Introduction

Global populations are aging at an escalating speed. The aging-related decline in physical function and quality of life imposes a huge health care burden. Frailty is a clinical aging syndrome attributed to chronic disease interactions and other factors, including an unbalanced diet and adverse lifestyle habits. In Taiwan, the prevalence of frailty and prefrailty in older adults aged 65 years and above is 5.4% and 41.5%, respectively, with a higher prevalence among females than males [-]. The clinical presentation of frailty includes decreased activity, abnormal weight loss, reduced appetite, fatigue without apparent cause, muscle loss, gait and balance abnormalities, and cognitive function impairment []. Frailty affects the endocrine, cardiovascular, and musculoskeletal systems []. Age-related muscle atrophy, known as sarcopenia, correlates highly with frailty []. The gait speed of older adults gradually decreases with age and is directly related to the bone and muscle mass of the lower limbs []. At present, lower limb rehabilitation involves repetitive training and often does not encourage older adults to perform independently [,].

Globally, the predicted demand for health care professionals will reach 80 million in 2030, while the number of available professionals will be less than 18 million []. To address this need, the health care system is using innovative technology such as intelligent telemedicine, smart sensor technology, and wearable devices [,]. An intelligent health care ecosphere created by information and communications technology is helpful for preventive health care, fitness, and health care services []. Interactive technology devices, as effective training tools, improve mobility and balance in patients []. Previous studies report significant improvements in several domains of physical function via game-related rehabilitation [,]. These findings suggest the effectiveness of exergames, which integrate games into exercise training, to improve physical function during rehabilitation. In addition, mental health is also improved by exergames, which increase the willingness of older adults to participate in long-term exercise []. Using exergames for rehabilitation to restore balance in older adults and to shorten the rehabilitation treatment period is an economically beneficial option for home training. Furthermore, exergames with visual feedback provide more effective rehabilitation; such improvement is unlikely to be achieved by traditional manual rehabilitation training, which diminishes with age [].

Reminiscence therapy, which incorporates life experiences using old photographs or scenarios into activities, is widely used in the care of older adults []. This method stimulates long-term memory and aims to maintain patients’ physical and mental health and quality of life, regardless of their cognitive function. The benefits of reminiscence therapy include increasing self-esteem, decreasing depression, and promoting mental health, thereby increasing life satisfaction [,].

Older adults are at high risk of COVID-19 infection due to frailty. The COVID-19 pandemic has placed immense pressure on health care systems; in response, a variety of exergames with interactive technology were launched during the pandemic for home use. These exergames can be used as activity tools to maintain muscle strength and decrease physical and mental stress during measures for COVID-19 control [,]. In the post–COVID-19 era [], exergames may help them to keep training and to reduce the burden on the health care system.

In this study, an Intelligent Rehabilitation Exergame System (IRES) that included a retro interactive exergame, real-time surface electromyography (sEMG) used to control the game, and reminiscence therapy was developed for use in the rehabilitation of frail older adults [,]. In addition, IRES records and provides data to health care professionals during rehabilitation to allow for the optimization of the rehabilitation protocol [,]. The System Usability Scale (SUS) was used to analyze the usability of IRES for rehabilitation [,], and the Taiwanese version of the EuroQol-5 Dimensions (EQ-5D) was used to evaluate changes in the quality of life after IRES training in frail older adults [].

MethodsIntelligent Rehabilitation Exergame System

IRES comprises an exergame for rehabilitation that interfaces with a virtual physician, historical photographs with constructed scenarios for reminiscence therapy, and an sEMG for controlling the exergame and recording electrophysiological data for muscle activity during rehabilitation.

sEMG Signal-Sensing Technology

sEMG is composed of electrode patches and sensors that are placed on the surface of the skin to detect signals useful for human-machine interactions, gesture recognition, and muscle function evaluation, as previously described [,]. Touch fasteners were used to position a kneepad with 3 electrode patches at the bottom of the sensor. The changes in muscle potential signals caused by muscle contractions during limb exercise were measured []. The signals were amplified and transmitted by the wireless transmission module to the main unit of the sEMG, then displayed on the screen and stored []. The signals were also transmitted to the Unity game software (Unity Technologies), which serves as an interactive multimedia medium to provide visual feedback to the user ().

‎Figure 1. Illustration of the real-time surface electromyography, circuit diagram, and electromyography signal. Design of the Exergame

The IRES system incorporates several innovative features: (1) it is designed to accommodate both medical professionals and frail older adults, (2) it records the sEMG data of muscle group movement during rehabilitation training, (3) this data is then sent for analysis, (4) feedback is provided to medical professionals to assess and adjust rehabilitation parameters, and (5) it recommends the most suitable rehabilitation treatment for the frail older user. Representative historical and cultural images were used for reminiscence therapy that targeted older adults. For example, photographs of the Hayashi Department Store in Tainan in the 1960s were used as the inspiration to draw the interface background, and the interactive components were also designed based on items that were well-known in Taiwan in the 1960s ().

‎Figure 2. The interface design with the retro style of the IRES (Intelligent Rehabilitation Exergame System).

Three scenes were constructed for 3 major lower limb rehabilitation movements—knee extension (), ankle plantar flexion, and ankle dorsiflexion for the training of the quadriceps, tibialis anterior, and gastrocnemius muscles, respectively. Participants were able to check the remaining time, remaining health points, and cumulative score on the screen and freely set the exergame to return to the main menu, retry, or continue. The virtual physician was also shown on the screen as a coach to guide and encourage the participants to perform correct movements during training. Misplacement of the kneepad was alerted by the virtual physician (Figure S1 in ).

‎Figure 3. The operation procedure of the IRES (Intelligent Rehabilitation Exergame System): (A) frail older adults wearing the kneepad and placing the surface electromyography sensors and (B) entering the exergame system to perform 3 major rehabilitation movements (ie, knee extension, plantar flexion, and dorsiflexion). Operation of the Exergame

Before the start of the experiment, the researcher explained how the IRES operates and how to accurately wear the kneepad and calibrate the placement of the device. After kneepad placement, the participants logged in to the IRES and selected one scene for training. The first try with maximum strength was used to calibrate the baseline, and then the training began. Each movement was repeated 20 times for each leg within 30 minutes. Training was performed 2 times per week (Monday and Wednesday) for 4 weeks. The training record was analyzed for frequency, intensity, time, and type by the IRES and health care professionals to customize the content for the next training (). The environment of the experimental setup is shown in Figure S2 in .

‎Figure 4. Flowchart for operating the IRES (Intelligent Rehabilitation Exergame System) for (A) health care professionals and (B) frail older adults. Participant Recruitment

This prospective cohort study was advertised in 2 community centers (Hesing Village and Liren Village, Tainan, Taiwan). Older adults over 60 years old with no mobility and communication difficulties who were willing to complete the 4-week study were enrolled as the inclusion criteria. The exclusion criteria were as follows: (1) unable to walk independently, (2) unable to understand or follow simple instructions, and (3) unwilling to complete the 4-week training.

Participant Assessment

The Clinical Frailty Scale from 1 (very fit) to 9 (terminally ill) was used to classify the health status of older adults by the physicians at the initial assessment (Table S1 in ) []. Participants assessment was performed after completing the first (week 1, baseline) and the last (week 4, one-month follow-up) training using the SUS for IRES usability [,] and the Taiwanese version of the EQ-­5D questionnaire for quality of life []. The items included in the SUS are listed in Table S2 in .

Ethical Considerations

This study was approved by the National Cheng Kung University Human Research Ethics Committee (NCKU HREC-F-109-497-2), and the participants provided their written informed consent to participate in this study after receiving a comprehensive explanation of the study from the investigators. All data were deidentified before analysis.

Statistical Analysis

As previously described by Chang et al [], at a 95% power and 5% 2-tailed significance level, complete data from at least 15 participants are required to accurately detect differences in the usability analysis. We analyzed power using the G*Power 3.1 software (University of Kiel) []. Descriptive statistical data are reported as the mean and SD. Distributions of continuous variables were evaluated using the Shapiro-Wilk test. To assess statistically significant differences in the studied parameters, the independent samples t test or Mann-Whitney U test was used to compare continuous variables based on the results of the normality tests. The t test was used to verify the correlation between usability or EQ-5D and sex or previous rehabilitation experience at baseline and 1-month follow-up. Statistical significance was defined as P<.05.

ResultsParticipant Demographics and Change in SUS After IRES Rehabilitation

A total of 55 older adults were recruited, including 27 with previous rehabilitation experience and 28 without. Of these participants, 6 were excluded according to the exclusion criteria, leaving 49 participants in the final cohort (). The frailty scale scores of the 49 participants ranged from 1 to 5 (score of 1, four participants; score of 2, ten participants; score of 3, twenty-five participants; score of 4, seven participants; and score of 5, three participants). The mean age of the 49 enrolled participants was 74.6 years.

‎Figure 5. Flowchart for participant recruitment into the study cohort. IRES: Intelligent Rehabilitation Exergame System; SUS: System Usability Scale; EQ-5D: EuroQol-5 Dimensions.

The SUS scores for 10 selected participants at baseline and 1-month follow-up are shown in . The mean SUS score at baseline was 82.09, with a rating of “good.” At 1-month follow-up, the mean SUS score increased to 87.14 with a rating of “good+.” We observed a significant difference in overall usability between the baseline and the 1-month follow-up (t96=−2.19; P=.03), indicating that the participants felt that the IRES was easy to operate and that its usability increased after 1 month of use.

Table 1. SUS scores for 10 selected participants at baseline (mean 82.09, SD 10.62) and 1-month follow-up (mean 87.14, SD 12.17). A significant difference is observed in overall usability between the baseline and the 1-month follow-up (t96=−2.19; P=.03).ParticipantSexCFSPrevious rehabilitation experienceSUS scoresBaseline1-month follow-up1Female5No8572.52Male3No95803Male3No90904Male3No92.587.55Female3No82.597.520Female4No709521Female1No709022Female2No87.510023Female3No10010024Female4Yes809025Female2Yes92.590

aSUS: System Usability Scale.

bCFS: Clinical Frailty Score.

Comparisons of the SUS score between baseline and 1-month follow-up for individual items are shown in . At baseline, only two items, item 6 (inconsistency) and item 7 (learnability), were “average”; all others achieved “good.” One month after completing the IRES training, item 6 (inconsistency) remained “average,” and the others achieved “good.” The scores increased from “average−” to “average+” for item 6 and from “average” to “good+” for item 7.

Table 2. SUS component and total scores at baseline and 1-month follow-up stratified by previous rehabilitation experience and sex.ItemPrevious rehabilitation experienceSexTotal (N=49)Item BenchmarkYes (n=26)No (n=23)Male (n=12)Female (n=37)Mean (SD)MaxMinMean (SD)MaxMinMean (SD)MaxMinMean (SD)MaxMinMean (SD)MaxMin1. WillingnessBaseline4.38 (0.69)534.52 (0.84)534.66 (0.65)534.37 (0.79)534.44 (0.76)53Good+1-month follow-up4.92 (0.27)545.00 (0.00)554.91 (0.28)544.97 (0.16)544.95 (0.19)54Good+2. ComplexityBaseline1.84 (1.04)411.52 (0.79)311.33 (0.88)411.81 (0.93)411.69 (0.93)41Good+1-month follow-up1.80 (1.41)511.43 (1.07)511.50 (1.24)511.67 (1.29)511.63 (1.26)51Good+3. ConvenienceBaseline4.53 (0.64)534.43 (0.84)534.50 (0.79)534.48 (0.73)534.48 (0.73)53Good+1-month follow-up4.34 (0.89)534.78 (0.51)534.58 (0.79)534.54 (0.76)534.55 (0.76)53Good+4. StressBaseline1.11 (0.32)211.04 (0.20)211.08 (0.28)211.08 (0.27)211.08 (0.27)21Good+1-month follow-up1.19 (0.80)511.34 (1.02)511.33 (1.15)511.24 (0.83)511.26 (0.90)51Good+5. IntegrationBaseline4.50 (0.64)534.56 (0.72)534.41 (0.79)534.56 (0.64)534.53 (0.68)53Good+1-month follow-up4.26 (1.28)514.56 (0.99)514.75 (0.62)534.29 (1.26)514.41 (1.15)51Good+6. InconsistencyBaseline2.03 (1.31)512.82 (1.52)512.25 (1.35)512.45 (1.50)512.40 (1.45)51Average–1-month follow-up2.07 (1.29)512.08 (1.20)512.16 (1.19)512.05 (1.26)512.08 (1.23)51Average+7. LearnabilityBaseline3.57 (1.33)513.86 (1.32)513.83 (1.26)523.67 (1.35)513.71 (1.32)51Average1-month follow-up4.81 (0.49)534.69 (0.92)514.66 (1.15)514.78 (0.53)534.75 (0.72)51Good+8. CumbersomenessBaseline1.69 (0.92)411.52 (1.20)511.33 (0.88)411.70 (1.10)511.61 (1.05)51Good1-month follow-up2.11 (1.68)511.82 (1.40)512.41 (1.83)511.83 (1.44)511.97 (1.54)51Good–9. ConfidenceBaseline3.96 (1.03)524.30 (1.06)514.08 (1.24)514.13 (1.00)524.12 (1.05)51Good–1-month follow-up4.26 (1.11)514.91 (0.28)544.50 (1.24)514.59 (0.76)524.57 (0.88)51Good+10. DifficultyBaseline1.80 (1.13)511.56 (0.89)411.83 (1.19)411.64 (0.97)511.69 (1.02)51Good1-month follow-up1.65 (1.19)511.26 (0.75)411.66 (1.30)511.41 (0.92)511.46 (1.02)51Good+

aSUS: System Usability Scale.

The Effect of Sex and Rehabilitation History on SUS

The effect of sex on differences in SUS scores between baseline and 1-month follow-up was evaluated (). No significant differences were observed between the sexes for any of the 10 items evaluated (P>.05), indicating that the usability of the IRES was consistent between male and female participants.

Table 3. Comparison of SUS scores before and after training according to sex (N=49; 12 men and 37 women).ItemBaseline1-month follow-upt test (df)P valuet test (df)P value1. Willingness−1.13 (47).260.84 (47).42. Complexity1.55 (47).120.41 (47).683. Convenience−0.05 (47).95−0.16 (47).864. Stress−0.02 (47).98−0.29 (47).765. Integration0.66 (47).5−1.18 (47).246. Inconsistency0.42 (47).66−0.27 (47).787. Learnability−0.35 (47).720.48 (47).638. Cumbersomeness1.05 (47).29−1.12 (47).269. Confidence0.14 (47).880.31 (47).7510. Difficulty−0.53 (47).59−0.76 (47).44

aSUS: System Usability Scale.

Whether previous rehabilitation experience affected the change in SUS scores between baseline and 1-month follow-up was evaluated (). At baseline, no significant differences were found between participants with and without rehabilitation experience for any of the 10 items (P>.05), indicating that experience does not affect usability. A significant difference between baseline and follow-up was observed for item 3 (convenience: t47=2.05; P=.04) and item 9 (confidence: t47=2.68; P<.001), indicating that IRES use can increase convenience and confidence in older adults without previous rehabilitation experience.

Table 4. SUS component scores before and after training according to previous rehabilitation experience (N=49; yes=26, no=23).ItemBaseline1-month follow-upt test (df)P valuet test (df)P value1. Willingness0.62 (47).531.35 (47).182. Complexity−1.21 (47).23−1.02 (47).313. Convenience−0.48 (47).622.05 (47).044. Stress−0.90 (47).360.59 (47).555. Integration0.33 (47).740.89 (47).376. Inconsistency1.94 (47).060.02 (47).977. Learnability0.76 (47).44−0.53 (47).598. Cumbersomeness−0.55 (47).57−0.64 (47).519. Confidence1.14 (47).252.68 (47)<.00110. Difficulty−0.82 (47).41−1.35 (47).18

aSUS: System Usability Scale.

bSignificant difference between groups.

The effect of previous rehabilitation experience on the difference between baseline and 1-month follow-up on each component of the SUS score was analyzed. A significant difference was observed for item 1 (willingness: t96=−4.51; P<.001), item 7 (learnability: t96=−4.83; P<.001), and item 9 (confidence: t96=−2.27; P=.02). Among older adults with previous rehabilitation experience, we observed significant differences in item 1 (willingness: t50=−3.66; P<.001) and item 7 (learnability: t50=−4.42; P<.001). Among older adults without previous rehabilitation experience, significant differences were observed in item 1 (willingness: t44=−2.71; P<.001), item 7 (learnability: t44=−2.45; P=.01), and item 9 (confidence: t44=−2.65; P=.021) ().

Table 5. Changes in SUS component scores between baseline and 1-month follow-up according to previous rehabilitation experience.ItemBaseline vs 1-month follow-upTotal (N=49)Previous rehabilitation experience (n=26)No rehabilitation experience (n=23)t test (df)P valuet test (df)P valuet test (df)P value1. Willingness−4.51 (96)<.001−3.66 (50)<.001−2.71 (44)<.0012. Complexity0.27 (96).780.11 (50).910.31 (44).753. Convenience−0.40 (96).680.89 (50).37−1.68 (44).094. Stress−1.35 (96).17−0.45 (50).65−1.39 (44).175. Integration0.64 (96).520.81 (50).410.00 (44)>.996. Inconsistency1.19 (96).23−0.11 (50).911.82 (44).077. Learnability−4.83 (96)<.001−4.42 (50)<.001−2.45 (44).018. Cumbersomeness−1.37 (96).17−1.12 (50).26−0.79 (44).439. Confidence−2.27 (96).02−1.02 (50).3−2.65 (44).0110. Difficulty1.08 (96).280.47 (50).631.24 (44).21

aSUS: System Usability Scale.

bSignificant difference between baseline and 1-month follow-up.

The Change in Quality of Life After IRES Rehabilitation

The change in participant quality of life between baseline and 1-month follow-up was evaluated using the Taiwanese version of the EQ-5D questionnaire (). For the total cohort, all males and all females quality of life improved significantly after the IRES training program (all P<.05), indicating a positive effect on the quality of life of older adults regardless of sex.

Table 6. Differences in the EQ-5D between baseline and 1-month follow-up according to sex.EQ-5DTotal (N=49)Male (n=12)Female (n=37)t test (df)P valuet test (df)P valuet test (df)P valueBaseline0.64 (96)0.120.65 (22)0.130.64 (72)0.121-Month Follow-Up0.74 (96)0.10.74 (22)0.110.74 (72)0.1Total6.03 (96)<.0012.47 (22)0.035.49 (72)<.001

aEQ-5D: EuroQol-5 Dimensions.

bSignificant difference between baseline and 1-month follow-up.

DiscussionPrincipal Findings

We examined the usability of IRES and its effects on the quality of life of frail older adults. The results indicate that IRES increased the willingness of frail older adults to undergo continuous long-term rehabilitation training. The learnability and confidence (ie, self-affirmation) were also higher after 1 month of training compared to those at the baseline. This result indicates that IRES that combines gamified rehabilitation content and movements instructed by a virtual coach can increase the user’s ability to operate and understand the system, thereby decreasing their uncertainty and increasing their confidence in using this novel technology product. In addition, no gender differences were found in the SUS scores between baseline and 1-month follow-up, indicating that both male and female older adults had consistent subjective willingness to use the IRES for the duration of the training. A significant difference was observed in the quality of life between baseline and 1 month after IRES training as assessed by the EQ-5D, further indicating that the quality of life of the participants improved, regardless of their sex.

The Evolution and Current Trends of Digital Rehabilitation Aids

Rehabilitation aids that combine exercise and games are effective training methods for alleviating frailty and maintaining the health and quality of life of older adults. Such aids also encourage older adults to participate in long-term rehabilitation training, effectively improving physical and mental function while decreasing societal health care burdens []. With the maturation and popularization of digital technology, digital games have been used in emerging cognitive training models. Such games, known as serious games, differ from normal entertainment games, as they are purposeful and beneficial to the players. Serious games can encourage older adults to exercise and then increase their physical activity level, further improving muscle strength and cardiopulmonary fitness. Serious games also overcome the low adherence that is generally observed in traditional rehabilitation training, thereby improving the motivation for long-term training []. By improving mental focus and increasing muscle mass, exergames can increase the balance, gait, and activity capacity of older adults. Moreover, the integration feature of exergames for older adults provides psychological health benefits [-]. The benefit of IRES on the self-esteem and quality of life of the older adults observed in this study likely results from the exergame-based training.

Indeed, information and communications technology and auxiliary life services can effectively improve the life satisfaction of older adults and may increase their willingness to use technology products or services. Considering the larger health care needs of this population, their travel limitations, and health care personnel shortages, intelligent telemedicine care models should be integrated into current health care systems as new tools or services to maintain patient health and prevent disease [,]. The following 3 major features need to be incorporated into future health care systems: (1) information and data platform improvements, including the collection and analysis of patient data, immediate provision of individual assistance, and data sharing among medical professionals; (2) health and care services, including the use of virtual and physical communities to provide customized products and care services for patients; and (3) care service support, including supporting and assisting patient care services and promoting health and welfare [,]. The appropriate incorporation of intelligent telemedicine can promote the reformation of traditional health care. In the UK National Health Service, the long-term plan is to make intelligent medicine a mainstream practice []. However, customized training modules and diverse service content are critical factors proven to affect user acceptance of intelligent telemedicine systems for both patients and health care professionals [].

Features and Advantages of the IRES Program Design

The IRES uses gamified training content to increase the pleasure of frail older adults during training, thereby increasing their motivation to continue long-term rehabilitation. In addition, the IRES effectively records, transmits, and analyzes data regarding lower limb muscle movement by sEMG. sEMG measures changes in muscle potential signals that occur when muscles in different motor units are stimulated by motor neurons during limb exercise. The contraction of individual muscles during movements and their potential differences were used to indirectly evaluate individual muscle strength []. sEMG is a clinical method widely used in human-machine interactions, hand gesture recognition, and muscle function evaluation. This technology provides data that can be used to evaluate muscle activity and joint movement []. These data can be reviewed and studied by health care professionals to evaluate and adjust exercise regimens to provide the most suitable customized rehabilitation treatment for frail older adults.