ArticleJMIR mHealth and uHealth2017
Accuracy and Adoption of Wearable Technology Used by Active Citizens: A Marathon Event Field Study.
Article in JMIR mHealth and uHealth, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
27 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Mobile Apps to Quantify Aspects of Physical Activity: a Systematic Review on its Reliability and Validity.Journal of medical systems · 2020Pooled it
- Comparison of Weekly Training Load and Acute: Chronic Workload Ratio Methods to Estimate Change in Training Load in Running.Journal of athletic training · 2024Trial
- The Effect of Wearable-Based Real-Time Feedback on Running Injuries and Running Performance: A Randomized Controlled Trial.The American journal of sports medicine · 2024Trial
- Article
- Recent Advancements in Wearable Hydration-Monitoring Technologies: Scoping Review of Sensors, Trends, and Future Directions.JMIR mHealth and uHealth · 2025Article
- A feasibility study on using soft insoles for estimating 3D ground reaction forces with incorporated 3D-printed foam-like sensors.Wearable technologies · 2025Article
- Instrumenting Parkrun: Usefulness and Validity of Inertial Sensors.Sensors (Basel, Switzerland) · 2024Article
- Exploring the Relationship between Running-Related Technology Use and Running-Related Injuries: A Cross-Sectional Study of Recreational and Elite Long-Distance Runners.Healthcare (Basel, Switzerland) · 2024Article
- A 0.05 m Change in Inertial Measurement Unit Placement Alters Time and Frequency Domain Metrics during Running.Sensors (Basel, Switzerland) · 2024Article
- Data-driven approach to quantify trust in medical devices using Bayesian networks.Experimental biology and medicine (Maywood, N.J.) · 2023Review
- Wearable Devices in Diving: Scoping Review.JMIR mHealth and uHealth · 2022Article
- Two of a Kind? Similarities and Differences between Runners and Walkers in Sociodemographic Characteristics, Sports Related Characteristics and Wearable Usage.International journal of environmental research and public health · 2022Article
- Feasibility and usability of GPS data in exploring associations between training load and running-related knee injuries in recreational runners.BMC sports science, medicine & rehabilitation · 2022Article
- Is This the Real Life, or Is This Just Laboratory? A Scoping Review of IMU-Based Running Gait Analysis.Sensors (Basel, Switzerland) · 2022Article
- A qualitative examination of the factors affecting the adoption of injury focused wearable technologies in recreational runners.PloS one · 2022Article
- Smart Watch Versus Classic Receivers: Static Validity of Three GPS Devices in Different Types of Built Environments.Sensors (Basel, Switzerland) · 2021Article
- Use of monitoring technology and injury incidence among recreational runners: a cross-sectional study.BMC sports science, medicine & rehabilitation · 2021Article
- Measuring Gait Velocity and Stride Length with an Ultrawide Bandwidth Local Positioning System and an Inertial Measurement Unit.Sensors (Basel, Switzerland) · 2021Article
- Evaluation of COVID-19 Restrictions on Distance Runners' Training Habits Using Wearable Trackers.Frontiers in sports and active living · 2021Article
- Patterns of Use and Key Predictors for the Use of Wearable Health Care Devices by US Adults: Insights from a National Survey.Journal of medical Internet research · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundToday, 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.
objectiveThe 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.
methodsA 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.
resultsA 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.
conclusionsA 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.
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Registered trials
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