Device-based monitoring in physical activity and public health research

A RIEMANN MANIFOLD MODEL FRAMEWORK FOR LONGITUDINAL CHANGES IN PHYSICAL ACTIVITY PATTERNS

Physical activity (PA) is significantly associated with many health outcomes. The wide usage of wearable accelerometer-based activity trackers in recent years has provided a unique opportunity for in-depth research on PA and its relations with health outcomes and interventions. Past analysis of activity tracker data relies heavily on aggregating minute-level PA records into day-level summary statistics in which important information of PA temporal/diurnal patterns is lost. In this paper we propose a novel functional data analysis approach based on Riemann manifolds for modeling PA and its longitudinal changes. We model smoothed minute-level PA of a day as one-dimensional Riemann manifolds and longitudinal changes in PA in different visits as deformations between manifolds. The variability in changes of PA among a cohort of subjects is characterized via variability in the deformation. Functional principal component analysis is further adopted to model the deformations, and PC scores are used as a proxy in modeling the relation between changes in PA and health outcomes and/or interventions. We conduct comprehensive analyses on data from two clinical trials: Reach for Health (RfH) and Metabolism, Exercise and Nutrition at UCSD (MENU), focusing on the effect of interventions on longitudinal changes in PA patterns and how different modes of changes in PA influence weight loss, respectively. The proposed approach reveals unique modes of changes, including overall enhanced PA, boosted morning PA, and shifts of active hours specific to each study cohort. The results bring new insights into the study of longitudinal changes in PA and health and have the potential to facilitate designing of effective health interventions and guidelines.

Longitudinal Associations Between Timing of Physical Activity Accumulation and Health: Application of Functional Data Methods

Accelerometers are widely used for tracking human movement and provide minute-level (or even 30 Hz level) physical activity (PA) records for detailed analysis. Instead of using day-level summary statistics to assess these densely sampled inputs, we implement functional principal component analysis (FPCA) approaches to study the temporal patterns of PA data from 245 overweight/obese women at three visits over a 1-year period. We apply longitudinal FPCA to decompose PA inputs, incorporating subject-specific variability, and then test the association between these patterns and obesity-related health outcomes by multiple mixed effect regression models. With the proposed methods, the longitudinal patterns in both densely sampled inputs and scalar outcomes are investigated and connected. The results show that the health outcomes are strongly associated with PA variation, in both subject and visit-level. In addition, we reveal that timing of PA during the day can impact changes in outcomes, a finding that would not be possible with day-level PA summaries. Thus, our findings imply that the use of longitudinal FPCA can elucidate temporal patterns of multiple levels of PA inputs. Furthermore, the exploration of the relationship between PA patterns and health outcomes can be useful for establishing weight-loss guidelines.

Association of inflammation and lung function decline caused by personal PM2.5 exposure: a machine learning approach in time-series data

Numerous studies focused on the association between lung function impairment and inflammation caused by fine particulate matter (PM_2.5), but the causal relationships are difficult to clarify. In the current study, twenty healthy Chinese young adults who participated in 7 days of observation every four seasons were enrolled, and autoregression models (AM) and classification and regression trees (CART) in a machine learning framework were applied to analyze the association among PM_2.5 exposure, inflammation, and lung function from a data structure perspective. There were strong cross-correlations between personal dose of PM_2.5 (Dw) and lung functions (vital capacity (VC), forced vital capacity (FVC), etc.). These cross-correlation coefficients were associated with inflammatory indicators (uteroglobin (UG), serum amyloid (SAA), and fractional exhaled nitric oxide (FeNO)). CART reported that inflammatory indicators UG and SAA had the predictive ability of the directional association between Dw and FVC at 1-day lag and that high levels of UG and SAA predicted that PM_2.5 exposure induced lung function decline. Consistently, lower lung function indicators at a 2-day lag after personal PM_2.5 exposure predicted the high value of inflammatory indicator FeNO. Taken together, we applied machine learning algorithms to analyze repeated measurement data, finding that inflammation and lung function decline caused by PM_2.5 could affect each other.

Development of a Non-Contacting Muscular Activity Measurement System for Evaluating Knee Extensors Training in Real-Time

To give people more specific information on the quality of their daily motion, it is necessary to continuously measure muscular activity during everyday occupations in an easy way. The traditional methods to measure muscle activity using a combination of surface electromyography (sEMG) sensors and optical motion capture system are expensive and not suitable for non-technical users and unstructured environment. For this reason, in our group we are researching methods to estimate leg muscle activity using non-contact wearable sensors, improving ease of movement and system usability. In a previous study, we developed a method to estimate muscle activity via only a single inertial measurement unit (IMU) on the shank. In this study, we describe a method to estimate muscle activity during walking via two IMU sensors, using an original sensing system and specifically developed estimation algorithms based on ANN techniques. The muscle activity estimation results, estimated by the proposed algorithm after optimization, showed a relatively high estimation accuracy with a correlation efficient of R² = 0.48 and a standard deviation STD = 0.10, with a total system average delay of 192 ms. As the average interval between different gait phases in human gait is 250–1000 ms, a 192 ms delay is still acceptable for daily walking requirements. For this reason, compared with the previous study, the newly proposed system presents a higher accuracy and is better suitable for real-time leg muscle activity estimation during walking.

Factors associated with long-term wearable physical activity monitor user engagement

Wearable physical activity monitors (PAMs) have potential to positively influence physical activity. However, high rates of disengagement have been reported, which dampens enthusiasm, as these devices are unlikely to impact habitual physical activity if they are not worn for a sustained period of time. The purpose of this study was to identify demographic and device-use characteristics (e.g., data sharing) associated with sustained device engagement. Current PAM users (n = 418; mean age: 35.0 ± 12.5; 78% female) from across the USA were recruited online and completed a baseline web-based survey in 2015-2016 comprising questions about demographics and device use. Participants were followed-up again in 2017, at which time they reported whether or not they still used a PAM. Sustained PAM engagement was defined as those who continued use at follow-up. The median follow-up time was 15.5 (±3.7) months. In fully adjusted models, the following were significantly associated with long-term engagement: age (odds ratio [OR]: 1.03; 95% confidence interval [CI]: 1.01-1.05, p = .014), Hispanic ethnicity (OR: 3.67; 95% CI: 1.20-11.26, p = .023), running as a preferred exercise (OR: 1.82; 95% CI: 1.02-3.24, p = .043), wanting to monitor health variables as a reason for choosing to use a PAM (OR: 1.73; 95% CI: 1.02-2.92, p = .042), and sharing data from the PAM publicly on social media (e.g., Facebook and Twitter; OR: 5.11; 95% CI: 1.64-15.93, p = .005). A number of sociodemographic and use characteristics were associated with sustained device use over a median follow-up of 1.3 years. One modifiable factor that may lead to longer device engagement is encouraging users to share data publicly.

Strategies to enhance routine physical activity in care home residents: the REACH research programme including a cluster feasibility RCT

Background

Care home residents are mainly inactive, leading to increased dependency and low mood. Although exercise classes may increase activity, a more sustainable model is to engage staff and residents in increasing routine activity.

Objectives

The objectives were to develop and preliminarily test strategies to enhance the routine physical activity of care home residents to improve their physical, psychological and social well-being through five overlapping workstreams.

Design

This trial had a mixed-methods research design to develop and test the feasibility of undertaking an evaluative study consisting of gaining an understanding of the opportunities for and barriers to enhancing physical activity in care homes (workstream 1); testing physical activity assessment instruments (workstream 2); developing an intervention through a process of intervention mapping (workstream 3); refining the provisional intervention in the care home setting and clarifying outcome measurement (workstream 4); and undertaking a cluster randomised feasibility trial of the intervention [introduced via three facilitated workshops at baseline (with physiotherapist input), 2 weeks (with artist input) and 2 months], with embedded process and health economic evaluations (workstream 5).

Setting

The trial was set in 12 residential care homes differing in size, location, ownership and provision in Yorkshire, UK.

Participants

The participants were elderly residents, carers, managers and staff of care homes.

Intervention

The intervention was MoveMore, designed for the whole home, to encourage and support the movement of residents in their daily routines.

Main outcome measures

The main outcome measures related to the feasibility and acceptability of implementing a full-scale trial in terms of recruitment and retention of care homes and residents, intervention delivery, completion and reporting of baseline data and outcomes (including hours of accelerometer wear, hours of sedentary behaviour and hours and type of physical activity), and safety and cost data (workstream 5).

Results

Workstream 1 – through a detailed understanding of life in a care home, a needs assessment was produced, and barriers to and facilitators of activity were identified. Key factors included ethos of care; organisation, management and delivery of care; use of space; and the residents’ daily routines. Workstream 2 – 22 (73.3%) out of 30 residents who wore a hip accelerometer had valid data (≥ 8 hours on ≥ 4 days of the week). Workstream 3 – practical mechanisms for increasing physical activity were developed, informed by an advisory group of stakeholders and outputs from workstreams 1 and 2, framed by the process of intervention mapping. Workstream 4 – action groups were convened in four care homes to refine the intervention, leading to further development of implementation strategies. The intervention, MoveMore, is a whole-home intervention involving engagement with a stakeholder group to implement a cyclical process of change to encourage and support the movement of residents in their daily routines. Workstream 5 – 12 care homes and 153 residents were recruited to the cluster randomised feasibility trial. Recruitment in the care homes varied (40–89%). Five care homes were randomised to the intervention and seven were randomised to usual care. Predetermined progression criteria were recruitment of care homes and residents (green); intervention delivery (amber); and data collection and follow-up – 52% of residents provided usable accelerometer data at 9 months (red), > 75% of residents had reported outcomes at 9 months (green, but self-reported resident outcomes were red), 26% loss of residents to follow-up at 9 months [just missing green criterion (no greater than 25%)] and safety concerns (green).

Limitations

Observations of residents’ movements were not conducted in private spaces. Working with care home residents to identify appropriate outcome measures was challenging. Take-up of the intervention was suboptimal in some sites. It was not possible to make a reliably informed decision on the most appropriate physical activity end point(s) for future use in a definitive trial.

Conclusions

A whole-home intervention was developed that was owned and delivered by staff and was informed by residents and staff. The feasibility of conducting a cluster randomised controlled trial was successfully tested: the target numbers of care homes and residents were recruited, demonstrating that it is possible to recruit care home residents to a cluster randomised trial, although this process was time-consuming and resource heavy. A large data set was collected, which provided a comprehensive picture of the environment, residents and staff in care homes. Extensive quantitative and qualitative work comprehensively explored a neglected area of health and social care research. Completion of ethnographic work in a range of settings enabled the production of an in-depth picture of life in care homes that will be helpful for other researchers considering organisational change in this setting.

Future work

The content and delivery of the intervention requires optimisation and the outcome measurement requires further refinement prior to undertaking a full trial evaluation. Consideration could be given to a recommended, simplified, core outcome set, which would facilitate data collection in this population.

Trial registration

Current Controlled Trials ISRCTN16076575.

Funding

This project was funded by the National Institute for Health Research (NIHR) Programme Grant for Applied Research programme and will be published in full in Programme Grant for Applied Research; Vol. 9, No. 9. See the NIHR Journals Library website for further project information.

Assessing personal exposure using Agent Based Modelling informed by sensors technology

Technology innovations create possibilities to capture exposure-related data at a great depth and breadth. Considering, though, the substantial hurdles involved in collecting individual data for whole populations, this study introduces a first approach of simulating human movement and interaction behaviour, using Agent Based Modelling (ABM). A city scale ABM was developed for urban Thessaloniki, Greece that feeds into population-based exposure assessment without imposing prior bias, basing its estimations onto emerging properties of the behaviour of the computerised autonomous decision makers (agents) that compose the city-system. Population statistics, road and buildings networks data were transformed into human, road and building agents, respectively. Survey outputs with time-use patterns were associated with human agent rules, aiming to model representative to real-world behaviours. Moreover, time-geography of exposure data, derived from a local sensors campaign, was used to inform and enhance the model. As a prevalence of an agent-specific decision-making, virtual individuals of different sociodemographic backgrounds express different spatiotemporal behaviours and their trajectories are coupled with spatially resolved pollution levels. Personal exposure was evaluated by assigning PM concentrations to human agents based on coordinates, type of location and intensity of encountered activities. Study results indicated that PM2.5 inhalation adjusted exposure between housemates can differ by 56.5% whereas exposure between two neighbours can vary by as much as 87%, due to the prevalence of different behaviours. This study provides details of a new methodology that permits the cost-effective construction of refined time-activity diaries and daily exposure profiles, taking into account different microenvironments and sociodemographic characteristics. The proposed method leads to a refined exposure assessment model, addressing effectively vulnerable subgroups of population. It can be used for evaluating the probable impacts of different public health policies prior to implementation reducing, therefore, the time and expense required to identify efficient measures.

Comparing the activPAL software's Primary Time in Bed Algorithm against Self-Report and van der Berg's Algorithm

The purpose of this study was to compare activPAL algorithm-estimated values for time in bed (TIB), wake time (WT) and bedtime (BT) against self-report and an algorithm developed by van der Berg and colleagues. Secondary analyses of baseline data from the Community Activity for Prevention Study (CAPS) were used in which adults ≥ 18 years wore the activPAL for seven days. Mixed-effects models compared differences between TIB, WT, and BT for all three methods. Bland-Altman plots examined agreement and the two-one-sided test examined equivalence. activPAL was not equivalent to self-report or van der Berg in estimating TIB, but was equivalent to self-report for estimating BT, and was equivalent to van der Berg for estimating WT. The activPAL algorithm requires adjustments before researchers can use it to estimate TIB. However, researchers can use activPAL's option to manually enter self-reported BT and WT to estimate TIB and better understand 24-hour movement patterns.

Comparison of Wearable and Clinical Devices for Acquisition of Peripheral Nervous System Signals

A key access point to the functioning of the autonomic nervous system is the investigation of peripheral signals. Wearable devices (WDs) enable the acquisition and quantification of peripheral signals in a wide range of contexts, from personal uses to scientific research. WDs have lower costs and higher portability than medical-grade devices. However, the achievable data quality can be lower, and data are subject to artifacts due to body movements and data losses. It is therefore crucial to evaluate the reliability and validity of WDs before their use in research. In this study, we introduce a data analysis procedure for the assessment of WDs for multivariate physiological signals. The quality of cardiac and electrodermal activity signals is validated with a standard set of signal quality indicators. The pipeline is available as a collection of open source Python scripts based on the pyphysio package. We apply the indicators for the analysis of signal quality on data simultaneously recorded from a clinical-grade device and two WDs. The dataset provides signals of six different physiological measures collected from 18 subjects with WDs. This study indicates the need to validate the use of WDs in experimental settings for research and the importance of both technological and signal processing aspects to obtain reliable signals and reproducible results.

Determining Physical Activity Characteristics from Wristband Data for Use in Automated Insulin Delivery Systems

Algorithms that can determine the type of physical activity (PA) and quantify the intensity can allow precision medicine approaches, such as automated insulin delivery systems that modulate insulin administration in response to PA. In this work, data from a multi-sensor wristband is used to design classifiers to distinguish among five different physical states (PS) (resting, activities of daily living, running, biking, and resistance training), and to develop models to estimate the energy expenditure (EE) of the PA for diabetes therapy. The data collected are filtered, features are extracted from the reconciled signals, and the extracted features are used by machine learning algorithms, including deep-learning techniques, to obtain accurate PS classification and EE estimation. The various machine learning techniques have different success rates ranging from 75.7% to 94.8% in classifying the five different PS. The deep neural network model with long short-term memory has 94.8% classification accuracy. We achieved 0.5 MET (Metabolic Equivalent of Task) root-mean-square error for EE estimation accuracy, relative to indirect calorimetry with randomly selected testing data (10% of collected data). We also demonstrate a 5% improvement in PS classification accuracy and a 0.34 MET decrease in the mean absolute error when using multi-sensor approach relative to using only accelerometer data.

Validating Accelerometers for the Assessment of Body Position and Sedentary Behavior

There is growing evidence that sedentary behavior is a risk factor for somatic and mental health. However, there is still a lack of objective field methods, which can assess both components of sedentary behavior: the postural (sitting/lying) and the movement intensity part. The purpose of the study was to compare the validity of different accelerometers (ActivPAL [thigh], ActiGraph [hip], move [hip], and move [thigh]). 20 adults (10 females; age 25.68 ± 4.55 years) participated in a structured protocol with a series of full- and semistandardized sessions under laboratory conditions. Direct observation via video recording was used as a criterion measure of body positions (sitting/lying vs. nonsitting/lying). By combining direct observation with metabolic equivalent tables, protocol activities were also categorized as sedentary or nonsedentary. Cohen’s kappa was calculated as an overall validity measure to compare accelerometer and video recordings. Across all conditions, for the measurement of sitting/lying body positions, the ActivPAL ([thigh], ĸ = .85) and Move 4 ([thigh], ĸ = .97) showed almost perfect agreement, whereas the Move 4 ([hip], ĸ = .78) and ActiGraph ([hip], ĸ = .67) showed substantial agreement. For the sedentary behavior part, across all conditions, the ActivPAL ([thigh], ĸ = .90), Move 4 ([thigh], ĸ = .95) and Move 4 ([hip], ĸ = .84) revealed almost perfect agreement, whereas the ActiGraph ([hip], ĸ = .69) showed substantial agreement. In particular, thigh-worn devices, namely the Move and the ActivPAL, achieved up to excellent validity in measuring sitting/lying body positions and sedentary behavior and are recommended for future studies.

Using Smart Bracelets to Assess Heart Rate Among Students During Physical Education Lessons: Feasibility, Reliability, and Validity Study

Background

An increasing number of wrist-worn wearables are being examined in the context of health care. However, studies of their use during physical education (PE) lessons remain scarce.

Objective

We aim to examine the reliability and validity of the Fizzo Smart Bracelet (Fizzo) in measuring heart rate (HR) in the laboratory and during PE lessons.

Methods

In Study 1, 11 healthy subjects (median age 22.0 years, IQR 3.75 years) twice completed a test that involved running on a treadmill at 6 km/h for 12 minutes and 12 km/h for 5 minutes. During the test, participants wore two Fizzo devices, one each on their left and right wrists, to measure their HR. At the same time, the Polar Team2 Pro (Polar), which is worn on the chest, was used as the standard. In Study 2, we went to 10 schools and measured the HR of 24 students (median age 14.0 years, IQR 2.0 years) during PE lessons. During the PE lessons, each student wore a Polar device on their chest and a Fizzo on their right wrist to measure HR data. At the end of the PE lessons, the students and their teachers completed a questionnaire where they assessed the feasibility of Fizzo. The measurements taken by the left wrist Fizzo and the right wrist Fizzo were compared to estimate reliability, while the Fizzo measurements were compared to the Polar measurements to estimate validity. To measure reliability, intraclass correlation coefficients (ICC), mean difference (MD), standard error of measurement (SEM), and mean absolute percentage errors (MAPE) were used. To measure validity, ICC, limits of agreement (LOA), and MAPE were calculated and Bland-Altman plots were constructed. Percentage values were used to estimate the feasibility of Fizzo.

Results

The Fizzo showed excellent reliability and validity in the laboratory and moderate validity in a PE lesson setting. In Study 1, reliability was excellent (ICC>0.97; MD<0.7; SEM<0.56; MAPE<1.45%). The validity as determined by comparing the left wrist Fizzo and right wrist Fizzo was excellent (ICC>0.98; MAPE<1.85%). Bland-Altman plots showed a strong correlation between left wrist Fizzo measurements (bias=0.48, LOA=–3.94 to 4.89 beats per minute) and right wrist Fizzo measurements (bias=0.56, LOA=–4.60 to 5.72 beats per minute). In Study 2, the validity of the Fizzo was lower compared to that found in Study 1 but still moderate (ICC>0.70; MAPE<9.0%). The Fizzo showed broader LOA in the Bland-Altman plots during the PE lessons (bias=–2.60, LOA=–38.89 to 33.69 beats per minute). Most participants considered the Fizzo very comfortable and easy to put on. All teachers thought the Fizzo was helpful.

Conclusions

When participants ran on a treadmill in the laboratory, both left and right wrist Fizzo measurements were accurate. The validity of the Fizzo was lower in PE lessons but still reached a moderate level. The Fizzo is feasible for use during PE lessons.

Who Uses Wearable Activity Trackers and Why? A Comparison of Former and Current Users in the United States

BACKGROUND

There has been an explosion in the use of wearable activity trackers (WATs), but we do not fully understand who wears them and why. This study's purpose was to describe the characteristics of WAT users and to compare current and former users.

MATERIALS AND METHODS

A variety of internet-based resources (eg, Craigslist, Facebook) were used to recruit current and former WAT users. Respondents completed a web-based survey, where they provided information on sociodemographic characteristics, health, physical activity behavior, and about their WAT use.

RESULTS

Of the 2826 respondents who gave informed consent, 70.8% (n = 2002) met inclusion criteria for this analysis. Respondents ranged from 18 to 81 years old (mean 32.9 ± 12.2 standard deviation) with 73.8% women. Most were current WAT users (68.7%), and the average length of WAT use overall was 9.3 ± 9.7 months. On average, current users wore the device for 3.7 months longer than former users. Compared to current users, former users had a lower body mass index (1.2 kg/m² less), reported fewer medical conditions, shared data from their device less often, and received the device as a gift more frequently.

CONCLUSIONS

Current and former users varied in their reasons for using a WAT and how they used their device. Differences identified between these groups support further exploration of associations between WAT users' profiles and their physical activity behavior.

Joint and Individual Representation of Domains of Physical Activity, Sleep, and Circadian Rhythmicity

Developments in wearable technology have enabled researchers to continuously and objectively monitor various aspects and physiological domains of real-life including levels of physical activity, quality of sleep, and strength of circadian rhythm in many epidemiological and clinical studies. Current analytical practice is to summarize each of these three domains individually via a standard inventory of interpretable features, and explore individual associations between the features and clinical variables. However, the features often exhibit significant interaction and correlation both within and between domains. Integration of features across multiple domains remains methodologically challenging. To address this problem, we propose to use joint and individual variation explained (JIVE), a dimension reduction technique that efficiently deals with multivariate data representing multiple domains. In this paper, we review the most frequently used features to characterize the domains of physical activity, sleep, and circadian rhythniicity and illustrate the approach using wrist-worn actigraphy data from 198 participants of the Baltimore Longitudinal Study of Aging.

Incorporating an exercise rehabilitation programme for people with intermittent claudication into an established cardiac rehabilitation service: A protocol for a pilot study

Introduction

Current UK guidance for the treatment of intermittent claudication (IC) states that supervised exercise programmes (SEPs) should be offered as first-line treatment [1], prior to surgical interventions. However, there is currently a national shortage of dedicated SEPs. It has been suggested that the established network of UK Cardiac Rehabilitation (CR) programmes could cater for IC patients. This study will investigate the feasibility of combining IC and CR patients into one SEP, and explore the patient perception's regarding the treatment programme, to establish whether CR could potentially fill the gap in service provision.

Methods and analysis

Patients diagnosed with IC will be incorporated into a CR programme where they will exercise alongside patients with coronary artery disease (CAD). Participants will attend a 2-h class, once a week for a total of 12 weeks. Another group of IC patients will attend an SEP consisting solely of other IC patients (control group).

Outcome measures

The study will evaluate the feasibility of recruitment, retention, and participant experience of the intervention as well as physiological outcomes (primary outcome: pain-free walking, and maximal walking distance; secondary outcomes: physical activity levels, perceived walking ability, and disease-specific quality of life). Outcomes will be assessed at baseline and on completion of the SEP.

Ethics and dissemination

Ethical approval for this study was obtained from North West- Greater Manchester West Research Ethics Committee on 13th June 2018 (IRAS ID: 230391). This study is registered with clinicaltrials. gov (NCT03564080) and conforms to the Declaration of Helsinki. Results will be disseminated via national conferences and published in peer-reviewed journals.

Longitudinal trajectories of physical activity and walking performance by gross motor function classification system level for children with cerebral palsy

Purpose: This study aims to present developmental trajectories for physical activity (PA) and walking performance for children with cerebral palsy (CP).Materials and methods: Seventy-nine children with CP, 39 (49%) female, Gross Motor Functional Classification System levels I-V, and mean age 91.3 months (+/-27.7 SD) participated. Participants in levels I-V wore the Actigraph to capture PA and children in levels I-II also wore a StepWatch (SW) (n = 43) to measure walking performance. Trajectories for average PA counts/minute and number of minutes of moderate to vigorous PA were generated for levels I, II, and III/IV/V (aggregate). Single leg strides/day and average strides faster than 30 strides/min trajectories were generated for levels I-II.Results: Participants did not display plateaus in PA or walking performance based on functional level. Children in all levels showed a decrease in amount and intensity of PA from 3.0 to 12 years old, with participants in level I demonstrating the steepest decline. Children in level I decreased slightly, and level II increased slightly in both walking performance measures from 3.0 to 12 years old.Conclusions: Longitudinal curves demonstrate variations in PA and walking performance by functional level and provide prognostic information as to what changes may be anticipated for children with CP.Implications for rehabilitationLongitudinal developmental trajectories for physical activity and walking performance for children with cerebral palsy across functional levels are documented.Trajectories have potential to support collaborative intervention planning between therapists and families relative to physical activity and walking performance.

Identifying the Incidence of Exercise Dependence Attitudes, Levels of Body Perception, and Preferences for Use of Fitness Technology Monitoring

Background: The study was focused on identifying the relationship between the incidence of exercise dependence attitudes, the level of body perception, and preferences for use of fitness technology monitoring. Methods: We investigated 241 students from physical education and sports specializations. We used a questionnaire structured in 5 parts: personal information (age, gender, weight, height, and institutional membership), Exercise Dependence Scale-R (EDS-21), the Compulsive Exercise Test (CET), Body perception questionnaire, and Fitness Monitoring Technology. Results: Application of EDS-21 revealed 8.3% with dependence and CET with 13.4%. Body mass index (BMI) in combination with self-image body: 18 (7.4%) of subjects over-estimated themselves, 18 (7.4%) of subjects underestimated themselves, 38.6% of the subjects were satisfied with their appearance, 17% were very satisfied and none were dissatisfied. A total of 36.1% thought they had the correct weight and 8.7% thought of losing weight. The most preferred monitoring technologies were the treadmill and the smartphone's fitness applications. A total of 11.6% of the subjects always used technologies, and 17% of subjects never did so. Conclusions: In order to identify the level of incidence of exercise dependence, body perception, and preference and frequency of use of monitoring technology, it is necessary to expand the level of knowledge about health and physical activities. The approach would diminish medical incidences of addiction, improve proactive behaviors, and correct prohealth.

Knee extensor muscular activity estimation during different walking patterns: flat normal and brisk walking, stair climbing

Preserving mobility, the ability to keep a correct posture and dynamic balance in order to walk properly, is fundamental to maintain autonomy in daily life. Based on the correlation between muscle groups and autonomy, previous research has suggested that maintaining muscular tone in knee extensors is critical. Continuous training of knee extensors during aging is therefore essential to maintain independence. In this work, it is hypothesized that it is possible to estimate knee extensor activity only from IMU data based on a simple lower limbs model. The accuracy of the knee extensor activity estimation algorithm has been tested using sEMG measurements as control data on three different walking patterns: normal walk, fast walk and stair climbing. Estimated knee torque area and measured muscular activity for each step were compared confirming a high estimation accuracy with a correlation efficient R=0.80. Moreover, muscular activity can be divided based on intensity in three groups of statistically significant difference confirmed by the Steel-Dwass method. Future works should test the usability of the algorithm for different walking patterns, and use the collected data and the refined algorithm to implement a smart resistive device to increase knee extensor exertion during each walking pattern to the level necessary for sufficient extensor training.

Assessing and enhancing the utility of low-cost activity and location sensors for exposure studies

Nowadays, the advancement of mobile technology in conjunction with the introduction of the concept of exposome has provided new dynamics to the exposure studies. Since the addressing of health outcomes related to environmental stressors is crucial, the improvement of exposure assessment methodology is of paramount importance. Towards this aim, a pilot study was carried out in the two major cities of Greece (Athens, Thessaloniki), investigating the applicability of commercially available fitness monitors and the Moves App for tracking people's location and activities, as well as for predicting the type of the encountered location, using advanced modeling techniques. Within the frame of the study, 21 individuals were using the Fitbit Flex activity tracker, a temperature logger, and the application Moves App on their smartphones. For the validation of the above equipment, participants were also carrying an Actigraph (activity sensor) and a GPS device. The data collected from Fitbit Flex, the temperature logger, and the GPS (speed) were used as input parameters in an Artificial Neural Network (ANN) model for predicting the type of location. Analysis of the data showed that the Moves App tends to underestimate the daily steps counts in comparison with Fitbit Flex and Actigraph, respectively, while Moves App predicted the movement trajectory of an individual with reasonable accuracy, compared to a dedicated GPS. Finally, the encountered location was successfully predicted by the ANN in most of the cases.

Validation of the ADAMO Care Watch for step counting in older adults

Background

Accurate measurement devices are required to objectively quantify physical activity. Wearable activity monitors, such as pedometers, may serve as affordable and feasible instruments for measuring physical activity levels in older adults during their normal activities of daily living. Currently few available accelerometer-based steps counting devices have been shown to be accurate at slow walking speeds, therefore there is still lacking appropriate devices tailored for slow speed ambulation, typical of older adults.

This study aimed to assess the validity of step counting using the pedometer function of the ADAMO Care Watch, containing an embedded algorithm for measuring physical activity in older adults.

Methods

Twenty older adults aged ≥ 65 years (mean ± SD, 75±7 years; range, 68–91) and 20 young adults (25±5 years, range 20–40), wore a care watch on each wrist and performed a number of randomly ordered tasks: walking at slow, normal and fast self-paced speeds; a Timed Up and Go test (TUG); a step test and ascending/descending stairs. The criterion measure was the actual number of steps observed, counted with a manual tally counter. Absolute percentage error scores, Intraclass Correlation Coefficients (ICC), and Bland–Altman plots were used to assess validity.

Results

ADAMO Care Watch demonstrated high validity during slow and normal speeds (range 0.5–1.5 m/s) showing an absolute error from 1.3% to 1.9% in the older adult group and from 0.7% to 2.7% in the young adult group. The percentage error for the 30-metre walking tasks increased with faster pace in both young adult (17%) and older adult groups (6%). In the TUG test, there was less error in the steps recorded for older adults (1.3% to 2.2%) than the young adults (6.6% to 7.2%). For the total sample, the ICCs for the ADAMO Care Watch for the 30-metre walking tasks at each speed and for the TUG test were ranged between 0.931 to 0.985.

Conclusion

These findings provide evidence that the ADAMO Care Watch demonstrated highly accurate measurements of the steps count in all activities, particularly walking at normal and slow speeds. Therefore, these data support the inclusion of the ADAMO Care Watch in clinical applications for measuring the number of steps taken by older adults at normal, slow walking speeds.

Identifying compensatory movement patterns in the upper extremity using a wearable sensor system

OBJECTIVE

Movement impairments such as those due to stroke often result in the nervous system adopting atypical movements to compensate for movement deficits. Monitoring these compensatory patterns is critical for improving functional outcomes during rehabilitation. The purpose of this study was to test the feasibility and validity of a wearable sensor system for detecting compensatory trunk kinematics during activities of daily living.

APPROACH

Participants with no history of neurological impairments performed reaching and manipulation tasks with their upper extremity, and their movements were recorded by a wearable sensor system and validated using a motion capture system. Compensatory movements of the trunk were induced using a brace that limited range of motion at the elbow.

MAIN RESULTS

Our results showed that the elbow brace elicited compensatory movements of the trunk during reaching tasks but not manipulation tasks, and that a wearable sensor system with two sensors could reliably classify compensatory movements (~90% accuracy).

SIGNIFICANCE

These results show the potential of the wearable system to assess and monitor compensatory movements outside of a lab setting.

Motion sensors in multiple sclerosis: Narrative review and update of applications

INTRODUCTION

The use of motion sensors for measuring physical activity in multiple sclerosis (MS) has evolved with increasing research particularly during the past decade. Areas covered: This manuscript reviews the literature regarding the application of motion sensors for measuring physical activity in MS. We first describe 'what is known' about their use in MS by examining the evidence generated between 1997 and 2012, including the psychometric properties of motion sensors in MS and the development of MS-specific accelerometer cut-points. We then evaluate 'what is new' based on research conducted between 2013 and 2017. This includes newer research on psychometric properties of motion sensors in MS, development of new MS-specific accelerometer and step-rate cut-points, sedentary behavior assessment, and research on fitness trackers and multisensors in MS. The final part presents a picture of 'what is next' for the applications of motion sensors in MS, especially pertaining new opportunities for testing and using fitness trackers in MS, and tracking disease and disability progression based on motion sensor output. Expert commentary: The use of motion sensors in MS has grown substantially over the years; however, a lot more can be done to explore the full potential and utility of these devices in MS.

Validity of an Integrative Method for Processing Physical Activity Data

Accurate assessments of both physical activity and sedentary behaviors are crucial to understand the health consequences of movement patterns and to track changes over time and in response to interventions.

PURPOSE

The study evaluates the validity of an integrative, machine learning method for processing activity monitor data in relation to a portable metabolic analyzer (Oxycon mobile [OM]) and direct observation (DO).

METHODS

Forty-nine adults (age 18-40 yr) each completed 5-min bouts of 15 activities ranging from sedentary to vigorous intensity in a laboratory setting while wearing ActiGraph (AG) on the hip, activPAL on the thigh, and OM. Estimates of energy expenditure (EE) and categorization of activity intensity were obtained from the AG processed with Lyden's sojourn (SOJ) method and from our new sojourns including posture (SIP) method, which integrates output from the AG and activPAL. Classification accuracy and estimates of EE were then compared with criterion measures (OM and DO) using confusion matrices and comparisons of the mean absolute error of log-transformed data (MAE ln Q).

RESULTS

The SIP method had a higher overall classification agreement (79%, 95% CI = 75%-82%) than the SOJ (56%, 95% CI = 52%-59%) based on DO. Compared with OM, estimates of EE from SIP had lower mean absolute error of log-transformed data than SOJ for light-intensity (0.21 vs 0.27), moderate-intensity (0.33 vs 0.42), and vigorous-intensity (0.16 vs 0.35) activities.

CONCLUSIONS

The SIP method was superior to SOJ for distinguishing between sedentary and light activities as well as estimating EE at higher intensities. Thus, SIP is recommended for research in which accuracy of measurement across the full range of activity intensities is of interest.

Systematic review of the effectiveness of health-related behavioral interventions using portable activity sensing devices (PASDs)

BACKGROUND

Portable activity sensing devices (PASDs) have received significant interest as tools for objectively measuring activity-related parameters and promoting health-related outcomes. Studies of PASDs suggest the potential value of integrating them with behavioral interventions to improve intermediate and downstream clinical outcomes.

OBJECTIVES

This systematic review describes and evaluates evidence from controlled studies of interventions using PASDs on their effectiveness in health-related outcomes. Study quality was also assessed.

METHODS

A systematic literature search was performed of MEDLINE, Cochrane Central Register of Controlled Trials, PsycINFO, EMBASE, and CINAHL databases. We included English-language papers of controlled trials through 2015 reporting the effectiveness of PASDs in improving health-related outcomes in any population. We extracted and analyzed data on study characteristics including design, target population, interventions, and findings.

RESULTS

Seventeen trials met the inclusion criteria from a total of 9553 unique records. Study objectives varied greatly, but most sought to increase physical activity. Studies with a "passive" intervention arm using a PASD with minimal behavioral support generally did not demonstrate effectiveness in improving health-related outcomes. Interventions integrating PASDs with multiple behavioral change techniques were more likely to be effective, particularly for intermediate outcomes such as physical activity and weight loss. Trials had small sample sizes but were generally free of bias, except for blinding and selection bias.

CONCLUSION

There is insufficient evidence to draw a conclusion about the general health-related benefits of PASD interventions. PASD interventions may improve intermediate outcomes when coupled with multiple behavioral change techniques. Devices alone or with minimal behavioral change support are insufficient to change health-related outcomes.

Development of a portable sensorised handle for the objective assessment of the effectiveness and concordance of intervention plans in dementia

Dementia is a global issue which is rising rapidly, with 46.8 million people worldwide living with dementia in 2015. This number is expected to increase to 74.7 million by 2030. Some researches show that some kinds of exercises, such as resistance band exercises, would help to improve the memory and other abilities in patients with dementia. However, the true effectiveness of these activities is not really known due to the lack of objective assessment of efforts done during the long intervention time. Moreover, there is no way of measuring the actual activities performed by the patient without direct observation. As a consequence, it is impossible to measure the concordance between the patient and the therapist.

Validation of the AX3 triaxial accelerometer in older functionally impaired people

BACKGROUND

Studying physical activity (PA) trends in older populations and potential interventions for increasing PA is important, as PA is a factor in many age-related health outcomes such as chronic disease, premature mortality, physical function and injuries from falls. Objective measures of PA provide valuable information regarding the functional impact that ageing and chronic disease states may have on a patient's life.

AIMS

The purpose of this study was to test the validity of the AX3 PA monitor in an older population and to investigate whether the AX3 is a valid measure of distinct types or levels of activity in older people with a spectrum of mobility.

METHODS

Validity of the AX3 PA monitor was tested using the RT3 as a means of cross-validating the AX3. Study participants wore both the AX3 and the RT3 accelerometers, positioned on their non-dominant side, whilst completing a series of standardised everyday activities.

RESULTS

Although overall correlation was high (r > 0.8) between the RT3 and lower-limb-mounted AX3 counts, the correlation between the two devices was much stronger for walking activity than for any of the non-walking activities.

DISCUSSION

Activity counts at all lower limb positions for the AX3 and RT3 were highly correlated. Correlation between wrist-mounted AX3 counts and lower limb AX3 counts was only moderate, and worsened when walking aids were in use.

CONCLUSIONS

The results of this study indicate that the AX3 monitor is a valid tool, which might be used to objectively measure walking activity in older functionally impaired adults, a welcome finding for this under-researched area.

How Sensors Might Help Define the External Exposome

The advent of the exposome concept, the advancement of mobile technology, sensors, and the “internet of things” bring exciting opportunities to exposure science. Smartphone apps, wireless devices, the downsizing of monitoring technologies, along with lower costs for such equipment makes it possible for various aspects of exposure to be measured more easily and frequently. We discuss possibilities and lay out several criteria for using smart technologies for external exposome studies. Smart technologies are evolving quickly, and while they provide great promise for advancing exposure science, many are still in developmental stages and their use in epidemiology and risk studies must be carefully considered. The most useable technologies for exposure studies at this time relate to gathering exposure-factor data, such as location and activities. Development of some environmental sensors (e.g., for some air pollutants, noise, UV) is moving towards making the use of these more reliable and accessible to research studies. The possibility of accessing such an unprecedented amount of personal data also comes with various limitations and challenges, which are discussed. The advantage of improving the collection of long term exposure factor data is that this can be combined with more “traditional” measurement data to model exposures to numerous environmental factors.

Long-term Evaluation of the “Get Fit for Active Living” Program

Cette étude a examiné les niveaux de 6 et 12 mois d’adhésion à l’activité physique, les changements fonctionnels et les déterminants psychosociaux de l’activité physique chez 176 adultes âgés qui ont participé au programme pilote «Get Fit for Active Living» (GFAL). Des mesures fonctionnelles et psychosociales ont été menées en personne à 6 mois; à 12 mois, les mesures psychosociales et la participation à l’activité physique ont été évaluées par interview téléphonique. Quatre-vingt-quinze pour cent ont été retenus dans l’étude au suivi de 6 mois, et 88 pour cent à 12 mois. Le taux d’adhésion autodéclaré à l’exercice à 12 mois était de 66%. La principale raison de la participation continue dans l’exercice était de maintenir la santé (45%). Les motifs de non-respect étaient la maladie (38%) et le manque de motivation (32%). Les résultats identifient les facteurs associés aux changements positifs de comportement que les promoteurs peuvent utiliser lorsqu’ils ciblent une population âgée. Les résultats du projet GFAL peuvent servir de modèle pour des programmes d’exercices durables et communautaires pour les aînés.

Exploration of Sedentary Behavior in Residential Substance Abuse Populations: Results From an Intervention Study

BACKGROUND

There is a growing body of research linking sedentary behavior with increased risk of chronic disease and all-cause mortality. It is increasingly recommended that service providers address the multiple behavioral risk factors associated with these chronic diseases as part of routine substance abuse treatment.

OBJECTIVES

The study objective was to investigate rates of physical activity and sedentary behavior in a residential substance abuse population. In addition, efficacy of a sedentary behavior intervention, "Sit Switch," was examined for feasibility in this context.

METHODS

Participants (n = 54) were residents of The Salvation Army Recovery Service Centres located in Canberra and on the Gold Coast, Australia. Actigraph GT3X+ accelerometers were used to measure rates and patterns of sedentariness and physical activity. A nonrandomized controlled study of a single-session group intervention aimed at decreasing prolonged sitting ("Sit Switch") was conducted. Education, motivational-interviewing, and goal setting components underpinned the "Sit Switch" intervention.

RESULTS

Individuals were highly sedentary, spending 73% of daily activity at sedentary intensity engaged in inadequate levels of moderate physical activity (6.6%/day). The single session educational program did not lead to any significant changes in sedentary behavior. Conclusion/Importance: High levels of sedentariness and low levels of physical activity engagement are evident in residents in substance abuse treatment programs. It is strongly recommended that sedentariness, a modifiable risk behavior with independent consequences for cardiovascular disease and cancer, be addressed within residential programs.

Rationale, design and protocol of a longitudinal study assessing the effect of total knee arthroplasty on habitual physical activity and sedentary behavior in adults with osteoarthritis

Background

Physical activity levels are decreased and sedentary behaviour levels are increased in patients with knee osteoarthritis (OA). However, previous studies have shown that following total knee arthroplasty (TKA), objectively measured physical activity levels do not change compared to before the surgery. Very few studies have objectively assessed sedentary behaviour following TKA. This study aims to assess patterns of objective habitual physical activity and sedentary behaviour in patients with knee OA and to determine whether these change following TKA.

Methods

Patients diagnosed with knee osteoarthritis and scheduled for unilateral primary total knee arthroplasty will be recruited from the Orthopaedic Division at the Charlotte Maxeke Johannesburg Academic Hospital. Eligible participants will have assessments completed one week before the scheduled arthroplasty, six weeks, and six months post-operatively. The primary outcomes are habitual physical activity and sedentary behaviour which will be measured using accelerometry (Actigraph GTX3+ and activPal monitors) at the specific time points. The secondary outcomes will be improvements in osteoarthritis-specific quality of life measures using the following questionnaires: Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), Knee injury and Osteoarthritis Outcome Score (KOOS), Oxford Knee Score (OKS), Knee Society Clinical Rating System (KSS), UCLA activity index; subjective pain scores, and self reported sleep quality.

Discussion

The present study will contribute to the field of musculoskeletal health by providing a rich detailed description of the patterns of accumulation of physical activity and sedentary behaviour in patients with knee OA. These data will contribute to existing knowledge using an objective measurement for the assessment of functional ability after total knee arthroplasty. Although studies have used accelerometry to measure physical activity in knee OA patients, the data provided thus far have not delved into the detailed patterns of how and when physical activity is accumulated before and after TKA. Accurate assessment of physical activity is important for physical activity interventions that target special populations.

Trial registration

NCT02675062 (4 February 2016).

Statistical approaches to account for missing values in accelerometer data: Applications to modeling physical activity

Physical inactivity is a recognized risk factor for many chronic diseases. Accelerometers are increasingly used as an objective means to measure daily physical activity. One challenge in using these devices is missing data due to device nonwear. We used a well-characterized cohort of 333 overweight postmenopausal breast cancer survivors to examine missing data patterns of accelerometer outputs over the day. Based on these observed missingness patterns, we created psuedo-simulated datasets with realistic missing data patterns. We developed statistical methods to design imputation and variance weighting algorithms to account for missing data effects when fitting regression models. Bias and precision of each method were evaluated and compared. Our results indicated that not accounting for missing data in the analysis yielded unstable estimates in the regression analysis. Incorporating variance weights and/or subject-level imputation improved precision by >50%, compared to ignoring missing data. We recommend that these simple easy-to-implement statistical tools be used to improve analysis of accelerometer data.

Interest and preferences for using advanced physical activity tracking devices: results of a national cross-sectional survey

OBJECTIVES

Pedometers are an effective self-monitoring tool to increase users' physical activity. However, a range of advanced trackers that measure physical activity 24 hours per day have emerged (eg, Fitbit). The current study aims to determine people's current use, interest and preferences for advanced trackers.

DESIGN AND PARTICIPANTS

A cross-sectional national telephone survey was conducted in Australia with 1349 respondents.

OUTCOME MEASURES

Regression analyses were used to determine whether tracker interest and use, and use of advanced trackers over pedometers is a function of demographics. Preferences for tracker features and reasons for not wanting to wear a tracker are also presented.

RESULTS

Over one-third of participants (35%) had used a tracker, and 16% are interested in using one. Multinomial regression (n=1257) revealed that the use of trackers was lower in males (OR=0.48, 95% CI 0.36 to 0.65), non-working participants (OR=0.43, 95% CI 0.30 to 0.61), participants with lower education (OR=0.52, 95% CI 0.38 to 0.72) and inactive participants (OR=0.52, 95% CI 0.39 to 0.70). Interest in using a tracker was higher in younger participants (OR=1.73, 95% CI 1.15 to 2.58). The most frequently used tracker was a pedometer (59%). Logistic regression (n=445) revealed that use of advanced trackers compared with pedometers was higher in males (OR=1.67, 95% CI 1.01 to 2.79) and younger participants (OR=2.96, 95% CI 1.71 to 5.13), and lower in inactive participants (OR=0.35, 95% CI 0.19 to 0.63). Over half of current or interested tracker users (53%) prefer to wear it on their wrist, 31% considered counting steps the most important function and 30% regarded accuracy as the most important characteristic. The main reasons for not wanting to use a tracker were, 'I don't think it would help me' (39%), and 'I don't want to increase my activity' (47%).

CONCLUSIONS

Activity trackers are a promising tool to engage people in self-monitoring a physical activity. Trackers used in physical activity interventions should align with the preferences of target groups, and should be able to be worn on the wrist, measure steps and be accurate.

Use of population-referenced total activity counts percentiles to assess and classify physical activity of population groups

OBJECTIVES

Population-referenced total activity counts per day (TAC/d) percentiles provide public health practitioners a standardized measure of physical activity (PA) volume obtained from an accelerometer that can be compared across populations. The purpose of this study was to describe the application of TAC/d population-referenced percentiles to characterize the PA levels of population groups relative to US estimates.

METHODS

A total of 679 adults participating in the 2011 NYC Physical Activity Transit survey wore an ActiGraph accelerometer on their hip for seven consecutive days. Accelerometer-derived TAC/d was classified into age- and gender-specific quartiles of US population-referenced TAC/d to compare differences in the distributions by borough (N=5).

RESULTS

Males in Brooklyn, Manhattan, and Staten Island had significantly greater TAC/d than US males. Females in Brooklyn and Queens had significantly greater levels of TAC/d compared to US females. The proportion of males in each population-referenced TAC/d quartile varied significantly by borough (χ(2)(12)=2.63, p=0.002), with disproportionately more men in Manhattan and the Bronx found to be in the highest and lowest US population-referenced TAC/d quartiles, respectively. For females, there was no significant difference in US population-reference TAC/d quartile by borough (χ(2)(12)=1.09, p=0.36).

CONCLUSIONS

These results demonstrate the utility of population-referenced TAC/d percentiles in public health monitoring and surveillance. These findings also provide insights into the PA levels of NYC residents relative to the broader US population, which can be used to guide health promotion efforts.

Physical Workload and Work Capacity across Occupational Groups

This study aimed to determine physical performance criteria of different occupational groups by investigating physical activity and energy expenditure in healthy Swiss employees in real-life workplaces on workdays and non-working days in relation to their aerobic capacity (VO_2max). In this cross-sectional study, 337 healthy and full-time employed adults were recruited. Participants were classified (nine categories) according to the International Standard Classification of Occupations 1988 and merged into three groups with low-, moderate- and high-intensity occupational activity. Daily steps, energy expenditure, metabolic equivalents and activity at different intensities were measured using the SenseWear Mini armband on seven consecutive days (23 hours/day). VO_2max was determined by the 20-meter shuttle run test. Data of 303 subjects were considered for analysis (63% male, mean age: 33 yrs, SD 12), 101 from the low-, 102 from the moderate- and 100 from the high-intensity group. At work, the high-intensity group showed higher energy expenditure, metabolic equivalents, steps and activity at all intensities than the other groups (p<0.001). There were no significant differences in physical activity between the occupational groups on non-working days. VO_2max did not differ across groups when stratified for gender. The upper workload limit was 21%, 29% and 44% of VO_2max in the low-, moderate- and high-intensity group, respectively. Men had a lower limit than women due to their higher VO_2max (26% vs. 37%), when all groups were combined. While this study did confirm that the average workload limit is one third of VO_2max, it showed that the average is misrepresenting the actual physical work demands of specific occupational groups, and that it does not account for gender-related differences in relative workload. Therefore, clinical practice needs to consider these differences with regard to a safe return to work, particularly for the high-intensity group.

Ecological Momentary Assessment in Eating Disorder and Obesity Research: a Review of the Recent Literature

Our current understanding of the etiology and maintenance of eating disorders and obesity continues to be far from complete. Similarly, our understanding of determinants of both successful and unsuccessful weight loss surgery is also quite limited. While a number of research methodologies have been applied to these areas, one methodology that has recently seen a rise in popularity is the use of ecological momentary assessment (EMA). EMA allows one to study a variety of variables of interest in the natural environment. The study of eating disorders, obesity, and bariatric surgery has all been conducted using EMA recently. The current study is a review of these areas and summarizes the recent literature (past 3 years) in eating disorders, obesity, and bariatric surgery using EMA methodology.

Identifying waking time in 24-h accelerometry data in adults using an automated algorithm

As accelerometers are commonly used for 24-h measurements of daily activity, methods for separating waking from sleeping time are necessary for correct estimations of total daily activity levels accumulated during the waking period. Therefore, an algorithm to determine wake and bed times in 24-h accelerometry data was developed and the agreement of this algorithm with self-report was examined. One hundred seventy-seven participants (aged 40-75 years) of The Maastricht Study who completed a diary and who wore the activPAL3™ 24 h/day, on average 6 consecutive days were included. Intraclass correlation coefficient (ICC) was calculated and the Bland-Altman method was used to examine associations between the self-reported and algorithm-calculated waking hours. Mean self-reported waking hours was 15.8 h/day, which was significantly correlated with the algorithm-calculated waking hours (15.8 h/day, ICC = 0.79, P = < 0.001). The Bland-Altman plot indicated good agreement in waking hours as the mean difference was 0.02 h (95% limits of agreement (LoA) = -1.1 to 1.2 h). The median of the absolute difference was 15.6 min (Q1-Q3 = 7.6-33.2 min), and 71% of absolute differences was less than 30 min. The newly developed automated algorithm to determine wake and bed times was highly associated with self-reported times, and can therefore be used to identify waking time in 24-h accelerometry data in large-scale epidemiological studies.

Cross-validation of waist-worn GENEA accelerometer cut-points

PURPOSE

The purpose of this study was to determine the classification accuracy of the waist gravity estimator of normal everyday activity (GENEA) cut-points developed by Esliger et al. for predicting intensity categories across a range of lifestyle activities.

METHODS

Each participant performed one of two routines, consisting of seven lifestyle activities (home/office, ambulatory, and sport). The GENEA was worn on the right waist, and oxygen uptake was continuously measured using the Oxycon mobile. A one-way chi-squared test was used to determine the classification accuracy of the GENEA cut-points. Cross-tabulation tables provided information on under- and overestimations, and sensitivity and specificity analyses of the waist cut-points were also performed.

RESULTS

Spearman rank order correlation for the GENEA gravity-subtracted signal vector magnitude and Oxycon mobile MET values was 0.73. For all activities combined, the GENEA accurately predicted intensity classification 55.3% of the time, and it increased to 58.3% when stationary cycling was removed from the analysis. The sensitivity of the cut-points for the four intensity categories ranged from 0.244 to 0.958, and the specificity ranged from 0.576 to 0.943.

CONCLUSION

In this cross-validation study, the proposed GENEA cut-points had a low overall accuracy rate for classifying intensity (55.3%) when engaging in 14 different lifestyle activities.

A Validation Study of the Web-Based Physical Activity Questionnaire Active-Q Against the GENEA Accelerometer

Background

Valid physical activity assessment in epidemiological studies is essential to study associations with various health outcomes.

Objective

To validate the Web-based physical activity questionnaire Active-Q by comparing results of time spent at different physical activity levels with results from the GENEA accelerometer and to assess the reproducibility of Active-Q by comparing two admissions of the questionnaire.

Methods

A total of 148 men (aged 33 to 86 years) responded to Active-Q twice and wore the accelerometer during seven consecutive days on two occasions. Time spent on six different physical activity levels including sedentary, light (LPA), moderate (MPA), and vigorous (VPA) as well as additional combined categories of sedentary-to-light and moderate-to-vigorous (MVPA) physical activity was assessed. Validity of Active-Q was determined using Spearman correlation coefficients with 95% confidence intervals (CI) and the Bland-Altman method. Reproducibility was assessed using intraclass correlation coefficients (ICCs) comparing two admissions of the questionnaire.

Results

The validity correlation coefficients were statistically significant for time spent at all activity levels; sedentary (r=0.19, 95% CI: 0.04-0.34), LPA (r=0.15, 95% CI: 0.00-0.31), sedentary-to-light (r=0.35, 95% CI: 0.19-0.51), MPA (r=0.27, 95% CI: 0.12-0.42), VPA (r=0.54, 95% CI: 0.42-0.67), and MVPA (r=0.35, 95% CI: 0.21-0.48). The Bland-Altman plots showed a negative mean difference for time in LPA and positive mean differences for time spent in MPA, VPA and MVPA. The ICCs of test-retest reliability ranged between r=0.51-0.80 for the different activity levels in Active-Q.

Conclusions

More moderate and vigorous activities and less light activities were reported in Active-Q compared to accelerometer measurements. Active-Q shows comparable validity and reproducibility to other physical activity questionnaires used today.

Accuracy of a custom physical activity and knee angle measurement sensor system for patients with neuromuscular disorders and gait abnormalities

Long-term assessment of ambulatory behavior and joint motion are valuable tools for the evaluation of therapy effectiveness in patients with neuromuscular disorders and gait abnormalities. Even though there are several tools available to quantify ambulatory behavior in a home environment, reliable measurement of joint motion is still limited to laboratory tests. The aim of this study was to develop and evaluate a novel inertial sensor system for ambulatory behavior and joint motion measurement in the everyday environment. An algorithm for behavior classification, step detection, and knee angle calculation was developed. The validation protocol consisted of simulated daily activities in a laboratory environment. The tests were performed with ten healthy subjects and eleven patients with multiple sclerosis. Activity classification showed comparable performance to commercially available activPAL sensors. Step detection with our sensor system was more accurate. The calculated flexion-extension angle of the knee joint showed a root mean square error of less than 5° compared with results obtained using an electro-mechanical goniometer. This new system combines ambulatory behavior assessment and knee angle measurement for long-term measurement periods in a home environment. The wearable sensor system demonstrated high validity for behavior classification and knee joint angle measurement in a laboratory setting.

The Effectiveness of a Weight Maintenance Intervention for Adults with Intellectual Disabilities and Obesity: A Single Stranded Study

BACKGROUND

The evidence base for weight management programmes incorporating a weight loss and a weight maintenance phase for adults with intellectual disabilities (ID) is limited. This study describes the weight maintenance phase of a multicomponent weight management programme for adults with intellectual disability and obesity (TAKE 5).

MATERIALS AND METHODS

Thirty-one participants who had completed the 16 week TAKE five weight loss intervention (Phase I) were invited to participate in a 12 month weight maintenance intervention (Phase II). Content included recommendations of the National Weight Control Registry.

RESULTS

Twenty-eight participants completed Phase II with 50.4% maintaining their weight (mean weight change -0.5 kg, SD 2.2), 28.7% gaining weight (mean weight gain 5.4 kg, SD 2.2) and 21.6% losing weight (mean weight loss -8.0 kg, SD 3.0) at 12 months.

CONCLUSION

Further research is justified to investigate the efficacy of weight loss maintenance interventions in adults with intellectual disability and obesity, using controlled study designs.

A random forest classifier for the prediction of energy expenditure and type of physical activity from wrist and hip accelerometers

Wrist accelerometers are being used in population level surveillance of physical activity (PA) but more research is needed to evaluate their validity for correctly classifying types of PA behavior and predicting energy expenditure (EE). In this study we compare accelerometers worn on the wrist and hip, and the added value of heart rate (HR) data, for predicting PA type and EE using machine learning. Forty adults performed locomotion and household activities in a lab setting while wearing three ActiGraph GT3X+ accelerometers (left hip, right hip, non-dominant wrist) and a HR monitor (Polar RS400). Participants also wore a portable indirect calorimeter (COSMED K4b2), from which EE and metabolic equivalents (METs) were computed for each minute. We developed two predictive models: a random forest classifier to predict activity type and a random forest of regression trees to estimate METs. Predictions were evaluated using leave-one-user-out cross-validation. The hip accelerometer obtained an average accuracy of 92.3% in predicting four activity types (household, stairs, walking, running), while the wrist accelerometer obtained an average accuracy of 87.5%. Across all 8 activities combined (laundry, window washing, dusting, dishes, sweeping, stairs, walking, running), the hip and wrist accelerometers obtained average accuracies of 70.2% and 80.2% respectively. Predicting METs using the hip or wrist devices alone obtained root mean square errors (rMSE) of 1.09 and 1.00 METs per 6 min bout, respectively. Including HR data improved MET estimation, but did not significantly improve activity type classification. These results demonstrate the validity of random forest classification and regression forests for PA type and MET prediction using accelerometers. The wrist accelerometer proved more useful in predicting activities with significant arm movement, while the hip accelerometer was superior for predicting locomotion and estimating EE.

Monitoring capabilities of the Omron HJ-720ITC pedometer

PURPOSE

The purpose of our study was to examine the monitoring capabilities of the Omron HJ-720ITC pedometer and determine the feasibility of using it in physical activity interventions.

METHODS

Using data from an 8-week lifestyle-intervention study, we tested the capabilities of the pedometer for recording physical activity data. Data from a total of 28 subjects were used in our analysis. The total number of patient steps per day was recorded over the duration of an intervention, as well as "aerobic steps per day" (ie, those taken at a cadence of > 60 steps per minute for ≥ 10 consecutive minutes). Daily wear time was computed in order to ascertain participant compliance in wearing the pedometer. In addition, aerobic steps per minute were computed, providing an assessment of activity intensity during continuous walking bouts.

RESULTS

Total steps per day and aerobic steps per day increased from baseline assessment to Week 8. Participants exceeded 100 steps/minute for 89% of their aerobic minutes, suggesting that they were in the moderate-intensity range (3-6 metabolic equivalent tasks) when performing continuous bouts of walking (> 10 minutes).

CONCLUSION

The new pedometer is a reasonably priced, wearable activity monitor that is feasible for use in clinical and research settings.

Does participation in physical education reduce sedentary behaviour in school and throughout the day among normal-weight and overweight-to-obese Czech children aged 9-11 years?

Participation of 9 to 11-year-old children in physical education lessons (PEL) contributes to a significantly higher duration of moderate-to-vigorous physical activity (MVPA) during the school day and, in overweight/obese girls and normal-weight boys, to an increase in overall daily MVPA as shown by previous research. However, it is not known whether this increase in MVPA is at the expense of light physical activity (LPA) or sedentary behaviour (SED). SED, LPA, and MVPA were assessed in 338 schoolchildren aged 9–11 years (50.3% girls; 29.6% overweight/obese) over two school days (with and without a PEL) using a triaxial accelerometer during various segments of the school day. SED, LPA, and MVPA were quantified based on the duration of the activity (minutes). Participation in PEL led to significantly higher school MVPA in the overweight/obese and normal-weight girls and boys (p < 0.005) compared to MVPA of those children on the school day without PEL. Participation in PEL led to a significantly higher overall daily MVPA duration compared to that during the day without PEL for the overweight/obese girls (p < 0.05), normal-weight girls (p < 0.05) and boys (p < 0.005). Participation in PEL contributed not only to significantly higher LPA in the normal-weight girls and boys (p < 0.01) during the school day but also reduced school-time SED in the overweight/obese children (p < 0.01) and normal-weight girls (p < 0.005). Moreover, participation in PEL significantly reduced the overall daily SED in the normal-weight children and overweight/obese boys (p < 0.05). Adding one PEL to the daily school routine appears to be a promising strategy for effectively reducing SED in children.

ActiTrainer-determined segmented moderate-to-vigorous physical activity patterns among normal-weight and overweight-to-obese Czech schoolchildren

This study uncovered the patterns of moderate-to-vigorous physical activity (MVPA) in normal-weight and overweight/obese children before, during, and after school lessons. ActiTrainer-based MVPA was assessed over 2 school days (with and without a physical education lesson [PEL] in a sample of three hundred thirty-eight 9- to 11-year-old children (50.3 % female; 29.6 % overweight/obese), in the Czech Republic, during spring 2012. MVPA was quantified based on the step count (number), MVPA duration, and MVPA heart-rate response (minutes). No differences in the MVPA based on the time during the school day were confirmed in normal-weight versus overweight/obese girls, whereas normal-weight boys significantly exceed their overweight/obese peers in step count and after-school and daylong MVPA. Active participation in PEL contributes to a significantly higher step count, exercise duration, and MVPA heart-rate response in normal-weight and overweight/obese boys (p < 0.001) and normal-weight (p < 0.001) and overweight/obese (p < 0.04) girls during school time. Moreover, active participation in PEL significantly (p < 0.04) increases the overall daily step count, duration, and MVPA heart-rate response in normal-weight boys and overweight/obese girls. Active exercise during PEL accounted for 16.7 % of the total number of steps, 25.1 % of overall MVPA duration, and 24.1 % of the MVPA heart-rate response in 9- to 11-year-old children. During days with a PEL, a significantly (p < 0.02) higher number of overweight/obese girls and a higher number of normal-weight boys achieved the duration of 60 min/day of MVPA compared with days without a PEL.

CONCLUSION

Adding one PEL or an equivalent amount of MVPA to the daily school routine appears to be a promising strategy to effectively increase daily MVPA, particularly among overweight/obese girls.

Cross-cultural validation of a simple self-report instrument of physical activity in immigrants from the Middle East and native Swedes

AIM

To investigate cross-cultural validity of a simple self-report instrument of physical activity intended to be used in Swedish health care.

METHODS

A validation study performed in 599 Iraqis (58% men) and 553 Swedes (53% men) aged 30-75 years living in the city of Malmö, Sweden. The self-report instrument by the Swedish National Board of Health and Welfare was compared to corresponding measures assessed from accelerometry as reference.

RESULTS

The agreement between the methods in assessing the participants as sufficiently/insufficiently physically active (cut-point 150 min/week) was 65% in the Iraqis and 52% in the Swedes (p<0.001). The proportion disagreement where the self-reported physical activity was sufficient but insufficient according to the accelerometry was 26% and 45% in Iraqis and Swedes, respectively. Physical activity time (min/week) was overestimated by self-report compared to accelerometry by 71% in the Iraqis and 115% in the Swedes (p<0.001). The smallest and largest overestimation was seen in Iraqi (57%) and Swedish (139%) women, respectively. The deviation of the self-report instrument compared to accelerometry was related to the physical activity level, as the overestimation mainly occurred at lower physical activity.

CONCLUSIONS

The self-report instrument proposed by the Swedish National Board of Health and Welfare may overestimate the proportion sufficiently physically active, but to an extent depending on cultural background and gender.