1 Introduction

Essential hypertension is a chronic medical condition that affects a large proportion of older adults in Germany. According to the GEDA 2014/2015-EHIS survey by the Robert Koch Institute, nearly two-thirds of individuals aged 65 years or above have self-reported, physician-diagnosed hypertension (63.8% in women and 65.1% in men ≥ 65 years) (Robert Koch-Institut 2017). Hypertension is associated with an increased risk of cardiovascular disease, stroke, and end-stage renal disease, making it crucial to manage hypertension effectively in order to reduce the risk of complications and further events (Lewington et al. 2002; Whelton et al. 2018). The condition is one of the most important risk factors for morbidity and mortality in Germany that can be influenced by those affected via drug treatment or lifestyle modifications (Ezzati et al. 2002; Unger et al. 2020). Exercise is an essential non-pharmacological intervention with high level of evidence for BP reduction in the management of hypertension in older adults (Whelton et al. 2018; Vogel et al. 2009; Neuhauser et al. 2013). The current guidelines recommend regular aerobic and resistance exercise for the treatment and prevention of hypertension in all age groups. The guidelines suggest 30 min of moderate-intensity aerobic exercise five to seven days a week and strength training two to three days a week (Unger et al. 2020). Aerobic exercise has been shown to reduce systolic BP by 5–8 mmHg in hypertensive patients (Cornelissen and Smart 2013; Whelton et al. 2018). A systematic review by Pescatello et al. (2019) indicated that physical activity and exercise may reduce resting BP to prehypertensive and normotensive ranges. Despite the numerous health benefits associated with exercise, adherence to physical activity is often challenging, especially in older adults (Chao et al. 2000).

Motivational aspects also play a critical role in promoting adherence to physical activity in older adults (Schutzer and Graves 2004). Education, previous experience with exercise, perceived social support, and attitudes toward exercise, as well as perceived behavioral control/self-efficacy and benefits/barriers to continued physical activity, positively influence the adherence of older adults to physical exercise. On the other hand, perceived physical frailty and poor health hinder exercise adoption and adherence (Rhodes et al. 1999).

Stehr et al. (2021) reported that exercise that is perceived as joyful and diverse may facilitate exercise adherence in older adults. Providing a motivational and enjoyable exercise environment may promote adherence to exercise. Virtual reality (VR) exergaming with head-mounted displays has emerged as a potential exercise modality to promote adherence to physical exercise (Mouatt et al. 2020). Currently, several VR exergames using a head-mounted display are commercially available, such as the rhythm game, Beatsaber (Beat Games, Prague, the Czech Republic); the boxing game, Creed: Rise to Glory (Survios, Los Angeles, California, USA); and FitXR (FitXR, London, United Kingdom), a game for high-intensity interval training. Although most studies investigating commercial applications of VR mostly focus on the acceptability and intention to use VR among older adults, some studies show the efficacy of specific VR games (Evans et al. 2021; Sousa et al. 2022). Stewart et al. (2022) reported that the perceived exertion during VR exergames was actually lower than the actual exertion, highlighting the potential of the application in terms of increasing exercise engagement and duration. The perceived ease of use, usefulness, and enjoyment are factors that significantly affect the intention to use VR, which has shown high acceptance among older adults in promoting active aging and physical activities (Syed-Abdul et al. 2019; Xu et al. 2023).

As VR exergaming combines physical activity with an immersive and enjoyable virtual environment. Several studies have investigated the effectiveness of VR exergaming in promoting cognitive as well as physical function and balance among both young and older adults (Kircher et al. 2022; Doré et al. 2023).

Exercise intensity for older adults with hypertension is an important consideration when developing or evaluating appropriate interventions. The American College of Sports Medicine (ACSM) recommends aerobic exercise for older adults with hypertension at 40–60% of heart rate reserve (HRR) (Liguori and American College of Sports Medicine (ACSM) 2021). However, older adults often have difficulty training at the appropriate intensity level.(British Heart Foundation 2017; Meghani et al. 2023) Although VR exergames have been shown to raise heart rates (HRs) during exercise compared to at rest (Kivela et al. 2019), when comparing exercise in VR to traditional exercise, exercise performance in VR seems to be associated with lower HR and, thus, a longer training time before subjects reach a target HR (Rutkowski et al. 2021).

However, there is more critical research regarding the efficacy of exergames in VR for physical activity. In a pilot study by Perrin et al. (2019), no significant differences in HR could be observed during exercise between a VR bow-shooting game and traditional exercise. McDonough et al. (2020) compared the effects of traditional stationary cycling with cycling training using PlayStation 4 VR (Sony Interactive Entertainment, Tokyo, Japan) and Xbox 360 (Microsoft Corporation, Redmond, Washington, USA) exergaming. Although enjoyment and self-efficacy were significantly higher in the group using VR, no significant change in systolic and diastolic BP between the groups was observed (McDonough et al. 2020).

There is also a need for long-term studies to investigate the efficacy of VR exergaming in older adults with essential hypertension. Efficacy refers to the performance of an intervention under controlled, ideal conditions, while effectiveness describes its performance in typical, real-world settings. Few studies have investigated the effectiveness of VR exergaming in hypertensive patients, and most of these studies have a short study period or do not specifically address older adults. Most recent systematic reviews and meta-analyses that demonstrated the effectiveness of VR in cardiac rehabilitation programs used VR systems such as a Wii (Nintendo Co., Kyoto, Japan) or Kinect (Microsoft Corporation, Redmond, Washington, USA), which do not use head-mounted virtual reality systems for fully immersive exercise environments (Htut et al. 2018; García-Bravo et al. 2020; Bond et al. 2021; da Cruz et al. 2021).

The objective of this randomized controlled trial is to investigate a VR exergame prototype. This will involve endurance and strength endurance exercise training for older adults with essential hypertension over a 6-week period. Conventional group training with identical training content served as the control group. The following primary research questions were to be answered:

  • RQ1: Can an improvement in health parameters (BP, body composition, lung function, physical working capacity, and mobility) be achieved by means of the 6-week VR training program and are there differences compared to the conventional training group?

  • RQ2: Which exercise intensities can be reached during the 6-week VR training compared to the conventional training and are these sufficient to achieve a reduction in BP?

2 Methods

2.1 Ethics review, consent, and trial registration

The study was carried out in accordance with the World Medical Association Declaration of Helsinki and was reviewed and approved by the ethics committee of the Charité-Universitätsmedizin Berlin (EA4/010/21). Informed and signed consent was obtained from all participants. In addition, the study was registered in the German Clinical Trials Register (03/05/2021, DRKS00022880).

2.2 Trial design and participants

The study was designed as a randomized controlled pilot study with two parallel groups (EG—exergame group; CTG—conventional training group) and prospective data collection. No changes were made to the methods after the study commencement. The target group of the study exclusively involved older adults with essential hypertension from the age of 65 years. For recruitment, an internal volunteer database as well as gatekeepers from various geriatric organizations (e.g., a senior research group) in Berlin were used. The recruitment of potential participants was conducted by telephone calls. Thereby, the exclusion criteria, such as cognitive status via the Telephone Interview for Cognitive Status (TCIS) (Brandt et al. 1988) and the presence of various diseases (Table 1), were clarified. Potential participants were informed about the study, in detail, by the study staff and could decide on their participation after a reflection period of at least 24 h. The study took place in the motion lab of the Charité Geriatrics Research Group on the campus of the Evangelisches Geriatriezentrum and ran from April to October 2021.

Table 1 Eligibility criteria for participants
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2.3 Study procedure and outcomes

The finally included participants attended 14 study appointments. On the first and last appointment, the initial (pre-test) and final examination (post-test) took place. For the training, 12 appointments were scheduled within six weeks. Figure 1 shows a flowchart of the study procedure. At the beginning of the pre-test, the risk of falling was checked as an additional exclusion criterion using the Tinetti test (Tinetti et al. 1986). Afterward, participants were asked to sign the informed consent form after verbal and written explanations and to fill out general data as well as health questions. In addition, various outcomes for checking physical performance were collected during both the pre-test and post-test:

  • BP: systolic, diastolic, and pulse (Beurer® BM 28 HSD upper-arm blood pressure monitor);

  • Body composition: weight, BMI, body fat percentage, visceral fat level, and skeletal muscle (Renpho® body analysis scale);

  • Lung function—spirometry: forced vital capacity (FVC), forced expiratory volume in one second (FEV1), and FEV1/FVC ratio (Vitalograph® micro spirometer);

  • Physical working capacity (PWC)(Rost and Hollmann 1982) on a bicycle ergometer (PWC 130): watt, pulse, and BP;

  • Mobility: balance, gait speed, and lower extremity strength (Short Physical Performance Battery (SPPB)(Guralnik et al. 1994)).

Fig. 1
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Flow chart of the study procedure

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Further outcomes were measured before, during and after a training session in both groups:

  • HR was measured and recorded for the entire duration of each training session with a Polar® M600 smartwatch.

  • BP was measured both immediately before and after each training session. Five minutes after the training session, a BP measurement was taken again. BP measurements were repeated three times.

No changes were made to all outcomes after the commencement of the study. Furthermore to the relevant outcomes mentioned in this paper, additional study results on the evaluation of rapport (Buchem et al. 2022a) and the efficacy of self-competitive gamification designs (Buchem et al. 2022b) have been published.

2.4 Intervention

The training intervention for both EG and CTG took place twice a week for six weeks. The participants were trained either using supervised individual training in virtual reality or a conventional training group. The participants completed one strength endurance training session and one endurance training session per training week. A training session lasted about 20–30 min, depending on the number of repetitions per exercise and the number of sets performed. The training content were identical in both groups. Figure 2a–c, for example, describes the exercise “matching hands” for both groups. However, one exercise in endurance training differed. The EG participants threw a ball into a virtual ring, whereas the CTG participants threw the ball to each other (Fig. 3a–c).

Fig. 2
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Same exercise “matching hands” in both groups. a A participant of EG during the exercise. b The virtual trainer, Anna, demonstrates the exercise in VR. c The participants and the trainer of CTG during the exercise

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Fig. 3
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Different exercise “throwing balls” in EG and CTG. A participant of EG during the exercise “throwing balls into a ring” (a). The virtual trainer, Anna, demonstrates the exercise in VR (b). The participants of CTG during the exercise “throwing a ball to each other” (c)

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Table 2 shows the differences and similarities of the training intervention in both groups. A training session consisted of 3 sequences (warm-up, training phase, and cool-down). In the first two weeks, there was an additional training sequence, the practice phase. In this phase, the participants learned how to correctly perform the exercises of the strength endurance training, on the one hand, and the dance steps or gripping movements for the endurance training, on the other hand. Only participants of the EG took part in a digital introductory sequence in VR at the beginning and an evaluation of the HR at the end of each training session.

Table 2 Differences and similarities of the training intervention in EG and CTG
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2.5 Materials

2.5.1 VR exergame demonstrator

The VR exergame demonstrator was developed during a three-and-a-half-year project funded by the German Federal Ministry of Education and Research. The aim of “BewARe” was the development of a sensor-based VR exergame for elderly hypertensive patients. Within the scope of this project, a unity game engine was used to create two virtual training applications in the training forms of strength endurance and endurance. In addition to the VR applications, BewARe includes an assistance system that measures and evaluates the correctness of the execution of the training. Motion tracking data is recorded with a Microsoft Kinect Azure and heart rate data with a Polar M600 smartwatch. The Kinect data is transferred directly to the assistance system and processed there. The heart rate data, on the other hand, is forwarded to the assistance system via the BewARe Datahub. The BewARe Datahub is a backend for communication, data storage and the provision of this stored data. The BewARe Datahub backend is executed in Docker containers on a local computer. BewARe also provides a dashboard application for user management, configuration and evaluation, which is usually run on the same computer as the datahub, but can also be connected via a local network and run on a different computer if required.

At the start of the application, the study personnel gave the participants a briefing and either loaded an existing patient profile or created a new profile in the dashboard during the first training session. The profile data entered or updated in the dashboard can be used e.g. to calculate and save the individual maximum and training heart rate within the program. After putting on the smartwatch for heart rate monitoring, the controller (only for endurance training) or the tracker on the back of the hand (only for strength endurance training) and the VR headset with the support of the study staff, the application was started in the safe laboratory environment. In the game, the participants stand in a virtual room with a wooden floor and light-colored walls. The front wall is wood-paneled. In the strength endurance game, there is a chair in a circle with a chair symbol on the left and two dumbbells in a circle with a dumbbell symbol to the right. In front of the participants is the virtual agent Anna, who is greeting them first by waving and introducing herself. Anna is a female trainer. Due to the demonstrator stage of the game, this agent is only a silhouette of a female character. In the first week, Anna explains the different game elements to everyone in both training games. Thus, the time bar is explained, which is displayed as a yellow bar behind the trainer. In addition, a counter is displayed for some exercises, which counts up or down depending on the exercise. On the right wall, a diagram is presented at the beginning for explanation and is displayed to the participants at the end of the training with the data of the heart rate during the training. The participant only sees his virtual hands in the game, during endurance training it is possible to move the fingers using the Valve Index Controllers. There is no avatar representation of the participants in the applications. The virtual wristwatch on the left wrist shows the current heart rate of the exerciser. A green face means that the heart rate is ideal during this moment, blue means too low and red means too high. The aim is to keep the heart rate in the green state as much as possible during training. For the supervisor or study staff, a live stream of the game on a screen also offers the opportunity to monitor the heart rate and intervene if necessary. Furthermore, the participants were informed about the possibility of taking a break, which can be verbally expressed by the participants, in which case the game is briefly stopped.

The strength endurance training exergame represented guided instruction-based training. The aim was to follow Anna’s instructions and perform the exercises at the same time as her. The exercises can be taken from Table 2. The special feature to increase immersion were the physical elements. For example, trackers were attached to real dumbbells (Fig. 4) so that they could be felt haptically and used as training objects.

Fig. 4
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Overhead press with dumbbells

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Between the sets there was a one-minute active break, during which the participants walked across the room and were supposed to burst colored bubbles by touching them (Fig. 5).

Fig. 5
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Active break

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The endurance training exergame was a more gamified training. In the hustle dance exercise, participants were instructed in various dance steps by the trainer (Fig. 6). They initially practiced these steps without music and subsequently with music. In Ballgame 1, participants threw a virtual ball into a virtual ring held by the trainer. The ball was gripped with the Valve Index controllers and released from the hand using a combination of finger spreading and a throwing motion. During Ballgame 2, the participants threw a virtual ball as quickly as possible against the wooden wall in front of them.

Fig. 6
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Hustle dance

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In matching hands, Anna positioned her hands in different positions. Participants were required to mirror her movements and touch the circles on Anna’s hands using the controllers. In both ball games and matching hands, a counter provided information about the number of successful completions.

Both exergames have an identical warm-up and cool-down. In the warm-up, the participants stand in front of Anna and are encouraged to get their circulation going by swinging their arms together to the music. The hands drag a blue tail behind them and make the trajectory visible. As a cool-down, there are two relaxation exercises for each exergame. The first exercise is an imitation of Anna’s movement. This involves an arc tension movement with combined breathing as in Qi Gong (Fig. 7a). The participants see their own hands and follow Anna’s pace. The second breathing exercise is about breathing slowly. The participants see a cloud of small pearls in front of them which move outwards with the instructions for inhaling and contract as they exhale (Fig. 7b).

Fig. 7
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Breathing exercises. Arc tension (a) and cloud of pearls (b)

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In addition, the VR system was supplemented by a motion-tracking system. By means of Azure Kinect®, the movement can be analyzed and specific auditive feedback are given to the participants during the exercises. The assistance system sequentially receives 3D position data from the participants’ body parts (full body) and RGB data and uses these to determine various information: joint angles, type of movement performed, number of repetitions of the movement performed, start and end of a repetition of the movement performed and thus the quality of the execution of the movement performed with reference to individual body parts. However, an evaluation of the motion tracking system was not part of the publication.

2.5.2 HTC Vive Pro

For VR training, we used the head-mounted display, HTC Vive pro®, with five Vive Trackers 2.0® and two Valve Index® controllers. In strength endurance training, the Vive trackers were applied to the hands of the participants as well as the dumbbells and chair in order to visualize them in the VR environment (Fig. 8a–b). The Valve Index® controllers tracked the finger movements during endurance training to allow gripping and throwing balls (Fig. 8c).

Fig. 8
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Tracker position of Vive Trackers® (a) and Valve Index® controllers (c). Visualization of the real tracked objects in the VR environment (b)

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2.5.3 Gaming laptop

To supervise the VR exergame, the study personnel used an XMG A517 gaming laptop with a 6-core 4.1 GHz processor and a 1060 NVIDIA GeForce GTX 1060 6 GB GDDR5 graphics card. In addition, there was another PC for the local docker and patient dashboard, in which the framework data (age, height, and weight), as well as the resting HR, were recorded to determine the individual moderate load intensity zone. There was also a PC for the feedback system. All components were connected via a local network.

2.5.4 Polar M600

For measuring HR, a Polar M600 smartwatch was put on the participant’s left wrist, which had an optical pulse measurement system. In VR, a virtual smartwatch was visible at the same location, on which participants could track their real HR. Depending on the individual load intensity, the display changed color to blue (underload), green (ideal load), or red (overload). This was used to control the training intensity. The collected HR data were saved in the dashboard and could be downloaded as a CSV file. Existing studies show that a Polar M600 can be used as a valid measurement tool (Horton et al. 2017; Zhang et al. 2020).

2.6 Sample size and randomization

Due to the lack of usable data on effect sizes regarding the efficacy of VR on hypertension, it was not possible to estimate the number of cases. Against this background, this pilot study serves to estimate an effect.

Stratified random allocation was used as the randomization method. The sample was stratified by gender. All included participants in male and female blocks were randomly assigned to study groups using an Excel spreadsheet with random numbers. The random allocation sequence was generated by two research assistants. Due to the priority of the VR intervention, an approximate 2:1 ratio of EG to CTG was considered in the allocation of groups (Fig. 9). A student assistant conducted the recruitment of participants and assignments to the groups. The blinding of study participants and study personnel was not implemented.

Fig. 9
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Stratified randomization of the sample by gender

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2.7 Statistical methods

To analyze the first research question (RQ1), which focused on an improvement in health parameters (BP, body composition, lung function, physical working capacity, and mobility), descriptive (frequencies, percentages, min, max, median, mean, and SD) and inductive statistical methods using IBM SPSS Statistics 28 were run. The alpha level was set at 5%. The data were examined for normal distribution using the Shapiro–Wilk test. To compare the samples, a paired/unpaired t-test, Wilcoxon signed-rank test, or Mann–Whitney U test was performed. The tests were conducted to check for improvements and group differences at one-sided and two-sided significance levels, respectively. The repeated measures were tested with ANOVA. Confidence intervals (95% CIs) and effect sizes (Cohen 1988) (d; r; partial η2) were also provided. Missing values were countered with a listwise case exclusion.

For the analysis of the second research question (RQ2), data preparation was needed to calculate the maximum HR and individual moderate load intensity. For maximum HR, the formula of Sally Edwards (1993) was used (men: HRmax = 214—0.5 × age—0.11 × bodyweight in kg; women: HRmax = 210—0.5 × age—0.11 × bodyweight in kg). The individual moderate load intensity from 40 to 60% for each participant was calculated using the Karvonen formula(Karvonen 1957) (HRtraining = ((HRmax—HRresting) * intensity %) + HRresting). Training HR was corrected for beta-blocker intake by subtracting 10 bpm from resting HR, according to the literature (Eston and Connolly 1996; Such and Meyer 2010). Testing for differences between groups was also performed using a two-tailed unpaired t-test. ANOVA with repeated measures was used to calculate changes over a period of time.

Correlations were calculated using chi-square tests and phi (φ).

3 Results

3.1 Participants

The study team identified 59 potential participants through recruitment efforts. Unfortunately, 20 participants were not included; they either declined to participate or had other reasons such as hospitalization or extended vacations. Therefore, 39 participants were randomly assigned to the groups (Fig. 10). The study was stopped because all included participants completed their appropriate intervention or were excluded due to completing less than 10 training sessions. Consequently, the following results include 35 participants.

Fig. 10
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Flowchart for group allocation of EG and CTG

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In addition, a random sample of 12 participants was selected from the 23 participants of the EG and compared with the CTG in a sub-analysis. These additional results have been added in brackets in the tables and charts of the EG. In most cases, this analysis leads to almost identical results.

3.2 Demographic data

Table 3 shows the basic characteristics of the 35 participants. All participants in the sample had essential hypertension. Of these, 91.3% in the EG and 75.0% in the CTG had been controlled with medications such as ACE inhibitors, AT1 blockers, diuretics, or calcium antagonists. There is no correlation to group membership with regard to the use of medication (x2(1) = 1.71, p = 0.191, φ = − 0.22). Of the medicated participants, 34.8% in EG and 41.7% in CTG were also taking beta-blockers (x2(1) = 1.60, p = 0.689, φ = 0.07). In addition, 82.6% of EG and 58.3% of CTG were receiving other medications for various medical conditions (e.g., thyroid disease, diabetes, or osteoporosis).

Table 3 Sample characteristics
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The frequency of sporting activity indicates a significant correlation with group membership (x2(1) = 5.11, p  = 0.024, φ = − 0.38). In the EG, 65.2% were physically active several times a week, while only 34.8% exercised 0–1 times a week. In contrast, in the CTG, only 25.0% exercised several times a week, while 75.0% exercised 0–1 times a week. On average, the EG exercised three times per week, and the CTG exercised two times per week. In the subjective assessment of general health condition, the EG (pre: MV = 2.96; post: MV = 3.00) rated themselves healthier than the CTG (pre: MV = 3.25; post: MV = 3.17) at both pre- and post-examinations. There were no significant differences between the groups and in the pre–post comparison.

3.3 Blood pressure (BP)

An examination of BP in the pre–post comparison (paired t-test, one-sided) showed a significant reduction in both systolic (t(22) = 3.49, p = 0.001, 95% CI [5.12; 20.07]) and diastolic (t(22) = 3.44, p = 0.001, 95% CI [2.42; 9.76]) values in the EG (Fig. 11a, b). In the CTG, only descriptive differences were found (systolic BP: t(11) = 1.42, p = 0.092, 95% CI [− 3.33; 15.39]; diastolic BP: t(11) = 0.61, p = 0.279, 95% CI [− 2.78; 4.89]). There were no statically significant differences between the groups.

Fig. 11
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Pre–post comparison of systolic (a) and diastolic (b) BP for EG and CTG

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In addition, BP was examined before, after, and 5 min after each training session (repeated measures ANOVA) (Fig. 12a, b). It was found that systolic BP in both groups was significantly lower after training than before training (EG: F(1.49, 32.83) = 49.90, p < 0.001, partial η2 = 0.69; CTG: F(2.00, 22.00) = 46.16, p  < 0.001, partial η2 = 0.81). Similar results were found for diastolic BP (EG: F(1.47, 32.48) = 17.54, p < 0.001, partial η2 = 0.44; CTG: F(1.32, 14.56) = 11.25, p = 0.003, partial η2 = 0.51). After 5 min, the EG had a significantly lower systolic BP than the CTG (t(33) = − 1.81, p = 0.039, 95% CI [− 17.28.33; 0.99], d = − 0.65).

Fig. 12
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Progression of systolic (a) and diastolic (b) BP before and after training for EG and CTG

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3.4 Body composition

To assess body composition in the pre–post comparison (paired t-test, one-sided), five different parameters were examined: weight, BMI, body fat percentage, visceral fat level, and skeletal muscle. In the EG, significant decreases in weight, BMI, body fat, and visceral fat levels, as well as a significant increase in skeletal muscle, were detected (Table 4). In the CTG, decreases in weight and BMI were significant (Table 5). There were no statically significant differences between the groups.

Table 4 Pre–post comparison of body composition parameters for EG
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Table 5 Pre–post comparison of the body composition parameters for CTG
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3.5 Lung function—spirometry

During the pulmonary function test, the parameters FVC, FEV1, and FEV1/FVC were included in the analysis (paired t-test, one-sided). The values of FVC and FEV1 represent the percentage achieved in relation to the normal values. In the pre-post comparison, significant increases in FEV1 (t(22) = − 2.33, p = 0.015, 95% CI [− 7.65; − 0.44], d = − 0.49) and FEV1/FVC (t(22) = − 2.66, p = 0.007, 95% CI [− 7.727; − 0.952], d = − 0.55) were observed for the EG (Fig. 13). There were no statistically relevant differences between EG and CTG.

Fig. 13
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Pre–post comparison of spirometry results for EG and CTG

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3.6 Physical working capacity test 130

During the PWC test, submaximal power was tested on a bicycle ergometer up to a maximum of 130 bmp. As the average maximum HR of both groups shows, a maximum of 130 bpm was reached rather rarely. This could be due to a correlation between BP medication, especially beta blockers, and the non-achievement of the 130 bpm target HR in both pre-test (χ2(1, N = 34) = 7.99, p = 0.005, φ = 0.49) and post-test (χ2(1, N = 35) = 12.61, p < 0.001, φ = 0.60).

The evaluation (paired t-test, one-sided) was based on the achieved wattage, maximum HR, and maximum systolic and diastolic BP. No significant differences were found in the pre–post comparison of the respective groups, except the maximum HR of EG (Table 6). There were also no significant differences between the groups.

Table 6 Pre–post comparison of PWC test results for EG and CTG
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In the recovery period, systolic and diastolic BP as well as HR were examined one, three, and five minutes after test termination. During the recovery period, a repeated measures ANOVA with Greenhouse–Geisser correction determined a significant decrease in HR values as well as systolic and diastolic BP in both groups at both time points (Table 7). For all group comparisons, no significant group differences (between-subjects effects) could be detected.

Table 7 Within-subjects effects for the recovery period of EG and CTG
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3.7 Short physical performance battery (SPPB)

The SPPB tested mobility in the areas of balance, gait speed, and lower extremity strength. There was a maximum of four points per test. According to the results, the 6-week VR and group training led to a significant increase (Wilcoxon test, one-sided) in the total score (Fig. 14). Additionally, the time taken to complete the chair–stand test improved significantly in both groups (EG: MD = 1.04 ± 1.38, p = 0.002, r = − 0.64; CTG: MD = 1.50 ± 1.92; p = 0.021, r = − 0.62). There were no significant differences between the groups.

Fig. 14
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Pre–post comparison of the SPPB score for EG and CTG

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3.8 Heart rate (HR)

Regarding the HR parameters, no significant differences between the groups (unpaired t-test, two-sided) could be found (Table 8). An exception was the maximum HR during strength endurance training (t(29.76) = 3.66, p < 0.001, 95% CI [6.36; 22.42], d = 1.00).

Table 8 HR parameters of EG andCTG
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In addition, HR was examined before, during, and after training. On average, the target HR zone (intensity of 40 to 60%) could only be reached in the EG (Fig. 15a, b). In the EG, 11 of 23 participants (47.83%) trained within the intensity zone. In the CTG, only 2 out of 12 participants trained within the intensity zone (16.67%). However, in both groups, a significant increase (EG: MD = 29.42 ± 1.97; CTG: MD = 22.76 ± 0.99) or decrease (EG: MD = − 26.61 ± 1.80; CTG: MD = − 22.91 ± 0.87) in HR compared to before, during, and after training was observed. Accordingly, significant within-subjects effects were identified for both the EG and CTG (EG: F(1.57, 34.51) = 165.30, p < 0.001, partial η2 = 0.88; CTG: F(2.26, 24.87) = 157.58, p < 0.001, partial η2 = 0.94). Overall, there were no differences in the individual training sequences between the EG and CTG (between-subjects effects). Considering strength endurance and endurance training separately (unpaired t-test, two-sided), significantly higher HR values were found in strength endurance training in the EG during set 1 (p = 0.044), set 2 (p = 0.025), set 3 (p = 0.025), and CD (p = 0.032).

Fig. 15
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(a) Progression of mean HR during 6 weeks of training in EG and  (b) CTG. WU: warm-up; CD: cool-down

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Considering beta-blocker intake, a significantly higher HR was found in both groups over the entire training course for those not taking beta blockers (between-subjects effects) (EG: F(1, 21) = 7.83, p = 0.011, partial η2 = 0.27; CTG: F(1, 10) = 5.63, p = 0.039, partial η2 = 0.36). Overall, in the EG, there was a mean difference of 11.86 ± 4.27 bpm between participants with and without beta-blocker intake; in the CTG, there was a mean difference of 7.35 ± 3.10 bpm. It was also recognized that the difference in HR between persons with and without beta-blocker intake increased under higher load intensity.

4 Discussion

4.1 Principal findings

This study investigated the efficacy of a 6-week VR exergame among older adults with essential hypertension. Two questions were to be answered in the context of the examination:

RQ1: Can an improvement in health parameters (BP, body composition, lung function, physical working capacity, and mobility) be achieved by means of the 6-week VR training program and are there differences compared to the conventional training group?

RQ2: Which exercise intensities can be reached during the 6-week VR training compared to conventional training and are these sufficient to achieve a reduction in BP?

RQ1: The results of this study showed that significant improvements in the pre–post comparison for four out of five parameters (BP, body composition, lung function, and mobility) were achieved, especially in the EG, through the VR exergame. Although the participants in the CTG also improved, there were only significant positive changes in two of the five parameters (body composition and mobility). Direct group differences for the parameters were not determined.

The significantly lower BP values in the EG due to the VR exergame are particularly noteworthy, especially when considering older adults with hypertension. As mentioned in the introduction, a reduction in BP in older adults with hypertension using physical activity has been demonstrated in several studies (Whelton et al. 2018; Vogel et al. 2009; Neuhauser et al. 2013; Cornelissen and Smart 2013; Pescatello et al. 2019). However, the evidence that VR exergames also lead to a reduction in BP in older adults with hypertension has not yet been sufficiently proven. Most of the existing studies refer to video-based exergames. A study by Hou et al. (2023) demonstrated a blood-pressure-reducing effect on systolic BP during exergame training using a Nintendo Wii in older adults with hypertension. In contrast to our results, there was no decrease in diastolic BP for the exergame intervention. McDonough et al. (2020) found although enjoyment and self-efficacy were significantly higher in the group using VR, no significant changes in systolic and diastolic BP were observed between IG and CG for college students. Yu et al. (2020) also found no significant change in BP in his study when comparing 10 weeks of training using Kinect Sports and no training. However, in a study by Zhao et al. (2022), significant BP differences between the IG and CG during video game exercises were identified for older adults.

Regarding body composition, our results showed a significant positive change in weight, BMI, body fat, visceral fat, and skeletal muscle parameters for the EG and weight and BMI parameters for the CTG. Comparing the BMI of our sample, it was within the national average of about 27 kg/m2, which can be considered the optimal range (25-30 kg/m2) in this age group (Landendörfer et al. 2013; Statistisches Bundesamt 2023). Studies on body composition in conjunction with a VR exergame in older patients with hypertension are also limited. However, video-game-based interventions have shown comparable results to ours. A study with children showed a significant BMI reduction using video game exercise in a pre–post comparison as well as in comparison to the CG (Irandoust et al. 2021). Similar results were also obtained by Zhao et al. (2022) in his study on video game exercises for older adults. However, he reported no evident training-related improvements for body fat (Zhao et al. 2022). Overall, other forms of intervention have also shown an improvement in body composition parameters. Accordingly, a systematic review demonstrated a significant effect of conventional resistance training on a reduction in body fat percentage, body fat mass, and visceral fat in adults (Wewege et al. 2022). In addition, a study on cardiac rehabilitation showed greater improvements in body fat mass, body fat percentage, abdominal fat percentage, and body lean mass for a high-intensity interval training group compared with continuous moderate-intensity training (Dun et al. 2019).

In the examination of lung function, the parameters FEV1 and FEV1/FVC were significantly improved exclusively in the EG. There were no differences between the groups. However, it should be emphasized that in both groups, the pre-test and post-test results were above the target values for elderly people (FVC > 80%; FEV1 > 80%; FEV1/FVC > 70%) (Criée et al. 2015). Therefore, an improvement in the values was not necessarily expected. However, other studies have also examined the impact of VR/AR training on lung function. A systematic review by Bashir et al. (2023) concluded that there were no significant improvements in lung function between VR-related cardiac rehabilitation and standard cardiac rehabilitation. In a study on an AR game (with a projection in space) for older adults, the authors found no significant effect on lung capacity in the pre–post comparison because the intensity of exercise was not high enough to increase lung capacity (Park and Shin 2023). Similar to our results, an analysis of an inpatient post-COVID-19 rehabilitation program featuring VR showed significant changes in FEV1/VC within the VR group in the pre–post comparison, while no group differences were observed (Rutkowski et al. 2023). In the context of video-based exergames, Mazzoleni et al. (2014) found improvements in the dyspnea index when playing Wii Fit Plus among older people with chronic respiratory diseases. Additionally, Zhao et al. (2022) found a significant improvement in VC within the IG using video game exercises for older adults.

Regarding the parameters of the physical working capacity test (PWC 130), no change between pre- and post-test values could be found for both groups. A literature search for exergame studies that also investigated the PWC test did not identify any results. However, in a study that investigated aerobic training with two different intensities over 10 weeks in middle-aged participants, a significant reduction in systolic BP and an increase in maximum HR using increased physical working capacity at a HR of 130 bpm (PWC 130) was found (Moreira et al. 1999). One reason for the unchanged parameters in our study may be that a large part of the participants did not reach an HR of 130 bpm on the bicycle ergometer, which may be related to the intake of beta blockers. The association between beta-blocker use and non-achievement was significant in our study. If beta blockers are taken, a target HR of 130 in a PWC 130 test is less likely to be reached. This effect was also demonstrated in a study by Wonisch et al. (2003). Accordingly, individuals taking beta blockers had a lower chance of reaching the target HR (Wonisch et al. 2003).

In the SPPB values, there was a significant improvement in both groups. Similar results were obtained by Mascarenhas et al. (2023). In their study, both the conventional training and exergame groups improved. Moreover, no significant changes in the SPPB occurred in the control group (without training) (Mascarenhas et al. 2023). A significant difference between the groups in our study would be unlikely because the same exercises were performed. In one subtest, the participants were asked to perform a single sit-to-stand movement in the SPPB. Similar results to ours were obtained with previous exergames that were not VR-HMD systems but showed that exergaming can lead to improvements in standing up. In a study by Albores et al. (2013), the number of sit-to-stand repetitions significantly improved after 12 weeks of Nintendo Wii home exercise training.

RQ2: The results show that a significant increase in HR values during training was observed in both groups compared to the start HR. There was no direct difference in HR values between the groups. However, the appropriate target HRR intensity of 40 to 60% could only be achieved on average in the EG. In addition, a significant reduction in post-exercise hypotension was observed in both groups on average over the entire training period. A significantly lower systolic BP was observed 5 min after training in the EG compared to the CTG. Thus, training at the recommended intensity of 40 to 60% indicates a greater reduction in BP after training and also led to a significant reduction in BP over the entire training period in the EG.

In contrast to our study, during which only the EG reached the target intensity in the same training time as the CTG, Rutkowski et al. (2021) found that when comparing exercise in VR to traditional exercise, exercise performance in VR seems to be associated with lower HR and, thus, a longer training time before participants reached a target HR. However, similar to our results, a study by Perrin et al. (2019) also confirmed that no significant differences in HR could be observed during exercise between a VR bow shooting game and traditional exercise. Despite the significant increase in HR during exercise, the influence of beta-blocker intake should not be ignored. In both groups, this led to a significantly lower training HR in people taking beta blockers, which was also shown by Wonisch et al. (2003) The efficacy of a VR exergame in lowering BP in groups taking beta blockers must be further investigated.

In addition, the question is why only the EG participants achieved the target intensity level in this pilot study. One possibility could suggest a higher motivation through the VR exergame in comparison to the conventional group training. In this regard, we examined intrinsic motivation using the Intrinsic Motivation Inventory (IMI) at three different time points (results are not part of this publication). However, the results did not reveal any significant differences in the parameters examined between the groups at any time. In contrast a study by Martin-Niedecken et al. (2020), which compared functional high-intensity interval training in conventional vs. exergame-based training, it was found that exergame-based training produces higher intensities on the one hand and also delivers better results for flow, fun and motivation on the other. Another reason may be the personal preference of the participants regarding the training mode. Whereas some people train more effectively in a collaborative setting, others train better alone or in a competitive setting, as described in Caserman et al. (2020). The participants preference regarding the training mode was not taken into account in this study and should be considered more in the future.

4.2 Strengths and limitations

Our pilot study had several notable strengths. First, it provided valuable preliminary data that can be used to guide the development of future research in the area of VR exergames for older adults with hypertension. By conducting a pilot study, we were able to identify potential challenges that may need to be addressed in future, larger-scale studies.

Although this pilot study provides valuable insights into the feasibility and potential of the research question, it has several limitations that should be considered. First, the sample size was small, limiting the generalizability of the findings. This study was conducted with a convenience sample of 35 participants, which may not be representative of the larger population. Due to the small sample size, this pilot study may not have enough statistical power to detect small but meaningful effects. With 12 participants included, the control group was on the borderline of the sample size requirements recommended for pilot studies (Moore et al. 2011). Nevertheless, a limitation is that there was a ratio of 2:1 for the EG. Future research should aim to recruit a more diverse and larger sample size to increase the generalizability of the results. Second, the study was conducted in a laboratory setting, which may not accurately reflect real-world scenarios of cardiac rehabilitation. Participants may have acted differently in the controlled environment. Future research could explore the effects of VR intervention in a rehabilitation facility or as envisioned in home settings to increase the external validity of the findings. Another limitation was that both the participants and study staff were not blinded. Blinding a study in physical therapy is difficult and sometimes impossible, especially when the patient knows about the procedure. The production of a placebo device (e.g., a placebo exergame) was not foreseen in the research project, and no resources were available for this purpose.

There were different limitations in the measurements. A limitation in body composition was that the bioelectrical impedance analysis used a commercial scale, which has not been clinically tested. The model had only foot sensors and did not have hand sensors. For the next study, we recommend a more precise and medically validated measuring device for a BIA. Our PWC 130 test also had some limitations. In the case of unusual or unexpected reactions, the test should be stopped immediately, i.e., the ergometer should be switched off, and the participant should be taken off the machine after a short “cycling out”. The reasons for discontinuation in our study were systolic BP above 200 mmHG, cadence less than 60 rpm, severe fatigue, or exhaustion. In total, 22 participants reached the target HR of 130 bmp in the pre-test, and 12 did not. In the post-test, 19 participants succeeded in reaching the target HR, and 16 did not. In our sample, we found an association between the achievement of the target HR zone and the intake of beta blockers during the intervention. If beta blockers are taken, the target HR zone (intensity of 40 to 60%) is less likely to be reached. For a future clinical trial, a large sample size would be advantageous as it would allow a subgroup analysis of participants taking beta-blockers and those not taking beta blockers.

In our study, the Microsoft Kinect Azure motion tracking system was utilized to assess participants’ joint angles and provide auditory feedback in response to movement execution. However, the system’s high sensitivity led to frequent error detection. In order to maintain a flow of exercises for the participants, the permitted number of auditory cues was limited directly after the first VR sessions, during which the older users were irritated by the quantity of feedback. To mitigate this, we limited the auditory feedback to only one instance per exercise in the set to avoid overwhelming participants with excessive corrections. This design choice, while necessary, may have impacted the participants’ ability to adjust their movements in real time, as the feedback was not continuous. Although the auditory feedback did prompt participants to attempt movement corrections, the limited frequency of feedback meant that some errors likely persisted unaddressed. During the evaluation, we observed that some participants hesitated after receiving feedback, indicating that they were aware of the system’s corrections, while other participants were able to implement the auditory feedback directly. However, further analysis is required to determine whether this led to significant improvements in movement accuracy, which is not part of this paper due to resource constraints.

Despite these limitations, this pilot study provides useful insights into the potential of the research question and sets the stage for future research.

4.3 Outlook

Our pilot study allowed us to test the feasibility of our research design and procedures. This helped us identify areas where modifications may be needed to improve the design and implementation of future studies.

In terms of the system, a prototype for future study should allow more training adaptation possibilities. At present, the adjustment in the training severity was only possible via the number of repetitions and dumbbell weight. The adaptability of exercise duration, for example, would be of interest for individualized training in intention-to-treat studies. An exergame could also be very well adapted in rehabilitation in the context of other indications, e.g., orthopedics and neurology. Concerning the sample, future studies should aim to replicate our findings across larger and more diverse populations. While our sample showed initial results among older essential hypertensive patients, it is possible that the relationships we observed may differ in other populations with different forms of hypertension or in different age groups.

5 Conclusion

In conclusion, this pilot study aimed to investigate the initial efficacy of the developed VR exergame in terms of health parameters, as well as exercise intensities during training, and was able to provide initial insights in this regard. This pilot study showed that a six-week workout with a VR exergame led to a significant reduction in BP only in the EG in older adults with essential hypertension, while this reduction was not observed in the CTG. Both groups experienced increases in HR during exercise. For the essential hypertensive user group, the application of VR exergames as part of a comprehensive treatment program to lower BP could be a promising option, as they combine physical activity and gamification elements. While the exergame may not necessarily outperform traditional personal training in terms of physical outcomes, its motivational aspects could offer a valuable supplement to regular exercise interventions aimed at managing hypertension. However, the limitations of this pilot study should be acknowledged, including the small sample size and limited generalizability. These limitations underscore the need for future research endeavors to address these issues and build upon the present findings. It should also be noted that VR exergames are not a complete alternative to other blood-pressure-lowering interventions but can be used as an adjunctive measure. It is important that players exercise regularly and for a sufficiently long time to achieve a lasting effect. In addition, players with high BP should seek medical advice and monitor their activities before starting to exercise.