Summary Editorial on Wearable Technology in Exercise and Sport Applications

We recently closed the second of two Special Issues centered around wearable technology use in exercise and sport applications. The utility of wearable trackers in exercise and sport contexts has grown exponentially [1,2], and their use has consistently been a top fitness trend over the past 6 years that shows no signs of slowing [3,4]. With new commercial wearables constantly being released to the market [5], this area of research will continue to be important for the foreseeable future [6].

The first Special Issue (Wearable Technologies II) closed in October 2021, when researchers were still grappling with the effects of the worldwide pandemic. The articles present in this Special Issue were timely, as many users and platforms had transitioned to remote work. Four papers were featured in the Special Issue (1–4 in the List of Contributions Section).

Adamakis evaluated the ability of common accelerometer-based phone applications (without turning on the GPS function) to return accurate step-count and distance data during treadmill walking and jogging. Assessed using both Android and iPhone, the applications were Runtastic Pedometer, Accupedo, Pacer, and Argus. All applications were considered valid for step-count measurements across the exercise intensities used, with Android having a lower error than iOS. Distance measurements were not valid for any application or exercise condition, leading the author to conclude that GPS-based applications should be used when distance is the primary objective.

Reece et al. evaluated heart rate validity with regard to several commercially available wearables (Apple Watch 4, Garmin Forerunner 735 XT, Jabra Elite earbuds, Scosche Rhythm 24 armband) using Consumer Technology Association recommendations. The Consumer Technology Association standards include sitting, activities of daily living (upper body and full body), and walking, jogging, running, and cycling at various intensities for 57 min. It was reported that the Apple Watch Series 4 and the Scosche Rhythm 24 were valid across all the conditions tested. The Jabra Elite earbuds were acceptable for the sedentary, running, and cycling conditions, while the Garmin Forerunner 735 XT did not meet the validity criterion for any condition tested.

Lewis et al. studied the ability of activity monitors to effect behavior change. Commonly used commercial devices were evaluated (Amazfit Bip, Apple watch Nike 5, Fitbit Charge 3, Fitbit Ionic, Fitbit Versa 2, Galaxy Watch Active, Garmin Vivomove HR, Garmin Vivosmart 4, Withings Steel HR) according to an established behavior change taxonomy devised by trained coders who wore the monitor for at least one week. Fitbit and Garmin devices were found to implement the most behavior change techniques (Fitbit: education, enablement, environmental restructuring, coercion, incentivization, modeling, and persuasion; Garmin: enablement, environmental restructuring, and training). Depending on the nature of the clinical trial or intervention, future researchers may wish to utilize different wearables to assist with behavior modification.

Carrier et al. conducted a rapid systematic review to summarize the available literature on validity and reliability testing conducted on commercially available wearable devices utilized outside of the laboratory. Nine articles fit the inclusion criteria (eight for validity, one for reliability), with measurements obtained across a variety of exercise situations (running, walking, cycling, hiking). Study designs centered on determining validity and/or reliability in outdoor settings were highly variable, leading the authors to recommend that future investigations align more closely with their laboratory-based counterparts. Specifically, including a wider range of skin tones and exercise intensities was recommended. It was also determined that no common analytical techniques or threshold criteria exist for wearable testing, making it difficult to compare device validity and/or reliability across investigations

The second Special Issue (Wearable Technologies III) closed in August 2023, and it featured five papers (5–9 in the List of Contributions Section). These investigations were a positive representation of how the field of wearable-based research evolved over a few years.

Carrier et al. reported on the Garmin fēnix 6 watch’s ability to accurately estimate common laboratory-based measures, aerobic capacity (VO_2max), and lactate thresholds in an athletic population. The Garmin fēnix 6 watch met the threshold for VO_2max validity when data for the laboratory criterion device were averaged over increments of at least 30 s. Additionally, the validity threshold for the lactate threshold was met in all cases. These findings make critical physiological training estimates more widely available to individuals who may not have access to expensive laboratory equipment.

Raza et al. designed an investigation utilizing a publicly available smart phone sensor dataset (Kaggle) and artificial intelligence-based machine learning to evaluate human motion detection (walking and running) for sensor exploratory data analysis. Six iterations of machine learning and deep learning methods (including random forest, logistic regression, decision tree, and support vector machine) were applied to the dataset and compared. Additionally, the authors proposed an ensemble learning-based technique, which outperformed other accepted methodologies, achieving a high accuracy score of 99%. These findings can be used to improve the way that smart phones detect when individuals complete walking or running tasks.

Adamakis carried out a study for validating step-count measurements in commercially available wearable devices (Yamax 3D Power-Walker, Garmin Vivofit 3, Medisana Vifit) and Android phone applications (Accupedo Pedometer, Pedometer 2.0). Participants completed two tests: walking 400 steps inside a building that included ascending and descending 20 stairs; a 3-day field study conducted with regard to the criterion (Actigraph wGT3X-BT) and wearable devices. Wearable devices were found to be acceptable during the semi-structured indoor walking session; however, the phone applications were outside of the threshold. During the 3-day field study, no device or application had acceptable validity. The author warned that some devices may be valid in some situations but not acceptable in other contexts.

Gardner et al. conducted a systematic review on the ability of wearable devices to return training impulse data in collegiate and professional soccer players. Ten papers met the inclusion criteria and centered on the following training impulse-specific comparisons: training versus match, preseason versus in-season, and positional load. Training impulse values obtained during practice were higher in reserve players than in starters, but values were greater in starters during actual match play. Training impulse was highest during the buildup to the season than in all other parts the season (early season, mid-season, late season). Wearable heart rate monitors and axial GPS sensors were the primary method for obtaining training impulse metrics. It was suggested that the future integration of virtual reality could be used to reduce player load during training, ensuring that athletes are in prime physical condition for matches.

Elder et al. evaluated the ability of twelve iOS phone applications to align with the American College of Sports Medicine’s intensity thresholds according to the FITT principle (frequency, intensity, time, and type). Intensity was determined based on the heart rate-tracking capabilities of each application and evaluated using the Fitness Apps Scoring Instrument. While all of the applications allowed users to their track heart rates, only two (Gentler Streak Workout Tracker and Cardiobot Heart Rate Tracker) provided real-time feedback about intensity during exercise. The majority of applications (83%) did not provide guidance or recommendations to users on how to manage exercise intensity while in use.

While the first Special Issue (Wearable Technologies II) was primarily concerned with detailing the validity and reliability of wearable devices, the second Special Issue (Wearable Technologies III) extended this knowledge for practical use. As wearable devices become ubiquitous, end users will demand greater control of the data generated for their specific purposes. It is important for researchers to remain abreast of those expectations to create relevant evidence-supported guidelines. This approach will be useful not only for individual users but also the population at large, as data will be used in large-scale digital clinical trials [7,8].