Accuracy of three Android-based pedometer applications in laboratory and free-living settings

Mandatory after-school use of step tracker apps improves physical activity, body composition and fitness of adolescents

Previous scientific research on the use of mobile applications to increase physical activity level and improve health among adolescents does not provide conclusive results, one of the main reasons being the lack of adherence to the intervention after the first weeks. For this reason, the main objectives of the research were to determine the changes produced by a compulsory ten-week period of after-school intervention with mobile step-tracking applications on adolescents' health; and the final objective to compare the benefits obtained by each of the mobile applications. To meet the objectives, a longitudinal study with non-probability convenience sampling was proposed. The sample consisted of 400 adolescents from two public compulsory secondary schools in the Region of Murcia, Spain, whose body composition, level of physical activity, adherence to the Mediterranean diet, and physical fitness were measured. The SPSS statistical software was used for statistical analysis. The results showed that adolescents in the experimental group showed a higher level of physical activity and better body composition and physical fitness variables after the intervention compared to the control group, with differences between the different applications used. In conclusion, this research shows the usefulness of mobile applications if they are used in a compulsory way after school hours. The relevance of these results for policymakers lies in the fact that they provide statistical data on the usefulness of mobile applications as an educational resource, being an option to make up for the lack of sufficient physical education teaching hours to meet global physical activity recommendations.

Smartphone Pedometer Sensor Application for Evaluating Disease Activity and Predicting Comorbidities in Patients with Rheumatoid Arthritis: A Validation Study

Smartphone-based pedometer sensor telemedicine applications could be useful for measuring disease activity and predicting the risk of developing comorbidities, such as pulmonary or cardiovascular disease, in patients with rheumatoid arthritis (RA), but the sensors have not been validated in this patient population. The aim of this study was to validate step counting with an activity-tracking application running the inbuilt Android smartphone pedometer virtual sensor in patients with RA. Two Android-based smartphones were tested in a treadmill test-bed setup at six walking speeds and compared to manual step counting as the gold standard. Guided by a facilitator, the participants walked 100 steps at each test speed, from 2.5 km/h to 5 km/h, wearing both devices simultaneously in a stomach pouch. A computer automatically recorded both the manually observed and the sensor step count. The overall difference in device step counts versus the observed was 5.9% mean absolute percentage error. Highest mean error was at the 2.5 km/h speed tests, where the mean error of the two devices was 18.5%. Both speed and cadence were negatively correlated to the absolute percentage error, which indicates that the greater the speed and cadence, the lower the resulting step counting error rate. There was no correlation between clinical parameters and absolute percentage error. In conclusion, the activity-tracking application using the inbuilt Android smartphone pedometer virtual sensor is valid for step counting in patients with RA. However, walking at very low speed and cadence may represent a challenge.

Virtual national workshop: preparation of multimedia modules for physical education teachers in accordance with COVID-19 prevention procedures

The COVID-19 pandemic has caused changes in the school learning system. Face-to-face learning shifted to remote learning using multimedia approaches. Online learning created particular difficulties for Physical Education (PE) teachers. Previously, they had to be role models in the teaching of physical activity. A national virtual workshop was conducted to support those teachers as they shift to remote learning. The purpose of the workshop was to provide PE instruction through social media and develop online learning modules. The 3 days of activities consisted of 4 lectures and 6 workshops provided to 177 PE teachers from 32 provinces in Indonesia. Participants were informed about the COVID-19 pandemic, its impact on children, and healthy life during the pandemic. Online applications that were free of charge, easy to use, highly rated, and widely downloaded were also introduced to them. These multimedia applications could help teachers develop and deliver remote learning modules to their students. The workshop supported the teachers as they adapted to interactive distance learning. The workshop also successfully illustrates an innovative distance learning module delivered through multimedia.

Supervised Exercise Therapy Using Mobile Health Technology in Patients With Peripheral Arterial Disease: Pilot Randomized Controlled Trial

Background

Mobile health interventions are intended to support complex health care needs in chronic diseases digitally, but they are mainly targeted at general health improvement and neglect disease-specific requirements. Therefore, we designed TrackPAD, a smartphone app to support supervised exercise training in patients with peripheral arterial disease.

Objective

This pilot study aimed to evaluate changes in the 6-minute walking distance (meters) as a primary outcome measure. The secondary outcome measures included changes in physical activity and assessing the patients’ peripheral arterial disease–related quality of life.

Methods

This was a pilot two-arm, single-blinded, randomized controlled trial. Patients with symptomatic PAD (Fontaine stage IIa/b) and access to smartphones were eligible. Eligible participants were randomly assigned to the study, with the control group stratified by the distance covered in the 6-minute walking test using the TENALEA software. Participants randomized to the intervention group received usual care and the mobile intervention (TrackPAD) for the follow-up period of 3 months, whereas participants randomized to the control group received routine care only. TrackPAD records the frequency and duration of training sessions and pain levels using manual user input. Clinical outcome data were collected at the baseline and after 3 months via validated tools (the 6-minute walk test and self-reported quality of life). The usability and quality of the app were determined using the Mobile Application Rating Scale user version.

Results

The intervention group (n=19) increased their mean 6-minute walking distance (83 meters, SD 72.2), while the control group (n=20) decreased their mean distance after 3 months of follow-up (–38.8 meters, SD 53.7; P=.01). The peripheral arterial disease–related quality of life increased significantly in terms of “symptom perception” and “limitations in physical functioning.” Users’ feedback showed increased motivation and a changed attitude toward performing supervised exercise training.

Conclusions

Besides the rating providing a valuable support tool for the user group, the mobile intervention TrackPAD was linked to a change in prognosis-relevant outcome measures combined with enhanced coping with the disease. The influence of mobile interventions on long-term prognosis must be evaluated in the future.

Trial Registration

ClinicalTrials.gov NCT04947228; https://clinicaltrials.gov/ct2/show/NCT04947228

Criterion Validity of iOS and Android Applications to Measure Steps and Distance in Adults

The growing popularity of physical activity (PA) applications (apps) in recent years and the vast amounts of data that they generate present attractive possibilities for surveillance. However, measurement accuracy is indispensable when tracking PA variables to provide meaningful measures of PA. The purpose of this study was to examine the steps and distance criterion validity of freeware accelerometer-based PA smartphone apps, during incremental-intensity treadmill walking and jogging. Thirty healthy adults (25.9 ± 5.7 years) participated in this cross-sectional study. They were fitted with two smartphones (one with Android and one with iOS operating systems), each one simultaneously running four different apps (i.e., Runtastic Pedometer, Accupedo, Pacer, and Argus). They walked and jogged for 5 min at each of the predefined speeds of 4.8, 6.0, and 8.4 km/h on a treadmill, and two researchers counted every step taken during trials with a digital tally counter. Validity was evaluated by comparing each app with the criterion measure using repeated-measures analysis of variance (ANOVA), mean absolute percentage errors (MAPEs), and Bland–Altman plots. For step count, Android apps performed slightly more accurately that iOS apps; nevertheless, MAPEs were generally low for all apps (<5%) and accuracy increased at higher speeds. On the other hand, errors were significantly higher for distance estimation (>10%). The findings suggest that accelerometer-based apps are accurate tools for counting steps during treadmill walking and jogging and could be considered suitable for use as an outcome measure within a clinical trial. However, none of the examined apps was suitable for measuring distance.

Evaluating Pedometer Algorithms on Semi-Regular and Unstructured Gaits

Pedometers are popular for counting steps as a daily measure of physical activity, however, errors as high as 96% have been reported in previous work. Many reasons for pedometer error have been studied, including walking speed, sensor position on the body and pedometer algorithm, demonstrating some differences in error. However, we hypothesize that the largest source of error may be due to differences in the regularity of gait during different activities. During some activities, gait tends to be regular and the repetitiveness of individual steps makes them easy to identify in an accelerometer signal. During other activities of everyday life, gait is frequently semi-regular or unstructured, which we hypothesize makes it difficult to identify and count individual steps. In this work, we test this hypothesis by evaluating the three most common types of pedometer algorithm on a new data set that varies the regularity of gait. A total of 30 participants were video recorded performing three different activities: walking a path (regular gait), conducting a within-building activity (semi-regular gait), and conducting a within-room activity (unstructured gait). Participants were instrumented with accelerometers on the wrist, hip and ankle. Collectively, 60,805 steps were manually annotated for ground truth using synchronized video. The main contribution of this paper is to evaluate pedometer algorithms when the consistency of gait changes to simulate everyday life activities other than exercise. In our study, we found that semi-regular and unstructured gaits resulted in 5-466% error. This demonstrates the need to evaluate pedometer algorithms on activities that vary the regularity of gait. Our dataset is publicly available with links provided in the introduction and Data Availability Statement.

Reliability of a smartphone application to measure physical activity

The aim of this study was to evaluate how accurate is a smartphone app to measure a physical activity parameter (steps). Physical Education undergraduate students (n = 46), both male and female, were recruited. A tally counter, a validated device (Xiaomi Mi Band 2®) and My Active Life app were used to perform the steps count. Each participant took three low-intensity treadmill walks (5 km h^-1), with a number of target steps (500-, 1000- and 1500-steps walk). Visual agreement analyses was performed through Bland-Altman plots. There was no significant interaction between steps walks and device during treadmill walking test (F_(2,84) = 3.854; p = 0.07). Differences in steps measured by Mi Band were not different from 0 in 500-steps walk (p = 0.243) and 1000-steps walk (p = 0.350), and in My Active Life in 500-steps walk (p = 0.177) and 1500-steps walk (p = 0.221). Bland-Altman analyses indicated an acceptable agreement between My active Life app and Mi Band devices for 1000-steps walk (-359.01; 310.43) and 1500-steps walk (-572.97; 377.11). In conclusion, My Active Life app showed accuracy in measuring total steps, in longer walking activities (e.g. higher than 1000 steps), and can be used on a daily basis and in research setting.

Scoping Review of Healthcare Literature on Mobile, Wearable, and Textile Sensing Technology for Continuous Monitoring

Remote monitoring of health can reduce frequent hospitalisations, diminishing the burden on the healthcare system and cost to the community. Patient monitoring helps identify symptoms associated with diseases or disease-driven disorders, which makes it an essential element of medical diagnoses, clinical interventions, and rehabilitation treatments for severe medical conditions. This monitoring can be expensive and time-consuming and provide an incomplete picture of the state of the patient. In the last decade, there has been a significant increase in the adoption of mobile and wearable devices, along with the introduction of smart textile solutions that offer the possibility of continuous monitoring. These alternatives fuel a technology shift in healthcare, one that involves the continuous tracking and monitoring of individuals. This scoping review examines how mobile, wearable, and textile sensing technology have been permeating healthcare by offering alternate solutions to challenging issues, such as personalised prescriptions or home-based secondary prevention. To do so, we have selected 222 healthcare literature articles published from 2007 to 2019 and reviewed them following the PRISMA process under the schema of a scoping review framework. Overall, our findings show a recent increase in research on mobile sensing technology to address patient monitoring, reflected by 128 articles published in journals and 19 articles in conference proceedings between 2014 and 2019, which represents 57.65% and 8.55% respectively of all included articles.

Smartphone Accelerometry: A Smart and Reliable Measurement of Real-Life Physical Activity in Multiple Sclerosis and Healthy Individuals

Background: Mobility impairment is common in persons with multiple sclerosis (pwMS) and can be assessed with clinical tests and surveys that have restricted ecological validity. Commercial research-based accelerometers are considered to be more valuable as they measure real-life mobility. Smartphone accelerometry might be an easily accessible alternative.

Objective: To explore smartphone accelerometry in comparison to clinical tests, surveys, and a wrist-worn ActiGraph in pwMS and controls.

Methods: Sixty-seven pwMS and 70 matched controls underwent mobility tests and surveys. Real-life data were collected with a smartphone and an ActiGraph over 7 days. We explored different smartphone metrics in a technical validation course and computed afterward correlation between ActiGraph (steps per minute), smartphone accelerometry (variance of vector magnitude), clinical tests, and surveys. We also determined the ability to separate between patients and controls as well as between different disability groups.

Results: Based on the technical validation, we found the variance of the vector magnitude as a reliable estimate to discriminate wear time and no wear-time of the smartphone. Due to a further association with different activity levels, it was selected for real-life analyses. In the cross-sectional study, ActiGraph correlated moderately (r = 0.43, p < 0.05) with the smartphone but less with clinical tests (rho between |0.211| and |0.337|). Smartphone data showed stronger correlations with age (rho = −0.487) and clinical tests (rho between |0.565| and |0.605|). ActiGraph only differed between pwMS and controls (p < 0.001) but not between disability groups. At the same time, the smartphone showed differences between pwMS and controls, between RRMS and PP-/SPMS, and between participants with/without ambulatory impairment (all p < 0.001).

Conclusions: Smartphone accelerometry provides better estimates of mobility and disability than a wrist-worn standard accelerometer in a free-living context for both controls and pwMS. Given the fact that no additional device is needed, smartphone accelerometry might be a convenient outcome of real-life ambulation in healthy individuals and chronic diseases such as MS.

Toward Harmonized Treadmill-Based Validation of Step-Counting Wearable Technologies: A Scoping Review

BACKGROUND

The authors conducted a scoping review as a first step toward establishing harmonized (ie, consistent and compatible), empirically based best practices for validating step-counting wearable technologies.

PURPOSE

To catalog studies validating step-counting wearable technologies during treadmill ambulation.

METHODS

The authors searched PubMed and SPORTDiscus in August 2019 to identify treadmill-based validation studies that employed the criterion of directly observed (including video recorded) steps and cataloged study sample characteristics, protocol details, and analytical procedures. Where reported, speed- and wear location-specific mean absolute percentage error (MAPE) values were tabulated. Weighted median MAPE values were calculated by wear location and a 0.2-m/s speed increment.

RESULTS

Seventy-seven eligible studies were identified: most had samples averaging 54% (SD = 5%) female and 27 (5) years of age, treadmill protocols consisting of 3 to 5 bouts at speeds of 0.8 (0.1) to 1.6 (0.2) m/s, and reported measures of bias. Eleven studies provided MAPE values at treadmill speeds of 1.1 to 1.8 m/s; their weighted median MAPE values were 7% to 11% for wrist-worn, 1% to 4% for waist-worn, and ≤1% for thigh-worn devices.

CONCLUSIONS

Despite divergent study methodologies, the authors identified common practices and summarized MAPE values representing device step-count accuracy during treadmill walking. These initial empirical findings should be further refined to ultimately establish harmonized best practices for validating wearable technologies.

Mobile Apps to Quantify Aspects of Physical Activity: a Systematic Review on its Reliability and Validity

The purpose of this study was to systematically review and evaluate the evidence on the accuracy (validity) and consistency (reliability) of mobile apps used to quantify physical activity. Systematic literature searches were conducted in Pubmed, Science Direct, Web of Science, Physiotherapy Evidence Database (PEDro), Academic Search Complete and IEEE Xplore. Studies were included if they reported on the validity and/or reliability of a mobile application aiming primarily at measuring physical activity in humans with or without pathology. The reference lists of included articles were also screened for reports not identified through electronic searches. The methodological quality of included studies was assessed by 2 independent reviewers and data extracted by one reviewer and checked for accuracy by a second reviewer. A total of 25 articles were included in this review, of which 18 refer to validity and 7 to both validity and reliability. Mean percentage difference was used as an indicator of validity and varied between 0.1% and 79.3%. Intraclass Correlation Coefficients varied between 0.02 and 0.99 indicating poor to excellent reliability. There is conflicting and insufficient evidence on the validity and reliability, respectively, of apps for measuring physical activity. Nevertheless, velocity and the place where the smartphone is carried seem to have an impact on validity.

Use of Mobile Applications to Collect Data in Sport, Health, and Exercise Science: A Narrative Review

Peart, DJ, Balsalobre-Fernández, C, and Shaw, MP. Use of mobile applications to collect data in sport, health, and exercise science: A narrative review. J Strength Cond Res 33(4): 1167-1177, 2019-Mobile devices are ubiquitous in the population, and most have the capacity to download applications (apps). Some apps have been developed to collect physiological, kinanthropometric, and performance data; however, the validity and reliability of such data is often unknown. An appraisal of such apps is warranted, as mobile apps may offer an alternative method of data collection for practitioners and athletes with money, time, and space constraints. This article identifies and critically reviews the commercially available apps that have been tested in the scientific literature, finding evidence to support the measurement of the resting heart through photoplethysmography, heart rate variability, range of motion, barbell velocity, vertical jump, mechanical variables during running, and distances covered during walking, jogging, and running. The specific apps with evidence, along with reported measurement errors are summarized in the review. Although mobile apps may have the potential to collect data in the field, athletes and practitioners should exercise caution when implementing them into practice as not all apps have support from the literature, and the performance of a number of apps have only been tested on 1 device.

Methodology and validation for identifying gait type using machine learning on IMU data

With the rising popularity of activity tracking, there is a desire to not only count the number of steps a person takes, but also identify the type of step (e.g., walking or running) they are taking. For rehabilitation and athletic training, this difference is important to the prescribed regiment. Fourteen healthy adults walked, jogged and ran on a treadmill at three different constant speeds (1.21, 2.01, 2.68 m/s) for 90 s. An inertial measurement unit (IMU) with accelerometer and gyroscope was affixed to their left ankle. Collected acceleration and angular velocity data were partitioned into individual time-normalised strides. These data were used as features in the artificial neural network (ANN) that classified the type of stride. Several ANN models were tested: using only acceleration, only angular velocity and both. Using primarily acceleration data in the trained ANN yielded the best results (>94% correct stride-type identification) after cross-validation. The ANN models were able to accurately classify the gait type of each stride using a single wearable IMU. The accuracy of the method should improve further as more data is added to the ANN training.

Assessing a novel way to measure step count while walking using a custom mobile phone application

Introduction

Walking speed has been associated with many clinical outcomes (e.g., frailty, mortality, joint replacement need, etc.). Accurately measuring walking speed (stride length x step count/time) typically requires significant clinician/staff time or a gait lab with specialized equipment (i.e., electronic timers or motion capture). In the present study, our goal was to measure “step count” via smartphones through novel software and to compare with step tracking software that come standard with iOS and Android smartphones as a first step in walking speed measurement.

Methods

A separate calibration and validation data collection was performed. Individuals in the calibration collection (n = 5) walked 20m at normal and slow speed (<1.0 m/s). Appropriate settings for the novel mobile application were chosen to measure step count. Individuals in the validation (n = 52) collection walked at 6m, 10m, and 20m at normal and slow walking speeds. We compared step difference (absolute difference) from observed step counts to native step tracking software and our novel software derived step counts. We used generalized estimated equation adjusted (participant level) negative binomial regression models of absolute step difference from observed step counts, to determine optimal settings (calibration) and subsequently to gauge performance of the shake algorithm settings and native step tracking software across different distances and speeds (validation).

Results

For iOS/iPhone 6, when compared to observed step count, the shake service (software driven approach) significantly outperformed the embedded native step tracking software across all distances at slow speed, and for short distance (6m) at normal speed. On the Android phone, the shake service outperformed the native step tracking software at slow speed at 6 meters and 20 meters, while its performance eclipsed the native step tracking software only at 20 meters at normal speed.

Discussion

Our software based approach outperformed native step tracking software across various speeds and distances and carries the advantage of having adjustable measurement parameters that can be further optimized for specific medical conditions. Such software applications will provide an effective way to capture standardized data across multiple commercial smartphone devices, facilitating the future capture of walking speed and other clinically important performance parameters that will influence clinical and home care in the era of value based care.

Validity of the "Samsung Health" application to measure steps: A study with two different samsung smartphones

The purpose of this study was to examine the validity of a highly popular pedometer application (Samsung Health). Sixteen adults (28.8 ± 8.9 years of age) wore two Samsung smartphone models, Samsung Galaxy Core Prime (SGCP) and Samsung Galaxy S4 (SGS4), at three body locations (waist, arm, and hand) while walking and running over a 50-m test. All trials were recorded using a video as a gold standard measure of step counts. Results indicated that the validity of Samsung Health varied depending on the smartphone model, its body location, and the type of gait (walking and running). Samsung Health showed acceptable validity when the SGCP was located on the hand (Bias = -8.3%; RMSE = 5.6), and especially on the arm (Bias = -7.2%; RMSE = 4.9) while running, and when the SGS4 was located on the arm (Bias = -7.5%; RMSE = 5.4), and especially on the waist (Bias = 5.4%; RMSE = 3.7) while walking. Samsung Health only showed good validity when the SGS4 was located on the arm (Bias = 2.9%; RMSE = 3.6), and especially on the hand (Bias = 0.5%; RMSE = 2.5) while running. This application showed unacceptable validity in the remaining options.

Accuracy of a smartphone pedometer application according to different speeds and mobile phone locations in a laboratory context

BACKGROUND

The purpose of this study was to compare the accuracy of a smartphone application and a mechanical pedometer for step counting at different walking speeds and mobile phone locations in a laboratory context.

METHODS

Seventeen adults wore an iPphone6© with Runtastic Pedometer© application (RUN), at 3 different locations (belt, arm, jacket) and a pedometer (YAM) at the waist. They were asked to walk on an instrumented treadmill (reference) at various speeds (2, 4 and 6 km/h).

RESULTS

RUN was more accurate than YAM at 2 km/h (p < 0.05) and at 4 km/h (p = 0.03). At 6 km/h the two devices were equally accurate. The precision of YAM increased with speed (p < 0.05), while for RUN, the results were not significant but showed a trend (p = 0.051). Surprisingly, YAM underestimates the number of step by 60.5% at 2 km/h. The best accurate step counting (0.7% mean error) was observed when RUN is attached to the arm and at the highest speed.

CONCLUSIONS

RUN pedometer application could be recommended mainly for walking sessions even for low walking speed. Moreover, our results confirm that the smartphone should be strapped close to the body to discriminate steps from noise by the accelerometers (particularly at low speed).

A comprehensive comparison of simple step counting techniques using wrist- and ankle-mounted accelerometer and gyroscope signals

The goal of this work is to compare the differences between various step counting algorithms using both accelerometer and gyroscope measurements from wrist and ankle-mounted sensors. Participants completed four different conditions on a treadmill while wearing an accelerometer and gyroscope on the wrist and the ankle. Three different step counting techniques were applied to the data from each sensor type and mounting location. It was determined that using gyroscope measurements allowed for better performance than the typically used accelerometers, and that ankle-mounted sensors provided better performance than those mounted on the wrist.

Monitoring sedentary patterns in office employees: validity of an m-health tool (Walk@Work-App) for occupational health

OBJECTIVE

This study validated the Walk@Work-Application (W@W-App) for measuring occupational sitting and stepping.

METHODS

The W@W-App was installed on the smartphones of office-based employees (n=17; 10 women; 26±3 years). A prescribed 1-hour laboratory protocol plus two continuous hours of occupational free-living activities were performed. Intra-class correlation coefficients (ICC) compared mean differences of sitting time and step count measurements between the W@W-App and criterion measures (ActivPAL3TM and SW200Yamax Digi-Walker).

RESULTS

During the protocol, agreement between self-paced walking (ICC=0.85) and active working tasks step counts (ICC=0.80) was good. The smallest median difference was for sitting time (1.5seconds). During free-living conditions, sitting time (ICC=0.99) and stepping (ICC=0.92) showed excellent agreement, with a difference of 0.5minutes and 18 steps respectively.

CONCLUSIONS

The W@W-App provided valid measures for monitoring occupational sedentary patterns in real life conditions; a key issue for increasing awareness and changing occupational sedentariness.

Accuracy and Adoption of Wearable Technology Used by Active Citizens: A Marathon Event Field Study

BACKGROUND

Today, runners use wearable technology such as global positioning system (GPS)-enabled sport watches to track and optimize their training activities, for example, when participating in a road race event. For this purpose, an increasing amount of low-priced, consumer-oriented wearable devices are available. However, the variety of such devices is overwhelming. It is unclear which devices are used by active, healthy citizens and whether they can provide accurate tracking results in a diverse study population. No published literature has yet assessed the dissemination of wearable technology in such a cohort and related influencing factors.

OBJECTIVE

The aim of this study was 2-fold: (1) to determine the adoption of wearable technology by runners, especially "smart" devices and (2) to investigate on the accuracy of tracked distances as recorded by such devices.

METHODS

A pre-race survey was applied to assess which wearable technology was predominantly used by runners of different age, sex, and fitness level. A post-race survey was conducted to determine the accuracy of the devices that tracked the running course. Logistic regression analysis was used to investigate whether age, sex, fitness level, or track distance were influencing factors. Recorded distances of different device categories were tested with a 2-sample t test against each other.

RESULTS

A total of 898 pre-race and 262 post-race surveys were completed. Most of the participants (approximately 75%) used wearable technology for training optimization and distance recording. Females (P=.02) and runners in higher age groups (50-59 years: P=.03; 60-69 years: P<.001; 70-79 year: P=.004) were less likely to use wearables. The mean of the track distances recorded by mobile phones with combined app (mean absolute error, MAE=0.35 km) and GPS-enabled sport watches (MAE=0.12 km) was significantly different (P=.002) for the half-marathon event.

CONCLUSIONS

A great variety of vendors (n=36) and devices (n=156) were identified. Under real-world conditions, GPS-enabled devices, especially sport watches and mobile phones, were found to be accurate in terms of recorded course distances.