, and should as a minimum report on dimensions of physical activity including energy expenditure, step count and time spent in different intensities and sedentary time. The DigiWalker pedometer offers an informed choice of a comparatively inexpensive method of obtaining some measurement of physical activity. The HAES represents an informed choice of questionnaire to assess physical activity. There is insufficient data to recommend the use of one diary over another. Future research should focus on providing additional evidence of clinimetric properties of these and new physical activity assessment tools, as well as further exploring the added value of physical activity assessment in CF.

The effect of changing the built environment on physical activity: a quantitative review of the risk of bias in natural experiments.

BACKGROUND:
Evidence regarding the association of the built environment with physical activity is influencing policy recommendations that advocate changing the built environment to increase population-level physical activity. However, to date there has been no rigorous appraisal of the quality of the evidence on the effects of changing the built environment. The aim of this review was to conduct a thorough quantitative appraisal of the risk of bias present in those natural experiments with the strongest experimental designs for assessing the causal effects of the built environment on physical activity.

METHODS:
Eligible studies had to evaluate the effects of changing the built environment on physical activity, include at least one measurement before and one measurement of physical activity after changes in the environment, and have at least one intervention site and non-intervention comparison site. Given the large number of systematic reviews in this area, studies were identified from three exemplar systematic reviews; these were published in the past five years and were selected to provide a range of different built environment interventions. The risk of bias in these studies was analysed using the Cochrane Risk of Bias Assessment Tool: for Non-Randomized Studies of Interventions (ACROBAT-NRSI).

RESULTS:
Twelve eligible natural experiments were identified. Risk of bias assessments were conducted for each physical activity outcome from all studies, resulting in a total of fifteen outcomes being analysed. Intervention sites included parks, urban greenways/trails, bicycle lanes, paths, vacant lots, and a senior citizen's centre. All outcomes had an overall critical (n = 12) or serious (n = 3) risk of bias. Domains with the highest risk of bias were confounding (due to inadequate control sites and poor control of confounding variables), measurement of outcomes, and selection of the reported result.

CONCLUSIONS:
The present review focused on the strongest natural experiments conducted to date. Given this, the failure of existing studies to adequately control for potential sources of bias highlights the need for more rigorous research to underpin policy recommendations for changing the built environment to increase physical activity. Suggestions are proposed for how future natural experiments in this area can be improved.