Sarcopenia, defined as an age-related loss of skeletal muscle mass and a decline in muscle strength and physical performance, is a well-known geriatric syndrome that has gained increasing attention in public health (Cruz-Jentoft et al., 2010). Using the definitions of the Asian Working Group for Sarcopenia, a recent cross-sectional study in Taiwan reported that 9.3% and 4.1% of community-dwelling older men and women, respectively, aged over 65 years had sarcopenia (Kuo et al., 2019). Several studies have shown that sarcopenia may increase the risk of dependence in activities of daily living, falls, fractures, disabilities, and even death (Ali & Garcia, 2014; Janssen, Heymsfield, & Ross, 2002). Many strategies have been proposed to prevent and treat sarcopenia; however, first-line strategies focus on preserving skeletal muscle mass and maintaining muscle strength (Lo et al., 2017) because muscular degeneration begins at the age of 30 years and accelerates with aging. People more than 70 years old may lose 15% of their muscle mass every decade (Melton et al., 2000). Progressive resistance training (PRT) has been proven to be effective in gaining lean body mass, thereby inducing muscle hypertrophy and increasing muscle strength (Christie, 2011; Daly et al., 2013; Papa, Dong, & Hassan, 2017); therefore, it should be considered the primary intervention for sarcopenia (Ali & Garcia, 2014), and older adults should perform PRT 2–3 days per week to attenuate the effects of sarcopenia (Papa et al., 2017).
The prevalence of sarcopenia is much higher in residential facilities than in the community, ranging between 17.7% and 73.3% in long-term nursing homes and between 22% and 87% in assisted-living facilities (Liu & Latham, 2009). Although the Taiwanese government has established a long-term health care system to guarantee suitable services, the shortage of health care professionals is still a serious issue because Taiwan is one of the fastest-aging countries in the world (Wang & Tsay, 2012). The principles of rehabilitation and PRT programs include repetitive, mass-practiced, overwhelming, and task-oriented exercises (Winstein et al., 2016). Achieving these principles would demand devoting time, workforce, and money to rehabilitation. Interactive video gaming and virtual reality (VR) provide new platforms for the delivery of exercise programs, particularly in settings facing workforce shortages (Scott, Callisaya, Duque, Ebeling, & Scott, 2018). With VR, individuals can practice activities within enriched, secure, and challenging environments, thereby favoring motor learning and neural plasticity (de Bruin, Schoene, Pichierri, & Smith, 2010). Furthermore, VR-based systems using a sensor technology to monitor whole-body movements can reduce staff time for intervention, encourage patients to perform relatively high-energy movements, and increase patients’ motivation (Chang, Chen, & Huang, 2011). Several studies have suggested that VR-based exercise in older patients improves their mobility, leg strength, balance, and executive functions (Molina, Ricci, de Moraes, & Perracini, 2014). Given that older adults with sarcopenia have a higher potential risk of fall, fully immersive VR-based rehabilitation should be supervised and reduce unnecessary head movement and walking. To our knowledge, no available studies have evaluated the viability of a combination of PRT and VR focusing on the training of the upper extremity in aged sarcopenic individuals before this study. Just a few studies demonstrated that PRT in older adults using wheelchairs can significantly improve their functional fitness and activities of daily living by training the upper extremities (Chen, Li, Chang, Huang, & Cheng, 2015; Chen et al., 2016), and only one small pilot study observed that a fully immersive VR program for rehabilitation of the upper extremities was practical without serious adverse effects (Lee, Jung, Yun, Oh, & Seo, 2020). In this study, we designed a PRT rehabilitation program focusing on the upper extremity provided via VR (VR-REH) and aimed to evaluate the clinical effectiveness of this VR-REH among older patients with sarcopenia in health care facilities.
Materials and Methods
Subject Characteristics and Ethical Statements
In this quasi-experimental study without a control group, preintervention and postintervention measures were taken (refer to the section below for detailed information regarding primary and secondary outcomes and interventions). This study was conducted in southern rural Taiwan from January 2019 to July 2019. Older adults aged over 60 years and living in a nursing home or regularly visiting a day-care center were recruited for screening. Residents with sufficient cognitive function and physical fitness to perform training lasting 30 min and who met the following criteria of the Asian Working Group for Sarcopenia were included as participants in the final VR-REH: (a) a muscle mass of <7.0 kg/m2 for men and 5.7 kg/m2 for women, estimated using bio-impedance analysis and (b) a handgrip strength (HGS) of <26 kg for men and 18 kg for women or a usual gait speed of <0.8 m/s. Those who had significant cardiopulmonary diseases and received oxygen supplementation or had uncontrollable hypertension, experienced a recent infection, or were diagnosed with other diseases and prohibited to participate in sports exercise according to the guidelines of the American College of Sports Medicine were excluded (Garber et al., 2011). In addition, patients who could not walk even with the use of assistive devices were excluded from this study. The G*Power software (version 3.1.9.2; Heinrich-Heine-Universität, Düsseldorf, Germany) for Windows was used to determine the sample size. F test family with a statistical test of repeated-measures one-way analysis of variance was chosen. The alpha level and power were set as .05 and 0.8, respectively. Due to a lack previous study data, a medium effect size with partial eta squared .06 (Lakens, 2013) was set, and the minimal estimated sample size was 23 subjects. Considering a dropout rate of 10%, the final minimal estimated sample size should be 26. Written informed consent was obtained from all participants. This study was approved by the institutional review board of a medical center in Taiwan (approval number: 18-034) and registered at ClinicalTrials.gov (registration number: NCT03809104).
VR-Based Rehabilitation Programs
The program contained PRT and functional movement of the dominant upper limb. The VR device used in this study comprised one computer, one Oculus Rift headset, one constellation, and one Leap Motion sensor (Figure 1). The Oculus Rift headset was equipped with an organic light-emitting diode panel for each eye, with each eye having a resolution of 1,080 × 1,200. These panels had a refresh rate of 90 Hz and were globally refreshed. The Oculus Rift headset is portable, lightweight, and works with a standard PC hardware. Constellation is the positional tracking system of Oculus Rift and is used to track the position of a user’s head and other VR devices; furthermore, it comprises external infrared tracking sensors that optically track specially designed VR devices (Gleasure & Joseph, 2016). The handheld sensor used in this study was the Leap Motion Controller instead of an Oculus Touch controller because the hand dexterity of our participants was insufficient. The Leap Motion Controller is a small universal serial bus peripheral device that can be mounted onto a VR headset. This controller is designed for hand tracking in VR using two monochromatic infrared cameras and three infrared light-emitting diodes. A roughly hemispherical area with a distance of approximately 1 m can be observed (Rosa & Elizondo, 2014).
—The hardware of the virtual reality–based rehabilitation. The participant sat or stood 1 m in front of the screen and was supervised by 1 well-trained caregiver: (a) Oculus Rift Headset, (b) Oculus constellation: sensor for headset, (c) leap motion: sensor for hand movement, and (d) handheld device.
Citation: Journal of Aging and Physical Activity 29, 5; 10.1123/japa.2020-0222
The software used in VR-REH contained four commercialized VR games, namely (a) Leap Motion Blocks®, (b) Slum Ball VR Tournament®, (c) VR Super Sports® 10th edition–Basketball, and (d) VR Super Sports® 10th edition–Soccer (Figure 2). The tasks were analyzed based on Gentile’s motor learning theory, and games were selected based on the body stability and stationary conditions of the participants in the initial practice (Wüest, van de Langenberg, & de Bruin, 2014; Table 1). Online supplementary appendices provide the comprehensive text descriptions for each game (see Supplementary Table 1 [available online]). Each game was performed by the participants for 6 min under the assistance of a well-trained physiotherapist. A break of 1–2 min was kept between each game to allow the participants to adjust to the settings and Oculus Rift headset. A designed mass was held on the dominant hands of the participants, and its weight was gradually increased during VR-REH and was adjusted based on the ability of each participant. Furthermore, the participants were provided with 5 min of warm-up and cooldown exercises before and after VR-REH, respectively. The warm-up and cooldown exercises were mainly composed of progressive static stretch of the neck, chest, arm, thighs, and legs, which were designed for flexibility exercise. According to the recommendations of the American College of Sports Medicine (Garber et al., 2011) and other studies (Chen et al., 2016), VR-REH was performed twice per week, 30 min per session, for 12 weeks. A break of at least 48 hr was set between trainings. All participants, whether in a nursing home or in a day-care center, were given the same VR-REH. This study did not have a control group.
—The software of the VR-based rehabilitation. (a) Leap Motion Blocks®. Leap Motion Blocks® offers a platform in which the subjects can play freely with blocks; they can create blocks, grab, push, pile, and throw blocks around. The subjects are guided to perform a sequence of actions in the game. First, they need to pinch and stretch with both of their hands to create several blocks. Then, they need to push, pile, and throw blocks around. (b) Slum ball VR tournament®. In Slum Ball VR Tournament®, objects such as tin cans and balls will keep flying toward the player, and the player needs to hit the upcoming objects back to a specific location. (c) VR Sports-basketball®. (d) VR Sports-soccer®. In VR Super Sports® 10th edition–Basketball, the player needs to keep grabbing a basketball and throwing it out into the basket, while the participant becomes the goalkeeper of a soccer game and needs to use both of their hands to block the upcoming ball. VR = virtual reality.
Citation: Journal of Aging and Physical Activity 29, 5; 10.1123/japa.2020-0222
VR Games Corresponding to Gentile’s Skill Categories (Wüest et al., 2014)
| Action function | |||||
|---|---|---|---|---|---|
| Environment contexta | Body stabilityc | Body transportc | |||
| No object manipulationd | Object manipulationd | No object manipulationd | Object manipulationd | ||
| Stationary regulatory conditions | No intertrial variabilityb | Leap motion block | |||
| Intertrial variabilityb | Basketball | ||||
| In-motion regulatory conditions | No intertrial variabilityb | ||||
| Intertrial variabilityb | Slum ball VR tournament | Soccer | |||
Note. VR = virtual reality.
aThe regulatory conditions indicate relevant environmental features that constrain movement execution and may either be stationary (stationary regulatory conditions) or moving (in-motion regulatory conditions). bWith the indicator, intertrial variability is used to differentiate between regulatory conditions that change between trials (intertrial variability) and those that do not (no intertrial variability). cBody orientation indicates whether an action requires the performer to move from one location to another (body transport) or not (body stability). dObject manipulation indicates whether an object has to be controlled during the action performance (object manipulation) or not (no object manipulation).
According to Gentile, the easiest skill category can be found at the top left position. Moving either rightward or downward in the table renders the skill category more difficult.
Baseline Screening and Outcome Measurements
All participants were subjected to four evaluations. The first evaluation was the baseline, which was conducted to ensure that the participants met the definition of sarcopenia before they were included in VR-REH. The second, third, and fourth evaluations were conducted at 4, 8, and 12 weeks, respectively. The clinical effectiveness of VR-REH in older patients with sarcopenia in residential health care facilities was evaluated. Therefore, the primary outcomes chosen for this study were criteria for the diagnosis of sarcopenia, including appendicular skeletal muscle mass index (ASMMI), dominant HGS, and usual gait speed.
The target population in this study was older adults in a nursing home and day-care center. Many of them had functional disabilities that led to sedentary lifestyles or even immobilization, resulting in complications such as joint contracture (Fischer et al., 2015), muscle atrophy (Resnick, 2000), and loss of manual dexterity (Julien et al., 2017). Therefore, the secondary outcomes were the range of motion (ROM) of the joints of the dominant upper limbs, biceps, and triceps brachii muscle strength and box and block test (BBT) scores.
Anthropometry and Body Composition
Dual-energy X-ray absorptiometry, computed tomography, magnetic resonance imaging, and bioelectrical impedance analysis (BIA) have been used to evaluate participants’ appendicular skeletal muscle mass (Heymsfield, Adamek, Gonzalez, Jia, & Thomas, 2014). Among these methods, BIA has been widely used in clinical settings because it is relatively simple, quick, and noninvasive. Multifrequency BIA with tetrapolar electrodes provides a reliable means to measure the body composition of healthy subjects and patients with stable water levels (Nuñez et al., 1997; Pietrobelli, Rubiano, St-Onge, & Heymsfield, 2004). Therefore, we used BIA to measure the body composition of the participants using a BIA vector analysis software with the resistance–reactance graph method (Guida et al., 2008). Zeus 9.9 PLUS (Jawon Medical Co., Ltd., Kungsang Bukdo, South Korea) was used to examine the participants’ body composition. The machine transmits a low electric current; body composition was measured using the participants’ personal data (height, weight, sex, age, and newly calculated body impedance) saved via the tetrapolar electrodes (i.e., electrodes were placed on both hands, both soles of the feet, and both ankles of the participants at frequencies of 1, 5, 50, 250, 550, and 1,000 kHz and a current of 360 μA). ASMMI was defined as the appendicular skeletal muscle mass (in kilograms) divided by the height squared (in meters squared; Chen et al., 2014).
Measurement of Dominant Hand Grip Strength
The HGS was measured using a JAMAR dynamometer (J A Preston Corporation, New York, NY) using all five notches. It is a hydraulic instrument measuring isometric strength in kilograms, and a study has investigated its reliability in various older populations (Vermeulen et al., 2015). Each participant was shown the correct handling and positioning of the instruments and was asked to sit straight with their upper arm in a neutral position and their elbow flexed at 90°. The forearm was held in a neutral position, and the wrist was subjected to 0°–30° extension. The instrument was freely held, that is, neither the hand nor the forearm was allowed to rest on a surface (Neumann, Kwisda, Krettek, & Gaulke, 2017). Three measurements were conducted, and the highest of the three measurements was recorded. The participants were allowed to rest for 1 min between measurements. The minimal clinically important difference (MCID), defined as the minimal amount of change that is required to distinguish a true performance change due to variability in performance or measurement error, is more important and practical to use when using scales (Bobos, Nazari, Lu, & MacDermid, 2020). In a recent meta-analysis, the MCID of HGS was 2.44–2.69 kg in the healthy group without conditions (Bobos et al., 2020).
Measurement of Gait Speed
The usual gait speed was evaluated by measuring the time spent by a participant to walk a 6-m long corridor without a barrier, as suggested by Asian Working Group for Sarcopenia (Chen et al., 2014). Timing was initiated once the participants started walking and stopped at the point when they reached a distance of 6 m. Gait speed was recorded twice, and the final measurement was the average of the two speeds. The participants were allowed to rest for 10 min between measurements. In a large systematic review, the MCID of gait speed across multiple patient groups was 0.10–0.20 m/s (Bohannon & Glenney, 2014).
Measurement of the ROM of the Joints of Dominant Upper Extremity
The ROMs of the dominant upper extremity of the participants were measured using a goniometer under standard positions (Norkin & White, 2009), including shoulder flexion, abduction, and external rotation; elbow flexion and extension; forearm supination and pronation and wrist flexion and extension.
Measurement of Dominant Biceps and Triceps Brachii Muscle Strength
The microFET® 3 (Hoggan Health Industries, West Jordan, UT) was used to measure the maximal voluntary isometric contraction (MVIC) of the biceps and triceps brachii. It is an electronic handheld dynamometer that can detect 0–150 lb of force with high reliability and validity (Buckinx et al., 2017). To measure the MVIC of the biceps brachii, the participants were asked to lie on the treatment table with their elbows forming a 90° angle to the horizontal such that the arm is perpendicular to the limb. The microFET 3 was placed on the ventral side of the arm and aligned with the ulnar styloid. Then, a resistant force was exerted toward the device, and the participants were encouraged to go against the force with their maximum strength. The same procedure was used to measure the MVIC of the triceps brachii, except that the device was placed on the dorsal side of the participants’ arms (Stark, Walker, Phillips, Fejer, & Beck, 2011). MVIC was determined twice, and the final measurement was the average of the two measurements. The participants were allowed to rest for 1 min between measurements.
Box and Block Test
The setup of BBT comprised a wooden box with 150 wooden blocks inside. The wooden box was divided into two compartments. One compartment was filled with blocks, whereas the other box was empty. During the test, the participants were seated upright in a chair and instructed to transfer the wooden blocks one by one from one compartment to the other. The participants were scored based on the number of blocks that they transferred from one compartment to the other in 60 s. BBT has high test–retest reliability and validity and can be used to measure the unilateral gross manual dexterity in various populations (Desrosiers, Bravo, Hebert, Dutil, & Mercier, 1994). Most studies on the MCID of the BBT have been conducted on patients with stroke, and the MCID was 5.5 cubes/min for the most affected side and 7.8 cubes/min for the least affected side (Chen, Chen, Hsueh, Huang, & Hsieh, 2009).
Statistical Analyses
For all analyses, Statistical Package for the Social Sciences (version 19.0; IBM Corp., Armonk, NY) was used. Continuous data are expressed as mean ± SD, and categorical variables are presented as absolute numbers or percentages. Normality and homoscedasticity were evaluated before each analysis. The chi-squared test, independent t test, and Mann–Whitney U test were conducted to examine the differences in the distribution between categorized, normally distributed and nonnormally distributed variables, respectively, and to compare the data between the participants in the nursing home (PNHs) and participants in the day-care center (PDCs). Changes in the outcome measures at the baseline and 1, 2, and 3 months after VR-REH were evaluated using repeated-measures one-way analysis of variance and Bonferroni’s post hoc test. Data with p values of ≤.05 were considered statistically significant.
Results
Patient Characteristics
Of the 33 participants recruited for VR-REH, 30 completed the study, and three withdrew during the study period (i.e., two dropped out during the visit at 4 weeks because of hospitalization, and one withdrew at 8 weeks because the patient was released from the day-care center). The male-to-female ratio was 1:2. The mean age was approximately 74.57 years. Fifteen PNHs and 15 PDCs were enrolled in the study. No significant differences in the basic characteristics between PNHs and PDCs were observed. The mean body weight of the PDCs was larger than that of the PNHs; however, the difference was not significant (p = .065; Table 2). Although all of the participants could walk (with/without assistive device), 6 of the 15 PNHs and 3 of the 15 PDCs performed VR-REH in the sitting position because (a) they were used to conducting community ambulation by a wheelchair (5 of 6 in PNHs) and (b) they had a history of falling based on their medical records and statements (1 of 6 in PNHs and 3 of 3 in PDCs). Regarding adverse events, no falling incidents occurred during the study period. Five participants developed lightheadedness at the beginning of the program; however, this condition subsided after they got used to VR. Three participants complained of sore eyes after the 30-min VR-based program; however, their condition improved after they rested. No participants dropped out of the study because of the aforementioned discomforts.
Demographic Characteristics of the Participants at Baseline Evaluation
| Characteristics | Total (n = 30) | Nursing home (n = 15) | Day care (n = 15) | p valuea |
|---|---|---|---|---|
| Gender (M:F) | 10:20 | 4:11 | 6:9 | .284 |
| Age (year) | 74.57 ± 12.83 | 72.36 ± 16.42 | 77.00 ± 7.35 | .422 |
| Height (cm) | 156.97 ± 8.76 | 158.30 ± 5.48 | 153.31 ± 11.92 | .489 |
| Weight (kg) | 58.74 ± 10.89 | 54.13 ± 9.35 | 63.93 ± 10.64 | .065 |
Note. Data are presented as mean ± SD or numbers.
aRefers to the p value of a Mann–Whitney U test (continuous variables) or chi-square test (categorical variables) between participants in nursing home and day care.
Primary Outcomes
The data of the primary outcomes at the baseline and three monthly visits are shown in Table 3. The mean HGS of all participants increased by 0.17, 2.24, and 5.02 kg from the baseline at the 1-, 2- and 3-month follow-ups. The improvement in the mean HGS at 3 months after VR-REH was beyond the MCID. The HGS significantly increased after VR-REH (p < .001). Bonferroni’s post hoc analysis revealed that the data obtained at 2 and 3 months after VR-REH significantly differed from the baseline data. In addition, gait speed significantly improved after VR-REH (p = .006). Bonferroni’s post hoc analysis revealed that the data obtained 3 months after VR-REH significantly differed from the baseline data and that the increase was beyond the MCID. The appendicular skeletal muscle mass, ASMMI, fat-free mass, and fat-free mass index did not significantly change from the baseline at the 1-, 2-, and 3-month follow-ups.
Data of Primary Outcomes (Body Composition, Hand Grip Strength, and Gait Speed) at Baseline, the First Month, the Second Month, and the Third Month
| Variables | Baseline | First month | Second month | Third month | F value | Effect size | p valuea |
|---|---|---|---|---|---|---|---|
| ASM of four extremities (kg) | |||||||
| Total | 38.28 ± 12.05 | 39.13 ± 11.27 | 39.11 ± 11.54 | 39.37 ±11.13 | 0.856 | 0.3273 | .474 |
| Nursing home | 30.60 ± 5.88 | 32.48 ± 3.74 | 31.62 ± 3.67 | 33.09 ± 3.59 | 1.147 | 0.4592 | .370 |
| Day care | 44.67 ± 12.43 | 44.67 ± 12.72 | 45.38 ± 12.36 | 44.59 ± 12.86 | 0.823 | 0.1329 | .406 |
| ASMI of four extremities (kg/m2) | |||||||
| Total | 15.47 ± 4.05 | 15.72 ± 3.44 | 16.13 ± 4.32 | 15.68 ± 3.53 | 0.874 | 0.2308 | .423 |
| Nursing home | 12.14 ± 1.67 | 12.94 ± 0.85 | 12.61 ± 1.17 | 12.90 ± 1.41 | 0.974 | 0.4674 | .390 |
| Day care | 18.25 ± 3.19 | 18.04 ± 3.00 | 19.06 ± 3.68 | 17.99 ± 3.02 | 1.30 | 0.0672 | .295 |
| FMI (kg/m2) | |||||||
| Total | 8.27 ± 1.48 | 8.44 ± 1.55 | 8.44 ± 1.64 | 8.41 ± 1.86 | 0.585 | 0.275 | .630 |
| Nursing home | 7.78 ± 0.82 | 7.69 ± 1.30 | 7.83 ± 1.16 | 7.13 ± 1.75 | 1.228 | 0.211 | .342 |
| Day care | 9.07 ± 2.19 | 9.68 ± 1.11 | 9.45 ± 2.07 | 9.47 ± 1.20 | 3.050 | 0.8227 | .061 |
| FFMI (kg/m2) | |||||||
| Total | 15.92 ± 2.86 | 16.02 ± 2.14 | 16.58 ± 3.33 | 15.96 ± 2.29 | 0.910 | 0.1501 | .400 |
| Nursing home | 13.50 ± 1.27 | 14.13 ± 0.68 | 13.90 ± 1.15 | 13.99 ± 1.34 | 0.763 | 0.4226 | .454 |
| Day care | 17.94 ± 2.06 | 17.59 ± 1.50 | 18.82 ± 2.82 | 17.60 ± 1.41 | 1.201 | 0.031 | .328 |
| HGS (kg) [MCID: 2.44–2.69 kg] | |||||||
| Total | 11.34 ± 5.09 | 11.51 ± 5.03 | 13.58 ± 3.83 | 16.36 ± 3.84d | 24.575 | 0.9574 | <.001*,b,c |
| Nursing home | 9.42 ± 4.92 | 10.47 ± 6.72 | 11.75 ± 3.40 | 14.91 ± 4.15d | 9.891 | 1.0085 | <.001*,c |
| Day care | 12.78 ± 5.03 | 12.29 ± 3.61 | 14.96 ± 3.74 | 17.81 ± 3.07 d | 15.203 | 0.896 | .001*,b,c |
| Gait speed (m/s) [MCID: 0.1–0.2 m/s] | |||||||
| Total | 0.42 ± 0.19 | 0.55 ± 0.33 | 0.62 ± 0.34 | 0.62 ± 0.32d | 8.658 | 1.0065 | .006*,c |
| Nursing home | 0.42 ± 0.07 | 0.42 ± 0.22 | 0.48 ± 0.18 | 0.53 ± 0.17d | 1.708 | 0.3598 | .265 |
| Day care | 0.42 ± 0.24 | 0.62 ± 0.38 | 0.70 ± 0.40 | 0.67 ± 0.38d | 10.432 | 1.6097 | <.001*,b |
Note. ASM = appendicular skeletal muscle mass; ASMI = appendicular skeletal muscle mass index; FMI = fat mass index; FFMI = fat-free mass index; HGS = hand grip strength; MCID = minimal clinically important difference.
aAnalysis by repeated-measures one-way analysis of variance. bPost hoc analysis by Bonferroni test showed significantly different data between baseline and the second month. cPost hoc analysis by Bonferroni test showed significantly different data between baseline and the third month. dThe change of data between the third month and the baseline was beyond the minimal clinically important difference.
*p < .05.
For PNHs, HGS significantly improved after the rehabilitation compared with that at the baseline HGS (p < .001). Furthermore, post hoc analysis revealed that the data obtained at 3 months after the rehabilitation significantly differed from the baseline data. The other primary outcomes did not significantly change. Furthermore, the HGS and gait speed of the PDCs significantly improved after the rehabilitation compared with the baseline HGS and gait speed (p = .001 and p < .001, respectively). Post hoc analysis indicated that the HGS obtained at 2 and 3 months after the rehabilitation and gait speed detected at 2 months after the rehabilitation significantly differed from those at the baseline. The appendicular skeletal muscle mass, ASMMI, fat-free mass, or fat-free mass index did not significantly change.
Secondary Outcomes
The data of the secondary outcomes (i.e., ROMs of the upper extremity, MVIC of the biceps and triceps brachii, and BBT scores) at the baseline and at the 1-, 2-, and 3-month follow-ups are presented in Table 4.
Data of Secondary Outcomes (ROM of Upper Extremity, Strength of Biceps and Triceps Brachii Muscle, and BBT) at Baseline, the First Month, the Second Month, and the Third Month
| Variable | Baseline | First month | Second month | Third month | F value | Effect size | p valuea |
|---|---|---|---|---|---|---|---|
| Shoulder flexion (°) | |||||||
| Total | 130.36 ± 14.32 | 139.07 ± 11.01 | 143.07 ± 8.43 | 149.79 ± 8.70 | 29.265 | 1.6565 | <.001*,b,c,d,e,f |
| Nursing home | 133.00 ± 7.87 | 141.67 ± 7.94 | 143.50 ± 4.59 | 148.83 ± 5.12 | 12.866 | 1.6429 | <.001*,b,d |
| Day care | 128.38 ± 18.05 | 137.13 ± 13.04 | 142.75 ± 10.81 | 150.50 ± 10.98 | 17.601 | 1.7524 | <.001*,d,e,f |
| Shoulder-external rotation (°) | |||||||
| Total | 71.68 ± 10.64 | 75.50 ± 5.95 | 77.36 ± 4.68 | 79.50 ± 3.70 | 6.905 | 0.835 | .013*,e |
| Nursing home | 66.83 ± 9.68 | 72.17 ± 6.24 | 76.33 ± 4.55 | 78.17 ± 2.86 | 8.349 | 1.2934 | .002*,e |
| Day care | 75.31 ± 10.38 | 78.00 ± 4.60 | 78.13 ± 4.94 | 80.50 ± 4.11 | 1.523 | 0.5357 | .238 |
| Shoulder abduction (°) | |||||||
| Total | 128.79 ± 25.61 | 130.71 ± 15.70 | 138.21 ± 9.94 | 141.93 ± 9.52 | 4.854 | 0.5295 | .022*,e |
| Nursing home | 130.00 ± 27.99 | 137.33 ± 14.83 | 142.17 ± 10.63 | 140.83 ± 12.35 | 1.264 | 0.5384 | .322 |
| Day care | 127.88 ± 25.61 | 125.75 ± 15.33 | 135.25 ± 8.91 | 142.75 ± 7.59 | 5.402 | 0.521 | .037*,e,f |
| Elbow flexion (°) | |||||||
| Total | 120.29 ± 10.89 | 117.29 ± 7.57 | 118.79 ± 8.54 | 119.93 ± 6.88 | 0.635 | 0.1745 | .498 |
| Nursing home | 115.67 ± 7.39 | 118.33 ± 4.84 | 117.17 ± 6.27 | 118.17 ± 4.50 | 1.687 | 0.6088 | .212 |
| Day care | 123.75 ± 12.22 | 116.50 ± 9.38 | 120.00 ± 10.17 | 121.25 ± 8.08 | 1.157 | 0.4142 | .349 |
| Elbow extension (°) | |||||||
| Total | 3.71 ± 2.27 | 3.43 ± 1.55 | 3.79 ± 1.12 | 4.64 ± 0.63 | 2.534 | 0.1356 | .121 |
| Nursing home | 2.17 ± 2.23 | 3.17 ± 1.47 | 3.33 ± 1.03 | 4.50 ± 0.55 | 6.979 | 1.9693 | .004* |
| Day care | 4.88 ± 1.55 | 3.63 ± 1.69 | 4.13 ± 1.12 | 4.75 ± 0.71 | 1.659 | 0.4074 | .237 |
| Elbow supination (°) | |||||||
| Total | 56.07 ± 15.27 | 56.64 ± 9.95 | 59.14 ± 9.24 | 66.86 ± 9.45 | 5.008 | 0.3954 | .026*,e,f |
| Nursing home | 51.00 ± 17.16 | 57.00 ± 10.77 | 59.50 ± 9.57 | 64.00 ± 10.22 | 5.839 | 1.0549 | .042* |
| Day care | 59.87 ± 13.58 | 56.38 ± 10.04 | 58.88 ± 9.64 | 69.00 ± 8.90 | 2.533 | 0.1156 | .140 |
| Elbow pronation (°) | |||||||
| Total | 59.43 ± 18.33 | 60.43 ± 13.23 | 63.21 ± 13.10 | 69.21 ± 9.37 | 5.406 | 0.4688 | .015*,e,f |
| Nursing home | 63.50 ± 15.37 | 60.83 ± 11.16 | 63.67 ± 6.92 | 68.17 ± 8.21 | 1.910 | 0.0848 | .171 |
| Day care | 56.38 ± 20.75 | 60.13 ± 15.37 | 62.88 ± 16.86 | 70.00 ± 10.65 | 4.074 | 0.7205 | .057 |
| Wrist flexion (°) | |||||||
| Total | 51.86 ± 9.80 | 56.36 ± 8.82 | 62.07 ± 5.90 | 66.71 ± 4.68 | 11.96 | 0.9578 | <.001*,c,d,e |
| Nursing home | 50.83 ± 7.44 | 61.50 ± 4.14 | 62.83 ± 5.98 | 66.67 ± 4.46 | 10.980 | 1.6193 | <.001*,d |
| Day care | 52.63 ± 11.72 | 52.50 ± 9.62 | 61.5 ± 6.19 | 66.75 ± 5.15 | 6.624 | 0.723 | .003*,d,e |
| Wrist extension (°) | |||||||
| Total | 52.50 ± 10.90 | 58.64 ± 12.60 | 65.93 ± 9.04 | 69.86 ± 7.49 | 18.265 | 1.2455 | <.001*,c,d,e |
| Nursing home | 54.33 ± 6.37 | 60.67 ± 9.46 | 64.67 ± 8.89 | 69.50 ± 8.64 | 9.204 | 1.2955 | .009*,d |
| Day care | 51.13 ± 13.66 | 57.13 ± 14.99 | 66.88 ± 9.64 | 70.13 ± 7.12 | 9.969 | 1.2649 | <.001*,c,d |
| Biceps strength (kg) | |||||||
| Total | 5.03 ± 1.31 | 5.48 ± 1.28 | 5.80 ± 1.22 | 6.44 ± 1.21 | 19.633 | 1.1963 | <.001*,c,d,e,f |
| Nursing home | 4.78 ± 1.01 | 4.87 ± 0.69 | 5.35 ± 0.73 | 6.03 ± 1.12 | 8.429 | 0.8336 | .002* |
| Day care | 5.21 ± 1.54 | 5.94 ± 1.46 | 6.14 ± 1.43 | 6.75 ± 1.26 | 12.095 | 1.5086 | .002*,d,e |
| Triceps strength (kg) | |||||||
| Total | 5.06 ± 1.20 | 5.55 ± 1.35 | 5.75 ± 1.12 | 6.15 ± 1.27 | 6.872 | 0.7992 | .001*,d,e |
| Nursing home | 4.95 ± 0.69 | 5.13 ± 0.85 | 5.40 ± 0.78 | 5.67 ± 0.92 | 8.710 | 1.0886 | .001*,c |
| Day care | 5.15 ± 1.52 | 5.86 ± 1.61 | 6.01 ± 1.30 | 6.51 ± 1.43 | 3.699 | 0.8632 | .028* |
| BBT (cubes/min) [MCID: 5.5 cubes/min] | |||||||
| Total | 26.86 ± 12.63 | 34.14 ± 13.12 | 36.04 ± 9.31 | 37.57 ± 12.91g | 6.743 | 0.9057 | .001*,b,e |
| Nursing home | 31.00 ± 11.34 | 38.33 ± 6.19 | 40.17 ± 8.99 | 41.42 ± 3.90g | 3.281 | 0.9001 | .049* |
| Day care | 23.75 ± 13.38 | 29.63 ± 14.41 | 32.00 ± 10.32 | 37.00 ± 16.78g | 4.835 | 0.9419 | .010*,d |
Note. BBT = box and block test; MCID = minimal clinically important difference; ROM = range of motion.
aAnalysis by repeated-measures one-way analysis of variance. bPost hoc analysis by Bonferroni test showed significantly different data between baseline and the first month. cPost hoc analysis by Bonferroni test showed significantly different data between baseline and the second month. dPost hoc analysis by Bonferroni test showed significantly different data between baseline and the third month. ePost hoc analysis by Bonferroni test showed significantly different data between the first month and the third month. fPost hoc analysis by Bonferroni test showed significantly different data between the second month and the third month. gThe change of data between the third month and the baseline was beyond the minimal clinically important difference.
*p < .05.
ROMs of the Dominant Upper Extremity
Among all participants, (a) shoulder flexion (p < .001), (b) shoulder external rotation (p = .013), (c) shoulder abduction (p = .022), (d) elbow pronation (p = .026), (e) elbow supination (p = .015), (f) wrist flexion (p < .001), and (g) wrist extension (p < .001) significantly increased after VR-REH. The findings observed in PNHs were similar to those found in all participants overall, except that (a) shoulder abduction and (b) elbow pronation did not significantly increase, whereas elbow extension (p = .004) significantly increased after VR-REH. The results detected in PDCs were similar to those in all participants overall; however, (a) shoulder external rotation and (b) elbow supination did not significantly increase after VR-REH.
Biceps and Triceps Brachii Muscle Strength of the Dominant Hand
The MVIC of the biceps and triceps brachii of all participants significantly increased after VR-REH (p < .001 and p = .001, respectively). Bonferroni’s post hoc analysis revealed that the MVIC of the biceps brachii significantly differed (a) at 2 and 3 months after the rehabilitation compared with the baseline data and (b) at 3 months after the rehabilitation compared with the data obtained at 1 and 2 months after the rehabilitation. Furthermore, Bonferroni’s post hoc analysis showed that the MVIC of the triceps brachii at 3 months after the rehabilitation significantly differed from the data at the baseline and 1 month after the rehabilitation.
The MVIC of the biceps brachii of PNHs significantly increased after VR-REH (p = .002); however, Bonferroni’s post hoc analysis showed no significant results. The strength of the triceps brachii significantly increased after rehabilitation (p = .001), and Bonferroni’s post hoc analysis revealed that the data obtained at 2 months after the rehabilitation significantly differed from the baseline data.
The MVIC of the biceps brachii significantly increased after VR-REH (p = .002), and Bonferroni’s post hoc analysis showed that the data collected at 3 months after the rehabilitation significantly differed from the data at the baseline and 1 month after the rehabilitation. The MVIC of the triceps brachii significantly increased after the rehabilitation (p = .0028); however, Bonferroni’s post hoc analysis found no significant results.
BBT Scores
The BBT scores of all participants, PNHs, and PDCs significantly increased after VR-REH (p = .001, p = .049, and p = .010, respectively). Bonferroni’s post hoc analysis indicated significant improvements in (a) the data obtained at 1 month after VR-REH compared with the baseline data and (b) the data collected at 3 months after the VR-REH compared with the data acquired at 1 month after VR-REH in all participants. Similar results were found at 3 months after VR-REH and at the baseline in PDCs. The BBT scores of both groups increased beyond the MCID at 3 months after VR-REH.
Discussion
To our knowledge, no study has evaluated the effects of VR-based PRT on the treatment of sarcopenia in older adults. The most important finding of this study was that the proposed VR-based program with PRT could improve two of the diagnostic criteria of sarcopenia, namely dominant HGS and usual gait speed of older adults with sarcopenia in health care facilities. The MVIC of the biceps and triceps brachii, ROMs of the dominant upper extremity, and BBT scores improved at 3 months after VR-REH.
Our results on HGS, ROMs of the dominant upper extremity, and strength of the biceps and triceps brachii were unsurprising because VR-REH mostly focused on the movements of the upper extremities. Furthermore, the completeness and good graphic quality of the Oculus Rift and Leap Motion commercial gaming system provide a fun, immersive, and extrinsically motivating environment for participants, offering an alternative to repetitive exercise programs (Scott et al., 2018). In addition, we found an improvement in the usual gait speed after the VR-REH. We focused on upper-extremity training in the VR-REH and the improvement of gait speed might be contributed to the following three reasons. First, walking is a complex movement requiring several functional tasks, such as ROMs, velocity, position, and trained muscles (Eng, 2004). Although we did not conduct PRT on the lower extremities, the participants usually had to balance in a different position, like leaning forward, forward reaching, side shuffle, or lateral shifting during the video games, no matter whether they were in a standing or sitting position to perform the games. That movement might occur at the trunk/hips/knees/ankles for those performed in the standing position and at the trunk for those performed in the sitting position. The VR-REH we used might costimulate core muscles partially and improve balance control; second, strengthening programs can induce adaptive processes, specifically in the neuromuscular system, thereby enhancing balance performance and functional mobility. Granacher, Lacroix, Muehlbauer, Roettger, and Gollhofer (2013) demonstrated that the ability of older adults to rise from a chair, ambulate, and make turns improves after 9 weeks of core muscle strength training (twice per week). Park, Gong, and Yim (2017) observed that walking speed increased significantly after a sitting boxing program focusing on upper-extremity stretching and strengthening for 6 weeks (three times per week). Both of the studies did not train lower extremities, primarily, but showed improvement in gait. Their findings conformed to the findings of our study. Third, interlimb and intralimb segments coordination is important for bipedal human gait (Dietz, 1996). Arm swing in the human gait cycle plays an active role in control of body posture. The gait speed will be increased when amplitude of the arm and leg is increased (Bovonsunthonchai, Hiengkaew, Vachalathiti, Vongsirinavarat, & Tretriluxana, 2012). Moreover, the trunk and upper limbs have been found to move simultaneously, and such harmony in movement is reported to play a major role in gait (Stephenson, Lamontagne, & De Serres, 2009). The VR-REH in our study increased the ROMs of the upper extremities of the participants, which might lead to improvement of the arm swing and interlimb and intralimb segments’ coordination, resulting in the increasing gait speed.
Studies have evaluated the effectiveness of PRT in community-dwelling older adults and proved it is easily available, low cost, and effective in improving physical function and strength (Bårdstu et al., 2020; Liu & Latham, 2009). However, such studies are difficult to conduct with residents of health care facilities because half of them use wheelchairs for ambulation (Hirsch, 2015). Although all of the participants could complete the measurement of gait speed, some of them were used to conducting community ambulation by wheelchair due to poor muscle endurance of the lower extremities and performed the VR-REH in the sitting position. Some discrepancies might be presented between the sitting and standing position when performing the VR-REH. Those who stood for the VR-REH had to use their lower extremities for weight-bearing purposes. They might also do more movements with their lower extremities during the VR-REH, while those who sat might need more trunk rotation and lateral movements during the games. As most of the outcomes were measured from the dominant upper extremity in this study, we thought these discrepancies had limited impacts except for the gait speed and the ASMMI.
No significant increase in the appendicular skeletal muscle mass or ASMMI was observed in this study. Several studies have evaluated the neuromuscular contributions of strength training in older adult populations; however, they have revealed controversial findings (McKinnon, Connelly, Rice, Hunter, & Doherty, 2017). Most studies have agreed that aging can attenuate the hypertrophic response of muscle groups to resistance training (Brook et al., 2016). Moritani and deVries (1980) conducted a classical study and observed that the effect of muscle training may entirely depend on the neuromuscular adaptation of older adults after an 8-week training course. These findings were different from young healthy adults, in whom neural factors account for the larger proportion of the initial strength increment and muscle hypertrophy becoming the dominant factor after the first 3–5 weeks (Moritani & deVries, 1979). The VR-REH in this study lasted 3 months, and this length might be insufficient for us to observe changes in muscle mass.
Many ROMs of the dominant upper-extremity and BBT scores increased after VR-REH. Studies involving VR systems in different populations showed similar findings and demonstrated that VR-REH programs encouraged a more intense concentration on the task; subsequently, the motor learning and physical performance of individuals are enhanced (Molina et al., 2014; Park, Lee, Lee, & Lee, 2017; Scott et al., 2018). Our findings indicate that VR-REH is practical for older adults in health care facilities and could improve the function and manual dexterity of the dominant upper extremity of those with sarcopenia.
Older patients with sarcopenia present with diminished mobility and balance control as a result of morphological and functional changes in their musculoskeletal and nervous systems (Ali & Garcia, 2014). Intensive and repetitive motor training via specific therapeutic exercises can be conducted to overcome the negative changes associated with aging (Molina et al., 2014). One major advantage of the VR system is that it can optimize motor learning by attractively and interactively combining physical and cognitive demands (Molina et al., 2014). The Oculus Rift is superior to other VR systems because it provides a more stereoscopic environment with a surround sound, which easily makes a player indulge in a game. VR with Leap Motion can detect changes in manual gestures in a short distance, and this advantage makes it more feasible in older adults. The VR system with the combination of Oculus Rift and Leap Motion in our study provided a sense of immediacy with real-time feedback to help participants perform a task and could be inexpensively used as well as in small spaces. Furthermore, the commercial games we used in VR-REH were easy to set and play. The participants were assisted and supervised by a well-trained physiotherapist, and no falling incidents occurred throughout the study period. VR-REH was safe and workforce-saving; therefore, it could be used in health care facilities.
Our study had some limitations. First, although the sample size was slightly higher than the minimum requirement for statistical analyses, our sample size was still relatively small. As such, our study was less representative, and obtaining statistically significant findings was more difficult. Second, the participants were recruited in one nursing home and one day-care center in rural Southern Taiwan; therefore, the results might be only generalizable to similar populations. Third, VR-REH and evaluation only lasted 3 months. This duration might be insufficient to observe an increase in the muscle mass of older patients with sarcopenia. Fourth, it is possible that the holding device in the dominant hand was the stimulus for adaptations in some improvements in the upper extremities. Because we did not measure the parameters of the nondominant hand, it was difficult to differentiate whether VR or the holding device (as resistance) resulted in more improvements in the outcomes. Finally, this study adopted a quasi-experimental, single-group pretest–posttest design method, which better matched the real-life situations of natural studies. However, such a method lacked a control group to enable a comparison between participants who underwent the VR-REH and those who did not. The data should be carefully and cautiously interpreted. In this quasi-experimental study, we observed that the 3-month VR-REH was effective in improving surrogate outcomes of sarcopenia, but not ASSMI. Based on these results, we would suggest conducting future studies to investigate the optimal volume and long-term effects of VR-REH in the treatment and prevention of sarcopenia (with respect to both surrogate outcomes and muscle mass) of older adults with a larger randomized allocation with a control group, a longer follow-up period, and a more accurate sample size estimation with reference to this current study.
Conclusions
The walking speed, HGS of the dominant hand, MVIC of the biceps and triceps brachii, BBT scores, and some ROMs of the dominant upper extremity improved after the VR-REH. The VR-REH might be feasible for patients with sarcopenia in senior health care facilities. The use of VR-REH in senior health care facilities, particularly in rural regions, could be promoted because VR-REH was safe and effective in most of the surrogate outcomes of sarcopenia. However, future studies with longer interventions, randomized-controlled design, and a larger sample size, should be conducted to evaluate the clinical effectiveness of VR-REH.
Acknowledgments
The authors would like to thank Professor Huey-Shyan Lin from the School of Nursing of Fooyin University (Taiwan) for her great help in statistical analysis. The authors also sincerely appreciate all the investigators and patients who participated in this study. The authors are grateful to Professor Hui-Hsien Lin for her kindly help of statistical analysis in this study. This work was supported by the Ministry of Health and Welfare of Taiwan (grant number: 10836). All authors declared that there is no conflict of interest.
Author Contributions
Dr. Tuan conceptualized and designed the study, drafted the initial manuscript, and revised the manuscript.
Dr. Chen conceptualized and designed the study, and drafted the initial manuscript.
Prof. Lin conceptualized and designed the study, and supervised the rehabilitation program.
Dr. Huang and Ms. Wu executed the rehabilitation program, collected the data, and carried out the initial analyses.
Dr. Su and Dr. Sun rechecked the initial analysis and revised the manuscript.
All authors approved the final manuscript as submitted and agree to be accountable for all aspects of the work.
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