Effectiveness of Lower-Cost Strategies for Running Gait Retraining: A Systematic Review

he effectiveness of lower-cost equipment used for running gait retraining is still unclear. The objective of this systematic review was to evaluate the effectiveness of lower-cost equipment used in running gait retraining in altering biomechanical outcomes that may be associated with injuries. The literature search included all documents from MEDLINE, Web of Science, CINAHL, SPORTDis- cus, and Scopus. The studies were assessed for risk of bias using an evaluation tool for cross-sectional studies. After screening 2167 initial articles, full-text screening was performed in42 studies, and 22 were included in the systematic review. Strong evidence suggested that metronomes, smartwatches, and digital cameras are effective in running gait retraining programs as tools for intervention and/or evaluation of results when altering step cadence and foot strike patterns. Strong evidence was found on the effectiveness of accelerometers in interventions with feedback to reduce the peak pos- itive acceleration (PPA) of the lower leg and/or footwear while running. Finally, we found a lack of studies that exclusively used lower-cost equipment to perform the intervention/assessment of running retraining.

and/or evaluation of results when altering step cadence and foot strike patterns. Strong evidence was found on the effectiveness of accelerometers in interventions with feedback to reduce the peak pos- itive acceleration (PPA) of the lower leg and/or footwear while running. Finally, we found a lack of studies that exclusively used lower-cost equipment to perform the intervention/assessment of running retraining. Keywords:biomechanics; runners; feedback; cadence; foot strike pattern 1. Introduction The number of people who run has increased in recent years due to the associated health bene ts, accessibility, and low cost, making it one of the most popular forms of exercise [1]. However, due to the repetitive nature, overuse injuries are common. Studies suggest that between 42.7% and 79% of runners experience an injury in any given year [2–4]. The etiology of running injuries is known to be multifactorial in nature and includes intrinsic (advanced age, higher body mass index, history of previous injury, discrepancy in limb length, abnormal anatomical alignment and foot posture, altered foot load patterns, individual abilities, and cognitive properties) and extrinsic (ground surface, footwear, and training load) factors [5–14]. Common running injuries include plantar fasciopathy, Achilles tendinopathy, medial tibial stress syndrome, patellofemoral pain, iliotibial band syndrome, patellar tendinopathy, tibial stress fracture, hamstring injury including proximal tendinopathy, and gluteal tendinopathy [2,15,16]. Several strategies for the prevention and treatment of running injuries are applied by coaches and runners themselves; these include stretching, warming up, technical training, and changing the running technique (called retraining) to reduce the load on certain muscle groups and joints [17]. Biomechanical studies have extensively examined running retraining strategies that include changes in the step cadence, stride length, distance between the heel and the center of mass at the initial foot contact with the ground, duration of ight phase, foot strike pattern, hip and knee movement, trunk position, step width, and impact load Appl. Sci.2023,13, 1376.

Appl. Sci.2023,13, 1376 2 of 18 variables, among others; these studies also reported changes in the variables of kinematics, kinetics, and electromyography [18]. In this sense, several studies have documented the alterations in running mechanics in runners who develop injuries such as excessive pronation of the foot [19,20], accentuated hip adduction [21], increased internal hip rotation, contralateral pelvic drop, and a reduction in the peak hip exion, among others [22]. In recent years, studies have proposed changes to running techniques (i.e., movement) through running retraining with feedback in order to reduce impact loads (force applied to the skeleton when contact with the ground occurs) as a way to reduce injury risk [23–32]. Outcome measures concentrated on determining the effectiveness of running retraining using visual and/or auditory feedback in real time to modify kinematics and kinetics during running [22,33–36]. The step rate was shown to signi cantly reduce impact forces in long-distance runners with an increase of only 5% in step cadence [24]. The study identi ed that by increasing the step rate, kinematic variables such as the step length, vertical oscillation of the center of mass, and angle of inclination of the foot were reduced. The reductions in these variables were associated with decreased impact forces that could theoretically reduce the risk of injuries. Other authors supported a reduction in impact forces by increasing the cadence or rate of steps [37–39]. Increasing step cadence with a proportional reduction in the stride length at a constant speed has been shown to facilitate a reduction in foot inclination angles and impact forces. This decreases the number of initial contacts on the ground by the hindfoot during the step [37,39,40]. In this sense, some studies analyzed the change in the strike pattern and proposed a shift from stepping on the rearfoot to stepping on the middle or forefoot because the impact forces on the knees and hips are typically higher for heel striking compared to midfoot or forefoot striking [37,41,42]. It was reported that approximately 80% of recreational runners who use traditional running shoes opted for heel striking [43,44]. Thus, changing the strike

proposed a shift from stepping on the rearfoot to stepping on the middle or forefoot because the impact forces on the knees and hips are typically higher for heel striking compared to midfoot or forefoot striking [37,41,42]. It was reported that approximately 80% of recreational runners who use traditional running shoes opted for heel striking [43,44]. Thus, changing the strike pattern through gait retraining could be a way to reduce impact forces and the risk of injuries related to running [35,45–48]. A systematic review with meta-analyses to assess the effectiveness of running retrain- ing on kinematics, kinetics, performance, pain, and injury in long-distance runners [49] found that gait retraining was effective in increasing the step cadence and reducing the mean vertical load rate. It was also observed that gait retraining to minimize heel striking increased the knee exion at the initial contact. However, trials that reported on peak tibial acceleration (in the skin surrounding the tibia) and the peak patellofemoral joint reaction force were too different to pool their data. Results from individual trials demon- strated reductions in these outcomes across multiple retraining interventions. However, in a recent systematic review of randomized clinical trials on strategies to prevent and manage running-related knee injuries [50], low-quality evidence was found to indicate that retraining on running techniques may reduce the risk of running-related knee injuries by two-thirds. These ndings highlighted the effectiveness of gait retraining in runners to alter movement-related risk factors that are potentially associated with the development of musculoskeletal injuries. Even though the ef ciency of running retraining in reducing the risk of injuries is debatable, many studies and clinicians still utilize these interventions to improve outcomes for individual runners. However, most studies seem to rely on high-cost instruments such as force platforms (on the ground or on treadmills) and three-dimensional (3D) motion-analysis systems to provide real-time biofeedback. Unfortunately, these devices are unaffordable to many and are rarely available to coaches or clinicians. Clinical equipment generally includes a simple treadmill, a high-de nition video camera, and computer applications or smartphones to identify variables such as the step cadence, foot strike

as force platforms (on the ground or on treadmills) and three-dimensional (3D) motion-analysis systems to provide real-time biofeedback. Unfortunately, these devices are unaffordable to many and are rarely available to coaches or clinicians. Clinical equipment generally includes a simple treadmill, a high-de nition video camera, and computer applications or smartphones to identify variables such as the step cadence, foot strike angle, and foot strike pattern [40,51]. There is still a need to examine the effectiveness of lower-cost equipment utilized in running retraining interventions. Therefore, the objective of this systematic review was to evaluate the effects of lower-cost equipment on running gait retraining. As a de nition, in

Appl. Sci.2023,13, 1376 3 of 18 this study we assumed that lower-cost equipment would have a reduced cost compared to gold-standard devices. 2. Materials and Methods 2.1. Databases and Search Strategy This systematic review was carried out in accordance with the principles of the Pre- ferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and regis- tered on the Open Science Framework (OSF) website (https://osf.io/z4uxm). Searches were performed in the MEDLINE, Web of Science, CINAHL, SPORTDiscus, and Scopus databases; the searches were limited to publications in English, excluded reviews and congress abstracts, and had no date restrictions. Articles were searched using the following search strategy (identical for all databases) from the rst year of database registration until September 2022: ((((((“running”) OR “jogging”) OR “run”) OR “track and eld”) OR “runners”)) AND (((((“retraining”) OR “retrain”) OR “feedback”) OR “biofeedback”)) AND ((((((((((“injury”) OR “Injuries”) OR “injured”) OR “lesion”) OR “disability”) OR “contusion”) OR “disease”) OR “disorder”) OR “pain”)). 2.2. Selection of Studies The studies were selected by two reviewers (L.M.D. and V.C.), and a third reviewer (R.R.B.) was available to resolve any disagreements regarding the nal eligibility of selected publications. All studies identi ed by the search strategy were exported to EndNote version X8 (Clarivate Analytics) by one investigator. First, the removal of duplicate articles was performed automatically. Next, an analysis of the titles of all identi ed studies was performed by the reviewers followed by abstracts and full text. Studies were accepted or excluded based on inclusion and exclusion criteria. To be included, studies needed to: (1) involve interventions using running retraining with feedback (no time limitation); (2) use lower-cost equipment as a tool for intervention and/or evaluation of the intervention results; (3) report biomechanical variables; and (4) usepredictors of risk of injury or pain attenuation [18]. Studies were excluded if they reported interventions that did not include running retraining, running without feedback, participants with prosthetic limbs, neurological or congenital impairments, the use of only expensive equipment in the study, or children or participants under 18 years of age. 2.3. Data Extraction and Analysis The data extracted from each article included:

of injury or pain attenuation [18]. Studies were excluded if they reported interventions that did not include running retraining, running without feedback, participants with prosthetic limbs, neurological or congenital impairments, the use of only expensive equipment in the study, or children or participants under 18 years of age. 2.3. Data Extraction and Analysis The data extracted from each article included: authorship, year of publication, sample characteristics, division of sample groups, participant demographics, intervention protocols, method of providing feedback, equipment used, analyzed variables related to lower-cost equipment, and the results of these variables. The data analysis was based on running parameters modi ed through retraining when lower-cost equipment was used. 2.4. Risk of Bias Analysis Eligible studies were assessed in terms of their risk of bias using an assessment tool for cross-sectional studies (the AXIS tool). This assessment tool, which was developed by Downes et al. [52], aims to assist in the interpretation of a study and inform decisions about the quality thereof. The AXIS tool consists of 20 components to examine study quality, study design, and the potential risk of bias in cross-sectional studies [52]. Each question can be answered as yes, no, unable to determine, or not applicable; the scoring consists of one point for “yes” and zero points for “no”, unable to determine”, or “not applicable”. Some criteria were excluded from the analysis because they were not related to the evaluated studies (criteria 7, 13, and 14); therefore, 17 criteria contributed to the nal score. The number of “yes” responses was calculated to determine the percentage of the criteria that were met in each study.

Appl. Sci.2023,13, 1376 4 of 18 2.5. Data Synthesis for Evidence-Based Recommendations Outcomes were synthesized for each study using a modi ed model of the van Tulder criteria [53]: Strong evidence: ndings were consistent across at least three studies, two of which were of high quality. Moderate evidence: ndings were consistent across at least two studies, one of which was of high quality. Limited evidence: ndings were consistent across one high-quality study or two low- or moderate-quality studies. Very limited evidence: ndings were consistent across a moderate or low-quality study. Inconsistent evidence: results were inconsistent across multiple studies. Con icting evidence: results were contradictory across multiple studies. No evidence: ndings were negligible regardless of study quality. 3. Results The literature search identi ed 2167 articles in the ve databases searched. Of these, 2125 studies were removed after screening for duplicates and reading of titles and abstracts, which resulted in 42 studies that were independently read by two reviewers. After reading the full-text articles, 12 studies were excluded due to not using equipment considered to be lower-cost (e.g., three-dimensional motion-analysis systems and force plate), 2 studies for presenting case studies, 1 for lack of focus on feedback as an intervention, 1 because it pro- vided data on the same sample as in another original study, and 4 articles for not analyzing outcomes for the lower-cost equipment used. Thus, a total of 22 studies were included for the analysis of this systematic review. FigureAppl. Sci. 2023, 12, x FOR PEER REVIEW 4 of 25 The number of “yes” responses was calculated to determine the percentage of the criteria that were met in each study. 2.5. Data Synthesis for Evidence-Based Recommendations Outcomes were synthesized for each study using a modified model of the van Tulder criteria [53]: • Strong evidence: findings were consistent across at least three studies, two of which were of high quality. • Moderate evidence: findings were consistent across at least two studies, one of which was of high quality. • Limited evidence: findings were consistent across one high-quality study or two low- or moderate-quality studies. • Very limited evidence:

van Tulder criteria [53]: • Strong evidence: findings were consistent across at least three studies, two of which were of high quality. • Moderate evidence: findings were consistent across at least two studies, one of which was of high quality. • Limited evidence: findings were consistent across one high-quality study or two low- or moderate-quality studies. • Very limited evidence: findings were consistent across a moderate or low-quality study. • Inconsistent evidence: results were inconsistent across multiple studies. • Conflicting evidence: results were contradictory across multiple studies. • No evidence: findings were negligible regardless of study quality. 3. Results The literature search identified 2167 articles in the five databases searched. Of these, 2125 studies were removed after screening for duplicates and reading of titles and ab- stracts, which resulted in 42 studies that were independently read by two reviewers. After reading the full-text articles, 12 studies were excluded due to not using equipment con- sidered to be lower-cost (e.g., three-dimensional motion-analysis systems and force plate), 2 studies for presenting case studies, 1 for lack of focus on feedback as an intervention, 1 because it provided data on the same sample as in another original study, and 4 articles for not analyzing outcomes for the lower-cost equipment used. Thus, a total of 22 studies were included for the analysis of this systematic review. Figure 1 presents the study-se- lection flowchart. Figure 1. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flowchart of the included studies. 3.1. Risk-of-Bias Assessment of Included Studies Figure 1. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) owchart of the included studies.

Appl. Sci.2023,13, 1376 5 of 18 3.1. Risk-of-Bias Assessment of Included Studies The risk of bias of all eligible studies was assessed using the AXIS tool [52]. The assessment of quality and risk of bias indicated that all studies were of a very high quality, with 54% of studies meeting 82% of the criteria, 32% meeting 88% of the criteria, and 14% meeting 94% or more of the criteria (Table). Among the main limitations of the studies were a lack of justi cation regarding the sample size, the non-representativeness of the sample as a target population, and a lack of clarity in terms of recruitment. 3.2. Analyzed Studies An analysis of the lower-cost equipment used in the 22 studies included in this systematic review (Tables–5) showed that 45.5% of the studies used equipment such as smartwatches with an accelerometer, metronomes, stopwatches, video cameras, platforms, instrumented socks and insoles with sensors to control cadence and/or the foot strike pattern, and the distribution of plantar pressure and the peak force in contact with the ground [40,47,48,54–60]. We also found that 54.5% of the studies used an accelerometer and/or an inertial central unit to identify the peak positive acceleration (PPA) of the tibia (the skin surrounding the tibia) and/or footwear [30–32,61–69]. The results of studies that aimed to increase the preferred cadence reported effec- tiveness in modifying the step frequency and presented signi cant increases after the intervention that varied from 6% to 8.6% [40,48,54,55,58–60]. In addition, studies that used lower-cost equipment to change the foot strike pattern by suggesting that runners not land on their heels also obtained very satisfactory clinical results [47,48,56]. In addition, a study by Goss et al. [47] that used a digital camera and instrumented socks showed that 95% of the runners transitioned to a foot strike pattern other than heel striking after the retraining (Tables). Studies that analyzed the tibial and/or shoe PPA with the use of accelerometers showed significant reductions in the tibial and/or shoe PPA after performing theretraining [ These reported signi cant reductions did not present normative values because diverse methodologies of interventions were

95% of the runners transitioned to a foot strike pattern other than heel striking after the retraining (Tables). Studies that analyzed the tibial and/or shoe PPA with the use of accelerometers showed significant reductions in the tibial and/or shoe PPA after performing theretraining [ These reported signi cant reductions did not present normative values because diverse methodologies of interventions were applied to the runners (Tables).

Appl. Sci.2023,13, 1376 6 of 18 Table 1. Assessment of methodological quality using the AXIS scale. Y = criterion met, N = criterion not met. Final score = sum of Ys and Ns in the case of criterion 19 (with the percentage value in parentheses). Some criteria were excluded from the analysis because they were not related to the studies evaluated (criteria 7, 13, and 14); therefore, 17 criteria contributed to the nal score. Included Studies Criteria Final Score (%) 1 2 3 4 5 6 8 9 10 11 12 15 16 17 18 19 20 Allen et al. (2016) [40] Y Y N Y Y Y Y Y Y Y Y Y Y Y Y N Y 16 (94) Baumgartner et al. (2019) [55] Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y N Y 17 (100) Cheung et al. (2018) [66] Y Y N Y N N Y Y Y Y Y Y Y Y Y N Y 14 (82) Cheung et al. (2019) [31] Y Y Y Y N N Y Y Y Y Y Y Y Y Y N Y 15 (88) Ching et al. (2018) [30] Y Y N Y N N Y Y Y Y Y Y Y Y Y N Y 14 (82) Clansey et al. (2014) [63] Y Y N Y N N Y Y Y Y Y Y Y Y Y N Y 14 (82) Creaby and Smith (2016) [65] Y Y N Y N N Y Y Y Y Y Y Y Y Y N Y 14 (82) Crowell et al. (2010) [61] Y Y N Y N N Y Y Y Y Y Y Y Y Y N Y 14 (82) Crowell and Davis (2011) [62] Y Y Y Y N N Y Y Y Y Y Y Y Y Y N Y 15 (88) Da Silva Neto, Lopes, and Ribeiro, (2021) [57] Y Y Y Y N N Y Y Y Y Y Y Y Y Y N Y 15 (88) Goss et al. (2021) [47] Y Y Y Y N

This systematic review evaluates lower-cost equipment for running gait retraining.