New horizons in sensor development

Raw Photoplethysmography as an Enhancement for Research-Grade Wearable Activity Monitors

Wearable monitors continue to play a critical role in scientific assessments of physical activity. Recently, research-grade monitors have begun providing raw data from photoplethysmography (PPG) alongside standard raw data from inertial sensors (accelerometers and gyroscopes). Raw PPG enables granular and transparent estimation of cardiovascular parameters such as heart rate, thus presenting a valuable alternative to standard PPG methodologies (most of which rely on consumer-grade monitors that provide only coarse output from proprietary algorithms). The implications for physical activity assessment are tremendous, since it is now feasible to monitor granular and concurrent trends in both movement and cardiovascular physiology using a single noninvasive device. However, new users must also be aware of challenges and limitations that accompany the use of raw PPG data. This viewpoint paper therefore orients new users to the opportunities and challenges of raw PPG data by presenting its mechanics, pitfalls, and availability, as well as its parallels and synergies with inertial sensors. This includes discussion of specific applications to the prediction of energy expenditure, activity type, and 24-hour movement behaviors, with an emphasis on areas in which raw PPG data may help resolve known issues with inertial sensing (eg, measurement during cycling activities). We also discuss how the impact of raw PPG data can be maximized through the use of open-source tools when developing and disseminating new methods, similar to current standards for raw accelerometer and gyroscope data. Collectively, our comments show the strong potential of raw PPG data to enhance the use of research-grade wearable activity monitors in science over the coming years.

Concurrent Validity and Reliability of a Free Smartphone Application for Evaluation of Jump Height

Background/Objectives: Jump test assessment is commonly used for physical tests, with different type of devices used for its evaluation. The purpose of the present study was to examine the validity and reliability of a freely accessible mobile application (VertVision, version 2.0.5) for measuring jump performance. Methods: With that intent, thirty-eight college age recreationally active subjects underwent test assessment after a specific warm-up, performing countermovement jumps (CMJs) and squat jumps (SJs) on a contact platform while being recorded with a smartphone camera. Jump height was the criterion variable, with the same formula being used for both methods. Data analysis was performed by two experienced observers. Results: The results showed strong correlations with the contact platform (ICC > 0.9) for both jumps. Furthermore, between-observer reliability was also high (ICC > 0.9; CV ≤ 2.19), with lower values for smallest worthwhile change (≤0.23) and typical error of measurement (≤0.14). Estimation error varied when accounting for both observers, with the SJ accounting for bigger differences (4.1-6.03%), when compared to the CMJ (0.73-3.09%). Conclusions: The study suggests that VertVision is a suitable and handy method for evaluating jump performance. However, it presents a slight estimation error when compared to the contact platform.

The impact of selected methodological factors on data collection outcomes in observational studies of device-measured physical behaviour in adults: A systematic review

BACKGROUND

Accelerometer measures of physical behaviours (physical activity, sedentary behaviour and sleep) in observational studies offer detailed insight into associations with health and disease. Maximising recruitment and accelerometer wear, and minimising data loss remain key challenges. How varying methods used to collect accelerometer data influence data collection outcomes is poorly understood. We examined the influence of accelerometer placement and other methodological factors on participant recruitment, adherence and data loss in observational studies of adult physical behaviours.

METHODS

The review was in accordance with the Preferred Reporting Items for Systematic Reviews and Meta Analyses (PRISMA). Observational studies of adults including accelerometer measurement of physical behaviours were identified using database (MEDLINE (Ovid), Embase, PsychINFO, Health Management Information Consortium, Web of Science, SPORTDiscus and Cumulative Index to Nursing & Allied Health Literature) and supplementary searches to May 2022. Information regarding study design, accelerometer data collection methods and outcomes were extracted for each accelerometer measurement (study wave). Random effects meta-analyses and narrative syntheses were used to examine associations of methodological factors with participant recruitment, adherence and data loss.

RESULTS

123 accelerometer data collection waves were identified from 95 studies (92.5% from high-income countries). In-person distribution of accelerometers was associated with a greater proportion of invited participants consenting to wear an accelerometer (+ 30% [95% CI 18%, 42%] compared to postal distribution), and adhering to minimum wear criteria (+ 15% [4%, 25%]). The proportion of participants meeting minimum wear criteria was higher when accelerometers were worn at the wrist (+ 14% [ 5%, 23%]) compared to waist. Daily wear-time tended to be higher in studies using wrist-worn accelerometers compared to other wear locations. Reporting of information regarding data collection was inconsistent.

CONCLUSION

Methodological decisions including accelerometer wear-location and method of distribution may influence important data collection outcomes including recruitment and accelerometer wear-time. Consistent and comprehensive reporting of accelerometer data collection methods and outcomes is needed to support development of future studies and international consortia. Review supported by the British Heart Foundation (SP/F/20/150002) and registered (Prospero CRD42020213465).

Physical Activity Surveillance in Children and Adolescents using Smartphone Technology: Systematic Review (Preprint)

BACKGROUND

Self-reported physical activity (PA) questionnaires have traditionally been used for PA surveillance in children and adolescents, especially in free-living conditions. Objective measures are more accurate at measuring PA, but high cost often creates a barrier for their use in low- and middle-income settings. The advent of smartphone technology has greatly influenced mobile health and has offered new opportunities in health research, including PA surveillance.

OBJECTIVE

This review aimed to systematically explore the use of smartphone technology for PA surveillance in children and adolescents, specifically focusing on the use of smartphone apps.

METHODS

A literature search was conducted using 5 databases (PubMed, Scopus, CINAHL, MEDLINE, and Web of Science) and Google Scholar to identify articles relevant to the topic that were published from 2008 to 2023. Articles were included if they included children and adolescents within the age range of 5 to 18 years; used smartphone technology as PA surveillance; had PA behavioral outcomes such as energy expenditure, step count, and PA levels; were written in English; and were published between 2008 and 2023.

RESULTS

We identified and analyzed 8 studies (5 cross-sectional studies and 3 cohort studies). All participants were aged 12-18 years, and all studies were conducted in high-income countries only. Participants were recruited from schools, primary care facilities, and voluntarily. Five studies used mobile apps specifically and purposely developed for the study, whereas 3 studies used mobile apps downloadable from the Apple App Store and Android Play Store. PA surveillance using these apps was conducted from 24 hours to 4 weeks.

CONCLUSIONS

Evidence of PA surveillance using smartphone technology in children and adolescents was insufficient, which demonstrated the knowledge gap. Additional research is needed to further study the feasibility and validity of smartphone apps for PA surveillance among children and adolescents, especially in low- and middle-income countries.

Characterisation of Temporal Patterns in Step Count Behaviour from Smartphone App Data: An Unsupervised Machine Learning Approach

The increasing ubiquity of smartphone data, with greater spatial and temporal coverage than achieved by traditional study designs, have the potential to provide insight into habitual physical activity patterns. This study implements and evaluates the utility of both K-means clustering and agglomerative hierarchical clustering methods in identifying weekly and yearlong physical activity behaviour trends. Characterising the demographics and choice of activity type within the identified clusters of behaviour. Across all seven clusters of seasonal activity behaviour identified, daylight saving was shown to play a key role in influencing behaviour, with increased activity in summer months. Investigation into weekly behaviours identified six clusters with varied roles, of weekday versus weekend, on the likelihood of meeting physical activity guidelines. Preferred type of physical activity likewise varied between clusters, with gender and age strongly associated with cluster membership. Key relationships are identified between weekly clusters and seasonal activity behaviour clusters, demonstrating how short-term behaviours contribute to longer-term activity patterns. Utilising unsupervised machine learning, this study demonstrates how the volume and richness of secondary app data can allow us to move away from aggregate measures of physical activity to better understand temporal variations in habitual physical activity behaviour.

Wearable Technology and Analytics as a Complementary Toolkit to Optimize Workload and to Reduce Injury Burden

Wearable sensors enable the real-time and non-invasive monitoring of biomechanical, physiological, or biochemical parameters pertinent to the performance of athletes. Sports medicine researchers compile datasets involving a multitude of parameters that can often be time consuming to analyze in order to create value in an expeditious and accurate manner. Machine learning and artificial intelligence models may aid in the clinical decision-making process for sports scientists, team physicians, and athletic trainers in translating the data acquired from wearable sensors to accurately and efficiently make decisions regarding the health, safety, and performance of athletes. This narrative review discusses the application of commercial sensors utilized by sports teams today and the emergence of descriptive analytics to monitor the internal and external workload, hydration status, sleep, cardiovascular health, and return-to-sport status of athletes. This review is written for those who are interested in the application of wearable sensor data and data science to enhance performance and reduce injury burden in athletes of all ages.

Random forest algorithms for recognizing daily life activities using plantar pressure information: a smart-shoe study

Background

Wearable activity trackers are regarded as a new opportunity to deliver health promotion interventions. Indeed, while the prediction of active behaviors is currently primarily relying on the processing of accelerometer sensor data, the emergence of smart clothes with multi-sensing capacities is offering new possibilities. Algorithms able to process data from a variety of smart devices and classify daily life activities could therefore be of particular importance to achieve a more accurate evaluation of physical behaviors. This study aims to (1) develop an activity recognition algorithm based on the processing of plantar pressure information provided by a smart-shoe prototype and (2) to determine the optimal hardware and software configurations.

Method

Seventeen subjects wore a pair of smart-shoe prototypes composed of plantar pressure measurement insoles, and they performed the following nine activities: sitting, standing, walking on a flat surface, walking upstairs, walking downstairs, walking up a slope, running, cycling, and completing office work. The insole featured seven pressure sensors. For each activity, at least four minutes of plantar pressure data were collected. The plantar pressure data were cut in overlapping windows of different lengths and 167 features were extracted for each window. Data were split into training and test samples using a subject-wise assignment method. A random forest model was trained to recognize activity. The resulting activity recognition algorithms were evaluated on the test sample. A multi hold-out procedure allowed repeating the operation with 5 different assignments. The analytic conditions were modulated to test (1) different window lengths (1–60 seconds), (2) some selected sensor configurations and (3) different numbers of data features.

Results

A window length of 20 s was found to be optimum and therefore used for the rest of the analysis. Using all the sensors and all 167 features, the smart shoes predicted the activities with an average success of 89%. “Running” demonstrated the highest sensitivity (100%). “Walking up a slope” was linked with the lowest performance (63%), with the majority of the false negatives being “walking on a flat surface” and “walking upstairs.” Some 2- and 3-sensor configurations were linked with an average success rate of 87%. Reducing the number of features down to 20 does not alter significantly the performance of the algorithm.

Conclusion

High-performance human behavior recognition using plantar pressure data only is possible. In the future, smart-shoe devices could contribute to the evaluation of daily physical activities. Minimalist configurations integrating only a small number of sensors and computing a reduced number of selected features could maintain a satisfying performance. Future experiments must include a more heterogeneous population.

Office-based physical activity: mapping a social ecological model approach against COM-B

BACKGROUND

There are growing concerns over the health impacts of occupational sedentary behaviour on office-based workers and increasing workplace recognition of the need to increase physical activity at work. Social ecological models provide a holistic framework for increasing opportunities for physical activity at work. In this paper we propose a social ecological model of office-based physical activity and map it against the Capability Motivation Opportunity (COM-B) framework to highlight the mechanisms of behaviour change that can increase levels of physical activity of office-based workers.

DISCUSSION

The paper proposes a social ecological model of physical activity associated with office-based settings. The model considers opportunities for both incidental and discretionary activities, as well as macro and micro factors on both socio-cultural and physical dimensions. The COM-B framework for characterising behaviour change interventions is used to highlight the underlying mechanisms of behaviour change inherent in the model. The broad framework provided by social ecological models is important for understanding physical activity in office-based settings because of the non-discretionary nature of sedentary behaviour of office-based work. It is important for interventions not to rely on individual motivation for behaviour change alone but to incorporate changes to the broader social ecological and physical context to build capability and create opportunities for more sustainable change.

Can self-monitoring mobile health apps reduce sedentary behavior? A randomized controlled trial

OBJECTIVE

To examine whether the self-monitoring interventions of a mobile health app reduce sedentary behavior in the short and long terms.

METHOD

We designed a double-blind randomized control trial. Participants were selected from among the staff of a medical institution and registrants of an online research firm. Forty-nine participants were randomly assigned to either a control group (n = 25) or an intervention group (n = 24). The control group was given only the latest information about sedentary behavior, and the intervention was provided real-time feedback for self-monitoring in addition to the information. These interventions provided for 5 weeks (to measure the short-term effect) and 13 weeks (to measure the long-term effect) via the smartphone app. Measurements were as follows: subjective total sedentary time (SST), objective total sedentary time (OST), mean sedentary bout duration (MSB), and the number of sedentary breaks (SB). Only SST was measured by self-report based on the standardized International Physical Activity Questionnaire and others were measured with the smartphone.

RESULTS

No significant results were observed in the short term. In the long term, while no significant results were also observed in objective sedentary behavior (OST, MSB, SB), the significant differences were observed in subjective sedentary behavior (SST, βint  - βctrl between baseline and 9/13 weeks; 1.73 and 1.50 h/d, respectively).

CONCLUSIONS

Real-time feedback for self-monitoring with smartphone did not significantly affect objective sedentary behavior. However, providing only information about sedentary behavior to users with smartphones may make misperception on the amount of their subjective sedentary behavior.

Lessons Learned: Recommendations For Implementing a Longitudinal Study Using Wearable and Environmental Sensors in a Health Care Organization

Although traditional methods of data collection in naturalistic settings can shed light on constructs of interest to researchers, advances in sensor-based technology allow researchers to capture continuous physiological and behavioral data to provide a more comprehensive understanding of the constructs that are examined in a dynamic health care setting. This study gives examples for implementing technology-facilitated approaches and provides the following recommendations for conducting such longitudinal, sensor-based research, with both environmental and wearable sensors in a health care setting: pilot test sensors and software early and often; build trust with key stakeholders and with potential participants who may be wary of sensor-based data collection and concerned about privacy; generate excitement for novel, new technology during recruitment; monitor incoming sensor data to troubleshoot sensor issues; and consider the logistical constraints of sensor-based research. The study describes how these recommendations were successfully implemented by providing examples from a large-scale, longitudinal, sensor-based study of hospital employees at a large hospital in California. The knowledge gained from this study may be helpful to researchers interested in obtaining dynamic, longitudinal sensor data from both wearable and environmental sensors in a health care setting (eg, a hospital) to obtain a more comprehensive understanding of constructs of interest in an ecologically valid, secure, and efficient way.

Identifying characteristics of indicators of sedentary behavior using objective measurements

Objective

Recent attention has been focused on sedentary behavior (SB) affecting health outcomes, but the characteristics of indicators reflecting SB remain to be identified. This cross‐sectional study aims to identify the characteristics of indicators of SB, focusing on the examination of correlations, reliability, and validity of sedentary variables assessed by the smartphone app.

Method

Objectively measured data of SB of eligible 46 Japanese workers obtained from smartphones were used. We assessed the characteristics of current indicators being used with a 10‐minute or 30‐minute thresholds, in addition to the conventional indicators of total sedentary time, mean sedentary bout duration, and total number of sedentary bouts. They were evaluated from three perspectives: (a) association among the indicators, (b) reliability of the indicators, and (c) criterion validity.

Results

Total sedentary time under 10 minutes (U10) and U30 had negative associations with Total sedentary time (r = −.47 and −.21 respectively). The correlation between Mean sedentary bout duration and Total number of sedentary bouts was −.84, whereas between Mean sedentary bout duration 10, 30 and Total number of sedentary bouts were −.54 and −.21, respectively. The intraclass correlation coefficients of almost all indicators were around .80. Mean sedentary bout duration, Mean sedentary bout duration 10, Total number of sedentary bouts, Total sedentary time 30, U30 and U10 have significant differences between three BMI groups.

Conclusion

This study comprehensively revealed the rationale of advantage in the current indicator being used with a 10‐minute or 30‐minute threshold, rather than the conventional total amount of SB.

Mobile Sensing in Environmental Health and Neighborhood Research

Public health research has witnessed a rapid development in the use of location, environmental, behavioral, and biophysical sensors that provide high-resolution objective time-stamped data. This burgeoning field is stimulated by the development of novel multisensor devices that collect data for an increasing number of channels and algorithms that predict relevant dimensions from one or several data channels. Global positioning system (GPS) tracking, which enables geographic momentary assessment, permits researchers to assess multiplace personal exposure areas and the algorithm-based identification of trips and places visited, eventually validated and complemented using a GPS-based mobility survey. These methods open a new space-time perspective that considers the full dynamic of residential and nonresidential momentary exposures; spatially and temporally disaggregates the behavioral and health outcomes, thus replacing them in their immediate environmental context; investigates complex time sequences; explores the interplay among individual, environmental, and situational predictors; performs life-segment analyses considering infraindividual statistical units using case-crossover models; and derives recommendations for just-in-time interventions.

Advances and Controversies in Diet and Physical Activity Measurement in Youth

Technological advancements in the past decades have improved dietary intake and physical activity measurements. This report reviews current developments in dietary intake and physical activity assessment in youth. Dietary intake assessment has relied predominantly on self-report or image-based methods to measure key aspects of dietary intake (e.g., food types, portion size, eating occasion), which are prone to notable methodologic (e.g., recall bias) and logistic (e.g., participant and researcher burden) challenges. Although there have been improvements in automatic eating detection, artificial intelligence, and sensor-based technologies, participant input is often needed to verify food categories and portions. Current physical activity assessment methods, including self-report, direct observation, and wearable devices, provide researchers with reliable estimations for energy expenditure and bodily movement. Recent developments in algorithms that incorporate signals from multiple sensors and technology-augmented self-reporting methods have shown preliminary efficacy in measuring specific types of activity patterns and relevant contextual information. However, challenges in detecting resistance (e.g., in resistance training, weight lifting), prolonged physical activity monitoring, and algorithm (non)equivalence remain to be addressed. In summary, although dietary intake assessment methods have yet to achieve the same validity and reliability as physical activity measurement, recent developments in wearable technologies in both arenas have the potential to improve current assessment methods.

THEME INFORMATION

This article is part of a theme issue entitled Innovative Tools for Assessing Diet and Physical Activity for Health Promotion, which is sponsored by the North American branch of the International Life Sciences Institute.

Classification and Processing of 24-Hour Wrist Accelerometer Data

Background: An important step in accelerometer data analysis is the classification of continuous, 24-hour data into sleep, wake, and non-wear time. We compared classification times and physical activity metrics across different data processing and classification methods.Methods: Participants (n = 576) from the Finnish Retirement and Aging Study (FIREA) wore an accelerometer on their non-dominant wrist for seven days and nights and filled in daily logs with sleep and waking times. Accelerometer data were first classified as sleep or wake time by log, and Tudor-Locke, Tracy, and ActiGraph algorithms. Then, wake periods were classified as wear or non-wear by log, Choi algorithm, and wear sensor. We compared time classification (sleep, wake, and wake wear time) as well as physical activity measures (total activity volume and sedentary time) across these classification methods.Results:M(SD) nightly sleep time was 467 (49) minutes by log and 419 (88), 522 (86), and 453 (74) minutes by Tudor-Locke, Tracy, and ActiGraph algorithms, respectively. Wake wear time did not differ substantially when comparing Choi algorithm and the log. The wear sensor did not work properly in about 29% of the participants. Daily sedentary time varied by 8–81 minutes after excluding sleep by different methods and by 1–18 minutes after excluding non-wear time by different methods. Total activity volume did not substantially differ across the methods.Conclusion: The differences in wear and sedentary time were larger than differences in total activity volume. Methods for defining sleep periods had larger impact on outcomes than methods for defining wear time.

Statistical machine learning of sleep and physical activity phenotypes from sensor data in 96,220 UK Biobank participants

Current public health guidelines on physical activity and sleep duration are limited by a reliance on subjective self-reported evidence. Using data from simple wrist-worn activity monitors, we developed a tailored machine learning model, using balanced random forests with Hidden Markov Models, to reliably detect a number of activity modes. We show that physical activity and sleep behaviours can be classified with 87% accuracy in 159,504 minutes of recorded free-living behaviours from 132 adults. These trained models can be used to infer fine resolution activity patterns at the population scale in 96,220 participants. For example, we find that men spend more time in both low- and high- intensity behaviours, while women spend more time in mixed behaviours. Walking time is highest in spring and sleep time lowest during the summer. This work opens the possibility of future public health guidelines informed by the health consequences associated with specific, objectively measured, physical activity and sleep behaviours.

A comparison of 10 accelerometer non-wear time criteria and logbooks in children

BACKGROUND

There are many unresolved issues regarding data reduction algorithms for accelerometry. The choice of criterion for removal of non-wear time might have a profound influence on physical activity (PA) and sedentary time (SED) estimates. The aim of the present study was to compare 10 different non-wear criteria and a log of non-wear periods in 11-year-old children.

METHODS

Children from the Active Smarter Kids study performed 7-days of hip-worn accelerometer monitoring (Actigraph GT3X+) and logged the number of non-wear periods each day, along with the approximate duration and reason for non-wear. Accelerometers were analyzed using 10 different non-wear criteria: ≥ 10, 20, 30, 45, 60, and 90 min of consecutive zero counts without allowance for interruptions, and ≥60 and 90 min with allowance for 1 and 2 min of interruptions.

RESULTS

891 children provided 5203 measurement days, and reported 1232 non-wear periods ranging from 0 to 3 periods per day: on most days children reported no non-wear periods (77.1% of days). The maximum number of non-wear periods per day was 2 for the 90-min criterion, 3 to 5 for most criteria, 7 for the 20-min criterion, and 20 for the 10-min criterion. The non-wear criteria influenced overall PA (mean values across all criteria: 591 to 649 cpm; 10% difference) and SED time (461 to 539 min/day; 17% difference) estimates, especially for the most prolonged SED bouts. Estimates were similar for time spent in intensity-specific (light, moderate, vigorous, and moderate-to-vigorous) PA, but varied 6-9% among the non-wear criteria for proportions of time spent in intensity-specific PA (% of total wear time).

CONCLUSIONS

Population level estimates of PA and SED differed between different accelerometer non-wear criteria, meaning that non-wear time algorithms should be standardized across studies to reduce confusion and improve comparability of children's PA level. Based on the numbers and reasons for non-wear periods, we suggest a 45 or 60-min consecutive zero count-criterion not allowing any interruptions to be applied in future pediatric studies, at least for children older than 10 years.

TRIAL REGISTRATION

The study is registered in Clinicaltrials.gov with identification number NCT02132494 . Registered 7 April 2014.

Physical Activity Barriers and Facilitators Among US Pacific Islanders and the Feasibility of Using Mobile Technologies for Intervention: A Focus Group Study With Tongan Americans

BACKGROUND

Pacific Islanders experience an elevated risk of health conditions such as obesity and diabetes, which are related to a lack of physical activity (PA). However, little attention has been paid to understanding the determinants of PA and promoting PA among this racial/ethnic group in the United States.

METHODS

We conducted focus group discussions with Tongan Americans, one of the major Pacific Islander groups in the United States, to gain a better understanding of their PA participation patterns, their barriers and facilitators, their attitudes toward PA, and their perceptions of how mobile technologies such as smartphones could help increase their PA levels.

RESULTS

Results indicate that although the participants understand the various benefits of PA, they do not engage in much leisure-time PA for exercise purposes. A lack of time is cited as an important reason for insufficient PA participation. In addition, most participants report familiarity with smartphones, positive views of mobile technology, and interest in using smartphones to measure and promote PA.

CONCLUSION

Multiple barriers were related with the low level of PA among Tongan Americans. Mobile technology is a promising way of enhancing PA among Tongan Americans and potentially other Pacific Islander subgroups. Culturally tailored strategies could significantly enhance the effectiveness of PA intervention.

Impact of anatomical placement of an accelerometer on prediction of physical activity energy expenditure in lower-limb amputees

Purpose

To assess the influence of the anatomical placement of a tri-axial accelerometer on the prediction of physical activity energy expenditure (PAEE) in traumatic lower-limb amputees during walking and to develop valid population-specific prediction algorithms.

Methods

Thirty participants, consisting of unilateral (n = 10), and bilateral (n = 10) amputees, and non-injured controls (n = 10) volunteered to complete eight activities; resting in a supine position, walking on a flat (0.48, 0.67, 0.89, 1.12, 1.34 m.s^-1) and an inclined (3 and 5% gradient at 0.89 m.s^-1) treadmill. During each task, expired gases were collected and an Actigraph GT3X+ accelerometer was worn on the right hip, left hip and lumbar spine. Linear regression analyses were conducted between outputs from each accelerometer site and criterion PAEE (indirect calorimetry). Mean bias ± 95% limits of agreement were calculated. Additional covariates were incorporated to assess whether they improved the prediction accuracy of regression models. Subsequent mean absolute error statistics were calculated for the derived models at all sites using a leave-one out cross-validation analysis.

Results

Predicted PAEE at each anatomical location was significantly (P< 0.01) correlated with criterion PAEE (P<0.01). Wearing the GT3X+ on the shortest residual limb demonstrates the strongest correlation (unilateral; r = 0.86, bilateral; r = 0.94), smallest ±95% limits of agreement (unilateral; ±2.15, bilateral ±1.99 kcal·min^-1) and least absolute percentage error (unilateral; 22±17%, bilateral 17±14%) to criterion PAEE.

Conclusions

We have developed accurate PAEE population specific prediction models in lower-limb amputees using an ActiGraph GT3X+ accelerometer. Of the 3 anatomical locations considered, wearing the accelerometer on the side of the shortest residual limb provides the most accurate prediction of PAEE with the least error in unilateral and bilateral traumatic lower-limb amputees.

The activPALTM Accurately Classifies Activity Intensity Categories in Healthy Adults

The activPAL (AP) monitor is well established for distinguishing sitting, standing, and stepping time. However, its validity in predicting time in physical activity intensity categories in a free-living environment has not been determined.

PURPOSE

This study aimed to determine the validity of the AP in estimating time spent in sedentary, light, and moderate-to-vigorous physical activity (MVPA) in a free-living environment.

METHODS

Thirteen participants (mean ± SD age = 24.8 ± 5.2 yr, BMI = 23.8 ± 1.9 kg·m) were directly observed for three 10-h periods wearing an AP. A custom R program was developed and used to summarize detailed active and sedentary behavior variables from the AP. AP estimates were compared with direct observation.

RESULTS

The AP accurately and precisely estimated time in activity intensity categories (bias [95% confidence interval]; sedentary = 0.8 min [-2.9 to 4.5], light = 1.7 min [2.2-5.7], and -2.6 min [-5.8 to 0.7]). The overall accuracy rate for time in intensity categories was 96.2%. The AP also accurately estimated guideline minutes, guideline bouts, prolonged sitting minutes, and prolonged sitting bouts.

CONCLUSION

The AP can be used to accurately capture individualized estimates of active and sedentary behavior variables in free-living settings.

Awareness of wearing an accelerometer does not affect physical activity in youth

BACKGROUND

This study aimed to investigate whether awareness of being monitored by an accelerometer has an effect on physical activity in young people.

METHODS

Eighty healthy participants aged 10-18 years were randomized between blinded and nonblinded groups. The blinded participants were informed that we were testing the reliability of a new device for body posture assessment and these participants did not receive any information about physical activity. In contrast, the nonblinded participants were informed that the device was an accelerometer that assessed physical activity levels and patterns. The participants were instructed to wear the accelerometer for 4 consecutive days (2 school days and 2 school-free days).

RESULTS

Missing data led to the exclusion of 2 participants assigned to the blinded group. When data from the blinded group were compared with these from the nonblinded group, no differences were found in the duration of any of the following items: (i) wearing the accelerometer, (ii) total physical activity, (iii) sedentary activity, and (iv) moderate-to-vigorous activity.

CONCLUSIONS

Our study shows that the awareness of wearing an accelerometer has no influence on physical activity patterns in young people. This study improves the understanding of physical activity assessment and underlines the objectivity of this method.

TRIAL REGISTRATION

NCT02844101 (retrospectively registered at July 13th 2016).

Design and Evaluation of a Computer-Based 24-Hour Physical Activity Recall (cpar24) Instrument

Background

Widespread access to the Internet and an increasing number of Internet users offers the opportunity of using Web-based recalls to collect detailed physical activity data in epidemiologic studies.

Objective

The aim of this investigation was to evaluate the validity and reliability of a computer-based 24-hour physical activity recall (cpar24) instrument with respect to the recalled 24-h period.

Methods

A random sample of 67 German residents aged 22 to 70 years was instructed to wear an ActiGraph GT3X+ accelerometer for 3 days. Accelerometer counts per min were used to classify activities as sedentary (<100 counts per min), light (100-1951 counts per min), and moderate to vigorous (≥1952 counts per min). On day 3, participants were also requested to specify the type, intensity, timing, and context of all activities performed during day 2 using the cpar24. Using metabolic equivalent of task (MET), the cpar24 activities were classified as sedentary (<1.5 MET), light (1.5-2.9 MET), and moderate to vigorous (≥3.0 MET). The cpar24 was administered twice at a 3-h interval. The Spearman correlation coefficient (r) was used as primary measure of concurrent validity and test-retest reliability.

Results

As compared with accelerometry, the cpar24 underestimated light activity by −123 min (median difference, P difference <.001) and overestimated moderate to vigorous activity by 89 min (P difference <.001). By comparison, time spent sedentary assessed by the 2 methods was similar (median difference=+7 min, P difference=.39). There was modest agreement between the cpar24 and accelerometry regarding sedentary (r=.54), light (r=.46), and moderate to vigorous (r=.50) activities. Reliability analyses revealed modest to high intraclass correlation coefficients for sedentary (r=.75), light (r=.65), and moderate to vigorous (r=.92) activities and no statistically significant differences between replicate cpar24 measurements (median difference for sedentary activities=+10 min, for light activities=−5 min, for moderate to vigorous activities=0 min, all P difference ≥.60).

Conclusion

These data show that the cpar24 is a valid and reproducible Web-based measure of physical activity in adults.

A method to estimate free-living active and sedentary behavior from an accelerometer

INTRODUCTION

Methods to estimate physical activity (PA) and sedentary behavior (SB) from wearable monitors need to be validated in free-living settings.

PURPOSE

The purpose of this study was to develop and validate two novel machine-learning methods (Sojourn-1 Axis [soj-1x] and Sojourn-3 Axis [soj-3x]) in a free-living setting.

METHODS

Participants were directly observed in their natural environment for 10 consecutive hours on three separate occasions. Physical activity and SB estimated from soj-1x, soj-3x, and a neural network previously calibrated in the laboratory (lab-nnet) were compared with direct observation.

RESULTS

Compared with lab-nnet, soj-1x and soj-3x improved estimates of MET-hours (lab-nnet: % bias [95% confidence interval] = 33.1 [25.9 to 40.4], root-mean-square error [RMSE] = 5.4 [4.6-6.2]; soj-1x: % bias = 1.9 [-2.0 to 5.9], RMSE = 1.0 [0.6 to 1.3]; soj-3x: % bias = 3.4 [0.0 to 6.7], RMSE = 1.0 [0.6 to 1.5]) and minutes in different intensity categories {lab-nnet: % bias = -8.2 (sedentary), -8.2 (light), and 72.8 (moderate-to-vigorous PA [MVPA]); soj-1x: % bias = 8.8 (sedentary), -18.5 (light), and -1.0 (MVPA); soj-3x: % bias = 0.5 (sedentary), -0.8 (light), and -1.0 (MVPA)}. Soj-1x and soj-3x also produced accurate estimates of guideline minutes and breaks from sedentary time.

CONCLUSIONS

Compared with the lab-nnet algorithm, soj-1x and soj-3x improved the accuracy and precision in estimating free-living MET-hours, sedentary time, and time spent in light-intensity activity and MVPA. In addition, soj-3x is superior to soj-1x in differentiating SB from light-intensity activity.

Personalized Technologies in Chronic Gastrointestinal Disorders: Self-monitoring and Remote Sensor Technologies

With increased access to high-speed Internet and smartphone devices, patients have started to use mobile applications (apps) for various health needs. These mobile apps are now increasingly used in integration with telemedicine and wearables to support fitness, health education, symptom tracking, and collaborative disease management and care coordination. More recently, evidence (especially around remote patient monitoring) has started to build in some chronic diseases, and some of the digital health technologies have received approval from the Food and Drug Administration. With the changing healthcare landscape and push for value-based care, adoption of these digital health initiatives among providers is bound to increase. Although so far there is a dearth of published evidence about effectiveness of these apps in gastroenterology care, there are ongoing trials to determine whether remote patient monitoring can lead to improvement in process metrics or outcome metrics for patients with chronic gastrointestinal diseases.

Wear Compliance and Activity in Children Wearing Wrist- and Hip-Mounted Accelerometers

PURPOSE

This study aimed to 1) explore children's compliance to wearing wrist- and hip-mounted accelerometers, 2) compare children's physical activity (PA) derived from raw accelerations of wrist and hip, and 3) examine differences in raw and counts PA measured by hip-worn accelerometry.

METHODS

One hundred and twenty-nine 9- to 10-yr-old children wore a wrist-mounted GENEActiv accelerometer (GAwrist) and a hip-mounted ActiGraph GT3X+ accelerometer (AGhip) for 7 d. Both devices measured raw accelerations, and the AGhip also provided count-based data.

RESULTS

More children wore the GAwrist than those from the AGhip regardless of wear time criteria applied (P < 0.001-0.035). Raw data signal vector magnitude (r = 0.68), moderate PA (MPA) (r = 0.81), vigorous PA (VPA) (r = 0.85), and moderate-to-vigorous PA (MVPA) (r = 0.83) were strongly associated between devices (P < 0.001). GAwrist signal vector magnitude (P = 0.001), MPA (P = 0.037), VPA (P = 0.002), and MVPA (P = 0.016) were significantly greater than those from the AGhip. According to GAwrist raw data, 86.9% of children engaged in at least 60 min · d(-1) of MVPA, compared with 19% for AGhip. ActiGraph MPA (raw) was 42.00 ± 1.61 min · d(-1) compared with 35.05 ± 0.99 min · d(-1) (counts) (P = 0.02). ActiGraph VPA was 7.59 ± 0.46 min · d(-1) (raw) and 37.06 ± 1.85 min · d(-1) (counts; P = 0.19).

CONCLUSIONS

In children, accelerometer wrist placement promotes superior compliance than the hip. Raw accelerations were significantly higher for GAwrist compared with those for AGhip possibly because of placement location and technical differences between devices. AGhip PA calculated from raw accelerations and counts differed substantially, demonstrating that PA outcomes derived from cut points for raw output and counts cannot be directly compared.

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.

Evaluation of the ability of three physical activity monitors to predict weight change and estimate energy expenditure

Activity monitors such as the Actical accelerometer, the Sensewear armband, and the Intelligent Device for Energy Expenditure and Activity (IDEEA) are commonly validated against gold standards (e.g., doubly labeled water, or DLW) to determine whether they accurately measure total daily energy expenditure (TEE) or activity energy expenditure (AEE). However, little research has assessed whether these parameters or others (e.g., posture allocation) predict body weight change over time. The aims of this study were to (i) test whether estimated energy expenditure or posture allocation from the devices was associated with weight change during and following a low-calorie diet (LCD) and (ii) compare free-living TEE and AEE predictions from the devices against DLW before weight change. Eighty-seven participants from 2 clinical trials wore 2 of the 3 devices simultaneously for 1 week of a 2-week DLW period. Participants then completed an 8-week LCD and were weighed at the start and end of the LCD and 6 and 12 months after the LCD. More time spent walking at baseline, measured by the IDEEA, significantly predicted greater weight loss during the 8-week LCD. Measures of posture allocation demonstrated medium effect sizes in their relationships with weight change. Bland-Altman analyses indicated that the Sensewear and the IDEEA accurately estimated TEE, and the IDEEA accurately measured AEE. The results suggest that the ability of energy expenditure and posture allocation to predict weight change is limited, and the accuracy of TEE and AEE measurements varies across activity monitoring devices, with multi-sensor monitors demonstrating stronger validity.

Does reactivity to accelerometers occur in a single trial? Brief report in a sample of young adults

This study aimed to test acute reactivity during a physical activity in an outdoor setting and to verify the relative perceived performance. In all, 38 volunteers wore accelerometers or not and completed two 20-minute sessions of self-selected pace physical activity. Covered distance, exertional responses, and perceived efficacy were recorded at the end of every session. Relevant finding of this study has been that reactivity to accelerometers also occurs in acute condition. Consequently, this condition leads to a better performance and a greater perceived exertion. Moreover, this situation seems to occur in a state of awareness.

Occupational physical activity assessment for chronic disease prevention and management: A review of methods for both occupational health practitioners and researchers

Occupational physical activity (OPA) is an occupational exposure that impacts worker health. OPA is amenable to measurement and modification through the hierarchy of controls. Occupational exposure scientists have roles in addressing inadequate physical activity, as well as excessive or harmful physical activity. Occupational health researchers can contribute to the development of novel OPA exposure assessment techniques and to epidemiologic studies examining the health impacts of physical activity at work. Occupational health practitioners stand to benefit from understanding the strengths and limitations of physical activity measurement approaches, such as accelerometers in smartphones, which are already ubiquitous in many workplaces and in some worksite health programs. This comprehensive review of the literature provides an overview of physical activity monitoring for occupational exposure scientists. This article summarizes data on the public health implications of physical activity at work, highlighting complex relationships with common chronic diseases. This article includes descriptions of several techniques that have been used to measure physical activity at work and elsewhere, focusing in detail on pedometers, accelerometers, and Global Positioning System technology. Additional subjective and objective measurement strategies are described as well.

Innovations in the Use of Interactive Technology to Support Weight Management

New and emerging mobile technologies are providing unprecedented possibilities for understanding and intervening on obesity-related behaviors in real time. However, the mobile health (mHealth) field has yet to catch up with the fast-paced development of technology. Current mHealth efforts in weight management still tend to focus mainly on short message systems (SMS) interventions, rather than taking advantage of real-time sensing to develop just-in-time adaptive interventions (JITAIs). This paper will give an overview of the current technology landscape for sensing and intervening on three behaviors that are central to weight management: diet, physical activity, and sleep. Then five studies that really dig into the possibilities that these new technologies afford will be showcased. We conclude with a discussion of hurdles that mHealth obesity research has yet to overcome and a future-facing discussion.

Physical activity intensity can be accurately monitored by smartphone global positioning system 'app'

Monitoring physical activity is important to better individualise health and fitness benefits. This study assessed the concurrent validity of a smartphone global positioning system (GPS) 'app' and a sport-specific GPS device with a similar sampling rate, to measure physical activity components of speed and distance, compared to a higher sampling sport-specific GPS device. Thirty-eight (21 female, 17 male) participants, mean age of 24.68, s = 6.46 years, completed two 2.400 km trials around an all-weather athletics track wearing GPSports Pro™ (PRO), GPSports WiSpi™ (WISPI) and an iPhone™ with a Motion X GPS™ 'app' (MOTIONX). Statistical agreement, assessed using t-tests and Bland-Altman plots, indicated an (mean; 95% LOA) underestimation of 2% for average speed (0.126 km·h(-1); -0.389 to 0.642; p < .001), 1.7% for maximal speed (0.442 km·h(-1); -2.676 to 3.561; p = .018) and 1.9% for distance (0.045 km; -0.140 to 0.232; p < .001) by MOTIONX compared to that measured by PRO. In contrast, compared to PRO, WISPI overestimated average speed (0.232 km·h(-1); -0.376 to 0.088; p < .001) and distance (0.083 km; -0.129 to -0.038; p < .001) by 3.5% whilst underestimating maximal speed by 2.5% (0.474 km·h(-1); -1.152 to 2.099; p < .001). Despite the statistically significant difference, the MOTIONX measures intensity of physical activity, with a similar error as WISPI, to an acceptable level for population-based monitoring in unimpeded open-air environments. This presents a low-cost, minimal burden opportunity to remotely monitor physical activity participation to improve the prescription of exercise as medicine.

Practical considerations in using accelerometers to assess physical activity, sedentary behavior, and sleep

Increasingly, behavioral and epidemiological research uses activity-based measurements (accelerometry) to provide objective estimates of physical activity, sedentary behavior, and sleep in a variety of study designs. As interest in concurrently assessing these domains grows, there are key methodological considerations that influence the choice of monitoring instrument, analysis algorithm, and protocol for measuring these behaviors. The purpose of this review is to summarize evidence-guided information for 7 areas that are of importance in the design and interpretation of studies using actigraphy: (1) choice of cut-points; (2) impact of epoch length; (3) accelerometer placement; (4) duration of monitoring; (5) approaches for distinguishing sleep, nonwear times, and sedentary behavior; (6) role for a sleep and activity diary; and (7) epidemiological applications. Recommendations for future research are outlined and are intended to enhance the appropriate use of accelerometry for assessing physical activity, sedentary behavior, and sleep behaviors in research studies.

Using activity triggered e-diaries to reveal the associations between physical activity and affective states in older adult's daily living

Background

Evidence suggests that older adults show positive affects after participating in exercise bouts. However, it is less clear, if and how physical activities in daily living enhance affective states, too. This is dissatisfying, as most of older adults’ physical activities are part of their daily living. To answer these questions we used activity-triggered e-diaries to investigate the within-subject effects of physical activity on three dimensions of affective states (valence, energetic arousal, calmness) during everyday life.

Methods

Older adults (N = 74) between 50 and 70 years took part in the study during three consecutive days. Physical activity in daily living was objectively assessed using accelerometers. Affects were measured 10 min after a study participant surpassed a predefined threshold for activity or inactivity. The participants were prompted by an acoustic signal to assess their momentary affective states on an e-diary. Data were analyzed with hierarchical multilevel analyses.

Results

Whenever older individuals were more physically active, they felt more energized (energetic arousal) and agitated (calmness). However, they did not feel better (valence). Interestingly, body mass index (BMI) and valence were associated in a significant cross-level interaction. BMI acts as a moderating variable in the way that lower BMI scores were associated with higher levels of valence scores after being physically active.

Conclusions

The innovative ambulatory assessment used here affords an interesting insight to the affective effects of daily activity of older adults. These effects are no simple and no linear ones, i.e. physical activity is not associated with positive affects per se as shown several times in experimental studies with single activity bouts. Rather there is a differentiating association seen as an enhanced feeling of energy and agitation, which is not accompanied by a better feeling. Socio-emotional selectivity theory may support the finding that older individuals are emotionally more stable during their day-to-day life, which might explain the non-significant effect on the affect dimension valence.

Variability and Stability in Daily Moderate-to-Vigorous Physical Activity among 10 Year Old Children

Day-to-day variability and stability of children's physical activity levels across days of the week are not well understood. Our aims were to examine the day-to-day variability of moderate-to-vigorous physical activity (MVPA), to determine factors influencing the day-to-day variability of MVPA and to estimate stability of MVPA in children. The sample comprises 686 Portuguese children (10 years of age). MVPA was assessed with an accelerometer, and BMI was computed from measured height and weight. Daily changes in MVPA and their correlates (gender, BMI, and maturity) were modeled with a multilevel approach, and tracking was calculated using Foulkes &amp; Davies γ. A total of 51.3% of boys and 26.2% of girls achieved 60 min/day of MVPA on average. Daily MVPA was lower during the weekend (23.6% of boys and 13.6% of girls comply with the recommended 60 min/day of MVPA) compared to weekdays (60.8% and 35.4%, boys and girls, respectively). Normal weight children were more active than obese children and no effect was found for biological maturation. Tracking is low in both boys (γ = 0.59 ± 0.01) and girls (γ = 0.56 ± 0.01). Children's MVPA levels during a week are highly unstable. In summary, boys are more active than girls, maturation does not affect their MVPA, and obese children are less likely to meet 60 min/day of MVPA. These results highlight the importance of providing opportunities for increasing children's daily MVPA on all days of week, especially on the weekend.

Influence of accelerometer type and placement on physical activity energy expenditure prediction in manual wheelchair users

PURPOSE

To assess the validity of two accelerometer devices, at two different anatomical locations, for the prediction of physical activity energy expenditure (PAEE) in manual wheelchair users (MWUs).

METHODS

Seventeen MWUs (36 ± 10 yrs, 72 ± 11 kg) completed ten activities; resting, folding clothes, propulsion on a 1% gradient (3,4,5,6 and 7 km·hr-1) and propulsion at 4km·hr-1 (with an additional 8% body mass, 2% and 3% gradient) on a motorised wheelchair treadmill. GT3X+ and GENEActiv accelerometers were worn on the right wrist (W) and upper arm (UA). Linear regression analysis was conducted between outputs from each accelerometer and criterion PAEE, measured using indirect calorimetry. Subsequent error statistics were calculated for the derived regression equations for all four device/location combinations, using a leave-one-out cross-validation analysis.

RESULTS

Accelerometer outputs at each anatomical location were significantly (p < .01) associated with PAEE (GT3X+-UA; r = 0.68 and GT3X+-W; r = 0.82. GENEActiv-UA; r = 0.87 and GENEActiv-W; r = 0.88). Mean ± SD PAEE estimation errors for all activities combined were 15 ± 45%, 14 ± 50%, 3 ± 25% and 4 ± 26% for GT3X+-UA, GT3X+-W, GENEActiv-UA and GENEActiv-W, respectively. Absolute PAEE estimation errors for devices varied, 19 to 66% for GT3X+-UA, 17 to 122% for GT3X+-W, 15 to 26% for GENEActiv-UA and from 17.0 to 32% for the GENEActiv-W.

CONCLUSION

The results indicate that the GENEActiv device worn on either the upper arm or wrist provides the most valid prediction of PAEE in MWUs. Variation in error statistics between the two devices is a result of inherent differences in internal components, on-board filtering processes and outputs of each device.

Validation of Physical Activity Tracking via Android Smartphones Compared to ActiGraph Accelerometer: Laboratory-Based and Free-Living Validation Studies

BACKGROUND

There is increasing interest in using smartphones as stand-alone physical activity monitors via their built-in accelerometers, but there is presently limited data on the validity of this approach.

OBJECTIVE

The purpose of this work was to determine the validity and reliability of 3 Android smartphones for measuring physical activity among midlife and older adults.

METHODS

A laboratory (study 1) and a free-living (study 2) protocol were conducted. In study 1, individuals engaged in prescribed activities including sedentary (eg, sitting), light (sweeping), moderate (eg, walking 3 mph on a treadmill), and vigorous (eg, jogging 5 mph on a treadmill) activity over a 2-hour period wearing both an ActiGraph and 3 Android smartphones (ie, HTC MyTouch, Google Nexus One, and Motorola Cliq). In the free-living study, individuals engaged in usual daily activities over 7 days while wearing an Android smartphone (Google Nexus One) and an ActiGraph.

RESULTS

Study 1 included 15 participants (age: mean 55.5, SD 6.6 years; women: 56%, 8/15). Correlations between the ActiGraph and the 3 phones were strong to very strong (ρ=.77-.82). Further, after excluding bicycling and standing, cut-point derived classifications of activities yielded a high percentage of activities classified correctly according to intensity level (eg, 78%-91% by phone) that were similar to the ActiGraph's percent correctly classified (ie, 91%). Study 2 included 23 participants (age: mean 57.0, SD 6.4 years; women: 74%, 17/23). Within the free-living context, results suggested a moderate correlation (ie, ρ=.59, P<.001) between the raw ActiGraph counts/minute and the phone's raw counts/minute and a strong correlation on minutes of moderate-to-vigorous physical activity (MVPA; ie, ρ=.67, P<.001). Results from Bland-Altman plots suggested close mean absolute estimates of sedentary (mean difference=-26 min/day of sedentary behavior) and MVPA (mean difference=-1.3 min/day of MVPA) although there was large variation.

CONCLUSIONS

Overall, results suggest that an Android smartphone can provide comparable estimates of physical activity to an ActiGraph in both a laboratory-based and free-living context for estimating sedentary and MVPA and that different Android smartphones may reliably confer similar estimates.

Establishing and evaluating wrist cutpoints for the GENEActiv accelerometer in youth

PURPOSE

This study aimed to establish physical activity (PA) intensity cutpoints for a wrist-mounted GENEActiv accelerometer (ACC) in elementary school-age children. A second purpose was to apply cutpoints to a free-living sample and examine the duration of PA based on continuous 1-s epochs.

METHODS

Metabolic and ACC data were collected during nine typical activities in 24 children age 6-11 yr. Measured VO2 values were divided by Schofield-estimated resting values to determine METs. ACC data were collected at 75 Hz, band pass filtered, and averaged over each 1-s interval. Receiver operator characteristic curves were used to establish cutpoints at sedentary (≤ 1.5 METs), light (1.6-2.99 METs), moderate (3.0-5.99 METs), and vigorous (≥ 6 METs) activities. These cutpoints were applied to a free-living independent data set to quantify the amount of moderate-vigorous PA (MVPA) and to examine how bout length (1, 2, 3, 5, 10, 15, and 60 s) affected the accumulation of MVPA.

RESULTS

Receiver operator characteristic yielded areas under the curve of 0.956, 0.946, and 0.940 for sedentary, moderate, and vigorous intensities, respectively. Cutpoints for sedentary, moderate, and vigorous intensities were 0.190 g, 0.314 g, and 0.998 g, respectively. Intensity classification accuracies ranged from 27.6% (light) to 88.7% (vigorous) when cutpoints were applied to the calibration data. When applied to free-living data (n = 47 children age 6-11 yr), estimated daily MVPA was 308 min and decreased to 14.3 min when only including 1-min periods of continuous MVPA.

CONCLUSIONS

Cutpoints that quantify movements associated with moderate-vigorous intensity, when applied to a laboratory protocol, result in large amounts of accumulated MVPA using the 1-s epoch compared to prior studies, highlighting the need for representative calibration activities and free-living validation of cutpoints and epoch length selection.

Automatic identification of physical activity types and sedentary behaviors from triaxial accelerometer: laboratory-based calibrations are not enough

"Objective" methods to monitor physical activity and sedentary patterns in free-living conditions are necessary to further our understanding of their impacts on health. In recent years, many software solutions capable of automatically identifying activity types from portable accelerometry data have been developed, with promising results in controlled conditions, but virtually no reports on field tests. An automatic classification algorithm initially developed using laboratory-acquired data (59 subjects engaging in a set of 24 standardized activities) to discriminate between 8 activity classes (lying, slouching, sitting, standing, walking, running, and cycling) was applied to data collected in the field. Twenty volunteers equipped with a hip-worn triaxial accelerometer performed at their own pace an activity set that included, among others, activities such as walking the streets, running, cycling, and taking the bus. Performances of the laboratory-calibrated classification algorithm were compared with those of an alternative version of the same model including field-collected data in the learning set. Despite good results in laboratory conditions, the performances of the laboratory-calibrated algorithm (assessed by confusion matrices) decreased for several activities when applied to free-living data. Recalibrating the algorithm with data closer to real-life conditions and from an independent group of subjects proved useful, especially for the detection of sedentary behaviors while in transports, thereby improving the detection of overall sitting (sensitivity: laboratory model = 24.9%; recalibrated model = 95.7%). Automatic identification methods should be developed using data acquired in free-living conditions rather than data from standardized laboratory activity sets only, and their limits carefully tested before they are used in field studies.

Reliability and validity of a GPS-enabled iPhone "app" to measure physical activity

This study assessed the validity and reliability of an iPhone "app" and two sport-specific global positioning system (GPS) units to monitor distance, intensity and contextual physical activity. Forty (23 female, 17 male) 18-55-year-olds completed two trials of six laps around a 400-m athletics track wearing GPSports Pro and WiSpi units (5 and 1 Hz) and an iPhone(TM) with a Motion X GPS(TM) "app" that used the inbuilt iPhone location services application programming interface to obtain its sampling rate (which is likely to be ≤1 Hz). Overall, the statistical agreement, assessed using t-tests and Bland-Altman plots, indicated an underestimation of the known track distance (2.400 km) and average speed by the Motion X GPS "app" and GPSports Pro while the GPSports WiSpi(TM) device overestimated these outcomes. There was a ≤3% variation between trials for distance and average speed when measured by any of the GPS devices. Thus, the smartphone "app" trialled could be considered as an accessible alternative to provide high-quality contextualised data to enable ubiquitous monitoring and modification of programmes to ensure appropriate intensity and type of physical activity is prescribed and more importantly adhered to.

Reactivity to accelerometer measurement of children and adolescents

Supplemental digital content is available in the text.

Purpose

Awareness of being monitored can influence participants’ habitual physical activity (PA) behavior. This reactivity effect may threaten the validity of PA assessment. Reports on reactivity when measuring the PA of children and adolescents have been inconsistent. The aim of this study was to investigate whether PA outcomes measured by accelerometer devices differ from measurement day to measurement day and whether the day of the week and the day on which measurement started influence these differences.

Methods

Accelerometer data (counts per minute [cpm]) of children and adolescents (n = 2081) pooled from eight studies in Switzerland with at least 10 h of daily valid recording were investigated for effects of measurement day, day of the week, and start day using mixed linear regression.

Results

The first measurement day was the most active day. Counts per minute were significantly higher than on the second to the sixth day, but not on the seventh day. Differences in the age-adjusted means between the first and consecutive days ranged from 23 to 45 cpm (3.6%–7.1%). In preschoolchildren, the differences almost reached 10%. The start day significantly influenced PA outcome measures.

Conclusions

Reactivity to accelerometer measurement of PA is likely to be present to an extent of approximately 5% on the first day and may introduce a relevant bias to accelerometer-based studies. In preschoolchildren, the effects are larger than those in elementary and secondary schoolchildren. As the day of the week and the start day significantly influence PA estimates, researchers should plan for at least one familiarization day in school-age children and randomly assign start days.

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

BACKGROUND

Accelerometers objectively assess physical activity (PA) and are currently used in several large-scale epidemiological studies, but there is no consensus for processing the data. This study compared the impact of wear-time assessment methods and using either vertical (V)-axis or vector magnitude (VM) cut-points on accelerometer output.

METHODS

Participants (7,650 women, mean age 71.4 y) were mailed an accelerometer (ActiGraph GT3X+), instructed to wear it for 7 days, record dates and times the monitor was worn on a log, and return the monitor and log via mail. Data were processed using three wear-time methods (logs, Troiano or Choi algorithms) and V-axis or VM cut-points.

RESULTS

Using algorithms alone resulted in "mail-days" incorrectly identified as "wear-days" (27-79% of subjects had >7-days of valid data). Using only dates from the log and the Choi algorithm yielded: 1) larger samples with valid data than using log dates and times, 2) similar wear-times as using log dates and times, 3) more wear-time (V, 48.1 min more; VM, 29.5 min more) than only log dates and Troiano algorithm. Wear-time algorithm impacted sedentary time (~30-60 min lower for Troiano vs. Choi) but not moderate-to-vigorous (MV) PA time. Using V-axis cut-points yielded ~60 min more sedentary time and ~10 min less MVPA time than using VM cut-points.

CONCLUSIONS

Combining log-dates and the Choi algorithm was optimal, minimizing missing data and researcher burden. Estimates of time in physical activity and sedentary behavior are not directly comparable between V-axis and VM cut-points. These findings will inform consensus development for accelerometer data processing in ongoing epidemiologic studies.

Measuring and influencing physical activity with smartphone technology: a systematic review

BACKGROUND

Rapid developments in technology have encouraged the use of smartphones in physical activity research, although little is known regarding their effectiveness as measurement and intervention tools.

OBJECTIVE

This study systematically reviewed evidence on smartphones and their viability for measuring and influencing physical activity.

DATA SOURCES

Research articles were identified in September 2013 by literature searches in Web of Knowledge, PubMed, PsycINFO, EBSCO, and ScienceDirect.

STUDY SELECTION

The search was restricted using the terms (physical activity OR exercise OR fitness) AND (smartphone* OR mobile phone* OR cell phone*) AND (measurement OR intervention). Reviewed articles were required to be published in international academic peer-reviewed journals, or in full text from international scientific conferences, and focused on measuring physical activity through smartphone processing data and influencing people to be more active through smartphone applications.

STUDY APPRAISAL AND SYNTHESIS METHODS

Two reviewers independently performed the selection of articles and examined titles and abstracts to exclude those out of scope. Data on study characteristics, technologies used to objectively measure physical activity, strategies applied to influence activity; and the main study findings were extracted and reported.

RESULTS

A total of 26 articles (with the first published in 2007) met inclusion criteria. All studies were conducted in highly economically advantaged countries; 12 articles focused on special populations (e.g. obese patients). Studies measured physical activity using native mobile features, and/or an external device linked to an application. Measurement accuracy ranged from 52 to 100% (n = 10 studies). A total of 17 articles implemented and evaluated an intervention. Smartphone strategies to influence physical activity tended to be ad hoc, rather than theory-based approaches; physical activity profiles, goal setting, real-time feedback, social support networking, and online expert consultation were identified as the most useful strategies to encourage physical activity change. Only five studies assessed physical activity intervention effects; all used step counts as the outcome measure. Four studies (three pre-post and one comparative) reported physical activity increases (12-42 participants, 800-1,104 steps/day, 2 weeks-6 months), and one case-control study reported physical activity maintenance (n = 200 participants; >10,000 steps/day) over 3 months.

LIMITATIONS

Smartphone use is a relatively new field of study in physical activity research, and consequently the evidence base is emerging.

CONCLUSIONS

Few studies identified in this review considered the validity of phone-based assessment of physical activity. Those that did report on measurement properties found average-to-excellent levels of accuracy for different behaviors. The range of novel and engaging intervention strategies used by smartphones, and user perceptions on their usefulness and viability, highlights the potential such technology has for physical activity promotion. However, intervention effects reported in the extant literature are modest at best, and future studies need to utilize randomized controlled trial research designs, larger sample sizes, and longer study periods to better explore the physical activity measurement and intervention capabilities of smartphones.

Evaluation of prompted annotation of activity data recorded from a smart phone

In this paper we discuss the design and evaluation of a mobile based tool to collect activity data on a large scale. The current approach, based on an existing activity recognition module, recognizes class transitions from a set of specific activities (for example walking and running) to the standing still activity. Once this transition is detected the system prompts the user to provide a label for their previous activity. This label, along with the raw sensor data, is then stored locally prior to being uploaded to cloud storage. The system was evaluated by ten users. Three evaluation protocols were used, including a structured, semi-structured and free living protocol. Results indicate that the mobile application could be used to allow the user to provide accurate ground truth labels for their activity data. Similarities of up to 100% where observed when comparing the user prompted labels and those from an observer during structured lab based experiments. Further work will examine data segmentation and personalization issues in order to refine the system.

Evolution of accelerometer methods for physical activity research

The technology and application of current accelerometer-based devices in physical activity (PA) research allow the capture and storage or transmission of large volumes of raw acceleration signal data. These rich data not only provide opportunities to improve PA characterisation, but also bring logistical and analytic challenges. We discuss how researchers and developers from multiple disciplines are responding to the analytic challenges and how advances in data storage, transmission and big data computing will minimise logistical challenges. These new approaches also bring the need for several paradigm shifts for PA researchers, including a shift from count-based approaches and regression calibrations for PA energy expenditure (PAEE) estimation to activity characterisation and EE estimation based on features extracted from raw acceleration signals. Furthermore, a collaborative approach towards analytic methods is proposed to facilitate PA research, which requires a shift away from multiple independent calibration studies. Finally, we make the case for a distinction between PA represented by accelerometer-based devices and PA assessed by self-report.

Realizing the Promise of Social Psychology in Improving Public Health

The theories, phenomena, empirical findings, and methodological approaches that characterize contemporary social psychology hold much promise for addressing enduring problems in public health. Indeed, social psychologists played a major role in the development of the discipline of health psychology during the 1970s and 1980s. The health domain allows for the testing, refinement, and application of many interesting and important research questions in social psychology, and offers the discipline a chance to enhance its reach and visibility. Nevertheless, in a review of recent articles in two major social-psychological journals ( Personality and Social Psychology Bulletin and Journal of Personality and Social Psychology), we found that only 3.2% of 467 studies explored health-related topics. In this article, we identify opportunities for research at the interface of social psychology and health, delineate barriers, and offer strategies that can address these barriers as the discipline continues to evolve.

Developing suitable buffers to capture transport cycling behavior

The association between neighborhood built environment and cycling has received considerable attention in health literature over the last two decades, but different neighborhood definitions have been used and it is unclear which one is most appropriate. Administrative or fixed residential spatial units (e.g., home-buffer-based neighborhoods) are not necessarily representative for environmental exposure. An increased understanding of appropriate neighborhoods is needed. GPS cycling tracks from 78 participants for 7 days form the basis for the development and testing of different neighborhood buffers for transport cycling. The percentage of GPS points per square meter was used as indicator of the effectiveness of a series of different buffer types, including home-based network buffers, shortest route to city center buffers, and city center-directed ellipse-shaped buffers. The results show that GPS tracks can help us understand where people go and stay during the day, which can help us link built environment with cycling. Analysis showed that the further people live from the city center, the more elongated are their GPS tracks, and the better an ellipse-shaped directional buffer captured transport cycling behavior. In conclusion, we argue that in order to be able to link built environment factors with different forms of physical activity, we must study the most likely area people use. In this particular study, to capture transport cycling, with its relatively large radius of action, city center-directed ellipse-shaped buffers yielded better results than traditional home-based network buffer types. The ellipse-shaped buffer types could therefore be considered an alternative to more traditional buffers or administrative units in future studies of transport cycling behavior.