Effects of Wearable Devices with Biofeedback on Biomechanical Performance of Running—A Systematic Review

his present review includes a systematic search for peer-reviewed articles published between March 2009 and March 2020 that evaluated the e ects of wearable devices with biofeedback on the biomechanics of running. The included articles did not focus on physiological and metabolic metrics. Articles with patients, animals, orthoses, exoskeletons and virtual reality were not included. Following the PRISMA guidelines, 417 articles were rst identi ed, and nineteen were selected following the removal of duplicates and articles which did not meet the inclusion criteria. Most reviewedarticles reported a signi cant reduction in positive peak acceleration, which was found to be related to tibial stress fractures in running. Some previous studies provided biofeedback aiming to increase stride frequencies. They produced some positive e ects on running, as they reduced vertical load in knee and ankle joints and vertical displacement of the body and increased knee exion. Some other parameters, including contact ground time and speed, were fed back by wearable devices for running. Such devices reduced running time and increased swing phase time. This article reviews challenges in this area and suggests future studies can evaluate the long-term e ects in running biomechanics produced by wearable devices

vertical displacement of the body and increased knee exion. Some other parameters, including contact ground time and speed, were fed back by wearable devices for running. Such devices reduced running time and increased swing phase time. This article reviews challenges in this area and suggests future studies can evaluate the long-term e ects in running biomechanics produced by wearable devices with biofeedback. Keywords: wearable device; running; biomechanics; performance; technique; biofeedback; gait retraining 1. Introduction Running requires e cient conversion of power output into translocation [1], initiated by a greater joint range of motion [2,3]. During running, while the hip generates power to accelerate the leg so as to optimize the position of the foot and center of body mass [3–6], the ankle stabilizes and further accelerates the limb forwards [3,7,8], and the knee absorbs the loading by increasing the muscular power [3,9,10]. Biomechanics plays an important role in running performance and energy cost [7,11]. Attempts to change the biomechanical parameters of runners, characterized by quantitative spatiotemporal, kinematic and kinetic data [12,13], have been found to have some positive e ects Sensors2020,20, 6637; doi:10.3390 /s20226637 /journal/sensors

Sensors2020,20, 6637 2 of 25 on running. For example, strategies to increase stride frequency (SF) and reduce vertical oscillation of the body as well as ground reaction forces allow e cient energy transfer [11,14–16]. Meanwhile, smaller dorsi exion and faster plantar exion are required [1,11,17] to achieve greater horizontal heel velocity and propulsion, knee exion angle at initial contact (IC) [18,19] and greater maximum hip extension [20]. These biomechanical considerations are particularly important for competitive runners to decrease the completion time for a race distance and reduce the risk of injury [21]. Most runners are able to integrate and accommodate their own unique combination of anthropometric dimensions and mechanical characteristics to nd a running motion, which is most economical for them [11]. Gait retraining has shown positive correlations in running mechanics, improvingperformance [ . Biofeedback is a potential strategy to modify motor performance [25–27], although other strategies, including muscle strengthening, and training on explosive movements [28] and endurance, have also been used [29]. Although feedback and retraining are necessary to increase physiological and metabolic performances, they face challenges in obtaining accurate, quantitative, repeatable and continuous data about the athlete in the field [30]. This could explain why some previous studies suggested verbal feedback provided in the form of increments of intensity, frequency of the running, speed and time, which has resulted in low or no improvements in individual running mechanics [29,31] and require high manpower demand. There is also a possibility to underreport metrics related to injury, such as joint loading impact, because the human eye is unable to identify and quantify it [32]. Quantification of the biomechanical variables relies mainly on expensive laboratories set up [ Meanwhile, wearable technologies are options, which allow real-time measurements to be conducted in outdoor environments [34]. Wearables devices are lightweight mechanical or electronic technologies that are worn close to and/or on the surface of the skin. They are typically sensor-based devices that detect internal and/or external variables and transmit the information to an external device [35]. They usually come with some electronic components that provide auditory, visual or somatosensory feedback to the users

outdoor environments [34]. Wearables devices are lightweight mechanical or electronic technologies that are worn close to and/or on the surface of the skin. They are typically sensor-based devices that detect internal and/or external variables and transmit the information to an external device [35]. They usually come with some electronic components that provide auditory, visual or somatosensory feedback to the users [35,36]. In some cases, these devices analyze and transmit information immediately, generating biofeedback in real time [37,38]. Some latest techniques were developed to track balance [39,40], joint load [41], symmetry in the movement and joint angles [42]. They also gave instant tactile, visual and auditory feedback, which were found to be bene cial for the movement of a wide type of population such as healthy adults [43], amputees [44], older adults [45], runners [46] and stroke survivors [47]. Wearable sensors were validated against gait laboratory equipment on kinematic and kinetic measurements [48–54] in sports-related movements [55]. Accuracies of wearable sensors varied, as there were always challenges in the accurate position of sensors [56], data processing and simpli cation methods in calculating joint angles and segment acceleration [57] and protocols to calibrate the devices [58]. A systematic review of the current literature about the e ectiveness of wearable devices with biofeedback in running biomechanics is needed. A previous review has looked into wearables devices without real-time feedback in sports players, including runners [59]. Another review [22] undertook a narrative review of psychometric parameters such as motivation for running, preferred characteristics about feedback platforms, content feedback programs including frequency, motor learning strategies and workload con gurations. However, these reviews did not focus on biomechanical parameters such as vertical reaction forces and running kinematics. The primary aim of this systematic review is to determine whether wearable devices with biofeedback are able to modify and impact the biomechanics of the running and its performance. To achieve this, the most key parameters that were used to analyze and modify running gait were identi ed, and the results from previous studies with the use of wearable devices with biofeedback programs were organized. We hypothesize that wearable

determine whether wearable devices with biofeedback are able to modify and impact the biomechanics of the running and its performance. To achieve this, the most key parameters that were used to analyze and modify running gait were identi ed, and the results from previous studies with the use of wearable devices with biofeedback programs were organized. We hypothesize that wearable devices providing real-time biofeedback would produce biomechanical changes in the running technique and thereby improve performance.

Sensors2020,20, 6637 3 of 25 2. Materials and Methods A systematic literature search and analysis was conducted and reported in line with the preferred reporting items for systematic reviews and meta-analysis (PRISMA) guidelines. 2.1. Study Inclusion and Exclusion Criteria This systematic review includes articles that focus on wearable technologies providing feedback on biomechanical parameters related to running. The search was limited to peer-reviewed journal articles written in English and published in the period from March 2009 to March 2020. Records published before 2009 were searched but not retrieved. Book chapters and conference papers were excluded. The articles include wearable devices that provided feedback to users regarding kinetic, kinematic or spatiotemporal variables of running and should report results related to the sports gesture and performance in healthy adults. Articles were excluded if they included patients, animals, robotic-assistive devices, orthoses, exoskeletons or virtual reality environments. Articles were also excluded if their primary outcome measures were related to heart rate, sweat, sleep or cognitive/emotional conditions, classification/recognition of patterns, level of physical activity or navigation. 2.2. Search Strategy and Study Selection The review process was completed in four steps. First, potentially relevant records were identi ed through a systematic search for published papers in four major scienti c databases of Scopus, Cochrane, EBSCOHOST (SportDiscus, CINAHL, MEDLINE AND PUBMED) and Web of Science databases. The search strategy involved a combination of keywords and subject headings terms presented in Table. The simpli ed strategy was (1 or 2) and 3 and (4 or 5 or 6 or 7) and (8 or 9) and not 10. According to the restrictions in each database, the identi er was searched by sections before being included in the main search strategy. Table 1.Keywords and subject headings descriptors grouped by means. Terms and Strategies Identi er Wear * (technolog * or device * or sensor *) or “real-time sensor” 1 Inertial sensor” or “inertial measurement unit” or gyroscope * or magnetometer * or acceleromet * or “cell phone” or “smart phone *” 2 Run * or running or “runner” or “running injuries” 3 Feedback * or biofeedback * or augment *

Terms and Strategies Identi er Wear * (technolog * or device * or sensor *) or “real-time sensor” 1 Inertial sensor” or “inertial measurement unit” or gyroscope * or magnetometer * or acceleromet * or “cell phone” or “smart phone *” 2 Run * or running or “runner” or “running injuries” 3 Feedback * or biofeedback * or augment * or “real time” 4 Visual (signal * or feedback * or cue or biofeed * or augment *) or (visual (train * or retrain *)) 5 Auditory (signal * or feedback * or cue * or biofeed * or augment *) or (auditory (train * or retrain *))6 Haptic (signal * or feedback * or cue * or biofeed * or augment *)) or (vibrat * (signal * or feedback * or cue or biofeed * or augment *)) or (haptic (train * or retrain *)) or (vibrat * (train * or retrain *)) 7 Mechanic * or load * or performance or postural * or “ambulatory monitoring” 8 “Ground reaction” or force or kinematic * or biomech * or acceleration * or cadence or “step length” or “step width” or “step time” or “stride length” or “stride time” or “stance phase” or “swing phase” or “stance time” or “swing time” or “single support” or “double support” or “ground contact” or “gait speed” or “walking speed” or “running speed” or “heel-strike” or “toe o ” or “speed” or “center of mass” or “center of mass” or “center of gravity (CG)” or “center of gravity” 9 Patient or stroke or Parkinson's 10 (*) used as a truncation command, searching for the root of the word and retrieving any alternate ending. During the second phase, the title and abstract of all records were screened for relevance. Full-text articles were retrieved and assessed if the relevance was unclear. For the third phase, all records not ful lling the inclusion criteria were excluded. Eligibility was assessed for each remaining full-text

were screened for relevance. Full-text articles were retrieved and assessed if the relevance was unclear. For the third phase, all records not ful lling the inclusion criteria were excluded. Eligibility was assessed for each remaining full-text

Sensors2020,20, 6637 4 of 25 article. Finally, relevant information was extracted from the included records. References of all included studies were checked for additional publications that could be included in this review. 2.3. Data Extraction The author who did the search extracted data from each included article. Information was compiled in separate tables from each article regarding the participants, protocol, sensor(s), feedback and analysis. Demographic information, type and location of the sensors, feedback devices, type of feedback, type of environment, length of protocols, length of interventions as well as each biomechanical variable were analyzed in each record. Means and standard deviations for outcome measures were also extracted from all included articles for data analyses. Further comparisons were made between protocols, hardware device characteristics, feedback modalities and e ects of biofeedback on running performance. 3. Results 3.1. Search Results A total of 417 articles were identi ed through the database search, and 5 additional articles were identi ed through a manual search from reference lists. Following the removal of duplicates, 375 articles remained. Screening of the titles and abstracts led to the removal of 256 articles. Upon assessing the full texts of the remaining 119 articles, 19 articles were found to have met the inclusion criteria (Table, Figure). A list of the abbreviations used in this manuscript and their de nitions is giving at the end of this manuscript.Sensors 2020, 20, x FOR PEER REVIEW 4 of 26 (*) used as a truncation command, searching for the root of the word and retrieving any alternate ending. 2.3. Data Extraction The author who did the search extracted data from each included article. Information was compiled in separate tables from each article regarding the participants, protocol, sensor(s), feedback and analysis. Demographic information, type and location of the sensors, feedback devices, type of feedback, type of environment, length of protocols, length of interventions as well as each biomechanical variable were analyzed in each record. Means and standard deviations for outcome measures were also extracted from all included articles for data analyses. Further comparisons were made between protocols, hardware device characteristics, feedback modalities and effects

and location of the sensors, feedback devices, type of feedback, type of environment, length of protocols, length of interventions as well as each biomechanical variable were analyzed in each record. Means and standard deviations for outcome measures were also extracted from all included articles for data analyses. Further comparisons were made between protocols, hardware device characteristics, feedback modalities and effects of biofeedback on running performance. 3. Results 3.1. Search Results A total of 417 articles were identified through the database search, and 5 additional articles were identified through a manual search from reference lists. Following the removal of duplicates, 375 articles remained. Screening of the titles and abstracts led to the removal of 256 articles. Upon assessing the full texts of the remaining 119 articles, 19 articles were found to have met the inclusion criteria (Table 2, Figure 1). A list of the abbreviations used in this manuscript and their definitions is giving at the end of this manuscript. Figure 1. Flow diagram of study selection. 3.2. Subjects The participants in the nineteen reviewed articles were healthy recreational or competition- oriented runners in both genders aged between 18 and 47 years with a median age of 26 years old. They ran a minimum distance of 10 to 17 km per week. Some studies recruited runners with a high-risk running technique [60–62] or a peak positive acceleration (PPA) higher than 8 g [63–66]. One study [67] included overweight children as participants in their experiments (Table 2). Figure 1.Flow diagram of study selection. 3.2. Subjects The participants in the nineteen reviewed articles were healthy recreational or competition-oriented runners in both genders aged between 18 and 47 years with a median age of 26 years old. They ran a minimum distance of 10 to 17 km per week. Some studies recruited runners with a high-risk running technique [60–62] or a peak positive acceleration (PPA) higher than 8 g [63–66]. One study [67] included overweight children as participants in their experiments (Table).

to 17 km per week. Some studies recruited runners with a high-risk running technique [60–62] or a peak positive acceleration (PPA) higher than 8 g [63–66]. One study [67] included overweight children as participants in their experiments (Table).

Sensors2020,20, 6637 5 of 25 3.3. Components of Wearable Biofeedback Devices 3.3.1. Use of Wearables Sensors The most popular sensors used to evaluate running performance were accelerometers [56,61–74]. Among fteen included articles using accelerometers, three used wired accelerometers [61,66,71], six articles used wireless technology [62,63,67,70,72,73] and the type of connection is not mentioned in the remaining six articles [56,64,65,68,69,74]. The number of axes in the accelerometers varied, including uniaxial [68], biaxial [61,63], triaxial [62,64,65,69–74]. Some studies used inertial motion units (IMU), which contained triaxial accelerometers together with triaxial magnetometers and gyroscopes [67,73]. Two included articles did not disclose the number of axes of their accelerometers [56,66], and in another two articles, the accelerometer was part of commercial devices such as Aximo PADIS [73] and Garmin FR70 [62,72]. The measurement range of the wireless accelerometers varied between 2.8 g to 50 g, whereas the laboratory-based did not include their range in their description. The sampling frequency (SF) of the accelerometers ranged between 100 and500 [ 500 [ The sampling frequency of accelerometers for the rest of the papers was not mentioned. The most popular body place among studies that used accelerometers was in the anteromedial aspect of the distal tibia, approximately 15 cm above the medial malleolus [56,61,63–70,74]. Two included articles placed accelerometers on the head [56,74] and at the top [62,72,73] and back [61,64,71] of the shoe (see FigureSensors 2020, 20, x FOR PEER REVIEW 5 of 26 3.3. Components of Wearable Biofeedback Devices 3.3.1. Use of Wearables Sensors The most popular sensors used to evaluate running performance were accelerometers [56,61– 74]. Among fifteen included articles using accelerometers, three used wired accelerometers [61,66,71], six articles used wireless technology [62,63,67,70,72,73] and the type of connection is not mentioned in the remaining six articles [56,64,65,68,69,74]. The number of axes in the accelerometers varied, including uniaxial [68], biaxial [61,63], triaxial [62,64,65,69–74]. Some studies used inertial motion units (IMU), which contained triaxial accelerometers together with triaxial magnetometers and gyroscopes [67,73]. Two included articles did not disclose the number of axes of their accelerometers [56,66], and in another two articles, the accelerometer was part of

A systematic review of wearable devices' impact on running biomechanics.