The contribution of Nintendo Wii Fit series in the field of health: a systematic review and meta-analysis (original) (raw)

Introduction

The past decade saw the emergence of home-based active video games (AVG), with the Wii (Nintendo Co. Ltd., Kyoto, Japan) being released in 2006, followed by the PlayStation Move (Sony Corp, Tokyo, Japan) and the Kinect (Microsoft, Redmond, WA, USA) in 2010. These systems take advantage of accelerometry and video camera-mediated motion detection technologies to track the player’s movements and convert them into gaming commands. The Wii offers an original game modality with the Wii Balance Board accessory, which can be used as a weighing scale or as a gamepad sensitive to body sway (Clark et al., 2010).

Among the home-based AVG, the well-known Wii Fit series (Nintendo, Japan) runs on the Wii console and consists of a combination of both serious and entertaining activities requiring body movement to fulfill gaming commands. The software displays various kinds of health metrics (body mass index, number of kilocalories burned over a given period) encouraging the players to improve their physical fitness. Whilst the Wii Fit was primarily designed to be used in homes by healthy individuals for health and fitness purposes, an overview of the literature indicates that physical therapists and physicians from different medical fields include the use of Wii Fit in their clinical practice. For instance, the National Stroke Audit: Rehabilitation Services Report recently indicated that 76% of Australian hospitals have a Wii console available to aid with the rehabilitation of stroke patients (the National Stroke Foundation, 2012 in Levac et al., 2010).

Many reviews have focused on AVG and their effects on health and describe mitigated outcomes (LeBlanc et al., 2013; Peng, Crouse & Lin, 2013). However, the distinction between Wii Fit and other AVG was not always clear, resulting in the inability to ascertain an objective picture of the contribution from the Wii Fit. The goals for this systematic review are as follows:

• Goal 1: Identifying the health-related domains (i.e., populations and clinical situations) in which the Wii Fit series has already been tested or used. A scientific database search with reasoned exclusion criteria was undertaken.
• Goal 2: Understanding the effect of Wii Fit in the identified populations (cf. Goal 1). A qualitative systematic review of studies including Wii Fit interventions was performed, with particular attention given to health and physical activity outcomes.
• Goal 3: When possible, quantification of the effect Wii Fit has on selected health-related domains was achieved by conducting meta-analyses.

Methods

Literature search

The selection process is summarized in a PRISMA flow diagram (Fig. 1). Several strategies were adopted: (1) The MEDLINE database was used to conduct a systematic search using the following keywords: “wii fit,” “wii-fit” and “wiifit” (occurrences: N = 122). (2) The same keywords were used to search for additional articles in the Mary Ann Liebert, Inc. Games and Health Journal (N = 121). (3) Additional peer-reviewed articles were identified during manual searches via Google Search (Google Inc., Mountain View, CA, USA) (N = 1). (4) Articles suggested by authors active in the field of AVG (N = 10) or identified in the reference section of eligible papers (N = 46). (5) Only papers in English, French or Japanese were eligible for this review. The search and data extraction were performed by two independent researchers (Murakami H and Tripette J) and any discrepancies were resolved by a third contributor (Miyachi M).

Figure 1: Flow diagram for the selection of studies included in the systematic review and the meta-analyses.

Details about exclusion criteria and the selection process can be found in Table 1 and the “Methods.”

The literature search was completed in June 2015. A total of 200 articles were identified. In order to meet the primary inclusion criteria, studies were required to: (1) have a primary focus on any software of the Wii Fit series, and (2) focus on a recognized health issue. A total of 279 articles were screened after the identification and removal of 21 duplicates (Fig. 1).

Goal 1: identification of health domains

The exclusion criteria applied to identify medical domains in which the Wii Fit has already been tested or used are described in Table 1. The identification process involved screening titles and abstracts. The full texts were read when the abstracts provided insufficient details (Fig. 1 and Table 1). The results are shown in Table 2.

Table 1:

Summary of exclusion criteria.

Table 2:

Wii Fit studies, health domains and populations of interest.

Goal 2: systematic review, data extraction and synthesis

A qualitative systematic review was performed to understand the effect of Wii Fit in the previously identified health domains. This study followed the 2009 PRISMA guidelines for the conductance of systematic reviews and meta-analyses (Liberati et al., 2009) (see, Data S1). The exclusion criteria which were applied at this stage are described in Table 1.

The content of each eligible article was extracted according to the following protocol: (1) Study identification (first author’s name, year and country), (2) methodological details (study design, sample size, population characteristics, etc.), (3) activities used, (4) description of each identified primary or secondary health and physical activity outcome and (5) key findings (i.e., pre- and post-intervention as well as differences between Wii Fit and control groups) (Tables 3 and 4).

Table 3:

Wii Fit interventions for health status and well-being improvement1

Table 4:

Wii Fit interventions for functional balance training.

Goal 3: meta-analyses

The effects of Wii Fit were quantified for selected health-related domains. The most recurrent outcomes noted were the activities-specific balance confidence test (ABC), Berg balance score (BBS) and the time-up-and-go test (TUG). These three tests are frequently used to assess patients’ balance abilities (Powell & Myers, 1995; Berg, 1989; Podsiadlo & Richardson, 1991). ABC is usually administered by a health care professional asking “How confident are you that you will not lose your balance or become unsteady when you…” for 16 different situations (e.g., “…walk around the house?,” “…walk up or down stairs”…). For each item, the participant should answer by expressing confidence in percentage (Powell & Myers, 1995). BBS is a scale able to measure balance in adults. The therapist asks participants to complete 14 different tasks (e.g., “sitting to standing,” “turning to look behind them”…) and evaluates each of them using a five-point score, ranging from 0 to 4 (Berg, 1989). TUG is a simple measure of the time taken by a subject to stand up from a chair, walk a distance of 3 m, turn, walk back to the chair, and sit down (time is expressed in seconds) (Podsiadlo & Richardson, 1991). Firstly, pre- and post-intervention meta-analyses were performed for each of these three outcomes. Secondly, Wii Fit vs. traditional therapy meta-analyses were completed, which only included results from randomized control (RCT) or two-arm trials. The exclusion criteria applied at this stage are described in Table 1. Only studies that used the 3 m version of the TUG test were included. Groups submitted to a combination of Wii Fit activities and more traditional therapy exercises were excluded from the pre- and post-intervention meta-analysis (Barcala et al., 2013; Daniel, 2012; Yatar & Yildirim, 2015). The pre- and post-intervention effect was calculated for the three selected outcomes. These meta-analyses used the mean difference between the reported pre-intervention and post-intervention values. For the Wii Fit vs. traditional therapy meta-analyses, the difference between the pre- and post-Wii Fit intervention changes and the pre- and post-traditional intervention changes were used as inputs in the meta-analysis. The variance imputation methods described by Follmann et al. (1992) were used to estimate the standard deviations of effect size when the authors did not report them. Heterogeneity between studies was assessed using the homogeneity test. A fixed-effect model was used when the _I_2 statistic, which is the index of heterogeneity, was under 75%. Sub-analyses were conducted in patients and healthy subjects. For ABC, because only two studies included a comparison between Wii Fit and traditional therapy (Yatar & Yildirim, 2015; Meldrum et al., 2015), only the pre- and post-intervention meta-analysis was performed. The risk of bias in each individual study included in the Wii Fit vs. traditional therapy meta-analysis was also assessed (Fig. 2). Meta-regression analyses were performed to assess the impact of intervention duration and volume (i.e., session duration × number of session) on ABC, BBS and TUG. p < 0.05 indicates statistical significance. Meta-analysis was performed using STATA 12.1 (StataCorp, College Station, TX, USA).

Figure 2: Assessment of risk of bias in individual studies included in the Wii Fit vs. traditional therapy meta-analyses.

The absence of ABC, BBS or TUG excluded de facto the studies from the meta-analyses. Therefore the usually reported “reporting bias” was not included in this assessment. No “other bias” was identified.

Results

The literature search provided a total of 279 references of interest (Fig. 1). Following the title and abstract screening process 138 studies were discarded, as they did not meet the selection criteria. One article was not accessible so was also discarded at this stage. An additional 25 references were removed after reading the full-text. Finally, 115 studies were included in the qualitative analysis, covering an approximately six-year period from July 2009 to June 2015.

Goal 1: health domains and populations of interest

The 115 selected studies focused on Wii Fit as a novel tool to improve physical function, fitness or health status. The content of the 115 articles was used to determine the different health domains in which Wii Fit may have potential benefits (Table 2).

Goal 2: systematic review of Wii-Fit interventions

From the 115 selected Wii Fit articles, 68 were intervention studies and met the selection criteria for inclusion in the systematic qualitative review. Overall, these studies involved 2,183 participants from both sexes (females: 1,161, males: 844, not specified: 178), with a wide age range (49 ± 6 months to 86 ± 6 years (Salem et al., 2012; Chao et al., 2013)), and various medical conditions. Primary and secondary outcomes, intervention content, as well as observation period vary from study to study. The intervention durations vary from 2 to 20 weeks (Bower et al., 2014; Toulotte, Toursel & Olivier, 2012), frequencies from 1 to 7 sessions per week (respectively, Toulotte, Toursel & Olivier, 2012; Chan et al., 2012 and Tripette et al., 2014b; Kempf & Martin, 2013) and session time from 10 to 60 min (respectively, Janssen, Tange & Arends, 2013; Franco et al., 2012 and Baltaci et al., 2013; Brichetto et al., 2013; Toulotte, Toursel & Olivier, 2012).

Six papers reported adverse effects: In young adults, light to moderate adverse effects (muscle soreness, pain, sprain, etc.) were observed (Tripette et al., 2014a). Among seniors, hip strain, neck strain, lower back pain as well as one fall were reported (Nicholson et al., 2015; Williams et al., 2010; Agmon et al., 2011). In multiple sclerosis patients, knee pain and lower back pain were also reported (Prosperini et al., 2013). Bower et al. (2014) observed a relatively high rate of falls in stroke patients (four events over a group of 30 patients).

Table 3 describes the characteristics and main results from studies with a primary focus on the effects of Wii Fit interventions on physical activity level, physical fitness or patients’ health status. Among 13 studies, 10 observed positive effects (Janssen, Tange & Arends, 2013; Daniel, 2012; Tripette et al., 2014b; Cutter et al., 2014; Albores et al., 2013; Kempf & Martin, 2013; Cho & Sohng, 2014; Kim et al., 2014; Hoffman et al., 2013, 2014; Chan et al., 2012) and three presented more contrasted results (Owens et al., 2011; Nitz et al., 2010; Albores et al., 2013). Interestingly, four intervention studies were conducted in patients with chronic diseases. They all reported a significant improvement in health status and well-being (chronic obstructive pulmonary disease, type two diabetes mellitus, chronic kidney disease and lower back pain) (Albores et al., 2013; Kempf & Martin, 2013; Cho & Sohng, 2014; Kim et al., 2014). Two reports described Wii Fit interventions as both feasible and effective methods for improving the overall physical fitness, mobility and independence of senior subjects (Janssen, Tange & Arends, 2013; Daniel, 2012; Chan et al., 2012).

For each population, Table 4 summarizes study characteristics and the main results for protocols with a primary focus on the effect of Wii Fit intervention on balance activities and related physical functions. Overall, the qualitative review of these studies supports a positive effect of Wii Fit interventions on balance outcomes. Among the 55 studies, 50 observed a positive effect of Wii Fit on at least one parameter (Morone et al., 2014; Wall et al., 2015; Bieryla & Dold, 2013; Rendon et al., 2012; Bainbridge et al., 2011; Fung et al., 2012; Salem et al., 2012; Barcala et al., 2013; Yatar & Yildirim, 2015; Meldrum et al., 2015; Ferguson et al., 2013; Hammond et al., 2014; Mombarg, Jelsma & Hartman, 2013; Jelsma et al., 2013, 2014; Esposito et al., 2013; Tarakci et al., 2013; Gioftsidou et al., 2013; Melong & Keats, 2013; Lee, Lee & Park, 2014; Cone, Levy & Goble, 2015; Baltaci et al., 2013; Sims et al., 2013; Punt et al., 2015; Brichetto et al., 2013; Nilsagård, Forsberg & von Koch, 2013; Prosperini et al., 2013; Robinson et al., 2015; Hung et al., 2014; Omiyale, Crowell & Madhavan, 2015; Williams et al., 2011; Bateni, 2012; Orsega-Smith et al., 2012; Toulotte, Toursel & Olivier, 2012; Chao et al., 2013, 2014; Cho, Hwangbo & Shin, 2014; Nicholson et al., 2015; Roopchand-Martin et al., 2015; Williams et al., 2010; Agmon et al., 2011; Jorgensen et al., 2013; Esculier et al., 2012; dos Santos Mendes et al., 2012; Padala et al., 2012; Pompeu et al., 2012; Mhatre et al., 2013; Esculier, Vaudrin & Tremblay, 2014; Goncalves et al., 2014; Liao et al., 2015). There were numerous examples were balance-related parameters improved to a similar or even higher extent when using Wii Fit compared to traditional therapies (Morone et al., 2014; Fung et al., 2012; Barcala et al., 2013; Daniel, 2012; Yatar & Yildirim, 2015; Meldrum et al., 2015; Gioftsidou et al., 2013; Melong & Keats, 2013; Lee, Lee & Park, 2014; Baltaci et al., 2013; Sims et al., 2013; Punt et al., 2015; Brichetto et al., 2013; Duclos et al., 2012; Hung et al., 2014; Bateni, 2012; Toulotte, Toursel & Olivier, 2012; Nicholson et al., 2015; Jorgensen et al., 2013; Pompeu et al., 2012; Liao et al., 2015). Only five papers described contrasted results or expressed some reservations about the ability of the software to induce benefits in balance skills (Janssen, Tange & Arends, 2013; Ramstrand & Lygnegård, 2012; Naumann et al., 2015; Bower et al., 2014; Franco et al., 2012), with three of these studies being conducted in healthy populations (Janssen, Tange & Arends, 2013; Naumann et al., 2015; Franco et al., 2012).

Goal 3: outcomes of meta-analyses

For the pre- and post-intervention meta-analyses, seven groups out of six studies were included for ABC, 13 groups out of 12 studies for BBS, and 12 groups out of 12 studies for TUG. For the Wii Fit vs. traditional therapy meta-analyses, 14 groups out of seven studies for BBS, and 12 groups out of six studies for TUG. Studies included in the different meta-analyses involved 595 participants from both sexes (females: 332, males: 242, not specified: 21), with a wide age range (12 ± 3 to 86 ± 5 years (Tarakci et al., 2013; Chao et al., 2013)) and various medical conditions. Whilst these papers all included a measure of ABC, BBS or TUG, the interventions content and duration vary from study to study. The assessment of individual studies revealed a low risk of bias (Fig. 2). Detailed results for ABC, BBS and TUG are presented in Figs. 3–5, and data included in the meta-analyses appears in Tables 3 and 4. Wii Fit interventions did not induce any change in ABC (2.02, 95% CI: −4.01–8.04). For BBS, significant improvements were noted in both healthy subjects and patients (2.00, 95% CI: 0.41–3.60 and 2.99, 95% CI: 0.08–5.90, respectively; 2.23, 95% CI: 0.84–3.63, overall). In addition, there was no significant difference in changes induced by traditional training and those induced by Wii Fit, suggesting that Wii Fit was as valid as traditional training. Regarding TUG, no significant reduction was noted after the Wii Fit intervention in either healthy subjects or patients (−0.34 s, 95% CI: −1.38 to 0.70 and −2.24 s, 95% CI: −5.17 to 0.69, respectively; −0.55 s, 95% CI: −1.53 to 0.43, overall). However, compared to traditional training programs, the Wii Fit did induced a more significant reduction in TUG, especially in patients (−1.76, 95% CI [−2.13 to −1.39], in patients; −1.31, 95% CI [−1.62 to −1.01], overall). The sets of studies included in both BBS and TUG pre- and post-intervention meta-analyses were statistically homogenous (_I_2 = 0.0%, p = 0.961 and _I_2 = 0.0%, p = 0.969, respectively for the overall analysis). Various levels of heterogeneity were observed in the Wii Fit vs. traditional therapy meta-analyses (_I_2 = 60.0%, p = 0.040 for BBS in patients and _I_2 = 74.3%, p = 0.002 for TUG overall (Figs. 4 and 5), indicating some inconsistencies in the literature. This was expected, however, since different populations were included in the analyses.

Figure 3: Pre- and post-intervention meta-analytic effect for the activities-specific balance confidence test (ABC).

The black point shows the average change for each study. The diamonds describe the pooled values respectively for the change in healthy subjects, patients and the overall population. The vertical black line refers to no change. For each analysis (overall population) or sub-analysis (healthy subjects or patients), a significant effect is observed if the diamond does not touch the black line. The horizontal black line shows the 95% CI and the gray square shows the study weight in percentage. Four-week Wii Fit intervention group (a) and eight-week Wii Fit intervention group (b) (Orsega-Smith et al., 2012). _I_2: index of heterogeneity.

Figure 4: Pre- and post-intervention meta-analytic effect (A) and Wii-Fit vs. traditional therapy meta-analytic effect (B) for the Berg balance score (BBS).

(A) The black point shows the average change for each study. The diamonds describe the pooled values respectively for the change in healthy subjects, patients and the overall population. The vertical black line refers to no change. For each analysis (overall population) or sub-analysis (healthy subjects or patients), a significant effect is observed if the diamond does not touch the black line. (B) The black point shows the difference of effect between Wii Fit and traditional therapy for each study. The diamonds describe the pooled values respectively for the difference of effect in healthy subjects, patients and the overall population. The vertical black line refers to no difference between _Wii Fit_-induced change and traditional therapy-induced change. For each analysis (overall population) or sub-analysis (healthy subjects or patients), a significant difference is observed if the diamond does not touch the black line. (A and B) The horizontal black line shows the 95% CI and the gray square shows the study weight in percentage. Four-week Wii Fit intervention group (a) and eight-week Wii Fit intervention group (b) (Orsega-Smith et al., 2012). _I_2: index of heterogeneity.

Figure 5: Pre- and post-intervention meta-analytic effect (A) and Wii-Fit vs. traditional therapy meta-analytic effect (B) for the time-up-and-go test (TUG).

(A) The black point shows the average change for each study. The diamonds describe the pooled values respectively for the change in healthy subjects, patients and the overall population. The vertical black line refers to no change. For each analysis (overall population) or sub-analysis (healthy subjects or patients), a significant effect is observed if the diamond does not touch the black line. (B) The black point shows the difference of effect between Wii Fit and traditional therapy for each study. The diamonds describe the pooled values respectively for the difference of effect in healthy subjects, patients and the overall population, the vertical black line refers to no difference between _Wii Fit_-induced change and traditional therapy-induced change. For each analysis (overall population) or sub-analysis (healthy subjects or patients), a significant difference is observed if the diamond does not touch the black line. (A and B) The horizontal black line shows the 95% CI and the gray square shows the study weight in percentage. _I_2: index of heterogeneity. Unlike ABC and BBS, which are scores, the TUG test results are expressed in time. A negative difference therefore indicates a higher performance.

Meta-regression analyses revealed no significant results (not shown), suggesting no relationships between improvements in balance outcomes and intervention duration or volume.

Discussion

The three main goals set for this review were as follows:

• Goal 1: Identify the health-related domains in which the Wii Fit series has been tested or used. A scientific database search was undertaken with reasoned exclusion criteria. We identified that the Wii Fit has been used for numerous health purposes and in various populations (Table 2). Balance training was identified as being the most recurrent topic in the literature and appears to be the field of predilection for the usage of the Wii Fit software. Another notable focus was the prevention of metabolic disorders as well as the improvement of health status in people with chronic disease.
• Goal 2: Understand the effect of Wii Fit in the identified populations (cf. Goal 1). A qualitative systematic review of studies including Wii Fit interventions was performed, with particular attention given to health and physical activity outcomes. Wii Fit was employed to prevent falls, to induce functional improvements in seniors or in subjects presenting neurodegenerative diseases, to treat orthopedic populations, etc. (Table 4). Overall, the effects of using Wii Fit were mainly positive, with the software being recurrently described as being able to induce similar benefits to traditional therapies. In addition, Wii-Fit interventions were linked to an improvement of health status in several different patients types (diabetic subjects, cancer patients …), however its preventive effect remains to be demonstrated.
• Goal 3: To conduct meta-analyses when possible to quantify the effect Wii Fit had on selected health-related domains. In regards to balance training, the results of meta-analyses revealed that Wii Fit interventions had a positive impact on BBS and TUG. Interestingly, Wii Fit interventions also appear very safe, with very low levels of injuries being reported.

Wii Fit for the prevention of metabolic disorders and health status improvement in patients

From light physical activity to moderate-to-vigorous physical activity, AVG elicit a wide range of intensities (Graves et al., 2010; O’Donovan, Roche & Hussey, 2014; Deutsch et al., 2011; Douris et al., 2012; Garn et al., 2012; Lyons et al., 2012; O’Donovan & Hussey, 2012; Tripette et al., 2014a, 2014b; Xian et al., 2014; Worley, Rogers & Kraemer, 2011; Guderian et al., 2010; Mullins et al., 2012; Peng, Lin & Crouse, 2011). However, it is difficult to state whether playing Wii Fit on a regular basis would allow one to meet the American College of Sports Medicine_’_s recommendations for physical activity or could induce beneficial effects on health. Intervention studies reviewed in this article indicate that playing Wii Fit is not a strategy to consider in young adults (and children) for the prevention of cardio-metabolic disease, because it does not induce any significant increase in physical activity or any improvement in physical fitness (Owens et al., 2011; Nitz et al., 2010). However, one study showed a significant and rapid weight loss during a Wii Fit intervention in postpartum women (Tripette et al., 2014b). Wii Fit may also be a promising tool to aid seniors in maintaining a healthy lifestyle. Intervention studies have reported an increase in physical activity (Janssen, Tange & Arends, 2013), physical fitness (Daniel, 2012) and functional skills (Chan et al., 2012). Playing Wii Fit also clearly appeared to be beneficial for various types of patients: Some studies have reported improvements to health status in chronic obstructive pulmonary disease, hemodialysis patients, diabetic subjects and cancer patients (Albores et al., 2013; Kempf & Martin, 2013; Cho & Sohng, 2014; Hoffman et al., 2013, 2014). While the preventive effects of Wii Fit remain to be demonstrated, the software may be of value in other clinical settings.

Wii Fit for balance training

Many of the intervention studies (55/68) were related to balance training or to the improvement of related functions, with a large majority of them (50/55, Table 4) describing a beneficial effect. The meta-analytic results supported these promising observations. Significant improvements were observed for BBS in both healthy subjects and patients, while a trend was noted for TUG improvements in patients. Interestingly, the meta-analyses also revealed no difference in improvements induced by traditional therapies and Wii Fit interventions for BBS, while TUG showed greater improvements following the Wii Fit intervention compared to after traditional therapy. Taken together, these outcomes suggest a possible therapeutic application for the software, with Wii Fit potentially being as valid as traditional training in some situations. However, a careful look at the qualitative analysis outcome (Table 4) mitigates the overall positive impression for some populations. For instance, Wii Fit intervention outcomes in children with cerebral palsy appeared somewhat contrasted, sometimes being successful (Tarakci et al., 2013), sometimes unsuccessful (Ramstrand & Lygnegård, 2012) or sometimes inducing improvements in some but not all of the parameters (Jelsma et al., 2013). In addition, BBS evaluates balance in isolated balance-related tasks, and TUG combines a limited set of very simple actions (standing-up, walking and sitting-down). _Wii Fit_-induced improvements were only observed in BBS score and TUG, in a clinical setting, and were not associated with improvements in self-confidence in balance abilities (no changes in ABC), therefore it is unclear whether these improvements can be transferred to activities that occur during daily-life and positively impact the quality of life. The general impressions about Wii Fit interventions are, however, currently positive. Our review should therefore encourage further research in order to assist physiotherapists and health professionals in their decision to incorporate the use of Wii Fit into their treatment regimes. Considering that contrasted observations do exist, prescribing Wii Fit should still be considered with caution.

Wii Fit therapeutic content and Wii Balance Board

It is unsurprising that Wii Fit has been the object of much attention among physical therapists. The specificities of the Wii Fit games taken together with the technical features of the Wii Balance Board tend to promote medial–lateral and anterior–posterior movements, mimicking exercises that are commonly used in physical rehabilitation programs (Levac et al., 2010; Michalski et al., 2012; Duclos et al., 2012). The board is composed of multiple pressure sensors able to work together to follow the displacement of the vertical projection of the center of gravity on the floor. Moreover, the device has been validated against the “gold standard” laboratory-grade force platform for assessing standing balance (Clark et al., 2010). In addition, high levels of adherence have frequently been reported in the reviewed studies (Tables 3 and 4). One may therefore hypothesize that key features of the Wii Fit are the ludic elements that promote adherence in individuals who are not interested in traditional training programs. However, Deutsch et al. (2011) emphasizes one limitation of the Wii Fit, which favors the “knowledge of results” rather than the “knowledge of performance” model, i.e., subjects focus on scores rather than on the quality of movements. This is an important finding, since this would limit the relevance of using Wii Fit at home without the supervision of a therapist checking the quality of movement. One difference with the traditional proprioceptive rehabilitation material is that the Wii Balance Board is unable to tilt. Medial–lateral and anterior–posterior displacements are the result of exteroceptive adaptive mechanisms triggered by visual and auditory feedback stimuli that depend on the game scenario.

Limitations

Firstly, the sub-analyses performed in patients included various pathologies. This was highlighted by the high level of heterogeneity between studies in the Wii Fit vs. traditional therapy meta-analyses (see _I_2 in Figs. 4B and 5B). While the overall meta-analyses described a positive effect, the results cannot be predictive of Wii Fit intervention-related changes in one specific population. This emphasizes the requirement for more research in order to determine the optimum usage of Wii Fit for each medical domain. Secondly, the attention given to AVG and other virtual reality devices for the purpose of promoting health has been constantly growing, even after the screening period of this review (July 2009 to June 2015). Therefore we encourage readers to also review the new literature on the subject.

Conclusion

Originally designed as a ludic health and fitness promotion software, the Wii Fit series grabbed the attention of physical therapists due to the panel of features favoring body movements. Initial promising observations encouraged physicians, from various medical fields, to test the Wii Fit software on numerous populations. The literature still remains contrasted on the preventive effects of Wii Fit on chronic diseases. However, Wii Fit interventions were shown to be effective for the improvement of health status in various types of patients (chronic obstructive pulmonary disease, hemodialysis, renal complications, diabetes, cancer, etc.). Our review identified that the most notable focus of Wii Fit interventions were balance training. The Wii Fit has indeed been successfully used to prevent falls or to induce functional improvements in a wide range of healthy or pathologic populations (e.g., seniors, subjects with neurodegenerative diseases, orthopedic patients, children with developmental delay, multiple sclerosis patients, etc.). Our meta-analysis supports the general positive impressions about Wii Fit, suggesting promising applications in a wide range of medical fields. The unexpected entry of a video game into the health device market could create innovative healthcare strategies, however, more research is required to validate these claims.

Additional Information and Declarations

Competing Interests

The authors declare that they have no competing interests.

Author Contributions

Julien Tripette conceived and designed the experiments, performed the experiments, analyzed the data, wrote the paper, prepared figures and/or tables, reviewed drafts of the paper.

Haruka Murakami performed the experiments, analyzed the data, prepared figures and/or tables, reviewed drafts of the paper.

Katie Rose Ryan performed the experiments, wrote the paper, reviewed drafts of the paper.

Yuji Ohta contributed reagents/materials/analysis tools, reviewed drafts of the paper.

Motohiko Miyachi conceived and designed the experiments, contributed reagents/materials/analysis tools, reviewed drafts of the paper.

Data Availability

The following information was supplied regarding data availability:

The work consists of a systematic review and a meta-analysis. No original data have been generated. The data used for the meta-analysis are reported in the tables in the manuscript.

Funding

This work was supported by the Fonds de recherche du Québec—Santé and the Japan Society for the Promotion of Science. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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