Effectiveness of virtual reality and feedback to improve gait and balance in patients with diabetic peripheral neuropathies: systematic review and meta-analysis

Citation: Alonso-Enríquez, L.; Gómez-Cuaresma, L.; Billot, M.; Garcia-Bernal, M.I.; Benitez-Lugo, M.L.; Casuso-Holgado, M.J.; Luque-Moreno, C. Effectiveness of Virtual Reality and Feedback to Improve Gait and Balance in Patients with Diabetic Peripheral Neuropathies: Systematic Review and Meta-Analysis. Healthcare 2023, 11, 3037. https://doi.org/10.3390/ healthcare11233037 Academic Editor: Giovanni Morone Received: 18 October 2023 Revised: 19 November 2023 Accepted: 23 November 2023 Published: 24 November 2023 Copyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). healthcare Review Effectiveness of Virtual Reality and Feedback to Improve Gait and Balance in Patients with Diabetic Peripheral Neuropathies: Systematic Review and Meta-Analysis Laura Alonso-Enríquez 1,2, Laura Gómez-Cuaresma 3, Maxime Billot 4, Maria Isabel Garcia-Bernal 5,6 , Maria Luisa Benitez-Lugo 6, María Jesús Casuso-Holgado 5,6 and Carlos Luque-Moreno 1,5,6,* 1Faculty of Nursing and Physiotherapy, University of Cadiz, 11009 Cadiz, Spain; [email protected] 2IntegraSalud Centro de Rehabilitación Funcional, El Puerto de Santa María, 11500 Cadiz, Spain 3Hôpital Du Pays De L’Autan, 81100 Castres, France; laura.gomezcuar[email protected] 4PRISMATICS Lab (Predictive Research in Spine/Neuromodulation Management and Thoracic Innovation/Cardiac Surgery), Poitiers University Hospital, 86021 Poitiers, France; [email protected] 5Instituto de Biomedicina de Sevilla (IBIS), 41013 Seville, Spain; [email protected] (M.I.G.-B.); [email protected] (M.J.C.-H.) 6Department of Physiotherapy, University of Seville, 41009 Seville, Spain; [email protected] *Correspondence: [email protected] Abstract: Diabetic peripheral neuropathy (DPN) is the primary complication in patients with diabetes mellitus, characterized by loss of sensation and function in the lower limbs. Virtual reality (VR) and/or sensory feedback (FB) therapy has shown positive effects in other neurologic conditions such as stroke. However, consensus regarding their effectiveness in the DPN population is lacking. This study aims to analyze existing scientific evidence about the effects of VR and/or FB on improving gait and balance and reducing the risk of falls in patients with DPN (pwDPN). A thorough search was conducted in scientific databases including PubMed, Scopus, and EMBASE, up until November 2023. CMSQ, the PEDro scale, and the Cochrane Collaboration’s tool were used to assess the methodological quality and risk of bias of the studies. A total of 10 studies were selected for qualitative analysis, with three contributing information to the meta-analysis. The combined results suggest a positive trend in favor of VR and FB rehabilitation; however, significant differences were not observed in balance (SMD = − 0.81, 95% CI = − 1.90, 0.29; p= 0.15; I 2 = 86%) or gait speed improvements (MD = − 1.05, 95% CI = − 2.96, 0.85; p= 0.28; I 2 = 89%). Therefore, further randomized controlled studies are still needed to achieve stronger conclusions regarding the benefits of VR and/or FB in pwDPN. Keywords: Diabetic Neuropathies; Virtual Reality; Feedback; Sensory; Gait Disorders; Neurologic; Postural Balance 1. Introduction Recently, diabetes mellitus (DM) has emerged as a significant disease with an increasing prevalence rate. It is estimated that 462 million individuals are affected by type 2 diabetes, accounting for approximately 6.3% of the world’s population, and this number has continued rising, reaching 6059 cases per 100,000 people a year [ 1 ]. Diabetic Peripheral Neuropathy (DPN) is the main neuromuscular complication and the most prevalent chronic consequence, affecting about 73% of the DM population [ 2 ]. DPN is characterized by progressive degeneration of nerve fibers, primarily in the lower limbs, leading to decreased signal conduction velocity [ 3 ] and resulting in symptoms such as hypoesthesia, poor proprioception, impaired reflexes [ 4 ], reduced strength [ 5 ] in lower limbs, and decreased step length and functional gait speed [ 6 ]. Furthermore, the causes of proprioception alteration Healthcare 2023,11, 3037. https://doi.org/10.3390/healthcare11233037 https://www.mdpi.com/journal/healthcare Healthcare 2023,11, 3037 2 of 17 do not fully explain the variability in pinch proprioception in the DPN population, indicating a greater role of muscle spindles [ 7 ]. People with DPN (pwDPN) exhibit a deficiency in both static (the ability to maintain an upright stance while managing center of mass (CoM) sway [ 8 ]) and dynamic balance (the ability to maintain balance during movement) [ 9 , 10 ]. Specifically, there is a notable decline in static balance, as patients appear to acquire anticipatory postural strategies during movement [ 11 ]. These strategies pertain to proactive balance aimed at preventing disturbances, in contrast with reactive balance which responds to perturbations [ 12 ]. While balance is regulated by proprioception and exteroception, and visual and vestibular systems, [ 8 , 9 , 13 ], their impairment in pwDPN compromises balance. Thereby, pwDPN heavily rely on visual input for standing and walking, increasing the likelihood of falls in closed-eye conditions [ 14 – 16 ]. This elevates the risk of falls (causing damage more than 15 times greater than in subjects without DPN [ 17 ]), especially in the medial–lateral plane due to increased CoM sway [ 18 – 20 ]. In pwDPN, gender and obesity seem to have more influence on balance control than age, as men exhibit greater sway than women, particularly without visual input, and obese subjects experience an increase in sway without vision [ 21 ]. On a global scale, alteration of visual cues due to diabetic retinopathy [ 22 , 23 ] and dysfunction in the vestibular system [ 24 ] contribute to balance deficits and an increased risk of falls in pwDPN. Instability and alteration in gait parameters constitute the primary causes of falls—one of the most serious complications experienced by pwDPN [ 9 , 14 , 15 , 19 ]. Even in the initial stages of the DPN disease, altered spatio-temporal gait parameters have been observed, both with eyes closed and open, likely due to an alteration of knee extensor activation, compensated for by ankle and hip strategies [ 25 ]. Furthermore, the long-term lack of sensory input leads to other severe complications such as ulceration, amputations, diabetic foot, and an increased risk of falls [ 8 , 26 ], reducing quality of life [ 20 , 27 ] and increasing healthcare costs. The primary goals of physiotherapy for pwDPN include enhancing balance, proprioception, and gait quality. This approach aims to limit mortality and morbidity by preventing foot deformities and improving the distribution of plantar load, which is related to high-pressure areas during walking, thereby decreasing the risk of ulceration [ 28 – 30 ]. To achieve these goals, conventional physiotherapy has focused on specific balance and gait programs [ 8 ] as well as tailored orthosis [ 31 ]. Various interventions, such as electrotherapy and magnetotherapy [ 32 ], exercise therapy [ 6 ], and programs specifically targeting balance and gait [ 8 ], have demonstrated a high level of evidence. Other complementary approaches, such as acupuncture [ 33 ] and kinesiotaping [ 34 ], have shown relative evidence, while offloading devices do not appear to improve static and dynamic balance [35]. Recently, innovative technology like virtual reality (VR) and feedback (FB) have arisen and are widely used as complementary therapies for gait and balance disorder rehabilitation in various neurological conditions [ 36 – 38 ] and, to a lesser extent, in DPN. VR-based treatment includes computerized programs specifically designed to recreate real environments [ 39 ], fostering a secure clinical space supported by medical staff while improving motivation and adherence [ 40 ]. There are three main types of VR based on interaction levels: non-immersive [ 41 ], featuring limited interaction and environments generated by small devices like screens or joysticks; semi-immersive systems, using larger devices or setups like Kinect [ 42 , 43 ]; and immersive environments, creating controlled and highly interactive settings with a greater sense of presence and stimulation. VR integrates the concept of FB, harnessing the ability to voluntarily control and modify certain bodily functions or biological processes with additional input [ 44 ]. FB is categorized based on the input: visual, auditory, or tactile/haptic [ 44 , 45 ]. FB appears crucial when shaping new movement patterns or balance schemas in patients experiencing sensory loss. Relevant learning occurs during both the execution of the movement or the goal of the exercise [ 46 ], providing crucial information and enhancing patients’ motor learning. The literature indicates that VR has shown clinical benefits in gait and balance rehabilitation among individuals with neurological disorders, such as stroke [ 37 ], Parkinson’s Healthcare 2023,11, 3037 3 of 17 disease [ 36 ], and cerebral palsy [ 38 ], and even in the recovery from orthopedic ankle injuries [ 47 ]. Despite extensive studies on pharmacological [ 48 ] and non-pharmacological therapies [ 31 – 34 ] for pwDPN rehabilitation, a systematic review focusing on the use of VR in pwDPN is lacking. Peripheral neurological involvement alters the body schema at the central level, suggesting potential benefits for pwDPN through tailored VR programs. The aim of the current study is to comprehensively review and synthesize existing evidence regarding the effects of both VR and FB on improving gait, balance, and risk of falls in pwDPN. 2. Materials and Methods The requirements of “Preferred Reporting Items for Systematic Review and Meta- Analysis (PRISMA) Model” [ 49 ] have been followed in this systematic review. The protocol of this systematic review has been registered in the PROSPERO database (CRD42021255708). 2.1. Search Strategy Up until November 2023, an exhaustive search of studies was conducted in the databases: Medline/PubMed, Scopus, and EMBASE. The search strategies in the different databases are shown in Table 1. Table 1. Search strategy for the different databases. Databases Search Medline/PubMed Scopus (“virtual reality” OR feedback OR biofeedback OR wii OR game OR “augmented reality” OR kinect OR videogam* OR “virtual training” OR “immersive training”) AND (“diabetic neuropath*” OR “diabetic polineuropath*”) AND (gait OR balance OR walking OR “risk of fall”) EMBASE (‘virtual reality’/exp OR ‘virtual reality’ OR ‘virtual reality exposure therapy’/exp OR ‘virtual reality exposure therapy’ OR ‘video game’/exp OR ‘video game’ OR ‘feedback system’/exp OR ‘feedback system’) AND ( ‘diabetic neuropathy’ /exp OR ‘diabetic neuropathy’ ) AND ( ‘gait’ /exp OR gait OR ‘balance’/exp OR balance OR ‘falls’/exp OR falls) 2.2. Eligibility Criteria The studies selected for this systematic review were chosen after applying the PICO inclusion criteria [ 50 ]: (P) in patients with peripheral neuropathy in lower limb due to diabetes, (I) is effective virtual reality and/or feedback (C) compared to other intervention, non-intervention, or without comparison (O) to improve gait, balance and/or risk of falls?(s) Study Design: Randomized control trials, non-randomized control trials, prospective cohort studies, case series, crossover trials, pilot studies, and clinical trials. The “human” search filter was used and the search was limited to studies in English and Spanish. The exclusion criteria were: studies did not carry out an intervention, were not focused on gait and/or balance, or were focused solely on the evaluation, treatment, and/or monitoring of plantar pressures predisposing to ulceration. 2.3. Assessment of the Methodological Quality and Risk of Bias The evaluation of methodological quality was carried out through the Physiotherapy Evidence Database (PEDro) scale [ 51 ] in the studies for which it was possible to implement this scale (RCTs), and the Checklist for Measuring Study Quality (CMSQ) [ 40 ] for all the studies included (to obtain a broader vision of all types of studies such as case series, etc.). The PEDro scale [ 51 ] was developed to identify studies that tend to be valid internally and to redirect clinical decision-making. It consists of 11 criteria, granting a point for each criterion met. This scale shows the degree of methodological quality of a given study, with a score ranging from 0 to 10. Those with a score of 9–10 points on the PEDro scale are considered to have excellent methodological quality; between 6–8 points, good Healthcare 2023,11, 3037 4 of 17 methodological quality; between 4–5 points, fair methodological quality; and fewer than 4 points, poor methodological quality [52]. The CMSQ [ 53 ] consists of 27 “yes” or “no” questions organized into five sections: (1) study quality; (2) external validity; (3) study bias; (4) confusion and selection bias; and (5) study power. It presents a scoring system for individual sections and an overall final score. The Cochrane Collaboration’s tool [ 54 ] was used to analyze the risk of bias in the RCTs. To evaluate, it includes six domains and uses three terms: “low risk”, “high risk”, and “unclear risk” and is represented by a table and a chart. Two authors (L.A.-E. and L.G.-C.) carried out quality assessment of the studies by implementing the different scales, and an additional reviewer (C.L.-M.) was considered for consensus when needed. 2.4. Selection Process and Data Extraction The selection process consisted of several stages. A first pre-search was performed in the main database used, PubMed, to obtain the most appropriate search terms for search strategies. A definitive search was subsequently implemented in all the databases with their different strategies, as shown in Table 1. Once complete, duplicates were removed and a detailed title and abstract began to be read, identifying studies of high relevance. After that, we verified the studies that fell within the inclusion criteria established for this review by reading the full text. Finally, a last bibliographic search was done manually, and two more studies were identified. Two authors (L.A.-E. and L.G.-C.) carried out the screening process, and an additional reviewer (C.L.-M.) was considered for consensus when needed. The data extracted from the selected studies were author, year of publication, patient characteristics (total number of participants and by groups, average age, sex), disease evaluation, intervention details such as type VR or BF, sessions/repetitions, follow-up/reviews, as well as outcomes, measurement instruments, and finally results. 2.5. Data Synthesis and Statistical Analysis The findings were described narratively and where possible, study results were pooled and meta-analysis was conducted based on the same underlying outcomes (gait parameters, balance). Mean differences or standard mean differences (SMD) were calculated with 95% confidence intervals (CI). Fixed or random effects models were used according to the degree of heterogeneity (I 2 coefficient ≤ 50%), assuming a 95% confidence interval (CI) for all analyses. Review Manager software (RevMan v.5.4.1, The Cochrane Collaboration, 2020) was used to summarize the effects and construct the forest plots. EPIDAT software version 3.1. was used to check the risk of publication bias of the studies included in our meta-analysis. Whenever possible, Begg and Egger values were obtained (p< 0.05). 3. Results A total of 137 studies were identified (the sum of the results found in the aforementioned databases), from which only 10 studies were finally selected for this systematic review, after applying the eligibility criteria (Figure 1). A list was made to show the excluded studies and the reasons for exclusion, which is found as Supplementary Materials (List S1). 3.1. Methodological Quality and Risk of Bias of Included Studies Table 2shows the score on the PEDro scale [ 51 ] resulting from the two RTCs [ 10 , 17 ] selected for this review. The overall score is 7 on average, illustrating the good methodological quality of the selected studies. Regarding the risk of bias of RCTs, all studies presented the same risk of bias. With respect to the evaluation of the different types of biases according to the selected studies, the items that obtained the highest biases were the blinding of participants and staff Healthcare 2023,11, 3037 5 of 17 (performance bias), and the blinding of outcome assessments (detection bias). The results are shown in Figures 2and 3. Healthcare 2023, 11, x FOR PEER REVIEW 5 of 18 Figure 1. Flowchart of the different phases of the systematic review [49]. 3.1. Methodological Quality and Risk of Bias of Included Studies Table 2 shows the score on the PEDro scale [51] resulting from the two RTCs [10,17] selected for this review. The overall score is 7 on average, illustrating the good methodological quality of the selected studies. Table 2. Score obtained on the PEDro scale [51] of each of the selected studies. Study Total 1 2 3 4 5 6 7 8 9 10 11 Grewal et al., 2015 [17] 7/10 - 1 1 1 0 0 0 1 1 1 1 Taveggia et al., 2014 [10] 7/10 - 1 0 1 0 0 1 1 1 1 1 Regarding the risk of bias of RCTs, all studies presented the same risk of bias. With respect to the evaluation of the different types of biases according to the selected studies, the items that obtained the highest biases were the blinding of participants and staff (performance bias), and the blinding of outcome assessments (detection bias). The results are shown in Figures 2 and 3. Figure 1. Flowchart of the different phases of the systematic review [49]. Table 2. Score obtained on the PEDro scale [51] of each of the selected studies. Study Total 1 2 3 4 5 6 7 8 9 10 11 Grewal et al., 2015 [17] 7/10 - 1 1 1 0 0 0 1 1 1 1 Taveggia et al., 2014 [10] 7/10 - 1 0 1 0 0 1 1 1 1 1 Healthcare 2023, 11, x FOR PEER REVIEW 6 of 18 Figure 2. Risk of bias [54] of the studies included in the systematic review [10,17]. Green: Low risk of bias; Yellow: Unclear risk of bias; Red: High risk of bias. Figure 3. Overall risk of bias [54]. The results are presented by percentages. Finally, the results obtained through the CMSQ [53] were highly variable, obtaining an average score of 16 and a total range of 5 to 22. In general, the best scores were obtained for the RCTs [10,17]. The results are shown in Table 3. Table 3. Score obtained on the CMSQ scale [53] of each of the selected studies. Study of Methodological Quality (Checklist for Measuring Quality), and Grade of Recommendation (Guidelines of the Oxford Centre for Evidence-Based Medicine). Chadrashe khar et al., 2021 [55] Stolarczyk et al., 2021 [56] Hung et al., 2019 [57] Carroll & Galles, 2018 [27] Grewal et al., 2015 [17] Mohield in et al., 2014 [58] Tavegg ia et al., 2014 [10] Grewal et al., 2013 [59] Salsabili et al., 2013 [60] Salsabili et al., 2011 [9] Checklist items for Measuring Quality 1. Study quality Hypothesis/aim 1 1 1 0 1 1 1 1 1 1 Outcomes 1 1 1 1 1 1 1 1 1 1 Eligibility criteria 1 1 1 0 1 1 1 1 1 1 Figure 2. Risk of bias [ 54 ] of the studies included in the systematic review [ 10 , 17 ]. Green: Low risk of bias; Yellow: Unclear risk of bias; Red: High risk of bias. Healthcare 2023,11, 3037 6 of 17 Healthcare 2023, 11, x FOR PEER REVIEW 6 of 18 Figure 2. Risk of bias [54] of the studies included in the systematic review [10,17]. Green: Low risk of bias; Yellow: Unclear risk of bias; Red: High risk of bias. Figure 3. Overall risk of bias [54]. The results are presented by percentages. Finally, the results obtained through the CMSQ [53] were highly variable, obtaining an average score of 16 and a total range of 5 to 22. In general, the best scores were obtained for the RCTs [10,17]. The results are shown in Table 3. Table 3. Score obtained on the CMSQ scale [53] of each of the selected studies. Study of Methodological Quality (Checklist for Measuring Quality), and Grade of Recommendation (Guidelines of the Oxford Centre for Evidence-Based Medicine). Chadrashe khar et al., 2021 [55] Stolarczyk et al., 2021 [56] Hung et al., 2019 [57] Carroll & Galles, 2018 [27] Grewal et al., 2015 [17] Mohield in et al., 2014 [58] Tavegg ia et al., 2014 [10] Grewal et al., 2013 [59] Salsabili et al., 2013 [60] Salsabili et al., 2011 [9] Checklist items for Measuring Quality 1. Study quality Hypothesis/aim 1 1 1 0 1 1 1 1 1 1 Outcomes 1 1 1 1 1 1 1 1 1 1 Eligibility criteria 1 1 1 0 1 1 1 1 1 1 Figure 3. Overall risk of bias [54]. The results are presented by percentages. Finally, the results obtained through the CMSQ [ 53 ] were highly variable, obtaining an average score of 16 and a total range of 5 to 22. In general, the best scores were obtained for the RCTs [10,17]. The results are shown in Table 3. Table 3. Score obtained on the CMSQ scale [53] of each of the selected studies. Study of Methodological Quality (Checklist for Measuring Quality), and Grade of Recommendation (Guidelines of the Oxford Centre for Evidence-Based Medicine). Chadrashekhar et al., 2021 [55] Stolarczyk et al., 2021 [56] Hung et al., 2019 [57] Carroll & Galles, 2018 [27] Grewal et al., 2015 [17] Mohieldin et al., 2014 [58] Taveggia et al., 2014 [10] Grewal et al., 2013 [59] Salsabili et al., 2013 [60] Salsabili et al., 2011 [9] Checklist items for Measuring Quality 1. Study quality Hypothesis/aim 1 1 1 0 1 1 1 1 1 1 Outcomes 1 1 1 1 1 1 1 1 1 1 Eligibility criteria 1 1 1 0 1 1 1 1 1 1 Interventions 1 1 1 1 1 1 1 1 1 1 Confounders 0 0 0 0 0 0 0 0 0 0 Findings 1 1 1 0 1 1 1 1 1 1 Random variability 0 0 0 0 0 0 0 0 0 0 Adverse events 0 0 1 0 0 0 1 1 0 0 Lost to follow-up 0 1 1 0 0 0 1 1 0 0 Probability values 0 1 1 0 0 0 1 1 0 0 2. External validity Source population 1 1 1 0 1 1 1 1 1 1 Illustrative sample 0 0 0 0 0 0 0 0 0 0 Illustrative treatment 1 1 1 1 1 1 1 1 1 1 3. Internal validity (study bias) Blinding of subjects 1 0 0 0 0 0 1 0 0 0 Blinding 0 0 0 0 0 0 1 0000 “Data extraction” 0 0 0 0 0 0 0 0 0 0 Follow-up adjustments 0 0 0 0 0 0 0 0 0 0 Statistical tests 1 1 1 1 1 1 1 1 1 1 Compliance 1 1 1 1 1 1 1 1 1 1 Outcomes 1 1 1 0 1 1 1 1 1 1 4. Internal validity (confounding and selection bias) 1 Source of patients 1 1 1 0 1 1 1 1 1 1 Recruitment period 1 1 1 0 1 1 1 1 1 1 Randomization 1 0 1 0 1 1 1 0 0 0 Concealment 0 0 1 0 1 1 1 0 0 0 Analysis 0 0 0 0 0 0 0 0 0 0 Loss to follow-up 0 1 1 0 0 0 0 1 0 0 5. Power Effect 2 5 2 0 5 5 5 2 2 2 Total score 16 20 19 5 19 19 22 18 14 14 Percentage (%) 50 62 59 16 59 59 69 56 44 44 3.2. Synthesis of Results Table 4shows the most relevant characteristics of the included studies. As is shown, all studies reported a positive effect of VR and FB interventions on balance and gait in pwPDN. When compared with other interventions, pooled data demonstrated a tendency in favor of VR and FB, but differences between groups were not statistically significant for balance (SMD = − 0.81, 95% CI = − 1.90, 0.29; p= 0.15; I 2 = 86%) or gait speed (MD = − 1.05, 95% CI = −2.96, 0.85; p= 0.28; I2= 89%) (Figures 4and 5). Healthcare 2023,11, 3037 7 of 17 Table 4. Synthesis of results. Authors (Year) Design Sample Age (Average, Standard Dev.) Intervention N Ses Performance of Measurement VR FB Results Chandrashekhar et al., (2021) [55] PS N = 23 66.6 ±9.9 Vibration wearables (Myovolt) 10 min 3 ses/week 4 weeks Static and dynamic Balance (BSS, TUG) Proactive Balance (TUG-Cognitive)Pain (BPI-DPN) - Haptic ↑TUG y TUG-Cognitive Stolarczyk et al., (2021) [56] N-RCT N = 77 73.22 ±7.57 BSSys 30 min 5 ses/week 12 weeks Static and Dynamic Balance (2 BSSys tests EO and EC: general data, AP, and ML) Risk of Fall (BSSys) Motor coordination (BSSys) -Visual and Haptic ↑static balance EO and EC, all parameters ↓risk of fall All parameters were significantly worse in EG (DPN patients) than CG (healthy participants) Hung et al., (2019) [57] CCT N = 24 A = 12 B = 12 49.9 ±12 Interactive game + mat (XavixPORT) 30 min 3 ses/week 6 weeks Static and Dynamic Balance (TUG, BSS) Risk of Fall (MFES) Monopodial Balance (UST) Semiimmersive Visual and auditive ↑BSS, MFES y TUG. = UST Carroll & Galles (2018) [27] CC N = 1 60 3 interactive games (OGRE) 45 min 6 ses Balance (TUG, 2◦session) Security (ABC) Semi-immersive Visual and auditive ↑TUG ↑ABC Plus positive verbal feedback Grewal et al., (2015) [17] RCT N = 35 CG = 20 EG = 19 63.7 ±8.2 CG: standard care EG: interactive game (MATLAB), 5 sensors (LegSys) hips and lower limbs. 45 min 2 ses/week 4 weeks Risk of Fall (FES-I) Quality of life (SF-12) Balance (double stance, CoM AP sway, LM sway and total sway) Daily physical activity (PAMSys) Nonimmersive Visual and auditive ↓CoM total sway and ML sway EO ↓sway hips–ankles ↑mental punctuation SF-12 Mohieldin et al., (2014) [58] N-RCT N = 87 CG = 30 EG = 57 49.9 ±12 CG: standard care EG: Dynamic posturography (SMART Balance Master) CG: 3 ses/week, 12 weeks EG: 2 ses/week, 12 weeks Static balance: visual and vestibular proportion EO and EC (SMART Balance Master) - Visual ↓ compost balance and SMTS relation DPN light and CG. ↓ compost balance and SMTS relation DPN severe and CG. ↑pre-post SMTS relation DPN light. Taveggia et al., (2014) [10] RCT N = 27 CG = 14 EG = 13 72 ±9CG: standar care EG: Biodex protocol 60 min 5 ses/week 4 weeks Gait (6MWT, 10MWT and Tinetti) Function (FIM and Tinetti) Balance (Tinetti) -Visual and haptic. ↑walking resistance EG ↑functionality (FIM) both groups Healthcare 2023,11, 3037 8 of 17 Table 4. Cont. Authors (Year) Design Sample Age (Average, Standard Dev.) Intervention N Ses Performance of Measurement VR FB Results Grewal et al., (2013) [59] PCS N = 29 57 ±10 Interactive gam (MATLAB) 6 inertial sensors (BalanSens) ankles and wrist. 10–15 min 1 ses Balance (CoM sway = BalanSens) Coordination between upper and lower limbs AP and ML (RCI) Nonimmersive Visual and auditive ↓CoM hips-ankles sway, EC. ↓CoM Sway, EO. ↑coordination AP EO. Salsabili et al., (2013) [60] T-SS N = 19 56 ±8.96 BSSys 30 min 10 ses/week 3 weeks Static Balance (torque platform) DPN severity (Valk score) - Visual = Valk Score: no ↓DPN severity. ↓CoM sway ML low-medium frequencies OE. Salsabili et al., (2011) [9] T-SS N = 19 56 ±8.96 BSSys 30 min 10 ses/week 3 weeks DPN severity (Valk score) Balance (CoM fluctuation = BSSys, 3 trials EO and 3 EC) - Visual = Valk Score: no ↓DPN severity. ↑stability total, AP, and ML EO. ↑stability total and AP EC (ABC) Activities-Specific Balance Confidence Scale; (AP) Anteroposterior; (BSS) Berg Scale Score; BSSys Biodex Stability System; (BPI-DPN) Brief Pain Inventory for patients with painful Diabetic Peripheral Neuropathy; (CC) Clinical Case; (CCT) Crossover Clinical Trial; (CG) Control Group; (CoM) Center of Mass; (DPN) Diabetic Peripheral Neuropathy (EC) Eyes Closed; EO (Eyes Opened); (EG) Experimental Group; (FES-I) Falls Efficacy Scale; (FIM) Functional Independence Measure; (MFES) Modified Falls Efficacy Scale; (ML) Medial-lateral; (min) minutes; (N-RCT) Non-Randomized Controlled Trial; (PCS) Prospective Cohort Study; (PS) Pilot Study; (RCI) Reciprocal Compensatory Index; (RCT) Randomized Controlled Trial; (SF-12) Short-form health survey; (Ses) session; (SMTS) somatosensorial; (T-SS) Time-Series Study; (TUG) Timed Up and Go; (6MWT) 6-Minute Walking Test; (10 MWT) 10-Meter Walking Test. Healthcare 2023,11, 3037 9 of 17 3.3. Participant and Intervention Characteristics The total number of participants was 251 patients with DPN plus 71 healthy participants: 30 in the study by Mohieldin et al. [ 58 ] and 40 in the study by Stolarczyk et al. [ 56 ]. Neither of these two studies specified the participants’ gender, nor did Grewal et al. [ 59 ] in 2013, but it was concluded that there were 137 women and 105 men, with ages between 49.7 and 80.8 years . All of the participants were elderly adults and only in Mohieldin et al. [58] was the average age under 50 years. Healthcare 2023, 11, x FOR PEER REVIEW 10 of 18 manual strengthening and stretching exercises, in addition to 20 min of walking and standard static and dynamic balance protocols. Mohieldin et al. [58] used dynamic posturography as the evaluation instrument in both the intervention and control group, but training was in the intervention group only, which received visual FB of their CoM, while the control group received verbal and/or haptic input. In this study, the doses were not similar: while both groups trained for three months, the intervention group had two training sessions a week and the control group had three. Using the OGRE game, Carroll & Galles [27] developed three specific interactive videogames for their patients. The first was to collect coins (visual FB), the second to steer a boat and shoot lasers (combining moves from MMII and MMSS), and the third to dodge obstacles with a plane. The patient interacted with the games through the Microsoft Kinect camera. Hung et al. [57] similarly proposed training based on the visual and auditory FB that the games created specifically for this purpose through the XavixPORT console. It was divided into four exercises: knee raises following the reference on the screen, stepping on hamsters, following the rhythm (first fast and then slow), and repeating the knee raises. In this study, the intervention was cross-sectional, which means that group A trained first and group B served as the control, while after the intervention time, group B trained and group A served as the control. Chandrashekhar et al. [55] were the only authors to use a localized wearable (Myovolt) in the tibialis anterior, quadriceps, and muscle belly of the gastrocnemius and soleus at different speed frequencies to provide different haptic FB to the participants in their study: group 1 received an intermittent 120 Hz vibration; group 2, a 35–120 Hz sinusoidal, and group 3, a 120 Hz continuous vibration. 3.4. Study Groups Included in the Statistical Analysis It was possible to pool data from only three studies [9,21,47] and two outcomes: balance and gait speed (Figures 4 and 5). In all the studies, to a greater or lesser extent, balance improved in some of its components: postural control or static balance assessed by stabilometric platform [9] or the Unipedal Stance Test [47] and functional or dynamic balance, assessed by the Berg Balance Scale [47] and Tinetti Scale [9]. Pooled data from these three clinical trials [9,21,47] showed better results for functional balance measures than for postural control. However, heterogeneity was generally high, and the low number of studies included in this synthesis compel us to interpret these findings with caution (Figure 4). Figure 4. Forest plot of the comparison of the difference between the subgroups in postural control and functional balance [10,17,57]. Pooled data from two clinical trials [9,47] point to the superiority of VR and FB rehabilitation for gait speed improvements in pwDPN, although statistical significance was not reached. Moreover, the low number of studies included in this analysis, the different Figure 4. Forest plot of the comparison of the difference between the subgroups in postural control and functional balance [10,17,57]. Healthcare 2023, 11, x FOR PEER REVIEW 11 of 18 assessment tools used (TUG and 10 mWT) [9,47], and the high heterogeneity observed prevent us from considering these findings as conclusive (Figure 5). Figure 5. Forest plot of the comparison of the difference between the subgroups in gait speed [10,57]. Finally, publication bias could be assessed only by funnel plot asymmetry inspection (Figures 6 and 7), as Begg’s or Egger’s statistical tests are recommended when at least 10 studies are included in each meta-analysis [63]. Figure 6. Funnel plot for balance. Where both slanting dotted lines represent the expected 95% CI; vertical dotted line referring to the position of no effect; points represent individual studies. Figure 7. Funnel plot for gait speed. Where both slanting dotted lines represent the expected 95% CI; vertical dotted line referring to the position of no effect; points represent individual studies. 4. Discussion This systematic review and meta-analysis aimed to summarize the current evidence regarding the effects of VR and/or FB on balance and gait parameters in pwDPN. Overall, interventions appeared to improve balance and gait outcomes. However, when compared with other interventions or no intervention, statistically significant differences have not been consistently observed. Figure 5. Forest plot of the comparison of the difference between the subgroups in gait speed [ 10 , 57 ]. The two publications reported by Salsabili et al. [ 8 , 49 ] seem to be part of the same study, since the number of participants, average age, and intervention dose were the same, distributing analysis of the outcome measures between the two publications. 3.3.1. Classification of Participants According to Peripheral Neuropathy Regarding the way to measure the severity or even the presence of DPN, different instruments and evaluative tests were used. Grewal et al. [ 17 , 59 ] chose the Vibration Perception Threshold (VPT), with a perception threshold >25 V or, if it was accessible, using the 10-g monofilament test on the heads of the first, third, and fifth metatarsal. In the studies by Salsabili et al. [ 9 , 60 ], the presence of DPN was evaluated by measuring the conduction velocity of the peroneal and tibial motor nerves and the sural sensory nerve. Once the presence of DPN is confirmed, its severity is tested with the Valk test, a 10-item instrument that assesses polyneuropathy severity according to the frequency of symptom appearance. Taveggia et al. [ 10 ] also used a monofilament test, in addition to the Diabetic Neuropathy Index criteria [ 61 ], an index used for over 20 years. Moreover, Mohieldin et al. [ 58 ] followed the criteria established by Dyck et al. [ 62 ] and divided all the participants into four categories: without DPN, asymptomatic DPN, moderate DPN, or severe DPN. The rest of the studies did not measure DPN severity. 3.3.2. Training Sessions Training session duration ranged from 10 to 60 min, including breaks between tasks. Interventions were carried out using videogames created specifically for rehabilitation: MatLab [ 17 , 59 ], OGRE [ 27 ], or XavixPORT [ 57 ] and/or dynamometric platforms, Biodex Stability System [ 9 , 10 , 56 , 60 ], and SMART Balance Master [ 58 ]. Only one of the studies Healthcare 2023,11, 3037 16 of 17 35. Horstink, K.A.; van der Woude, L.H.V.; Hijmans, J.M. Effects of offloading devices on static and dynamic balance in patients with diabetic peripheral neuropathy: A systematic review. Rev. Endocr. Metab. Disord. 2021,22, 325–335. [CrossRef] [PubMed] 36. Lei, C.; Sunzi, K.; Dai, F.; Liu, X.; Wang, Y.; Zhang, B.; He, L.; Ju, M. 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