Virtual Reality Versus Conventional Exercise in Patients with Type 1 Diabetes: A Feasibility Randomized Crossover Trial

Abstract

Exercise plays a key role in managing type 1 diabetes mellitus (T1DM), and virtual reality (VR)-based exercise offers an innovative solution to increase motivation and deliver meaningful health benefits to patients who are often hesitant to engage in physical activity. The purpose of this study was to assess the acceptability, usability, intention for future use, and preference of a VR-based cycling application, as well as to investigate the effects of VR-based exercise on the physiological, biochemical, and psychological parameters of individuals with T1DM compared to conventional exercise. This study represents a preliminary investigation with a small sample size of 11 patients with T1DM. Each participant underwent two 20 min low-intensity exercise trials. One session involved conventional cycling on a stationary ergometer, while the other used a VR-based cycling application. The two exercise conditions were conducted 48 h apart, without a formal washout period. According to the results, high scores were observed for preference, acceptance, and usability of the VR-based cycling application, and statistically significant improvements in mood and enjoyment were observed following the VR-based cycling compared to conventional cycling. Additionally, while no statistically significant differences were found in physiological parameters (blood glucose, blood pressure, and heart rate) between the two conditions, the VR-based session showed a trend toward greater reductions. In conclusion, the use of VR technology in the field of cycling exercise has great significance in improving the mood and engagement of T1DM patients in exercise programs, providing a user-friendly and well-accepted VR cycling application; subsequently, it has also shown preliminary potential for the regulation of biological parameters. Healthcare professionals could easily expand exercise protocols with the strengths of the VR technologies along with other health-related programs.

1. Introduction

According to the World Health Organization (WHO), diabetes is a chronic disease that occurs either when the pancreas does not produce enough insulin or when the body cannot effectively use the insulin it does produce. In 2019, diabetes was the direct cause of 1.5 million deaths, with 48% of these deaths occurring before the age of 70. In particular, type 1 diabetes mellitus (T1DM) is characterized by insufficient insulin production, requiring daily insulin administration. According to WHO, in 2017, there were 9 million people with T1DM worldwide, with the majority living in high-income countries. However, the exact cause and prevention methods for T1DM remain unknown [1,2,3].

The importance of exercise in the prevention and management of diabetes has been extensively documented in the literature [4,5]. Exercise modulates immune function by reducing systemic inflammation and enhancing regulatory T cell activity, mechanisms that may help delay autoimmune β-cell destruction and thus contribute to the prevention and improved management of T1DM [6]. Moreover, physical activity improves blood glucose control, insulin sensitivity, cardiovascular health, and overall well-being while reducing insulin requirements and long-term complications in individuals living with T1DM [7]. However, for individuals with T1DM, exercise can pose risks, such as hypoglycemia, which may lead to acute symptoms like tremors, sweating, dizziness, and confusion, and in severe cases can result in loss of consciousness, seizures, or even coma if not promptly treated. Moreover, repeated episodes of hypoglycemia can blunt autonomic warning signals, increasing the risk of unrecognized (hypoglycemia unawareness) and severe hypoglycemia events during or after exercise [8,9].

Another critical factor influencing diabetes management is an individual’s psychological state. Increased stress can negatively impact glycemic control and overall disease management in individuals with diabetes [10,11]. Stress triggers the release of hormones like cortisol and adrenaline, which impair insulin effectiveness, often resulting in hyperglycemia [12]. Additionally, blood glucose levels and mood are closely interconnected, with elevated glucose levels having an adverse impact on mood [13]. According to Van Tilburg et al., glycemic control in individuals with T1DM is significantly impacted by subclinical depressive moods, while diabetes management may be positively influenced by improved mood [14]. Another systematic review and meta-analysis supports that regular exercise improves health-related quality of life and consequently glycemic control in patients with type 2 diabetes mellitus (T2DM) [15].

In this context, enjoyment emerges as an essential factor in promoting adherence to exercise programs [16]. Patients with T1DM experienced improved quality of life, heightened motivation to exercise, and increased enjoyment—key contributors to psychological well-being—after participating in a 6-week HIIT exercise program [17]. In addition, intrinsic motivation plays a vital role in participation and commitment to exercise programs [18]. Notably, enjoyment and intrinsic motivation for exercise are strongly linked; when individuals find an activity enjoyable, their intrinsic motivation to engage in it increases [19]. Moreover, according to Bandura’s [20] theory, self-efficacy refers to an individual’s belief in his or her ability to organize and execute the actions required to achieve specific goals. It influences motivation, effort, resilience, and performance in various tasks, with higher self-efficacy leading to greater persistence and success in challenging situations [20]. It is highlighted in the literature that increased self-efficacy has great potential in exercise performance and maintaining exercise behaviors [21]. Furthermore, self-efficacy, along with its interaction with expectations of positive outcomes, is strongly associated with adherence to diabetes self-management and better glycemic control, while the influence of self-efficacy may be stronger when patients have a strong belief that adherence to diabetes management would lead to positive outcomes. Thus, increasing self-efficacy through exercise could improve diabetes self-management [22].

The use of virtual reality (VR) technologies/applications is increasingly being incorporated into the management of diabetes, particularly in education, prevention, and treatment strategies [23]. VR-based education programs are considered powerful tools for promoting self-management of diabetes due to their immersive learning experiences and enhanced communication with patients [24]. Studies have shown that VR-based interventions effectively reduce pain and anxiety while improving glycemic control, adherence to treatment protocols, and satisfaction in children with T1DM [25]. The improvements in balance and fall prevention were supported by a systematic review and meta-analysis, but the findings indicated no significant reduction in glycated hemoglobin (HbA1c) levels [26]. It is also important to mention that VR-based exercise can be an attractive tool that decreases dyspnea symptoms and can enhance performance during exercise, and consequently can increase exercise engagement in patients [27]. In addition, VR technologies/applications can activate the brain’s reward system by providing engaging and immersive experiences that trigger dopamine release—a chemical linked to pleasure and motivation [28]. Enjoyable VR activities, like games or social interactions, can enhance feelings of enjoyment and encourage continued participation [29,30].

Research on VR-based exercise in diabetes management is still in its early stages. VR technologies/applications have shown promising results among patients with T2DM, demonstrating high adherence to exercise intervention programs and improving diabetes-related factors. For instance, VR-based exercise intervention programs can improve exercise outcomes by increasing motivation, engagement, and accessibility and potentially reducing perceived exertion [30] while also improving balance, strength, and gait and reducing the risk of falls in T2DM patients [31]. According to Lee et al., such interventions can positively affect blood glucose levels, muscle mass, physical activity, and glycemic control [32]. Furthermore, another study indicated that physical activity levels significantly increased after participating in a VR-based exercise intervention program [33]. For T1DM, the available research is even more limited. Preliminary findings suggest that video games and VR environments are being explored for T1DM education and management. These tools have proven to be safe, engaging, and effective in fostering confidence in diabetes management and promoting positive behavior changes, such as increased exercise adherence, consistent glucose monitoring, and better overall self-management habits [34,35]. According to a recent study, exergames may offer similar glycemic responses to traditional aerobic exercise with fewer hypoglycemic episodes, making them a promising alternative for T1DM management [36]. Finally, another study reported that active video games produced cardiovascular responses similar to those of running while significantly improving endothelial function and enjoyment levels in T1DM patients [37].

The purpose of the study was to assess the acceptability, usability, intention for future use, and preference of a VR-based cycling application, as well as to investigate the effects of VR-based exercise on the physiological, biochemical (heart rate, blood glucose, and mean arterial pressure), and psychological (mood, interest/enjoyment, self-efficacy, and attitudes) parameters of individuals with T1DM compared to conventional exercise.

2. Materials and Methods

2.1. Trial Design and Ethics Approval

The present study followed a randomized crossover design consisting of two cycling exercise trials (VR-based and non-VR cycling). The study experimental protocol was approved by the Institutional Ethics Committee (ref: 1829/13.10.2021), and all patients gave written informed consent according to the Helsinki Declaration on Human Research Participation [38].

2.2. Participants

Originally, 20 patients with T1DM (recruited from the Endocrinology Clinic of the General University Hospital of Larissa, Greece) were sampled (from October 2022 to February 2023). For the baseline, 17 patients completed the initial assessment. Among the remaining 17 patients, 6 did not participate in the second visit. As a result, the final study sample consisted of 11 patients (6 male, 5 female) (see study flow, Figure 1). The patients had a mean age of 31.7 years (±9.7 years). The inclusion criteria included having a normal Body Mass Index (BMI), with the patients presenting a mean BMI of 22.8 kg/m² and a mean waist circumference of less than 1.00 m. Additionally, inclusion criteria included being physically inactive, and all patients in the study reported mean exercise duration of less than 100 min per week, as assessed by the International Physical Activity Questionnaire (IPAQ) [39]. Patients were excluded from the study if they had ophthalmological disorders (and/or wore glasses), a history of COVID-19 infection and hospitalization [27], contraindications to exercise including physical limitations such as neurological, orthopedic, cognitive, and/or psychiatric problems [40], a sleep quality score > 5 according to the Pittsburgh Sleep Quality Index (PSQI) [41,42], a quality of life score < 45 according to the 12-item Short Form Survey (SF-12) [43], and an anxiety and depression symptom score < 7 according to the Hospital Anxiety and Depression Scale (HADS) for each subscale [44,45].

2.3. VR Cycling Application

This study consisted of two exercise trials that were performed on a stationary seated cycle ergometer (Toorx, Chrono Line, BRX R 300). Each trial comprised 20 min of low-intensity cycling exercise, with one trial conducted using a conventional exercise method and the other using a VR-based cycling application (referred to as “VRADA”, which was originally developed for use in patients with dementia and Alzheimer’s disease), following the methodology outlined in previous studies [27,40,46,47]. Specifically, for the VRADA cycling application [47], the selection was based on the hypothesis that it would reduce stress, potentially resulting in a reduction in blood glucose levels, increased motivation to engage in exercise, and a safe and controlled approach to managing glucose levels during exercise. The patients were able to receive feedback on aspects of the exercise (e.g., duration, distance, speed, etc.) but also to customize other aspects, such as the choice of virtual landscape (forest, beach, and snowy terrain), music, and motivational phrases during the exercise (using a VR controller with ray-casting interaction for choices). In addition, the duration of the exercise was determined by the participant, allowing for personalization to increase motivation. Finally, the present study used version 4.1 of the VRADA cycling application, with the exception of some parts that were originally designed for patients with dementia and Alzheimer’s disease (e.g., cognitive tasks) and adapted for the T1DM population; this adaptation allowed us to provide a safe, controlled, and immersive exercise environment tailored to this population.

2.4. Measures

Initially, anthropometric characteristics were measured (e.g., weight and height). After that, a number of questionnaires were used during the baseline and first visit phase. All patients were asked about demographic characteristics (e.g., gender, age, etc.) and technology use (e.g., frequency of using mobile phones, computers, and video games) before participating in the trials (VR-based and non-VR cycling).

In addition, they completed the Profile of Mood States (POMS) before and after each trial, which consists of 14 items assessing mood states (e.g., “I feel calm”) (5-point Likert scale, Cronbach’s α 0.78) [48,49]. Afterwards, and specifically after each trial, they completed the Intrinsic Motivation Inventory (IMI), consisting of 6 items assessing interest and enjoyment of the task (e.g., “This exercise was enjoyable”) (5-point Likert scale, Cronbach’s α 0.85) [50,51], the Exercise Self-Efficacy Scale, consisting of 10 items assessing how confident they are in completing the exercise (e.g., “I will exercise even when I am tired”) (5-point Likert scale, Cronbach’s α 0.88) [52,53], and the Self-Efficacy Expectations scale, assessing perceived ability to sustain exercise for increasing durations (e.g., 20–40 min) (answered Yes/No, e.g., “I can sustain the task for 30 min” and rated confidence on a 10-point scale, Cronbach’s α 0.84) [52,53]; in addition, attitudes were assessed using 6 bipolar items (e.g., “Pleasant–Unpleasant”) (7-point semantic differential scale, Cronbach’s α 0.92) [54].

Furthermore, after exercise in the VR environment, the patients also completed questionnaires regarding personal innovativeness, which consisted of 4 items (e.g., “I am eager to experiment with new technologies”, Cronbach’s α 0.72) [54], usability, which consisted of 10 items (e.g., “I found this virtual reality system complex”, Cronbach’s α 0.82) [55], VR equipment satisfaction, which consisted of 9 items (e.g., “I felt comfortable using the headset”, Cronbach’s α 0.68) [56], acceptance, such as perceived enjoyment, which consisted of 6 items (e.g., “I enjoyed exercising with the virtual reality system”, Cronbach’s α 0.80) [57], and intention for future use, which consisted of 3 items (e.g., “I intend to use VR-based exercise when it becomes available”, Cronbach’s α 0.86) [57,58]. All questionnaires were scored on a 5-point Likert scale.

At the end of the last visit, and after getting the experience of the two trials (VR-based and non-VR cycling), the patients also completed the final questionnaire about preference for the two cycling exercise protocols, which consisted of eight items (e.g., “Exercise was more pleasant…”). Finally, a semi-structured interview was conducted to gather additional qualitative information, allowing us to identify important topics regarding patients’ experiences, attitudes, and perceptions of VR versus conventional exercise protocols.

2.5. Procedure and Experimental Protocol

Initially, the patients were randomly assigned to groups using a block randomization technique [59]. All patients completed two separate exercise sessions, one in each condition (VR-based and non-VR cycling), following a randomized crossover design. Each patient served as their own control by completing one session in each condition, with 48 h between sessions to reduce potential carryover effects. The entire protocol was completed within one week, with the experimental sessions conducted on Tuesdays and Thursdays and the participants continuing their usual daily routine for the remaining time. Then, at the baseline and first visit, the patients were informed of the study objectives, signed the informed consent form, and were encouraged to ask questions. The patients subsequently adjusted their position on the cycle ergometer and, for those exercising with the VR cycling application for the first time, familiarized themselves with the equipment of the VR system. The principles of cycling biomechanics were followed, including proper spinal alignment, joint angle adjustments, and optimal foot positioning on the pedals, as outlined in the relevant literature [60]. The cycling protocol in our experiment was adapted accordingly to ensure comfort, symmetric force distribution, and minimal musculoskeletal stress. Finally, they were given detailed instructions on the procedure and use of the equipment.

Thereafter, physiological and biochemical parameters such as heart rate (chest strap sensor, Polar H9, Kempele, Finland), blood glucose (manual sphygmomanometer BP, Mac, Nagoya, Japan), and arterial pressure (using a device placed on the patient’s arm that provides glucose readings via smartphone scanning, Freestyle Libre) were assessed before and after each trial: a 20 min low-intensity (approximately 30% of the predicted watts) cycling exercise trial. It is noted that the mean arterial blood pressure (MAP) was calculated according to the following equation [61]:

$${\mathrm{MAP}}_{\left(\mathrm{mmHg}\right)}={\displaystyle \frac{\mathbf{s}\mathbf{y}\mathbf{s}\mathbf{t}\mathbf{o}\mathbf{l}\mathbf{i}\mathbf{c} \mathbf{b}\mathbf{l}\mathbf{o}\mathbf{o}\mathbf{d} \mathbf{p}\mathbf{r}\mathbf{e}\mathbf{s}\mathbf{s}\mathbf{u}\mathbf{r}\mathbf{e}\left(\mathbf{m}\mathbf{m}\mathbf{H}\mathbf{g}\right) + 2 \mathbf{x} \mathbf{d}\mathbf{i}\mathbf{a}\mathbf{s}\mathbf{t}\mathbf{o}\mathbf{l}\mathbf{i}\mathbf{c} \mathbf{b}\mathbf{l}\mathbf{o}\mathbf{o}\mathbf{d} \mathbf{p}\mathbf{r}\mathbf{e}\mathbf{s}\mathbf{s}\mathbf{u}\mathbf{r}\mathbf{e} \left(\mathbf{m}\mathbf{m}\mathbf{H}\mathbf{g}\right)}{3}}$$

The environment provided optimal conditions to facilitate cycling exercise. The temperature was 21 ± 1 °C, and the relative humidity was maintained at 48 ± 5%. The room was quiet, with noise levels maintained below 20 decibels. Adequate ventilation was provided, and the patients exercised without the use of protective face masks, allowing for natural breathing and comfort. Lighting was adequate, further supporting a conducive exercise environment. The intervention was conducted by an exercise-specialized researcher and a doctor with expertise in diabetes, ensuring appropriate instruction and safety throughout the study. Finally, before and after the bicycle exercise trial, all patients completed a few questionnaires at each visit (as described in the previous section), and at the very last stage, the patients participated in the semi-structured interview.

With regard to the cycling exercise protocols, the patients participated in 20 min of low-intensity cycling exercise in both trials (VR-based and non-VR cycling). The cycling trials consisted of three consecutive stages: the first stage was a warm-up and familiarization with the procedures and equipment (1 min duration, 15–20 rpm, and 0-watt load), the second stage was a cycling trial with a constant resistance work rate (20 min duration, 50–60 rpm, and 30% of the predicted value load), and the third stage was a recovery stage (1 min duration, 15–20 rpm, and 0-watt load). All predicted values and maximum loads were calculated according to the equation of Wasserman et al. [62]:

V̇O_2max [mL·min⁻¹] = (height_(cm) − age_(yrs)) × 14 (female) or × 20 (male)

V̇O_2unloaded [mL·min⁻¹] = 150 + (6 × body mass_(kg))

$$\mathrm{Load} [\mathrm{work} \mathrm{rate}/{\min }^{-1}]={\displaystyle \frac{\left(\mathrm{V}\mathrm{O}2\mathrm{m}\mathrm{a}\mathrm{x}-\mathrm{V}\mathrm{O}2\mathrm{u}\mathrm{n}\mathrm{l}\mathrm{o}\mathrm{a}\mathrm{d}\mathrm{e}\mathrm{d}\right)}{100}}$$

2.6. Statistical Analysis

A power of 85% and a confidence interval of 95%, with an estimated Type I error of 5%, were assumed for the sample size calculation in this study. The Kolmogorov–Smirnov test was used to assess the normality of the data and determine whether parametric or non-parametric statistical analyses were appropriate. Based on the results, the Mann–Whitney U test was used for between-group comparisons where applicable, and the Wilcoxon Signed Rank test for distributed data was conducted to compare the two cycling exercise conditions (VR-based and non-VR cycling). Cohen’s d was calculated from the mean difference between groups (M1 and M2) and by the pooled SD:

$$\mathrm{Cohen}’\mathrm{s} \mathrm{d}={\displaystyle \frac{\mathbf{M}2-\mathbf{M}1}{{\mathbf{S}\mathbf{D}}_{\mathbf{p}\mathbf{o}\mathbf{o}\mathbf{l}\mathbf{e}\mathbf{d}}}} \mathrm{and} {\mathrm{SD}}_{\mathrm{pooled}}=\sqrt{{\displaystyle \frac{{\mathbf{S}\mathbf{D}1}^2+{\mathbf{S}\mathbf{D}2}^2}{2}}}$$

Effect sizes were interpreted according to standard guidelines: small (d = 0.2), medium (d = 0.5), and large (d = 0.8) effects [63]. Due to the crossover design, sessions were administered in randomized order. Although no formal analysis of carryover effects was conducted, the scheduling ensured sufficient washout time between sessions to minimize potential bias.

Semi-structured interviews were audio-recorded and transcribed verbatim. The qualitative data were then analyzed using a thematic analysis. Two independent researchers familiarized themselves with the transcripts by reading them multiple times and engaged in an inductive coding process to identify initial codes reflecting emerging patterns. These codes were collated into potential themes and iteratively refined through discussions to resolve any discrepancies, ensuring consistency and rigor. This systematic approach allowed us to identify key themes related to participants’ experiences, attitudes, and perceptions of both VR-based and traditional exercise interventions.

Finally, the IBM SPSS 21 statistical package (SPSS Inc., Chicago, IL, USA) was used for all statistical analyses.

3. Results

3.1. Psychological Parameters and Feasibility of the VR System

A statistically significant difference was found for interest/enjoyment, with participants reporting higher scores following the VR-based cycling condition compared to the conventional condition (Z = −2.874, p < 0.001). Furthermore, in terms of feasibility, according to the assessment of VR environment, the results showed high scores of personal innovativeness, perceived enjoyment, intention for future use, usability, and VR equipment satisfaction. Means scores and standard deviations for psychological variables and VR environment are presented in

Table 1. Mean scores and standard deviations for psychological and VR environment variables.

		VR Condition	Non-VR Condition
Post VR—Non-VR Comparison		Mean ± SD	Mean ± SD
	Self-Efficacy	41.4 ± 6.4	40.2 ± 10.9
	Self-Efficacy Expectations	54.5 ± 10.2	49.5 ± 13.5
	Interest/Enjoyment	4.5 ± 0.5 *	3.4 ± 0.9
	Attitudes	6.2 ± 0.7	5.7 ± 1.0
Post VR Trial	Personal Innovativeness	3.9 ± 0.7	-
	Perceived Enjoyment	4.6 ± 0.3	-
	IFU	4.4 ± 0.6	-
	Usability	83.4 ± 12.1	-
	VR Equipment	4.1 ± 0.5	-

Abbreviations: IFU = intention for future use; * statistically significant differences (p < 0.05).

. These findings support the feasibility and acceptability of the VR-based cycling protocol among individuals with T1DM. In contrast, the results obtained immediately after each cycling exercise trial showed no statistically significant differences between the VR and non-VR conditions for exercise self-efficacy (p = 0.869, Cohen’s d = 0.05), self-efficacy expectations (p = 0.399, Cohen’s d = 0.40), and attitudes (p = 0.22). Finally, after completing both exercise trials, the participants expressed a statistically significant preference for the VR-based cycling condition (Z = −2.836, p < 0.05), suggesting that they favored the immersive VR experience over conventional cycling.

3.2. Physiological and Biochemical Parameters and Mood

All patients were classified as physically inactive, reporting a mean weekly exercise duration of less than 100 min, according to the International Physical Activity Questionnaire [39]. The results of the physiological and biochemical parameters showed no statistically significant changes from the pre- to post-cycling exercise trials (in either condition) for blood glucose and mean arterial pressure. However, clinical changes were observed in both conditions, with the VR condition showing greater changes: lower values for blood glucose and smaller increases in mean arterial pressure (Figure 2). In addition, no statistically significant changes were observed in heart rate at baseline or during any minute of exercise between the two conditions, but statistically significant changes were found during the recovery phase (p = 0.045), with lower heart rate values following the VR-based cycling condition (80.83 ± 14.35) compared to the non-VR cycling condition (87.55 ± 8.97) (Figure 3). Furthermore, the examination of the patients’ moods revealed a statistically significant improvement in patients’ mood following the cycling exercise in the VR condition, with mean mood scores increasing from pre- to post-intervention (p = 0.05). This suggests that engaging in exercise within a virtual reality environment had a positive effect on the participants’ moods. The values of the patients’ physiological and biochemical parameters and moods are presented in

Table 2. Values of the physiological and biochemical parameters and moods.

		VR Condition				Non-VR Condition
Variable	Unit	Baseline	After Trial	p-Value	Cohen’s d	Baseline	After Trial	p-Value	Cohen’s d
HR	bpm	83.0 ± 10.6	80.8 ± 14.3	0.594	0.17	83.5 ± 9.2	87.3 ± 8.8	0.202	0.42
BG	mg/dL	173.2 ± 61.6	144.6 ± 55.1	0.091	0.49	185.9 ± 64.4	181.6 ± 58.2	0.990	0.07
MAP	mmHg	92.1 ± 24.3	95.7 ± 10.7	0.858	0.19	88.2 ± 25.8	96.7 ± 10.9	0.328	0.43
Mood	score	59.1 ± 5.9	64.0 ± 3.9	0.05 *	0.98	59.7 ± 6.6	61.5 ± 5.0	0.439	0.31

Abbreviations: HR = heart rate, BG = blood glucose, MAP = mean arterial blood pressure. Note: p-value describes the comparison pre/post-trial. * (p = 0.05).

.

3.3. Semi-Structured Interview

The semi-structured interview included questions related to the patients’ emotions, expectations, and perceptions of the usability and tolerability of the VR cycling application and equipment. Most patients reported that they would use this VR cycling application for exercise, reported no difficulty, and required no additional time to understand how it worked. In addition, most of the patients felt a sense of presence and control, although they noted that the VR environment appeared artificial. None of them reported any feelings of discomfort, and only one patient mentioned that he was distracted during the experience. Furthermore, patients described the exercise using the VR cycling application as enjoyable, motivating them to engage in physical activity, and effective in making time pass more quickly. However, a small percentage of patients (27.3%) reported that they would not want to use the VR cycling application for regular exercise. Finally, the patients suggested that young people, individuals unable to exercise outdoors, and those who do not typically enjoy exercise would find this type of exercise (VR cycling application) particularly beneficial.

4. Discussion

The purpose of the present study was to assess the acceptability, usability, intention for future use, and preference of a VR-based cycling application, as well as to investigate the effects of VR-based exercise on the physiological, biochemical (heart rate, blood glucose, and mean arterial pressure), and psychological (mood, interest/enjoyment, self-efficacy, attitudes) parameters of individuals with T1DM compared to conventional cycling exercise. The findings provide preliminary insights into the effects of the VR-based exercise on the investigated physiological, biochemical, and psychological variables compared to conventional cycling exercise in patients with T1DM and highlight the use of VR-based cycling applications. The results provided significant insight into the patients’ acceptance of and preference for the VR-based cycling application, as well as its usability. In particular, positive results were revealed from our main goal regarding the patients’ acceptance and usability of the VR-based cycling application, as well as their preference for this type of exercise, which supports the potential of this approach as a complementary tool in T1DM management. These findings are consistent with those of previous studies, which suggested that VR-based exercise is well accepted, user-friendly, and provides positive benefits in diverse populations [27,40,46]. Additionally, the results showed an 8% decrease in blood glucose levels after 20 min of exercise in the VR-based cycling condition compared to the non-VR cycling condition, which showed a smaller decrease. Although this difference was not statistically significant, the direction and magnitude of the change are clinically meaningful, particularly considering the short duration of the intervention.

Relevant studies in the literature indicate that VR-based interventions are effective in reducing pain and anxiety, improving glycemic control, promoting adherence to treatment protocols, and increasing satisfaction in children with T1DM [25]. It is also supported that VR-based interventions can have a positive impact on blood glucose levels, muscle mass, physical activity, and overall glycemic control [25]. Similarly, Senior et al. reported a significant increase in physical activity levels after participating in a VR-based exercise program [33]. Moreover, a recent study by Elsholz et al. explored the taxonomy of research and commercial VR-based exercise applications, highlighting the different priorities of each domain [64]. Research applications often focus on scientific precision and skill development under controlled conditions, whereas commercial applications emphasize motivation, fun, and competitiveness, catering to a broader audience. This distinction is critical to designing VR-based interventions that not only target physiological, biochemical, and psychological outcomes of T1DM patients but also ensure long-term engagement and adherence [64].

Additionally, previous studies have shown that increased stress raises blood glucose levels and leads to poor glucose control [65]. As it is supported by Shaw and Lubetzky, a short bout of VR-based exercise can reduce stress and anxiety [66]. This suggests that the immersive and enjoyable nature of VR-based exercise may contribute to improved glucose regulation not only through physical mechanisms but also by reducing psychological stress. Therefore, VR-based exercise could provide an enjoyable activity in a pleasant environment, which might help reduce stress and, consequently, lead to a greater reduction in glucose levels compared to regular exercise. According to Lee et al., a VR-based exercise program can help regulate glucose control in patients with T2DM [32]. Further, a two-week VR exercise program consisting of 30 min sessions at an intensity of 65–70% maximum heart rate produced effects similar to running, with fewer hypoglycemic episodes in T1DM patients [36]. However, a systematic review and meta-analysis concluded that longer duration VR-based exercise programs did not show statistically significant improvements in glycated hemoglobin in diabetic patients [26]. There are only a few studies in the literature that have evaluated the direct effects of VR-based exercise in comparison to conventional exercise on physiological and biochemistry parameters. Another study found that biological responses to exercise stimuli were similar in both exercise trials (VR and non-VR conditions) when conducted at the same duration and intensity [37], which is consistent with the results of the present study. However, the greater decrease in glucose observed in the VR-based cycling condition in our study highlights the potential of this modality to elicit beneficial physiological effects in a more engaging way, although it was not statistically significant.

Regarding the psychological variables, the present findings support a statistically significant improvement in the patients’ moods following exercise in the VR-based cycling condition, while no significant change was observed after exercise in the non-VR cycling condition. Similar results were found regarding patients’ interest/enjoyment and preference for VR-based cycling condition. The combination of exercise and VR increases enjoyment, boosts energy, and reduces fatigue, as supported by other studies, e.g., [67]. Previous research has emphasized that interactive and engaging formats such as VR applications may be more effective than conventional exercise methods in supporting long-term physical activity adherence [68]. VR-based exercise has been shown to enhance motivation and promote consistent participation by providing enjoyable and immersive experiences [69]. While some VR environments may not necessarily facilitate high-intensity exercise, they can maximize enjoyment, which is a key factor in sustaining engagement over time [70]. Moreover, active VR games can induce varying levels of physical intensity, with those perceived as highly enjoyable and low in perceived exertion being particularly effective in promoting continued involvement in physical activity [71]. Also, according to Plante et al., using VR without exercise increased fatigue and tension (approximately 60%) and decreased energy levels (approximately 7%) [67]. Consistent with this, Gomes et al. [37] found that participants found VR gaming exercises more enjoyable than running [37]. Mood improvements following VR-based cycling exercise compared to conventional cycling are also supported by Ochi et al., who posited that this type of exercise could improve well-being in diverse populations [72]. In addition, intrinsic motivation plays a key role in exercise participation and adherence [14]. The strong relationship between enjoyment and intrinsic motivation suggests that when people enjoy exercising, their motivation to continue exercising increases [15]. However, another study comparing VR-based cycling exercise to outdoor cycling reported that while VR-based cycling exercise positively influenced engagement and physiological responses, outdoor cycling provided greater benefits in terms of motivation, enjoyment, and exercise adherence [73]. This is supported by the activation of specific neural and endocrine systems. The dopaminergic mesolimbic reward pathway reinforces pleasure and motivation during enjoyable exercise, thereby enhancing adherence. Meanwhile, exercise modulates the hypothalamic–pituitary–adrenal (HPA) axis and the autonomic nervous system. These systems are crucial for regulating glucose metabolism and physical responses to activity, such as stress control and energy balance [74].

Patients with T1DM, in addition to the inability to produce insulin, are at risk for developing cardiovascular diseases, as hyperglycemia and insulin deficiency are associated with strain on the cardiovascular system [75]. Changes in mean arterial pressure levels are influenced by cardiac output and systemic vascular resistance [61]. Additionally, the autonomous nervous system plays a crucial role in the regulation of mean arterial pressure. When mean arterial pressure is elevated, parasympathetic activity increases, resulting in a decrease in cardiac output [61]. Conversely, when mean arterial pressure is low, sympathetic tone increases, resulting in an increase in cardiac output and systemic vascular resistance [61]. Increased sympathetic tone also occurs during exercise and periods of psychological stress [61]. The autonomous nervous system plays a critical role in the body’s response to stress. Stress reduces parasympathetic tone and increases sympathetic activity, leading to an elevated heart rate, blood pressure, and the release of hormones such as cortisol. Reduced parasympathetic activity negatively affects the regulation of the vagus nerve, which is important for heart rate variability and recovery. Long term, this can impact cardiovascular health. Strengthening parasympathetic tone through exercise or relaxation techniques can help mitigate these effects [76]. According to the present results, the mean arterial pressure did not show significant changes overall; however, it was observed that there was a greater increase in mean arterial pressure in the non-VR condition, with an increase of 9.6%. This phenomenon can be attributed to the fact that during the VR condition, stress was reduced, leading to a lower sympathetic tone and consequently a smaller change in mean arterial pressure, with a 3.9% increase. This is important because during this form of exercise, there is a reduced need for blood circulation from an already burdened cardiovascular system due to the disease [77].

Finally, in terms of the patients’ experiences during the VR condition, they scored high on personal innovativeness, indicating a preference for experimenting with new technologies, which likely contributed to the high scores for perceived enjoyment and future use intention. Patients also rated the usability of the VR cycling application and equipment highly, suggesting it could increase acceptance and engagement [78]. Moreover, enjoyment is directly correlated with adherence to VR condition, and VR cycling application can be used as a tool to improve adherence [79]. Lastly, the semi-structured interviews yielded encouraging results regarding participants’ experiences with the VR cycling application and equipment. The reported sense of presence in the VR environment could lead to changes in attitudes towards this exercise modality and increased enjoyment. Positive attitudes may increase intentions for future use [80].

Strengths and Limitations of the Study

To the best of our knowledge, no studies have assessed the physiological, biochemical, and psychological parameters in individuals with T1DM while comparing VR-based exercise to non-VR exercise. Based on our results, it appears that exercise performed in a pleasant environment can have a positive impact on physiological, biochemical, and psychological parameters in individuals with T1DM. Therefore, we recommend the inclusion of outdoor activities or VR exercise or the integration of new technologies into their exercise programs to improve both their physical and psychological well-being. Despite the preliminary encouraging results, the present study has several limitations that should be mentioned. First, the small sample size limits the generalizability of the findings and the statistical power to detect potential differences in physiological, biochemical, and psychological parameters. Although 20 patients were initially recruited from the wider hospital area, a significant number of patients failed to attend the first or second visit; personal and economic issues were identified as reasons for dropout. Recruitment of patients was subsequently stopped due to the high workload of the clinic. Second, the short duration of the intervention prevents conclusions about long-term effects or adherence to VR-based exercise, although the study showed improvements in mood and enjoyment. The lack of a non-exercising control group limits the ability to isolate the specific effects of VR-based cycling exercise compared to a non-exercise condition. Although efforts were made to control for potential confounding factors such as diet, sleep, and insulin use by instructing participants to follow their usual routines, individual variations in these factors could not be entirely eliminated. In addition, the absence of a longitudinal follow-up or repeated exposure to VR-based cycling exercise makes it difficult to assess the sustained impact and long-term adherence. Finally, due to the nature of the intervention, blinding of participants and researchers to the cycling exercise condition (VR or non-VR) was not feasible. This may have introduced potential performance or expectation bias. However, all outcome assessments were standardized and conducted using the same procedures across both conditions to reduce bias. The results of the present study provide preliminary insights that can inform larger-scale studies with extended intervention periods and more diverse samples to validate these findings.

Future studies should include larger, more diverse samples and extended intervention periods to confirm these findings and further investigate the role of VR-based exercise in T1DM management. Additionally, exploring bioinformatic indicators could provide deeper insights into the underlying mechanisms of VR-based exercise benefits, while at the same time aiding in the development of more engaging and enjoyable VR environments. Furthermore, studies could also consider implementing single-blind designs where possible, such as blinding raters to the condition, to further minimize potential bias. Moreover, future research should incorporate objective stress-related biomarkers, such as salivary cortisol or heart rate variability, to more accurately assess the stress-reducing effects of VR-based exercise interventions. Finally, the implementation of rigorous monitoring procedures, such as dietary logs, continuous glucose monitoring before and after exercise, and sleep assessments, would help to better control for potential confounders.

5. Conclusions

In conclusion, a single session of VR-based cycling exercise improved mood and increased interest/enjoyment compared to conventional exercise. However, no statistically significant differences were observed in the other physiological and biochemistry parameters examined in the present study. The findings of this study support the feasibility of implementing a single session of VR-based cycling exercise, as patients showed a strong preference for using the VR-based cycling application and reported high levels of acceptance, usability, and no adverse effects, indicating its potential for integration in clinical and health promotion settings. Healthcare professionals could easily expand exercise protocols with the strengths of the VR technologies along with other health-related programs.