Efficacy of video game-based interventions for active aging. A systematic literature review and meta-analysis

Fernando L. Vázquez; Patricia Otero; J. Antonio García-Casal; Vanessa Blanco; Ángela J. Torres; Manuel Arrojo

doi:10.1371/journal.pone.0208192

Abstract

Background

Due to the appeal and recent technological advances of video games, the games have gained interest as an intervention tool for active aging. The aim of this systematic literature review and meta-analysis was to determine the efficacy of video games for active aging and to examine the influence of potential moderator variables.

Methods

A systematic search was done using the following databases: Medline, PsycINFO, EMBASE, CINAHL and the Cochrane Central Register of Controlled Trials. In addition, previous reviews and meta-analyses were used to identify randomized controlled trials (RCT) of video game-based interventions for active aging published through February 28, 2018. An evaluation of the methodological quality of the articles and a meta-analysis and moderator analysis was conducted.

Results

A total of 22 articles depicting 21 RCT with 1125 participants were included. The results indicated that video game-based interventions produced positive effects on objectively measured physical health, negative affect and social health, with small effect sizes (d = 0.41, d = 0.26 and d = 0.40, respectively). The magnitude of this effect was moderated by the presence of subclinical conditions of participants, the type of game (exergames), the presence of physical activity, the type of prevention (indicated), non-blinded assignation, and older age of participants. The methodological quality of the studies was acceptable, the weakest area being external validity.

Conclusion

These finding indicate that video game-based interventions may assist adults in leading active aging processes and preventing secondary aging. Although more research is needed, video game-based interventions are a promising and accessible tool for active aging promotion.

Citation: Vázquez FL, Otero P, García-Casal JA, Blanco V, Torres ÁJ, Arrojo M (2018) Efficacy of video game-based interventions for active aging. A systematic literature review and meta-analysis. PLoS ONE 13(12): e0208192. https://doi.org/10.1371/journal.pone.0208192

Editor: Catherine Haighton, Northumbria University, UNITED KINGDOM

Received: July 13, 2018; Accepted: November 13, 2018; Published: December 11, 2018

This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.

Data Availability: All relevant data are within the paper and its Supporting Information files.

Funding: This study was funded by Center for Industrial Technological Development and the European Regional Development Fund (Grant: EXP - 00091195 / ITC-20161137 to FLV). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Europe is an aging society. According to Eurostat, people aged 50 or more currently represent 37% of the population, and population projections estimate that the number of people aged over 60 will increase by about two million people per annum in the coming decades [1]. These demographic changes compromise the sustainability of health care systems, with more healthcare resources needed to care for the aging population [2].

Therefore, it is essential to develop programs and interventions to foster active aging, intending to preserve health during the aging process. In this context, active aging is defined as the process of optimizing opportunities for health, participation and security to enhance quality of life as people age [3]. Because the aging process is composed of primary aging (i.e., innate maturational processes) and secondary aging (i.e., effects of environment and disease) [4], psychosocial interventions have the potential to prevent secondary aging.

To foster an active aging process, it is necessary to take a life course approach [3], especially because middle adulthood (established from 45 years of age according to the life cycle theory [5]) is when the decline in functional capacity due to age is accentuated [6]. In addition, interventions need to focus on health promotion, disease prevention and equitable access to health care because timely interventions are crucial for better results in age-related conditions [7].

One way to make these interventions more accessible is through video games. A video game is any game played on a digital device and encompasses a wide range of interfaces [8]. Exergames are video games that require physical activity when played. Serious video games are games or programs with gaming features which differ from casual video games in their aim to promote behavior change and/or educate for purposes such as health or learning, and might also offer opportunities to increase the appeal of computerized therapies [9]. Serious video games are rich, role-playing, story-based environments that aim to teach, train and change knowledge, attitudes and behavior [10]. There is evidence indicating that video games are a valuable tool for active aging promotion. Playing video games has been associated with an increase in hippocampal grey matter in older adults [11], changes in brain structure and improved aspects of cognitive functioning [12]. Furthermore, previous research has indicated that video games improve adherence to treatment [13], foster vicarious learning through the modelling of positive health behaviors [9], and promote behavioral change based on essay and feedback [14].

Previous systematic literature reviews reported positive results of video game-based interventions for physical health and cognition in older adults [15, 16], but they only focused on personal computers [15] or Nintendo Wii [16] video games. The only meta-analytic study in older adults focused on cognitive function and found that video game training enhanced several aspects of cognition including reaction time, attention, memory, and global cognition [17]. However, all of these studies included only adults over 60 years-old and were non-randomized controlled trials with the associated risk of bias.

To the best of our knowledge, no previous systematic literature review or meta-analysis has analyzed the efficacy of video game-based interventions for active aging through a life course preventive perspective, despite the World Health Organization recommendations [3]. Moreover, none of them based their findings on randomized controlled trials (RCT), nor included all kinds of video games or analyzed diverse health areas. The main aim of this meta-analysis was to determine the efficacy of video game-based interventions for active aging from middle adulthood (i.e., ≥ 45 years-old) in RCT. The secondary aim was to identify the specific moderating variables for the efficacy of the interventions.

Methods

This systematic literature review and meta-analysis was developed in adherence with the guidelines by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) [18] (see PRISMA checklist at S1 File). The protocol for this review was registered in the International Prospective Register of Systematic Reviews (CRD42018086870) and fulfilled the AMSTAR quality criteria for Systematic Reviews [19].

Search strategy

Studies published through February 28, 2018 were retrieved through systematic literature searches in the databases of Medline, PsycINFO, EMBASE, CINAHL and the Cochrane Central Register of Controlled Trials. The search terms were: (game* OR gami*) AND (serious OR interactive OR computer OR video OR multimedia OR internet OR Wii OR online) AND (health* OR behav* OR wellbeing OR prevent* OR social OR exer* OR acti* OR edu* OR optimal OR positive OR successful OR engagement OR habit* OR affect* OR mood OR emotion* OR self-efficacy OR self-esteem OR nutrition OR diet OR food OR cognitive OR physical OR mental) AND (adult* OR old* OR eld* OR geront* OR aged OR aging) AND (RCT OR randomi* controlled trial). Further studies were included through hand search, tracking cited references in other studies and relevant previous literature reviews.

Selection procedure

Studies identified in electronic searches, after exclusion of duplicates, were screened for relevance based on titles, abstracts and keywords. Full texts of articles considered relevant were obtained and fulfillment of inclusion and exclusion criteria was evaluated independently by two reviewers. Any disagreement was discussed in a consensus meeting. If consensus was not achieved, a third independent reviewer adopted a decision.

Studies were included if: (a) they were a RCT; (b) they assessed the efficacy of interventions for active aging; (c) the intervention received by the experimental group (EG) was delivered through a video game format; (d) the participants were healthy adults older than 44; (e) they used at least one standardized outcome measure; (f) they reported at least pre-treatment and post-treatment quantitative results that permitted computation of the effect size; and (g) were written in English or Spanish language.

Studies were excluded if they: (a) were pilot, feasibility, preliminary or proof of concept studies; (b) included mixed participants (e.g. young and older adults) without differentiating the results of each group; (c) reported multimodal interventions, not being able to discriminate which outcomes were associated with the video game intervention.

Data extraction

Data of the selected studies were extracted independently by two reviewers using a standardized data extraction protocol and coded based on a coding manual (S2 File) as suggested by the Cochrane Handbook for Systematic Reviews of Interventions [20].

Data coding

Descriptive information extracted from selected studies (when available) was comprised of the following: type of technology/device; name and type of video game (serious, casual, exergame); participants’ demographic characteristics (sample size, age, gender, education, civil status, socioeconomic status, urban or rural context and attrition); characteristics of the interventions received by the experimental and control groups (format, duration, number of sessions, presence of professional, individual tailoring, dosage, time of outcome assessment, follow up); outcome measures and findings.

For the purpose of this review, a video game was considered “serious” when it included gaming features aimed to promote behavior change and/or improve health [9]; “casual” when it was used in a leisure context with the sole aim of entertaining and without a specific aim of improving health; and “exergame” if it required physical activity when played [21].

To select primary outcomes, we based on health and social services, behavioral, personal and social determinant factors of active aging in an individual level according to the theoretical model of the World Health Organization [3]. In addition, we focused on health area of action of active aging [22]. Health was defined as a state of complete physical, mental and social wellbeing and not merely the absence of disease [23], and conceptualized as a three domain concept including physical, mental and social health [24]. Therefore, the primary outcomes were a change from baseline to post-treatment of physical, mental and social health domains.

Physical health was divided into objective (e.g., motor functioning, cardiovascular functioning) and self-reported health measures. Mental health was divided into cognitive health and emotional health, as recommended by Duncan and Barret [25]. Cognitive health included the cognitive domains described by Strauss, Sherman and Spreen [26]: executive functioning (working memory, inhibitory control, task switching/flexibility and reasoning/problem solving), visuospatial skills, immediate memory, delayed memory, language, attention and processing speed. Emotional health included positive and negative affect, according to the affective structure established by Watson, Clark and Tellegen [27]. Social health included the capacity to fulfil one’s potential and obligations, the ability to manage life with some degree of security and independence despite a medical condition, and the ability to participate in social activities [24]. When the authors did not report a global measure of a particular domain, a composite change score was calculated as a combined average of the mean change (and variance) across all outcomes reported for that specific domain, as suggested in previous meta-analyses [28, 29].

To analyze the influence of the characteristics of the studies on the effect sizes, potential moderating variables of participants, interventions, methods, context and extrinsic were coded [30]. Note that the potential moderating variable “type of prevention” distinguished between universal, selective, and indicated prevention, according to the US Institute of Medicine [31]. Universal prevention targets the general population; selective prevention targets segments of the population with an increased risk of developing a disorder because they have been exposed to risk factors; and indicated prevention targets people who have some symptoms of the disorder but do not yet meet the full diagnostic criteria. The moderator analysis was performed for a composite construct, combining the three health domains together (physical health, mental health and social health). See S2 File for a comprehensive list of the moderating variables and the outcome measures used to calculate the composite scores.

To assess the methodological quality of the included studies, we used Downs and Black’s checklist [32], which assesses reporting, external and internal validity, bias, confounding variables and power, comprising a total of 27 items and a maximum score of 32. Risk of bias of the selected studies was assessed using the instrument of the Cochrane Collaboration, which evaluates selection (random sequence generation and allocation concealment), performance, detection, attrition, reporting and other bias [20]. Studies were considered of low risk if none of the items were considered as high risk and not more than one item was coded as unclear or not reported. If one or more items were considered as high risk, the risk of bias of the study was considered high.

Inter-rater reliability of the data coding was evaluated using Cohen’s Kappa concordance index. It was excellent for the moderating variables coding (Kappa = 0.89), moderate for Downs and Black checklist (Kappa = 0.51) and substantial for Cochrane risk of bias assessment (Kappa = 0.78).

Data analysis

A meta-analysis was conducted with a fixed effect model if there was not heterogeneity (I² ≤ 50%) or random effect model if there was heterogeneity (I² > 50%) [33] through the Cochrane Review Manager software RevMan 5.3. Publication bias was assessed with the Begg’s test. Effect sizes for each meta-analysis were estimated as Standardized Mean Change Index (d_MR) for within-group pre-posttreatment comparisons, and as Standardized Mean Index difference (d) for between-group comparisons at posttreatment [34]. Effect sizes of 0.2 were considered small, 0.5 medium and 0.8 large [35], and outliers were excluded. For those studies that involved more than one experimental group (EG), the same control group (CG) was used for the calculation of separate effect sizes (comparisons between experimental and control groups). For studies reporting more than one CG, data for the control with the most neutral activity (i.e., usual care if available) were selected for computing the effect size. Effect sizes of follow-up assessments were not included in the meta-analysis because they were scarce (only four studies conducted follow ups) and follow up times were not comparable (ranging from 4 to 48 weeks).

A meta-analysis of variance (meta-ANOVA) statistic for categorical variables and a meta-regression analysis for quantitative variables (using IBM SPSS software, Version 21.0) was used to examine the contribution of moderating variables to the variance.

Results

Study selection

A total of 661 articles were identified after removing duplicates. Of these, full texts of 44 articles were assessed for inclusion (Fig 1). We requested additional data from authors of 12 studies, of which 8 provided the additional data [36–43]. Twenty-two articles were excluded: two because they were not RCT [42, 44]; one because the intervention was not delivered through a video game format [45]; four because the participants were not healthy older adults [46–49]; four because they did not use standardized outcome measures [50–53]; three because they did not report the minimum data necessary to calculate the effect sizes and it could not be obtained from the authors [54–56]; four because they were pilot, feasibility or proof of concept studies [57–60]; and four because they reported on multimodal interventions and were not able to discriminate which outcomes were associated to video games only [41, 61–63]. Two articles reporting outcomes from the same RCT were combined into one study [36, 64]. Finally, 22 articles depicting 21 RCT (22 EG and 23 CG) were included. Their characteristics and findings are presented in Table 1.

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Fig 1. PRISMA flow diagram.

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Table 1. Characteristics of the studies included in the meta-analysis.

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Descriptive characteristics of the studies

The 21 studies comprised a total sample of 1125 participants, with a mean of 53.57 (Standard Deviation (SD) = 30.13) participants per study. The mean age of the participants was 73.28 years (SD = 4.99), with means ranging from 66.50 (SD = 5.80) years [65] to 82.35 years (SD = 30.13) [66]. When gender was reported, the percentage of women ranged from 53.6% [67] to 90.0% [64] (Mean (M) = 63.41; SD = 17.63). In addition, the participants had 13.21 mean years (SD = 1.15) of education, and urban provenance [68]. None of the studies provided information about the civil status or the socioeconomic characteristics. Although all the studies included healthy older adults, three of them included participants with sub-clinical conditions such as subthreshold depression [69] or reduced mobility [38, 66].

The aims of the interventions were diverse and mainly focused on mental and physical health. Almost half (47.6%) of the studies were focused on improving mental health or preventing cognitive deterioration, 19.2% on improving physical functions and 33.3% were multi-domain, targeting physical and mental health. None of the studies focused on social health; however, two studies assessed social-health related outcomes [38, 64]. The majority (85.7%) of the studies assessed interventions considered as universal prevention programs, and 14.3% were indicated prevention programs [38, 66, 69]. Only five (23.8%) of the interventions were designed based on a theoretical model; specifically, the cognitive psychology theoretical models [67, 70–73].

Regarding the interface of the game, 38.1% of the interventions used movement to register the players’ performance (Wii or Kinect technology); 23.8% used manual devices (buttons, keyboards, gamepad or console) [37, 64, 65, 68, 70]; 23.8% used a digital carpet or balance board [39, 66, 74–76]; and 14.3% used a touchscreen [43, 67, 71]. A minority (28.6%) of the interventions used serious video games (brain training interventions) [37, 43, 64, 65, 67, 71], 57.1% used exergames [38–40, 66, 69, 72–78] and 14.3% used casual video games [65, 68, 79].

The video game-based interventions ranged from 6 to 60 sessions (M = 23.60; SD = 12.56), occurring from 4 up to 20 weeks (M = 8.48; SD = 4.18). When reported, the duration of the exact video game playing time per session ranged from 15 to 30 minutes (M = 20.00; SD = 7.07) [39, 67, 71, 72]. The total dosage of treatment received varied between 25 [68] and 1,980 [65] hours (M = 935.83; SD = 512.24). Only the 19.0% of the interventions were tailored to the sociodemographic characteristics of the participants (age and gender) [43, 69, 74–76], and 28.6% were tailored to their performance level (e.g., reaction time, level of difficulty) [39, 67, 71, 73, 78]; one intervention was tailored to both [37]. None of the programs were tailored to the health needs of the participants. The majority (71.4%) of the interventions were delivered face to face (e.g., [65, 69, 77]), while 28.6% were self-administered at home [37, 43, 67, 68, 71, 75].

The 57.1% of the interventions were delivered in individual format, 19.0% in a group format [39, 69, 74, 79], 9.5% in dyads with a partner [40, 78], and 9.5% were individual but simultaneously played in the same room as other participants [65, 70].

A professional was present in 81.0% of the interventions, but in 28.6% of cases only during the participants’ training [37, 39, 43, 67, 71, 75]; this information was not available in 14.3% of the studies [64, 70, 79], and in one the intervention was completely self-administered [68]. The type of professional who delivered the intervention was a researcher in 33.3% of studies, and a health professional in 28.6%; 19.0% of studies did not specify the type of professional [37, 67, 71, 73]. Only one study [37] stated that the professionals had been trained, and 47.6% of the studies trained the participants before starting the intervention [37, 39, 43, 64, 65, 67, 70–72, 75], through training sessions ranging from 15 to 60 minutes long (M = 43.38; SD = 16.86).

Most of the interventions (61.9%) were delivered in controlled settings, such as research or health care facilities (e.g., [64, 69, 76]), 28.6% were delivered at the participants’ homes [40, 43, 67, 68, 71, 75], 4.7% were delivered at a care home [66] and 4.7% at a social care facility [39].

Methodological characteristics of the studies

Twelve (57.1%) studies specified their randomization method: 10 through computer software [37, 39, 64, 66, 67, 69, 71, 72, 74, 75], one with a random number table [77], and one with a number extraction method [38]. The participants were blind to their assigned condition (experimental or control) in 28.6% of the studies [37, 64, 67, 71, 72, 74], and the assessment was conducted by blind researchers in the 23.8% [37, 66, 71, 72, 75].

Regarding control conditions, 19.0% of the studies had a usual care CG [65, 68, 77, 78], and of the 17 studies (81.0%) that had an active CG, 28.6% received a video game intervention [37, 43, 67, 69–71]. The 42.9% of studies were based on a research protocol [37, 43, 64, 67, 71, 72, 74, 75], but none of them used a manualized intervention.

Most studies (81.0%) evaluated the participants only at the end of the intervention. Follow-up assessments were infrequent and mostly brief. Only 19.0% of the studies conducted a follow up assessment: 9.5% of them at four weeks [37, 72], 4.7% at 12 weeks [64], and 4.7% at 48 weeks [70]. Longer term benefits were evident in one study [72], partially present in another study [64], and not maintained in one study [37]. Additionally, in one study, the follow up assessment was only conducted with non-standardized measures [70]. Attrition was assessed in all studies, and ranged from 0% in five (23.8%) [65, 68, 70, 73, 79] to 25% in one [64], with a mean of 7.6% of dropouts.

Quality assessment and risk of bias

The total score of the studies in Downs & Black’s checklist (Table 2) ranged from 13/32 (41% of the possible marks) [79] to 27/32 (84%) [37]. The average score was 20.52 (SD = 3.22). Reporting was the strongest domain and external validity the weakest. Assessment of risk of bias across studies is shown globally in Fig 2 and detailed in Table 2. The risk of bias was considered low for 2 studies (9.5%) [37, 72], unclear for 12 (57.1%) [38–40, 43, 65, 69, 71, 73, 76–79], and high for 7 (33.3%) [64, 66–68, 70, 74, 75].

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Fig 2. Risk of bias graph: Risk of bias presented as percentages across all included studies.

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Table 2. Methodological quality of included studies (Risk of bias / Downs & Black’s criteria).

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Meta-analysis of the efficacy of video game-based interventions for active aging

Begg’s rank correlation test confirmed the absence of publication bias (Kendall’s tau b = 0.01; p = .454, 1-tailed). A total of 14 studies assessed physical health; 13 of these were included in the meta-analysis. Of the 14 studies, 10 studies assessed objective measures between experimental and control groups and 4 studies assessed self-reported measures. Only one study that assessed physical health was excluded from the meta-analysis because it was identified as an outlier [73]. This study found that adapted physical activities training (alone or combined with Wii Fit) was more effective than Wii Fit alone at improving the balance of independent senior participants. Additionally, 18 studies assessing mental health were included in the meta-analysis (16 comparisons for cognitive mental health and 12 for emotional mental health), and 2 studies assessing social health were included in the meta-analysis. No study was excluded from the meta-analysis for mental or social health.

Regarding physical health, data were pooled from 10 comparisons for objective measures [38, 39, 66, 72–78] (with a total of 263 participants in the EG and 249 in the CG), and 4 comparisons for self-reported measures [38, 40, 79, 80] (with 95 participants in the EG and 81 in the CG) of physical health. The effect size indicated that participants experienced beneficial effects from the video game-based interventions when compared to those in the CG on objective measures (Standardized Mean Difference (SMD) 0.41, 95% CI = 0.23 to 0.59, p < .001; I² = 63%; p = .006), but not on self-reported measures (SMD 0.03, 95% CI = -0.27 to 0.33, p = .83; I² = 23%, p = .28) (Fig 3).

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Fig 3. Forest plot of comparisons: Experimental vs. control group change in physical health.

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For mental health, separate meta-analyses for cognitive and emotional health were conducted. Regarding cognition, data from 16 comparisons were pooled [37, 39, 40, 43, 64, 65, 67, 68, 70–72, 74, 75, 78, 79] (with a total of 394 participants in the EG and 387 in CG). It was found that participants of the EG did not demonstrate beneficial effects from the video game-based interventions compared to those of the CG (SMD 0.14, 95% CI = 0.00 to 0.29, p = .05; I² = 0%, p = .96) (Fig 4).

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Fig 4. Forest plot of comparisons: Experimental vs. control group change in cognitive and emotional mental health.

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Regarding emotional health, data were pooled from 6 comparisons for positive affect [38, 40, 64, 68, 69, 71] (with 152 participants in EG and 139 in CG), and 6 comparisons for negative affect [38, 40, 69, 71, 74, 75] (with 191 participants in EG and 171 in CG). Participants of the EG experienced beneficial effects from the video game-based interventions compared to the CG for negative (SMD 0.26, 95% CI = 0.05 to 0.47, p = .01; I² = 0%, p = .99), but not positive (SMD 0.22, 95% CI = -0.01 to 0.45, p = .07; I² = 0%, p = .47) affect (Fig 4).

Finally, in social health data were pooled from two comparisons [38, 64] (with 65 participants in EG and 55 in CG). Participants of the EG experienced higher beneficial effects from video game-based interventions than those from the CG (SMD 0.40, 95% CI = 0.04 to 0.77, p = .03; I² = 0%, p = .49) (Fig 5).

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Fig 5. Forest plot of comparisons: Experimental vs. control group change in social health.

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Analysis of moderating variables

The health status of participants, the type of game, the presence of physical activity, the type of prevention program, blinded assignment and participants’ age acted as moderating variables. The rest of the variables analyzed (see S2 File) did not have any significant moderating effect. Differences in health status (Q = 6.18; p = .046) demonstrated that participants with mental health symptoms such as subclinical depression (d = 0.49; 95% CI = 0.08–0.89; p = .017) and people with physical symptoms such as reduced mobility (d = 0.38; 95% CI = 0.21–0.55; p < .001) got more benefits than healthy individuals (d = 0.17; 95% CI = 0.07–0.27; p = .001). Considering differences in the type of game (Q = 6.15; p = .046), it was found that health benefits were associated with exergame-based interventions (d = 0.31; 95% CI = 0.21–0.42; p < .001) but not with serious games or casual video games. Consistently with this finding, the presence of physical activity (Q = 5.68; p = .017) also improved the efficacy of the interventions (d = 0.31; 95% CI = 0.21–0.42; p < .001) compared to those that did not utilize physical activity (d = 0.10; 95% CI = -0.05–0.24; p = .183). Regarding differences in type of prevention (Q = 5.95; p = .015), better results were found for indicated prevention programs (d = 0.40; 95% CI = 0.24–0.55; p < .001). Differences were found based on blinding (Q = 12.36; p = .002), participants not blinded benefited more from the interventions (d = 0.44; 95% CI = 0.14–0.74; p = .004) than those who were blinded (d = 0.25; 95% CI = -0.12–0.17; p = .736). The age of participants demonstrated a significant moderating effect on the effect size in health (Q = 4.93; B = .20; 95% CI = 0.002–0.037; p = .027), with older participants benefiting more.

Discussion

In this systematic review and meta-analysis, we analyzed the efficacy of video game-based interventions for active aging in adults older than 44. Based on 21 RCTs, it was found that video game-based interventions produced small positive effects on objectively measured physical health, negative affect and social health. These findings are similar to a previous systematic review for older adults [81], which found significant mental health outcomes in the majority of the reviewed studies, followed by some physical and social health benefits. However, the results of the current meta-analysis also contradict findings from a previous meta-analysis which reported that video game training enhanced several aspects of cognition in older adults including reaction time, attention, memory, and global cognition [17], although in the current meta-analysis there was a trend in this direction. There are some possible explanations for this trend. Firstly, the conclusions of our meta-analysis may be more rigorous and conservative, because it included only RCTs, while the meta-analysis by [17] included RCTs and other studies. Secondly, while the most frequently assessed cognitive outcomes in the current meta-analysis were global cognition and executive functioning followed by memory and lastly attention and speed of processing, attention and speed of processing have demonstrated to be the cognitive functions that improve the most after video game training [17, 68].

Regarding physical health, the benefits of video games are encouraging. They seem to improve some physical health variables in the older adults, for whom aging-related progressive degeneration in muscle strength and balance control system can lead to motor impairment, disability and falls [82, 83]. Although the effect sizes found were small, this may be due to the healthy status of the participants. In addition, while objectively measured physical health had significant benefits, self-reported physical health did not. One explanation for this finding is that self-reported measures refer to subjective health issues that are subject to high personal variability. These subjective health issues can also be confounded with changes that occur during normal aging (perceived exertion, perceived health, pain intensity). Furthermore, most of the studies in the current review assessed the same objectively measured variables that were trained during the video game-based intervention, which may have resulted in more positive outcomes. Physical health was assessed as muscle strength [77], balance [38, 39, 74, 76, 77], falls [66, 75], functional fitness [78] postural control and gait [72].

The positive effects of video games on negative affect and social health are of particular importance as depression and social isolation in older adults are risk factors that double the risk of subsequent dementia [84] and mortality [85]. Previous research has demonstrated that playing video games could lead to greater social interaction, less loneliness, a sense of accomplishment, and positive mood [40]. Our results confirm that video games can play a protective role in this area. However, it is unknown if the quality of mood enhancement and social participation derived from playing videogames is equivalent to non-virtual social participation. Social health is an emerging domain, and would greatly benefit from future research. Some of the outcome measures used in the reviewed studies, like The Short Form Health Survey [86], or the World Health Organization Quality of Life Scale Brief Version [87] include subdomains assessing social health (e.g. Social Role Functioning, Social relationships), though information about social health cannot be assessed if the authors only report the total score. This was the case in two of the studies analyzed for inclusion in the current study [40, 63].

The magnitude of the effects of video game-based interventions were moderated by the health status of participants, the type of game, the presence of physical activity, the type of prevention program, blinded assignment and participants’ age. Specifically, participants with subclinical conditions benefited more from the interventions than healthy ones, which is consistent with the larger effect size obtained by studies on indicated prevention programs [88] and could also be caused by their greater statistical power.

Exergames resulted in better outcomes than other types of video game interventions. This finding may be due to exergames accounting for the beneficial effects in cognition and physical condition, while serious and casual video games lost explanatory power when this variable was controlled. This can be explained by the fact that exercise training induced functional brain plasticity and prefrontal adaptations that were correlated with improved performance in executive functions and processing speed. This is likely a result of reducing the need for prefrontal resources of executive functions and attention in dual tasks [74]. Similarly, cognitive decline is associated with impaired gait in older adults [89]. Another hypothesis is that cerebral metabolic activity that occurs with physical activity training requires increased availability of oxygen [90]. In addition, exergames train different motor and cognitive abilities such as multidirectional displacements, weight transfer, attention, planning, decision making and concentration [91]. This is consistent with previous research that demonstrated that combining not only different cognitive abilities but also combining cognitive and physical training improved cognitive performance in older age to a greater extent, suggesting the implementation of combined cognitive—physical interventions [41]. Previous reviews on exergames in adults and older adults concluded that exergaming provided a novel method for increasing or substituting physical activity, and resulted in improved physical function, depression and cognitive function [16, 92, 93]. The significant effects still existed when excluding waitlist-only controlled studies, and when comparing to physical activity interventions [29]. However, our results partially contradict a previous study that found that serious games have small positive effects for healthy lifestyle promotion in all ages [21]. Our results might be due to the fact that our study focused on adults older than 45, and that serious games seem to be less effective than exergames in healthy life style promotion.

Furthermore, the fact that non-blinded participants had better outcomes could be explained as a placebo effect. However, due to the few blinded studies in this review (n = 6) this should be further explored in future studies. Lastly, the fact that older participants benefited more from the intervention may be related to age-related decline in physical, cognitive, emotional and social functioning that video game interventions can prevent. In addition, these benefits may also be due to the fact that older adults may start the training program with lower physical, cognitive and emotional functioning scores related to aging decline [94], which result in larger effect sizes after the intervention. This finding is consistent with a previous meta-analysis on video games aimed at older adults [17].

However, no moderating effects were found in the other studies’ participant characteristics (e.g., gender, education, marital status, socioeconomic class, or region), intervention variables (e.g., number of sessions, play duration, dosage of interventions, format, interface) or methodological variables (e.g., randomization method, type of control group, drop outs). These results indicate that video game-based interventions are broadly applicable across a wide range of participants and are equally effective on different dosages and formats. One reason for this may be that video game-based interventions are usually friendly and intuitive so people of any educational, social class or region can play them. In contrast to face to face interventions, video game based-interventions maintain fun, motivation, commitment with the task and allow for different activity levels, preventing fatigue [8]. No heterogeneity of the results was found, except for objective measured physical health, due to two studies [72, 74] which had results inconsistent with those of the other studies included in the meta-analysis. These differences may be due to the small sample size (n = 33 and n = 46, respectively) and the younger age of participants in these two studies (M ages 75.3 and 69.3, respectively). The small sample size and younger ages could prevent the generalization of the results and result in a ground effect that could impede the appreciation of improvements. The results of those studies should be considered with caution. Consequently, in the current meta-analysis a random effect model was applied to correct for these effects [33].

This review has a number of strengths, including a registered protocol, rigorous evaluation of the quality and risk of bias of the studies and rigorous methods of quantitative synthesis. As far as we know, it is the first literature review and meta-analysis focused on video game-based interventions for active aging.

Some limitations of the reviewed studies must be considered: most of the reviewed studies had small sample sizes, a lack of theoretical model-based interventions, none of the interventions were based on a manualized treatment and only 42.9% were based on a standardized protocol. Furthermore, there was a wide use of non-standardized measures, especially of criterion outcome measures (e.g., playing score, reaction time) and computerized non-validated adaptations of tests (e.g., Stroop test, Wisconsin Card Sorting Test), although it must be noted that only the results emerging from standardized instruments were included in this study. Follow-ups were scarce and generally brief and in 57.1% of studies the risk of bias was unclear. Maybe for these reasons, the effect sizes in significant health domains were small.

In addition, conclusions drawn from this meta-analysis must be considered in the context of some limitations. Firstly, we included RCT which did not use intent-to-treat analyses, introducing the possibility of survival bias. In order to control them, we carried out a moderator analysis including attrition as moderator. Secondly, social health domain was only measured in two studies; therefore, social health results should be interpreted with caution.

Conclusions

Despite these limitations, the findings suggest that video game-based interventions are a promising and effective intervention for active aging promotion. Future studies to increase methodological rigor are needed. Additionally, more studies are suggested to assess adults older than 44 but younger than 60, with longitudinal studies analyzing the preventive efficacy of interventions in the aging process. RCT of serious video-game-based interventions targeting health domains other than cognition are recommended.

Supporting information

S1 File. PRISMA checklist.

https://doi.org/10.1371/journal.pone.0208192.s001

(PDF)

S2 File. Coding protocol and manual.

https://doi.org/10.1371/journal.pone.0208192.s002

(PDF)

Acknowledgments

Our thanks to IMATIA Innovation and SIVSA Informatic Solutions.

References

1. Eurostat.European-Comission. Eurostat publications and databases. Eurostat, 2016.
2. European-Social-Network. Services for older people in Europe. Facts and figures about long term care services in Europe.2008.
3. World Health Organization. Active ageing: A policy framework. Geneva: World Health Organization; 2002.
4. Anstey K, Stankov L, Lord S. Primary aging, secondary aging, and intelligence. Psychol Aging. 1993;8:562–570. pmid:8292284
- View Article
- PubMed/NCBI
- Google Scholar
5. Woods R, Clare L. Handbook of the clinical psychology of ageing. 2nd ed. Chichester, United Kingdom: John Wiley & Sons; 2015.
6. Fjell AM, Walhovd KB, Westlye LT, Østby Y, Tamnes CK, Jernigan TL, et al. When does brain aging accelerate? Dangers of quadratic fits in cross-sectional studies. Neuroimage. 2010;50:1376–1383. pmid:20109562
- View Article
- PubMed/NCBI
- Google Scholar
7. Barnett JH, Lewis L, Blackwell AD, Taylor M. Early intervention in Alzheimer’s disease: A health economic study of the effects of diagnostic timing. BMC Neurol. 2014;14:101. pmid:24885474
- View Article
- PubMed/NCBI
- Google Scholar
8. Baranowski T, Buday R, Thompson DI, Baranowski J. Playing for real: Video games and stories for health-related behavior change. Am J Prev Med. 2008;34:74–82. e10. pmid:18083454
- View Article
- PubMed/NCBI
- Google Scholar
9. Fleming T, Cheek C, Merry S, Thabrew H, Bridgman H, Stasiak K, et al. Serious games for the treatment or prevention of depression: A systematic review. Rev Psicopatología Psicol Clin. 2014;19:227–42.
- View Article
- Google Scholar
10. Garris R, Ahlers R, Driskell JE. Games, motivation, and learning: A research and practice model. Simul Gaming. 2002;33:441–467.
- View Article
- Google Scholar
11. West GL, Zendel BR, Konishi K, Benady-Chorney J, Bohbot VD, Peretz I, et al. Playing Super Mario 64 increases hippocampal grey matter in older adults. PLoS One. 2017;12:e0187779. pmid:29211727
- View Article
- PubMed/NCBI
- Google Scholar
12. Shams TA, Foussias G, Zawadzki JA, Marshe VS, Siddiqui I, Muller DJ, et al. The effects of video games on cognition and brain structure: Potential implications for neuropsychiatric disorders. Curr Psychiatry Rep. 2015;17:71. pmid:26216589
- View Article
- PubMed/NCBI
- Google Scholar
13. Murray E, Burns J, See TS, Lai R, Nazareth I. Interactive health communication applications for people with chronic disease. Cochrane Database Syst Rev. 2005;(4): CD004274.
- View Article
- Google Scholar
14. Read JL, Shortell SM. Interactive games to promote behavior change in prevention and treatment. JAMA. 2011;305:1704–1705. pmid:21447802
- View Article
- PubMed/NCBI
- Google Scholar
15. Bleakley CM, Charles D, Porter-Armstrong A, McNeill MDJ, McDonough SM, McCormack B. Gaming for health: A systematic review of the physical and cognitive effects of interactive computer games in older adults. J Appl Gerontol. 2015;34:NP166–89. pmid:24652863
- View Article
- PubMed/NCBI
- Google Scholar
16. Chao YY, Scherer YK, Montgomery CA. Effects of using Nintendo Wii exergames in older adults: A review of the literature. J Aging Health. 2015;27:379–402. pmid:25245519
- View Article
- PubMed/NCBI
- Google Scholar
17. Toril P, Reales JM, Ballesteros S. Video game training enhances cognition of older adults: A meta-analytic study. Psychol Aging. 2014;29:706–716. pmid:25244488
- View Article
- PubMed/NCBI
- Google Scholar
18. Moher D, Liberati A, Tetzlaff J, Altman DG. Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. PLoS Med. 2009;6(7):e1000097. pmid:19621072
- View Article
- PubMed/NCBI
- Google Scholar
19. Shea BJ, Grimshaw JM, Wells GA, Boers M, Andersson N, Hamel C, et al. Development of AMSTAR: A measurement tool to assess the methodological quality of systematic reviews. BMC Med Res Methodol. 2007;7:10. pmid:17302989
- View Article
- PubMed/NCBI
- Google Scholar
20. Higgins J, Green S. Cochrane handbook for systematic reviews of interventions Version 5.1. 0 (updated March 2011) The Cochrane Collaboration; 2011.
21. DeSmet A, Van Ryckeghem D, Compernolle S, Baranowski T, Thompson D, Crombez G, et al. A meta-analysis of serious digital games for healthy lifestyle promotion. Prev Med. 2014;69:95–107. pmid:25172024
- View Article
- PubMed/NCBI
- Google Scholar
22. Sidorenko A, Zaidi A. Active ageing in CIS countries: Semantics, challenges, and responses. Curr Gerontol Geriatr Res. 2013;261819:17. pmid:23346109
- View Article
- PubMed/NCBI
- Google Scholar
23. World Helath Organization. Constitution of the World Health Organization. www.who.int/governance/eb/who_constitution_en.pdf.: World Health Organization; 2006.
24. Huber M, Knottnerus JA, Green L, van der Horst H, Jadad AR, Kromhout D, et al. How should we define health? BMJ. 2011;343: d4163. pmid:21791490
- View Article
- PubMed/NCBI
- Google Scholar
25. Duncan S, Barrett LF. Affect is a form of cognition: A neurobiological analysis. Cogn Emot. 2007;21:1184–1211. pmid:18509504
- View Article
- PubMed/NCBI
- Google Scholar
26. Strauss E, Sherman EM, Spreen O. A compendium of neuropsychological tests: Administration, norms, and commentary. Oxford: Oxford University Press; 2006.
27. Watson D, Clark LA, Tellegen A. Development and validation of brief measures of positive and negative affect: the PANAS scales. J Pers Soc Psychol. 1988;54:1063–1070. pmid:3397865
- View Article
- PubMed/NCBI
- Google Scholar
28. Hill NT, Mowszowski L, Naismith SL, Chadwick VL, Valenzuela M, Lampit A. Computerized cognitive training in older adults with mild cognitive impairment or dementia: A systematic review and meta-analysis. Am J Psychiatry. 2017;174:329–340. pmid:27838936
- View Article
- PubMed/NCBI
- Google Scholar
29. Stanmore E, Stubbs B, Vancampfort D, de Bruin ED, Firth J. The effect of active video games on cognitive functioning in clinical and non-clinical populations: A meta-analysis of randomized controlled trials. Neurosci Biobehav Rev. 2017;78:34–43. pmid:28442405
- View Article
- PubMed/NCBI
- Google Scholar
30. Lipsey MW, Wilson DB. Practical meta-analysis. Thousand Oaks, CA, US: Sage Publications, Inc.; 2001.
31. Haggerty RJ, Mrazek PJ. Reducing risks for mental disorders: Frontiers for preventive intervention research. Washington, DC.: National Academies Press; 1994.
32. Downs SH, Black N. The feasibility of creating a checklist for the assessment of the methodological quality both of randomised and non-randomised studies of health care interventions. J Epidemiol Community Health. 1998;52:377–384. pmid:9764259
- View Article
- PubMed/NCBI
- Google Scholar
33. Borenstein M, Hedges LV, Higgins J, Rothstein HR. A basic introduction to fixed-effect and random-effects models for meta-analysis. Res Synth Methods. 2010;1:97–111. pmid:26061376
- View Article
- PubMed/NCBI
- Google Scholar
34. Botella-Ausina J, Sánchez-Meca J. Meta-análisis en ciencias sociales y de la salud [Meta-analysis in social sciences and health]. Madrid: Síntesis; 2015.
35. Cohen J. Statistical power for the social sciences. Hillsdale, NJ: Laurence Erlbaum and Associates. 1988.
36. Ballesteros S, Mayas J, Prieto A, Toril P, Pita C, de León LP, et al. A randomized controlled trial of brain training with non-action video games in older adults: Results of the 3-month follow-up. Front Aging Neurosci. 2015;7:45. pmid:25926790
- View Article
- PubMed/NCBI
- Google Scholar
37. Buitenweg JIV, van de Ven RM, Prinssen S, Murre JMJ, Ridderinkhof KR. Cognitive flexibility training: A large-scale multimodal adaptive active-control intervention study in healthy older adults. Front Hum Neurosci. 2017;11:529. pmid:29209183
- View Article
- PubMed/NCBI
- Google Scholar
38. Karahan AY, Tok F, Taskin H, Kucuksarac S, Basaran A, Yildirim P. Effects of exergames on balance, functional mobility, and quality of life of geriatrics versus home exercise programme: Randomized controlled study. Cent Eur J Public Health. 2015; 23:S14–18. pmid:26849537
- View Article
- PubMed/NCBI
- Google Scholar
39. Schattin A, Arner R, Gennaro F, Bruin ED. Adaptations of prefrontal brain activity, executive functions, and gait in healthy elderly following exergame and balance training: A randomized-controlled study. Front Aging Neurosci. 2016; 8:278. pmid:27932975
- View Article
- PubMed/NCBI
- Google Scholar
40. Kahlbaugh PE, Sperandio AJ, Carlson AL, Hauselt J. Effects of playing Wii on well-being in the elderly: Physical activity, loneliness, and mood. Act Adapt Aging. 2011;35(4):331–344.
- View Article
- Google Scholar
41. Eggenberger P, Schumacher V, Angst M, Theill N, de Bruin ED. Does multicomponent physical exercise with simultaneous cognitive training boost cognitive performance in older adults? A 6-month randomized controlled trial with a 1-year follow-up. Clin Interv Aging. 2015;10:1335–1349. pmid:26316729
- View Article
- PubMed/NCBI
- Google Scholar
42. Belchior P, Marsiske M, Leite WL, Yam A, Thomas K, Mann W. Older adults’ engagement during an intervention involving off-the-shelf videogame. Games Health J. 2016;5:151–156. pmid:27310479
- View Article
- PubMed/NCBI
- Google Scholar
43. Souders DJ, Boot WR, Blocker K, Vitale T, Roque NA, Charness N. Evidence for narrow transfer after short-term cognitive training in older adults. Front Aging Neurosci. 2017; 9: 41. pmid:28293188
- View Article
- PubMed/NCBI
- Google Scholar
44. West GL, Zendel BR, Konishi K, Benady-Chorney J, Bohbot VD, Peretz I, et al. Playing Super Mario 64 increases hippocampal grey matter in older adults. PLoS One. 2017; 12. Available from: http://onlinelibrary.wiley.com/o/cochrane/clcentral/articles/603/CN-01442603/frame.html.
- View Article
- Google Scholar
45. Peretz C, Korczyn AD, Shatil E, Aharonson V, Birnboim S, Giladi N. Computer-based, personalized cognitive training versus classical computer games: A randomized double-blind prospective trial of cognitive stimulation. Neuroepidemiology. 2011;36:91–99. pmid:21311196
- View Article
- PubMed/NCBI
- Google Scholar
46. Whitton JP, Hancock KE, Shannon JM, Polley DB. Audiomotor perceptual training enhances speech intelligibility in background noise. Curr Biol. 2017;27:3237–47.e6. pmid:29056453
- View Article
- PubMed/NCBI
- Google Scholar
47. Jorgensen MG, Laessoe U, Hendriksen C, Nielsen OB, Aagaard P. Efficacy of Nintendo Wii training on mechanical leg muscle function and postural balance in community-dwelling older adults: A randomized controlled trial. J Gerontol A Biol Sci Med Sci. 2013;68:845–852. pmid:23114461
- View Article
- PubMed/NCBI
- Google Scholar
48. Morone G, Paolucci T, Luziatelli S, Iosa M, Piermattei C, Zangrando F, et al. Wii Fit is effective in women with bone loss condition associated with balance disorders: A randomized controlled trial. Aging Clin Exp Res. 2016;28:1187–1193. pmid:27154875
- View Article
- PubMed/NCBI
- Google Scholar
49. van den Berg M, Sherrington C, Killington M, Smith S, Bongers B, Hassett L, et al. Video and computer-based interactive exercises are safe and improve task-specific balance in geriatric and neurological rehabilitation: A randomised trial. J Physiother. 2016;62:20–28. pmid:26701163
- View Article
- PubMed/NCBI
- Google Scholar
50. Ballesteros S, Mayas J, Prieto A, Ruiz-Marquez E, Toril P, Reales JM. Effects of video game training on measures of selective attention and working memory in older adults: Results from a randomized controlled trial. Front Aging Neurosci. 2017;9:354. pmid:29163136
- View Article
- PubMed/NCBI
- Google Scholar
51. Mayas J, Parmentier FBR, Andrés P, Ballesteros S. Plasticity of attentional functions in older adults after non-action video game training: A randomized controlled trial. PLoS One. 2014;9:e92269–e. pmid:24647551
- View Article
- PubMed/NCBI
- Google Scholar
52. Basak C, Boot WR, Voss MW, Kramer AF. Can training in a real-time strategy video game attenuate cognitive decline in older adults? Psychol Aging. 2008;23:765. pmid:19140648
- View Article
- PubMed/NCBI
- Google Scholar
53. Sadeghi H, Hakim MN, Hamid TA, Amri SB, Razeghi M, Farazdaghi M, et al. The effect of exergaming on knee proprioception in older men: A randomized controlled trial. Arch Gerontol Geriatr. 2017;69:144–150. pmid:27923177
- View Article
- PubMed/NCBI
- Google Scholar
54. Marston HR, Kroll M, Fink D, Gschwind YJ. Flow experience of older adults using the iStoppFalls exergame. Games Cult. 2016;11:201–222.
- View Article
- Google Scholar
55. Wolinsky FD, Vander Weg MW, Howren MB, Jones MP, Dotson MM. Effects of cognitive speed of processing training on a composite neuropsychological outcome: Results at one-year from the IHAMS randomized controlled trial. Int Psychogeriatr. 2016;28:317–330. pmid:26364892
- View Article
- PubMed/NCBI
- Google Scholar
56. Torres ACS. Cognitive effects of video games on old people. Int J Disabil Hum Dev. 2011;10:55–58.
- View Article
- Google Scholar
57. Bozoki A, Radovanovic M, Winn B, Heeter C, Anthony JC. Effects of a computer-based cognitive exercise program on age-related cognitive decline. Arch Gerontol Geriatr. 2013;57:1–7. pmid:23542053
- View Article
- PubMed/NCBI
- Google Scholar
58. Hsu JK, Thibodeau R, Wong SJ, Zukiwsky D, Cecile S, Walton DM. A ''Wii'' bit of fun: The effects of adding Nintendo Wii Bowling to a standard exercise regimen for residents of long-term care with upper extremity dysfunction. Physiother Theory Pract. 2011;27:185–193. pmid:20698793
- View Article
- PubMed/NCBI
- Google Scholar
59. Rendon AA, Lohman EB, Thorpe D, Johnson EG, Medina E, Bradley B. The effect of virtual reality gaming on dynamic balance in older adults. Age Ageing. 2012;41:549–552. pmid:22672915
- View Article
- PubMed/NCBI
- Google Scholar
60. Szturm T, Betker AL, Moussavi Z, Desai A, Goodman V. Effects of an interactive computer game exercise regimen on balance impairment in frail community-dwelling older adults: A randomized controlled trial. Phys Ther. 2011;91:1449–1462. pmid:21799138
- View Article
- PubMed/NCBI
- Google Scholar
61. Eggenberger P, Theill N, Holenstein S, Schumacher V, de Bruin ED. Multicomponent physical exercise with simultaneous cognitive training to enhance dual-task walking of older adults: A secondary analysis of a 6-month randomized controlled trial with 1-year follow-up. Clin Interv Aging. 2015;10:1711–1732. pmid:26604719
- View Article
- PubMed/NCBI
- Google Scholar
62. Pichierri G, Murer K, de Bruin ED. A cognitive-motor intervention using a dance video game to enhance foot placement accuracy and gait under dual task conditions in older adults: A randomized controlled trial. BMC Geriatr. 2012;12:74. pmid:23241332
- View Article
- PubMed/NCBI
- Google Scholar
63. Franco JR, Jacobs K, Inzerillo C, Kluzik J. The effect of the Nintendo Wii Fit and exercise in improving balance and quality of life in community dwelling elders. Technol Health Care. 2012;20:95–115. pmid:22508022
- View Article
- PubMed/NCBI
- Google Scholar
64. Ballesteros S, Prieto A, Mayas J, Toril P, Pita C, de León LP, et al. Brain training with non-action video games enhances aspects of cognition in older adults: A randomized controlled trial. Front Aging Neurosci. 2014;6:277. pmid:25352805
- View Article
- PubMed/NCBI
- Google Scholar
65. Dustman RE, Emmerson RY, Steinhaus LA, Shearer DE, Dustman TJ. The effects of videogame playing on neuropsychological performance of elderly individuals. J Gerontol. 1992;47:P168–P171. pmid:1573200
- View Article
- PubMed/NCBI
- Google Scholar
66. Fu AS, Gao KL, Tung AK, Tsang WW, Kwan MM. Effectiveness of exergaming training in reducing risk and incidence of falls in frail older adults with a history of falls. Arch Phys Med Rehabil. 2015;96:2096–2102. pmid:26360975
- View Article
- PubMed/NCBI
- Google Scholar
67. Nouchi R, Taki Y, Takeuchi H, Hashizume H, Akitsuki Y, Shigemune Y, et al. Brain training game improves executive functions and processing speed in the elderly: A randomized controlled trial. PLoS One. 2012;7: e29676. pmid:22253758
- View Article
- PubMed/NCBI
- Google Scholar
68. Goldstein J, Cajko L, Oosterbroek M, Michielsen M, Van Houten O, Salverda F. Video games and the elderly. Soc Behav Pers. 1997;25:345–352.
- View Article
- Google Scholar
69. Li J, Theng YL, Foo S. Exergames for older adults with subthreshold depression: Does higher playfulness lead to better improvement in depression? Games Health J. 2016;5:175–182. pmid:27135146
- View Article
- PubMed/NCBI
- Google Scholar
70. Grönholm-Nyman P, Soveri A, Rinne JO, Ek E, Nyholm A, Neely AS, et al. Limited effects of set shifting training in healthy older adults. Front Aging Neurosci. 2017;9:69. pmid:28386226
- View Article
- PubMed/NCBI
- Google Scholar
71. Nouchi R, Saito T, Nouchi H, Kawashima R. Small acute benefits of 4 weeks processing speed training games on processing speed and inhibition performance and depressive mood in the healthy elderly people: Evidence from a randomized control trial. Front Aging Neurosci. 2016;8:302. pmid:28066229
- View Article
- PubMed/NCBI
- Google Scholar
72. Ribeiro JM, Gomes GCV, de Freitas TB, Viveiro LAP, da Silva KG, Bueno GC, et al. Effects of Kinect adventures games versus conventional physical therapy on postural control in elderly people: A randomized controlled trial. Games Health J. 2018;7:24–36. pmid:29239677
- View Article
- PubMed/NCBI
- Google Scholar
73. Toulotte C, Toursel C, Olivier N. Wii Fit training vs. adapted physical activities: Which one is the most appropriate to improve the balance of independent senior subjects? A randomized controlled study. Clin Rehabil. 2012;26:827–35. pmid:22324055
- View Article
- PubMed/NCBI
- Google Scholar
74. Eggenberger P, Wolf M, Schumann M, de Bruin ED. Exergame and balance training modulate prefrontal brain activity during walking and enhance executive function in older adults. Front Aging Neurosci. 2016;8:66. pmid:27148041
- View Article
- PubMed/NCBI
- Google Scholar
75. Schoene D, Valenzuela T, Toson B, Delbaere K, Severino C, Garcia J, et al. Interactive cognitive-motor step training improves cognitive risk factors of falling in older adults—A randomized controlled trial. PLoS One. 2015;10: e0145161. pmid:26673919
- View Article
- PubMed/NCBI
- Google Scholar
76. Whyatt C, Merriman NA, Young WR, Newell FN, Craig C. A Wii bit of fun: A novel platform to deliver effective balance training to older adults. Games Health J. 2015; 4:423–33. pmid:26469308
- View Article
- PubMed/NCBI
- Google Scholar
77. Sato K, Kuroki K, Saiki S, Nagatomi R. Improving walking, muscle strength, and balance in the elderly with an exergame using Kinect: A randomized controlled trial. Games Health J. 2015;4:161–167. pmid:26182059
- View Article
- PubMed/NCBI
- Google Scholar
78. Maillot P, Perrot A, Hartley A. Effects of interactive physical-activity video-game training on physical and cognitive function in older adults. Psychol Aging. 2012;27:589–600. pmid:22122605
- View Article
- PubMed/NCBI
- Google Scholar
79. Kim KW, Choi Y, You H, Na DL, Yoh MS, Park JK, et al. Effects of a serious game training on cognitive functions in older adults. J Am Geriatr Soc. 2015; 63:603–605. pmid:25800915
- View Article
- PubMed/NCBI
- Google Scholar
80. Ballesteros S, Prieto A, Mayas J, Toril P, Pita C, Ponce de León L, et al. Corrigendum: Brain training with non-action video games enhances aspects of cognition in older adults: A randomized controlled trial. Front Aging Neurosci. 2015;7:82. pmid:26042032
- View Article
- PubMed/NCBI
- Google Scholar
81. Hall AK, Chavarria E, Maneeratana V, Chaney BH, Bernhardt JM. Health benefits of digital videogames for older adults: A systematic review of the literature. Games Health J. 2012;1:402–410. pmid:26192056
- View Article
- PubMed/NCBI
- Google Scholar
82. Cayley P. Functional exercise for older adults. Heart Lung Circ. 2008;17:S70–S72. pmid:18952500
- View Article
- PubMed/NCBI
- Google Scholar
83. Nikolić M, Vranić TS, Arbanas J, Cvijanović O, Bajek S. Muscle loss in elderly. Coll Antropol. 2010;34:105–108.
- View Article
- Google Scholar
84. Ownby RL, Crocco E, Acevedo A, John V, Loewenstein D. Depression and risk for Alzheimer disease: Systematic review, meta-analysis, and metaregression analysis. Arch Gen Psychiatry. 2006;63:530–538. pmid:16651510
- View Article
- PubMed/NCBI
- Google Scholar
85. Holt-Lunstad J, Smith TB, Layton JB. Social relationships and mortality risk: A meta-analytic review. PLoS Med. 2010;7:e1000316. pmid:20668659
- View Article
- PubMed/NCBI
- Google Scholar
86. Ware JE Jr, Sherbourne CD. The MOS 36-item short-form health survey (SF-36): I. Conceptual framework and item selection. Med Care. 1992:473–483. pmid:1593914
- View Article
- PubMed/NCBI
- Google Scholar
87. World Health Organization. WHOQOL-BREF quality of life assessment. Psychol Med. 1998;28(3):551–558. pmid:9626712
- View Article
- PubMed/NCBI
- Google Scholar
88. Cuijpers P. Examining the effects of prevention programs on the incidence of new cases of mental disorders: The lack of statistical power. Am J Psychiatry. 2003;160:1385–1391. pmid:12900296
- View Article
- PubMed/NCBI
- Google Scholar
89. de Bruin ED, Schmidt A. Walking behaviour of healthy elderly: Attention should be paid. Behav Brain Funct. 2010;6:59. pmid:20939911
- View Article
- PubMed/NCBI
- Google Scholar
90. Dustman RE, Ruhling RO, Russell EM, Shearer DE, Bonekat HW, Shigeoka JW, et al. Aerobic exercise training and improved neuropsychological function of older individuals. Neurobiol Aging. 1984;5:35–42. pmid:6738784
- View Article
- PubMed/NCBI
- Google Scholar
91. Green CS, Bavelier D. Exercising your brain: A review of human brain plasticity and training-induced learning. Psychol Aging. 2008;23:692–701. pmid:19140641
- View Article
- PubMed/NCBI
- Google Scholar
92. Street TD, Lacey SJ, Langdon RR. Gaming your way to health: A systematic review of exergaming programs to increase health and exercise behaviors in adults. Games Health J. 2017;6:136–146. pmid:28448175
- View Article
- PubMed/NCBI
- Google Scholar
93. Chao Y-Y, Scherer YK, Montgomery CA. Effects of using Nintendo Wii exergames in older adults: A review of the literature. J Aging Health. 2015;27:379–402. pmid:25245519
- View Article
- PubMed/NCBI
- Google Scholar
94. Schaie KW, Willis SL. Handbook of the psychology of aging. London: Academic Press; 2010.

[ref1] 1. Eurostat.European-Comission. Eurostat publications and databases. Eurostat, 2016.

[ref2] 2. European-Social-Network. Services for older people in Europe. Facts and figures about long term care services in Europe.2008.

[ref3] 3. World Health Organization. Active ageing: A policy framework. Geneva: World Health Organization; 2002.

[ref4] 4. Anstey K, Stankov L, Lord S. Primary aging, secondary aging, and intelligence. Psychol Aging. 1993;8:562–570. pmid:8292284
View Article
PubMed/NCBI
Google Scholar

[5] View Article

[6] PubMed/NCBI

[7] Google Scholar

[ref5] 5. Woods R, Clare L. Handbook of the clinical psychology of ageing. 2nd ed. Chichester, United Kingdom: John Wiley & Sons; 2015.

[ref6] 6. Fjell AM, Walhovd KB, Westlye LT, Østby Y, Tamnes CK, Jernigan TL, et al. When does brain aging accelerate? Dangers of quadratic fits in cross-sectional studies. Neuroimage. 2010;50:1376–1383. pmid:20109562
View Article
PubMed/NCBI
Google Scholar

[10] View Article

[11] PubMed/NCBI

[12] Google Scholar

[ref7] 7. Barnett JH, Lewis L, Blackwell AD, Taylor M. Early intervention in Alzheimer’s disease: A health economic study of the effects of diagnostic timing. BMC Neurol. 2014;14:101. pmid:24885474
View Article
PubMed/NCBI
Google Scholar

[14] View Article

[15] PubMed/NCBI

[16] Google Scholar

[ref8] 8. Baranowski T, Buday R, Thompson DI, Baranowski J. Playing for real: Video games and stories for health-related behavior change. Am J Prev Med. 2008;34:74–82. e10. pmid:18083454
View Article
PubMed/NCBI
Google Scholar

[18] View Article

[19] PubMed/NCBI

[20] Google Scholar

[ref9] 9. Fleming T, Cheek C, Merry S, Thabrew H, Bridgman H, Stasiak K, et al. Serious games for the treatment or prevention of depression: A systematic review. Rev Psicopatología Psicol Clin. 2014;19:227–42.
View Article
Google Scholar

[22] View Article

[23] Google Scholar

[ref10] 10. Garris R, Ahlers R, Driskell JE. Games, motivation, and learning: A research and practice model. Simul Gaming. 2002;33:441–467.
View Article
Google Scholar

[25] View Article

[26] Google Scholar

[ref11] 11. West GL, Zendel BR, Konishi K, Benady-Chorney J, Bohbot VD, Peretz I, et al. Playing Super Mario 64 increases hippocampal grey matter in older adults. PLoS One. 2017;12:e0187779. pmid:29211727
View Article
PubMed/NCBI
Google Scholar

[28] View Article

[29] PubMed/NCBI

[30] Google Scholar

[ref12] 12. Shams TA, Foussias G, Zawadzki JA, Marshe VS, Siddiqui I, Muller DJ, et al. The effects of video games on cognition and brain structure: Potential implications for neuropsychiatric disorders. Curr Psychiatry Rep. 2015;17:71. pmid:26216589
View Article
PubMed/NCBI
Google Scholar

[32] View Article

[33] PubMed/NCBI

[34] Google Scholar

[ref13] 13. Murray E, Burns J, See TS, Lai R, Nazareth I. Interactive health communication applications for people with chronic disease. Cochrane Database Syst Rev. 2005;(4): CD004274.
View Article
Google Scholar

[36] View Article

[37] Google Scholar

[ref14] 14. Read JL, Shortell SM. Interactive games to promote behavior change in prevention and treatment. JAMA. 2011;305:1704–1705. pmid:21447802
View Article
PubMed/NCBI
Google Scholar

[39] View Article

[40] PubMed/NCBI

[41] Google Scholar

[ref15] 15. Bleakley CM, Charles D, Porter-Armstrong A, McNeill MDJ, McDonough SM, McCormack B. Gaming for health: A systematic review of the physical and cognitive effects of interactive computer games in older adults. J Appl Gerontol. 2015;34:NP166–89. pmid:24652863
View Article
PubMed/NCBI
Google Scholar

[43] View Article

[44] PubMed/NCBI

[45] Google Scholar

[ref16] 16. Chao YY, Scherer YK, Montgomery CA. Effects of using Nintendo Wii exergames in older adults: A review of the literature. J Aging Health. 2015;27:379–402. pmid:25245519
View Article
PubMed/NCBI
Google Scholar

[47] View Article

[48] PubMed/NCBI

[49] Google Scholar

[ref17] 17. Toril P, Reales JM, Ballesteros S. Video game training enhances cognition of older adults: A meta-analytic study. Psychol Aging. 2014;29:706–716. pmid:25244488
View Article
PubMed/NCBI
Google Scholar

[51] View Article

[52] PubMed/NCBI

[53] Google Scholar

[ref18] 18. Moher D, Liberati A, Tetzlaff J, Altman DG. Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. PLoS Med. 2009;6(7):e1000097. pmid:19621072
View Article
PubMed/NCBI
Google Scholar

[55] View Article

[56] PubMed/NCBI

[57] Google Scholar

[ref19] 19. Shea BJ, Grimshaw JM, Wells GA, Boers M, Andersson N, Hamel C, et al. Development of AMSTAR: A measurement tool to assess the methodological quality of systematic reviews. BMC Med Res Methodol. 2007;7:10. pmid:17302989
View Article
PubMed/NCBI
Google Scholar

[59] View Article

[60] PubMed/NCBI

[61] Google Scholar

[ref20] 20. Higgins J, Green S. Cochrane handbook for systematic reviews of interventions Version 5.1. 0 (updated March 2011) The Cochrane Collaboration; 2011.

[ref21] 21. DeSmet A, Van Ryckeghem D, Compernolle S, Baranowski T, Thompson D, Crombez G, et al. A meta-analysis of serious digital games for healthy lifestyle promotion. Prev Med. 2014;69:95–107. pmid:25172024
View Article
PubMed/NCBI
Google Scholar

[64] View Article

[65] PubMed/NCBI

[66] Google Scholar

[ref22] 22. Sidorenko A, Zaidi A. Active ageing in CIS countries: Semantics, challenges, and responses. Curr Gerontol Geriatr Res. 2013;261819:17. pmid:23346109
View Article
PubMed/NCBI
Google Scholar

[68] View Article

[69] PubMed/NCBI

[70] Google Scholar

[ref23] 23. World Helath Organization. Constitution of the World Health Organization. www.who.int/governance/eb/who_constitution_en.pdf.: World Health Organization; 2006.

[ref24] 24. Huber M, Knottnerus JA, Green L, van der Horst H, Jadad AR, Kromhout D, et al. How should we define health? BMJ. 2011;343: d4163. pmid:21791490
View Article
PubMed/NCBI
Google Scholar

[73] View Article

[74] PubMed/NCBI

[75] Google Scholar

[ref25] 25. Duncan S, Barrett LF. Affect is a form of cognition: A neurobiological analysis. Cogn Emot. 2007;21:1184–1211. pmid:18509504
View Article
PubMed/NCBI
Google Scholar

[77] View Article

[78] PubMed/NCBI

[79] Google Scholar

[ref26] 26. Strauss E, Sherman EM, Spreen O. A compendium of neuropsychological tests: Administration, norms, and commentary. Oxford: Oxford University Press; 2006.

[ref27] 27. Watson D, Clark LA, Tellegen A. Development and validation of brief measures of positive and negative affect: the PANAS scales. J Pers Soc Psychol. 1988;54:1063–1070. pmid:3397865
View Article
PubMed/NCBI
Google Scholar

[82] View Article

[83] PubMed/NCBI

[84] Google Scholar

[ref28] 28. Hill NT, Mowszowski L, Naismith SL, Chadwick VL, Valenzuela M, Lampit A. Computerized cognitive training in older adults with mild cognitive impairment or dementia: A systematic review and meta-analysis. Am J Psychiatry. 2017;174:329–340. pmid:27838936
View Article
PubMed/NCBI
Google Scholar

[86] View Article

[87] PubMed/NCBI

[88] Google Scholar

[ref29] 29. Stanmore E, Stubbs B, Vancampfort D, de Bruin ED, Firth J. The effect of active video games on cognitive functioning in clinical and non-clinical populations: A meta-analysis of randomized controlled trials. Neurosci Biobehav Rev. 2017;78:34–43. pmid:28442405
View Article
PubMed/NCBI
Google Scholar

[90] View Article

[91] PubMed/NCBI

[92] Google Scholar

[ref30] 30. Lipsey MW, Wilson DB. Practical meta-analysis. Thousand Oaks, CA, US: Sage Publications, Inc.; 2001.

[ref31] 31. Haggerty RJ, Mrazek PJ. Reducing risks for mental disorders: Frontiers for preventive intervention research. Washington, DC.: National Academies Press; 1994.

[ref32] 32. Downs SH, Black N. The feasibility of creating a checklist for the assessment of the methodological quality both of randomised and non-randomised studies of health care interventions. J Epidemiol Community Health. 1998;52:377–384. pmid:9764259
View Article
PubMed/NCBI
Google Scholar

[96] View Article

[97] PubMed/NCBI

[98] Google Scholar

[ref33] 33. Borenstein M, Hedges LV, Higgins J, Rothstein HR. A basic introduction to fixed-effect and random-effects models for meta-analysis. Res Synth Methods. 2010;1:97–111. pmid:26061376
View Article
PubMed/NCBI
Google Scholar

[100] View Article

[101] PubMed/NCBI

[102] Google Scholar

[ref34] 34. Botella-Ausina J, Sánchez-Meca J. Meta-análisis en ciencias sociales y de la salud [Meta-analysis in social sciences and health]. Madrid: Síntesis; 2015.

[ref35] 35. Cohen J. Statistical power for the social sciences. Hillsdale, NJ: Laurence Erlbaum and Associates. 1988.

[ref36] 36. Ballesteros S, Mayas J, Prieto A, Toril P, Pita C, de León LP, et al. A randomized controlled trial of brain training with non-action video games in older adults: Results of the 3-month follow-up. Front Aging Neurosci. 2015;7:45. pmid:25926790
View Article
PubMed/NCBI
Google Scholar

[106] View Article

[107] PubMed/NCBI

[108] Google Scholar

[ref37] 37. Buitenweg JIV, van de Ven RM, Prinssen S, Murre JMJ, Ridderinkhof KR. Cognitive flexibility training: A large-scale multimodal adaptive active-control intervention study in healthy older adults. Front Hum Neurosci. 2017;11:529. pmid:29209183
View Article
PubMed/NCBI
Google Scholar

[110] View Article

[111] PubMed/NCBI

[112] Google Scholar

[ref38] 38. Karahan AY, Tok F, Taskin H, Kucuksarac S, Basaran A, Yildirim P. Effects of exergames on balance, functional mobility, and quality of life of geriatrics versus home exercise programme: Randomized controlled study. Cent Eur J Public Health. 2015; 23:S14–18. pmid:26849537
View Article
PubMed/NCBI
Google Scholar

[114] View Article

[115] PubMed/NCBI

[116] Google Scholar

[ref39] 39. Schattin A, Arner R, Gennaro F, Bruin ED. Adaptations of prefrontal brain activity, executive functions, and gait in healthy elderly following exergame and balance training: A randomized-controlled study. Front Aging Neurosci. 2016; 8:278. pmid:27932975
View Article
PubMed/NCBI
Google Scholar

[118] View Article

[119] PubMed/NCBI

[120] Google Scholar

[ref40] 40. Kahlbaugh PE, Sperandio AJ, Carlson AL, Hauselt J. Effects of playing Wii on well-being in the elderly: Physical activity, loneliness, and mood. Act Adapt Aging. 2011;35(4):331–344.
View Article
Google Scholar

[122] View Article

[123] Google Scholar

[ref41] 41. Eggenberger P, Schumacher V, Angst M, Theill N, de Bruin ED. Does multicomponent physical exercise with simultaneous cognitive training boost cognitive performance in older adults? A 6-month randomized controlled trial with a 1-year follow-up. Clin Interv Aging. 2015;10:1335–1349. pmid:26316729
View Article
PubMed/NCBI
Google Scholar

[125] View Article

[126] PubMed/NCBI

[127] Google Scholar

[ref42] 42. Belchior P, Marsiske M, Leite WL, Yam A, Thomas K, Mann W. Older adults’ engagement during an intervention involving off-the-shelf videogame. Games Health J. 2016;5:151–156. pmid:27310479
View Article
PubMed/NCBI
Google Scholar

[129] View Article

[130] PubMed/NCBI

[131] Google Scholar

[ref43] 43. Souders DJ, Boot WR, Blocker K, Vitale T, Roque NA, Charness N. Evidence for narrow transfer after short-term cognitive training in older adults. Front Aging Neurosci. 2017; 9: 41. pmid:28293188
View Article
PubMed/NCBI
Google Scholar

[133] View Article

[134] PubMed/NCBI

[135] Google Scholar

[ref44] 44. West GL, Zendel BR, Konishi K, Benady-Chorney J, Bohbot VD, Peretz I, et al. Playing Super Mario 64 increases hippocampal grey matter in older adults. PLoS One. 2017; 12. Available from: http://onlinelibrary.wiley.com/o/cochrane/clcentral/articles/603/CN-01442603/frame.html.
View Article
Google Scholar

[137] View Article

[138] Google Scholar

[ref45] 45. Peretz C, Korczyn AD, Shatil E, Aharonson V, Birnboim S, Giladi N. Computer-based, personalized cognitive training versus classical computer games: A randomized double-blind prospective trial of cognitive stimulation. Neuroepidemiology. 2011;36:91–99. pmid:21311196
View Article
PubMed/NCBI
Google Scholar

[140] View Article

[141] PubMed/NCBI

[142] Google Scholar

[ref46] 46. Whitton JP, Hancock KE, Shannon JM, Polley DB. Audiomotor perceptual training enhances speech intelligibility in background noise. Curr Biol. 2017;27:3237–47.e6. pmid:29056453
View Article
PubMed/NCBI
Google Scholar

[144] View Article

[145] PubMed/NCBI

[146] Google Scholar

[ref47] 47. Jorgensen MG, Laessoe U, Hendriksen C, Nielsen OB, Aagaard P. Efficacy of Nintendo Wii training on mechanical leg muscle function and postural balance in community-dwelling older adults: A randomized controlled trial. J Gerontol A Biol Sci Med Sci. 2013;68:845–852. pmid:23114461
View Article
PubMed/NCBI
Google Scholar

[148] View Article

[149] PubMed/NCBI

[150] Google Scholar

[ref48] 48. Morone G, Paolucci T, Luziatelli S, Iosa M, Piermattei C, Zangrando F, et al. Wii Fit is effective in women with bone loss condition associated with balance disorders: A randomized controlled trial. Aging Clin Exp Res. 2016;28:1187–1193. pmid:27154875
View Article
PubMed/NCBI
Google Scholar

[152] View Article

[153] PubMed/NCBI

[154] Google Scholar

[ref49] 49. van den Berg M, Sherrington C, Killington M, Smith S, Bongers B, Hassett L, et al. Video and computer-based interactive exercises are safe and improve task-specific balance in geriatric and neurological rehabilitation: A randomised trial. J Physiother. 2016;62:20–28. pmid:26701163
View Article
PubMed/NCBI
Google Scholar

[156] View Article

[157] PubMed/NCBI

[158] Google Scholar

[ref50] 50. Ballesteros S, Mayas J, Prieto A, Ruiz-Marquez E, Toril P, Reales JM. Effects of video game training on measures of selective attention and working memory in older adults: Results from a randomized controlled trial. Front Aging Neurosci. 2017;9:354. pmid:29163136
View Article
PubMed/NCBI
Google Scholar

[160] View Article

[161] PubMed/NCBI

[162] Google Scholar

[ref51] 51. Mayas J, Parmentier FBR, Andrés P, Ballesteros S. Plasticity of attentional functions in older adults after non-action video game training: A randomized controlled trial. PLoS One. 2014;9:e92269–e. pmid:24647551
View Article
PubMed/NCBI
Google Scholar

[164] View Article

[165] PubMed/NCBI

[166] Google Scholar

[ref52] 52. Basak C, Boot WR, Voss MW, Kramer AF. Can training in a real-time strategy video game attenuate cognitive decline in older adults? Psychol Aging. 2008;23:765. pmid:19140648
View Article
PubMed/NCBI
Google Scholar

[168] View Article

[169] PubMed/NCBI

[170] Google Scholar

[ref53] 53. Sadeghi H, Hakim MN, Hamid TA, Amri SB, Razeghi M, Farazdaghi M, et al. The effect of exergaming on knee proprioception in older men: A randomized controlled trial. Arch Gerontol Geriatr. 2017;69:144–150. pmid:27923177
View Article
PubMed/NCBI
Google Scholar

[172] View Article

[173] PubMed/NCBI

[174] Google Scholar

[ref54] 54. Marston HR, Kroll M, Fink D, Gschwind YJ. Flow experience of older adults using the iStoppFalls exergame. Games Cult. 2016;11:201–222.
View Article
Google Scholar

[176] View Article

[177] Google Scholar

[ref55] 55. Wolinsky FD, Vander Weg MW, Howren MB, Jones MP, Dotson MM. Effects of cognitive speed of processing training on a composite neuropsychological outcome: Results at one-year from the IHAMS randomized controlled trial. Int Psychogeriatr. 2016;28:317–330. pmid:26364892
View Article
PubMed/NCBI
Google Scholar

[179] View Article

[180] PubMed/NCBI

[181] Google Scholar

[ref56] 56. Torres ACS. Cognitive effects of video games on old people. Int J Disabil Hum Dev. 2011;10:55–58.
View Article
Google Scholar

[183] View Article

[184] Google Scholar

[ref57] 57. Bozoki A, Radovanovic M, Winn B, Heeter C, Anthony JC. Effects of a computer-based cognitive exercise program on age-related cognitive decline. Arch Gerontol Geriatr. 2013;57:1–7. pmid:23542053
View Article
PubMed/NCBI
Google Scholar

[186] View Article

[187] PubMed/NCBI

[188] Google Scholar

[ref58] 58. Hsu JK, Thibodeau R, Wong SJ, Zukiwsky D, Cecile S, Walton DM. A ''Wii'' bit of fun: The effects of adding Nintendo Wii Bowling to a standard exercise regimen for residents of long-term care with upper extremity dysfunction. Physiother Theory Pract. 2011;27:185–193. pmid:20698793
View Article
PubMed/NCBI
Google Scholar

[190] View Article

[191] PubMed/NCBI

[192] Google Scholar

[ref59] 59. Rendon AA, Lohman EB, Thorpe D, Johnson EG, Medina E, Bradley B. The effect of virtual reality gaming on dynamic balance in older adults. Age Ageing. 2012;41:549–552. pmid:22672915
View Article
PubMed/NCBI
Google Scholar

[194] View Article

[195] PubMed/NCBI

[196] Google Scholar

[ref60] 60. Szturm T, Betker AL, Moussavi Z, Desai A, Goodman V. Effects of an interactive computer game exercise regimen on balance impairment in frail community-dwelling older adults: A randomized controlled trial. Phys Ther. 2011;91:1449–1462. pmid:21799138
View Article
PubMed/NCBI
Google Scholar

[198] View Article

[199] PubMed/NCBI

[200] Google Scholar

[ref61] 61. Eggenberger P, Theill N, Holenstein S, Schumacher V, de Bruin ED. Multicomponent physical exercise with simultaneous cognitive training to enhance dual-task walking of older adults: A secondary analysis of a 6-month randomized controlled trial with 1-year follow-up. Clin Interv Aging. 2015;10:1711–1732. pmid:26604719
View Article
PubMed/NCBI
Google Scholar

[202] View Article

[203] PubMed/NCBI

[204] Google Scholar

[ref62] 62. Pichierri G, Murer K, de Bruin ED. A cognitive-motor intervention using a dance video game to enhance foot placement accuracy and gait under dual task conditions in older adults: A randomized controlled trial. BMC Geriatr. 2012;12:74. pmid:23241332
View Article
PubMed/NCBI
Google Scholar

[206] View Article

[207] PubMed/NCBI

[208] Google Scholar

[ref63] 63. Franco JR, Jacobs K, Inzerillo C, Kluzik J. The effect of the Nintendo Wii Fit and exercise in improving balance and quality of life in community dwelling elders. Technol Health Care. 2012;20:95–115. pmid:22508022
View Article
PubMed/NCBI
Google Scholar

[210] View Article

[211] PubMed/NCBI

[212] Google Scholar

[ref64] 64. Ballesteros S, Prieto A, Mayas J, Toril P, Pita C, de León LP, et al. Brain training with non-action video games enhances aspects of cognition in older adults: A randomized controlled trial. Front Aging Neurosci. 2014;6:277. pmid:25352805
View Article
PubMed/NCBI
Google Scholar

[214] View Article

[215] PubMed/NCBI

[216] Google Scholar

[ref65] 65. Dustman RE, Emmerson RY, Steinhaus LA, Shearer DE, Dustman TJ. The effects of videogame playing on neuropsychological performance of elderly individuals. J Gerontol. 1992;47:P168–P171. pmid:1573200
View Article
PubMed/NCBI
Google Scholar

[218] View Article

[219] PubMed/NCBI

[220] Google Scholar

[ref66] 66. Fu AS, Gao KL, Tung AK, Tsang WW, Kwan MM. Effectiveness of exergaming training in reducing risk and incidence of falls in frail older adults with a history of falls. Arch Phys Med Rehabil. 2015;96:2096–2102. pmid:26360975
View Article
PubMed/NCBI
Google Scholar

[222] View Article

[223] PubMed/NCBI

[224] Google Scholar

[ref67] 67. Nouchi R, Taki Y, Takeuchi H, Hashizume H, Akitsuki Y, Shigemune Y, et al. Brain training game improves executive functions and processing speed in the elderly: A randomized controlled trial. PLoS One. 2012;7: e29676. pmid:22253758
View Article
PubMed/NCBI
Google Scholar

[226] View Article

[227] PubMed/NCBI

[228] Google Scholar

[ref68] 68. Goldstein J, Cajko L, Oosterbroek M, Michielsen M, Van Houten O, Salverda F. Video games and the elderly. Soc Behav Pers. 1997;25:345–352.
View Article
Google Scholar

[230] View Article

[231] Google Scholar

[ref69] 69. Li J, Theng YL, Foo S. Exergames for older adults with subthreshold depression: Does higher playfulness lead to better improvement in depression? Games Health J. 2016;5:175–182. pmid:27135146
View Article
PubMed/NCBI
Google Scholar

[233] View Article

[234] PubMed/NCBI

[235] Google Scholar

[ref70] 70. Grönholm-Nyman P, Soveri A, Rinne JO, Ek E, Nyholm A, Neely AS, et al. Limited effects of set shifting training in healthy older adults. Front Aging Neurosci. 2017;9:69. pmid:28386226
View Article
PubMed/NCBI
Google Scholar

[237] View Article

[238] PubMed/NCBI

[239] Google Scholar

[ref71] 71. Nouchi R, Saito T, Nouchi H, Kawashima R. Small acute benefits of 4 weeks processing speed training games on processing speed and inhibition performance and depressive mood in the healthy elderly people: Evidence from a randomized control trial. Front Aging Neurosci. 2016;8:302. pmid:28066229
View Article
PubMed/NCBI
Google Scholar

[241] View Article

[242] PubMed/NCBI

[243] Google Scholar

[ref72] 72. Ribeiro JM, Gomes GCV, de Freitas TB, Viveiro LAP, da Silva KG, Bueno GC, et al. Effects of Kinect adventures games versus conventional physical therapy on postural control in elderly people: A randomized controlled trial. Games Health J. 2018;7:24–36. pmid:29239677
View Article
PubMed/NCBI
Google Scholar

[245] View Article

[246] PubMed/NCBI

[247] Google Scholar

[ref73] 73. Toulotte C, Toursel C, Olivier N. Wii Fit training vs. adapted physical activities: Which one is the most appropriate to improve the balance of independent senior subjects? A randomized controlled study. Clin Rehabil. 2012;26:827–35. pmid:22324055
View Article
PubMed/NCBI
Google Scholar

[249] View Article

[250] PubMed/NCBI

[251] Google Scholar

[ref74] 74. Eggenberger P, Wolf M, Schumann M, de Bruin ED. Exergame and balance training modulate prefrontal brain activity during walking and enhance executive function in older adults. Front Aging Neurosci. 2016;8:66. pmid:27148041
View Article
PubMed/NCBI
Google Scholar

[253] View Article

[254] PubMed/NCBI

[255] Google Scholar

[ref75] 75. Schoene D, Valenzuela T, Toson B, Delbaere K, Severino C, Garcia J, et al. Interactive cognitive-motor step training improves cognitive risk factors of falling in older adults—A randomized controlled trial. PLoS One. 2015;10: e0145161. pmid:26673919
View Article
PubMed/NCBI
Google Scholar

[257] View Article

[258] PubMed/NCBI

[259] Google Scholar

[ref76] 76. Whyatt C, Merriman NA, Young WR, Newell FN, Craig C. A Wii bit of fun: A novel platform to deliver effective balance training to older adults. Games Health J. 2015; 4:423–33. pmid:26469308
View Article
PubMed/NCBI
Google Scholar

[261] View Article

[262] PubMed/NCBI

[263] Google Scholar

[ref77] 77. Sato K, Kuroki K, Saiki S, Nagatomi R. Improving walking, muscle strength, and balance in the elderly with an exergame using Kinect: A randomized controlled trial. Games Health J. 2015;4:161–167. pmid:26182059
View Article
PubMed/NCBI
Google Scholar

[265] View Article

[266] PubMed/NCBI

[267] Google Scholar

[ref78] 78. Maillot P, Perrot A, Hartley A. Effects of interactive physical-activity video-game training on physical and cognitive function in older adults. Psychol Aging. 2012;27:589–600. pmid:22122605
View Article
PubMed/NCBI
Google Scholar

[269] View Article

[270] PubMed/NCBI

[271] Google Scholar

[ref79] 79. Kim KW, Choi Y, You H, Na DL, Yoh MS, Park JK, et al. Effects of a serious game training on cognitive functions in older adults. J Am Geriatr Soc. 2015; 63:603–605. pmid:25800915
View Article
PubMed/NCBI
Google Scholar

[273] View Article

[274] PubMed/NCBI

[275] Google Scholar

[ref80] 80. Ballesteros S, Prieto A, Mayas J, Toril P, Pita C, Ponce de León L, et al. Corrigendum: Brain training with non-action video games enhances aspects of cognition in older adults: A randomized controlled trial. Front Aging Neurosci. 2015;7:82. pmid:26042032
View Article
PubMed/NCBI
Google Scholar

[277] View Article

[278] PubMed/NCBI

[279] Google Scholar

[ref81] 81. Hall AK, Chavarria E, Maneeratana V, Chaney BH, Bernhardt JM. Health benefits of digital videogames for older adults: A systematic review of the literature. Games Health J. 2012;1:402–410. pmid:26192056
View Article
PubMed/NCBI
Google Scholar

[281] View Article

[282] PubMed/NCBI

[283] Google Scholar

[ref82] 82. Cayley P. Functional exercise for older adults. Heart Lung Circ. 2008;17:S70–S72. pmid:18952500
View Article
PubMed/NCBI
Google Scholar

[285] View Article

[286] PubMed/NCBI

[287] Google Scholar

[ref83] 83. Nikolić M, Vranić TS, Arbanas J, Cvijanović O, Bajek S. Muscle loss in elderly. Coll Antropol. 2010;34:105–108.
View Article
Google Scholar

[289] View Article

[290] Google Scholar

[ref84] 84. Ownby RL, Crocco E, Acevedo A, John V, Loewenstein D. Depression and risk for Alzheimer disease: Systematic review, meta-analysis, and metaregression analysis. Arch Gen Psychiatry. 2006;63:530–538. pmid:16651510
View Article
PubMed/NCBI
Google Scholar

[292] View Article

[293] PubMed/NCBI

[294] Google Scholar

[ref85] 85. Holt-Lunstad J, Smith TB, Layton JB. Social relationships and mortality risk: A meta-analytic review. PLoS Med. 2010;7:e1000316. pmid:20668659
View Article
PubMed/NCBI
Google Scholar

[296] View Article

[297] PubMed/NCBI

[298] Google Scholar

[ref86] 86. Ware JE Jr, Sherbourne CD. The MOS 36-item short-form health survey (SF-36): I. Conceptual framework and item selection. Med Care. 1992:473–483. pmid:1593914
View Article
PubMed/NCBI
Google Scholar

[300] View Article

[301] PubMed/NCBI

[302] Google Scholar

[ref87] 87. World Health Organization. WHOQOL-BREF quality of life assessment. Psychol Med. 1998;28(3):551–558. pmid:9626712
View Article
PubMed/NCBI
Google Scholar

[304] View Article

[305] PubMed/NCBI

[306] Google Scholar

[ref88] 88. Cuijpers P. Examining the effects of prevention programs on the incidence of new cases of mental disorders: The lack of statistical power. Am J Psychiatry. 2003;160:1385–1391. pmid:12900296
View Article
PubMed/NCBI
Google Scholar

[308] View Article

[309] PubMed/NCBI

[310] Google Scholar

[ref89] 89. de Bruin ED, Schmidt A. Walking behaviour of healthy elderly: Attention should be paid. Behav Brain Funct. 2010;6:59. pmid:20939911
View Article
PubMed/NCBI
Google Scholar

[312] View Article

[313] PubMed/NCBI

[314] Google Scholar

[ref90] 90. Dustman RE, Ruhling RO, Russell EM, Shearer DE, Bonekat HW, Shigeoka JW, et al. Aerobic exercise training and improved neuropsychological function of older individuals. Neurobiol Aging. 1984;5:35–42. pmid:6738784
View Article
PubMed/NCBI
Google Scholar

[316] View Article

[317] PubMed/NCBI

[318] Google Scholar

[ref91] 91. Green CS, Bavelier D. Exercising your brain: A review of human brain plasticity and training-induced learning. Psychol Aging. 2008;23:692–701. pmid:19140641
View Article
PubMed/NCBI
Google Scholar

[320] View Article

[321] PubMed/NCBI

[322] Google Scholar

[ref92] 92. Street TD, Lacey SJ, Langdon RR. Gaming your way to health: A systematic review of exergaming programs to increase health and exercise behaviors in adults. Games Health J. 2017;6:136–146. pmid:28448175
View Article
PubMed/NCBI
Google Scholar

[324] View Article

[325] PubMed/NCBI

[326] Google Scholar

[ref93] 93. Chao Y-Y, Scherer YK, Montgomery CA. Effects of using Nintendo Wii exergames in older adults: A review of the literature. J Aging Health. 2015;27:379–402. pmid:25245519
View Article
PubMed/NCBI
Google Scholar

[328] View Article

[329] PubMed/NCBI

[330] Google Scholar

[ref94] 94. Schaie KW, Willis SL. Handbook of the psychology of aging. London: Academic Press; 2010.

Figures

Abstract

Background

Methods

Results

Conclusion

Introduction

Methods

Search strategy

Selection procedure

Data extraction

Data coding

Data analysis

Results

Study selection

Descriptive characteristics of the studies

Methodological characteristics of the studies

Quality assessment and risk of bias

Meta-analysis of the efficacy of video game-based interventions for active aging

Analysis of moderating variables

Discussion

Conclusions

Supporting information

S1 File. PRISMA checklist.

S2 File. Coding protocol and manual.

Acknowledgments

References