| Literature DB >> 35860203 |
Xiang Peng1,2, Rashid Menhas3, Jianhui Dai2, Muhammad Younas4.
Abstract
Background: Virtual reality applications are made for various objectives such as business, entertainment, education, and healthcare. Anxiety, phobias, distress, disordered eating, drug addiction, panic disorder, post-traumatic stress disorder, schizophrenia, bipolar disorder, psychosis, depression, and autism spectrum disorders may benefit from virtual reality-based approaches. The 2019 coronavirus (COVID-19) pandemic has impacted the way we live, enjoy, study, sport, and socialize significantly. Virtual reality fitness technology gained much attention during the COVID-19 preventive measures time. Objective: The present study explores the role of virtual reality fitness in improving overall wellbeing during the COVID-19 pandemic home isolation period in terms of physical-psychological health and physical activity.Entities:
Keywords: COVID-19; lockdown; overall wellbeing; psychological and physical health; virtual reality fitness
Year: 2022 PMID: 35860203 PMCID: PMC9289576 DOI: 10.2147/PRBM.S369020
Source DB: PubMed Journal: Psychol Res Behav Manag ISSN: 1179-1578
Figure 1Proposed relationship of the study variables.
Distribution Statistics of the Questionnaire
| Items | Frequency/Percentage |
|---|---|
| Total Questionnaire sent | 2300(100.0%) |
| Received back | 2300(100.0%) |
| Excluded | 100(4.35%) |
| Included in the final analysis | 2200(95.65%) |
Study Participant’s Background Information
| Variables | Categorization | Frequency/Percentage |
|---|---|---|
| Gender | Male | 1175(53.41%) |
| Female | 947(43.05%) | |
| Others | 78(3.54%) | |
| Age | 19–20 | 549(24.95%) |
| 21–25 | 564(25.64%) | |
| 26–30 | 450(20.45%) | |
| 31–35 | 379(17.23%) | |
| 36–40 | 121(5.5%) | |
| 41+ | 137(6.23%) | |
| Education | High School | 407(18.5%) |
| College Graduate | 693(31.5%) | |
| University Graduate | 1100(50.0%) | |
| Occupation | Government Employees | 183(8.32%) |
| Self-employed | 567(25.77%) | |
| Private Companies Employees | 383(17.41%) | |
| Students | 751(34.14%) | |
| No occupation, Retired | 297(13.5%) | |
| Others | 19(0.86%) | |
| Marital Status | Single (unmarried) | 993(45.14%) |
| Cohabitant | 75(3.41%) | |
| Married | 925(42.04%) | |
| Divorced | 108(4.91%) | |
| Widowed | 55(2.5%) | |
| Separated | 44(2.0%) | |
| Cohabitation at home | Live alone | 513(23.32%) |
| Live with family | 729(33.14%) | |
| Live with pets | 579(26.32%) | |
| Others | 379(17.22%) | |
| Consciousness about COVID-19 | Low | 21(0.95%) |
| Moderate | 479(21.77%) | |
| High | 1700(77.27%) |
Construct Validity and Reliability
| Constructs/Items | F.L | CA | CR | AVE |
|---|---|---|---|---|
| 0.966 | 0.975 | 0.907 | ||
| C-19 P1 | 0.949 | |||
| C-19 P2 | 0.952 | |||
| C-19 P3 | 0.957 | |||
| C19 P4 | 0.951 | |||
| 0.892 | 0.917 | 0.649 | ||
| PM1 | 0.750 | |||
| PM2 | 0.772 | |||
| PM3 | 0.807 | |||
| PM4 | 0.836 | |||
| PM5 | 0.840 | |||
| PM6 | 0.823 | |||
| 0.850 | 0.893 | 0.627 | ||
| OaW1 | 0.769 | |||
| OaW2 | 0.836 | |||
| OaW3 | 0.834 | |||
| OaW4 | 0.814 | |||
| OaW5 | 0.698 | |||
| 0.867 | 0.904 | 0.652 | ||
| PE1 | 0.795 | |||
| PE2 | 0.832 | |||
| PE3 | 0.759 | |||
| PE4 | 0.848 | |||
| PE5 | 0.802 | |||
| 0.823 | 0.876 | 0.586 | ||
| PhyH1 | 0.745 | |||
| PhyH2 | 0.803 | |||
| PhyH3 | 0.830 | |||
| PhyH4 | 0.742 | |||
| PhyH5 | 0.701 | |||
| 0.890 | 0.910 | 0.507 | ||
| VrF1 | 0.653 | |||
| VrF10 | 0.709 | |||
| VrF2 | 0.691 | |||
| VrF3 | 0.502 | |||
| VrF4 | 0.754 | |||
| VrF5 | 0.752 | |||
| VrF6 | 0.810 | |||
| VrF7 | 0.775 | |||
| VrF8 | 0.700 | |||
| VrF9 | 0.731 | |||
| 0.959 | 0.967 | 0.830 | ||
| PsyH1 | 0.878 | |||
| PsyH2 | 0.892 | |||
| PsyH3 | 0.936 | |||
| PsyH4 | 0.903 | |||
| PsyH5 | 0.925 | |||
| PsyH6 | 0.930 |
Abbreviations: FL, factor loadings; CA, Cronbach’s Alpha; CR, composite reliability; AVE, average variance extracted; C-19 P, COVID-19 pandemic; PM, preventive measures; OaW, overall wellbeing; PE, physical exercise; PhyH, physical health; VrF, virtual reality fitness; PsyH, psychological health.
Figure 2Factor loadings, path coefficient, and R-square result (PLS-Algorithm).
Discriminant Validity: Fornell Larcker
| Constructs | C-19 P | PM | OaW | PE | PhyH | VrF | PsyH |
|---|---|---|---|---|---|---|---|
| C-19 P | 0.952 | ||||||
| PM | 0.032 | 0.806 | |||||
| OaW | −0.046 | −0.388 | 0.792 | ||||
| PE | −0.015 | −0.473 | 0.687 | 0.808 | |||
| PhyH | −0.141 | −0.493 | 0.549 | 0.587 | 0.766 | ||
| VrF | 0.007 | −0.372 | 0.494 | 0.554 | 0.479 | 0.712 | |
| PsyH | 0.567 | 0.110 | −0.138 | −0.094 | −0.123 | −0.047 | 0.911 |
Abbreviations: C-19 P, COVID-19 pandemic; PM, preventive measures; OaW, overall well-being; PE, physical exercise; PhyH, physical health; VrF, virtual reality fitness; PsyH, psychological health.
Discriminant Validity (HTMT)
| Constructs | C-19 P | PM | OaW | PE | PhyH | VrF | PsyH |
|---|---|---|---|---|---|---|---|
| C-19 P | |||||||
| PM | 0.071 | ||||||
| OaW | 0.060 | 0.435 | |||||
| PE | 0.039 | 0.527 | 0.803 | ||||
| PhyH | 0.150 | 0.555 | 0.656 | 0.701 | |||
| VrF | 0.065 | 0.409 | 0.552 | 0.617 | 0.551 | ||
| PsyH | 0.586 | 0.118 | 0.155 | 0.105 | 0.133 | 0.075 |
Abbreviations: C-19 P, COVID-19 pandemic; PM, preventive measures; OaW, overall well-being; PE, physical exercise; PhyH, physical health; VrF, virtual reality fitness; PsyH, psychological health.
Assessment of Structural Model
| OaW | 0.294 | 0.293 | 0.26: Substantial | ||||
| PE | 0.390 | 0.389 | |||||
| PhyH | 0.362 | 0.361 | |||||
| VrF | 0.139 | 0.138 | |||||
| PsyH | 0.330 | 0.329 | |||||
| OaW | 0.177 | 0.437 | A value larger than (0) indicates Predictive Relevance | ||||
| PE | 0.246 | 0.472 | |||||
| PhyH | 0.206 | 0.380 | |||||
| VrF | 0.068 | 0.405 | |||||
| PsyH | 0.271 | 0.756 | |||||
| C-19 P | 0.002 | 0.000 | 0.027 | 0.000 | 0.475 | 0.26: Substantial | |
| PM | 0.067 | 0.135 | 0.175 | 0.161 | 0.009 | ||
| VrF | 0.202 | 0.273 | 0.162 | 0.001 | |||
| C-19 P | 1.001 | 1.001 | 1.001 | 1.001 | 1.001 | VIF ≤ 5.0 | |
| PM | 1.162 | 1.162 | 1.162 | 1.001 | 1.162 | ||
| VrF | 1.161 | 1.161 | 1.161 | 1.161 | |||
Abbreviations: C-19 P, COVID-19 pandemic; PM, preventive measures; OaW, overall well-being; PE, physical exercise; PhyH, physical health; VrF, virtual reality fitness; PsyH, psychological health.
Path Coefficient (Direct Effect) Result
| Hypothesis | OS/Beta | T values | P values | Decision |
|---|---|---|---|---|
| C-19 P -> OaW | −0.041 | 2.296 | 0.022 | Significant |
| C-19 P -> PE | −0.008 | 0.492 | 0.623 | Not Significant |
| C-19 P -> PhyH | −0.132 | 7.357 | 0.000 | Significant |
| C-19 P -> PsyH | 0.564 | 47.057 | 0.000 | Significant |
| PM -> OaW | −0.235 | 8.191 | 0.000 | Significant |
| PM -> PE | −0.309 | 11.266 | 0.000 | Significant |
| PM -> PhyH | −0.360 | 12.003 | 0.000 | Significant |
| PM -> PsyH | 0.084 | 4.911 | 0.000 | Significant |
Abbreviations: C-19 P, COVID-19 pandemic; PM, preventive measures; OaW, overall well-being; PE, physical exercise; PhyH, physical health; VrF, virtual reality fitness; PsyH, psychological health.
Mediation (Indirect Effect) Result
| Hypotheses | OS/Beta | Lower Limit | Upper Limit | T values | P values | Decision | Mediation |
|---|---|---|---|---|---|---|---|
| C-19 P -> VrF -> OaW | 0.008 | −0.009 | 0.021 | 1.067 | 0.286 | Not Significant | No Mediation |
| PM -> VrF ->OaW | −0.151 | −0.187 | −0.117 | 8.034 | 0.000 | Significant | Partial |
| C-19 P -> VrF -> PE | 0.008 | −0.009 | 0.022 | 1.066 | 0.287 | Not Significant | No Mediation |
| PM -> VrF -> PE | −0.164 | −0.207 | −0.128 | 8.102 | 0.000 | Significant | Partial |
| C-19 P -> VrF -> PhyH | 0.007 | −0.007 | 0.018 | 1.064 | 0.288 | Not Significant | No Mediation |
| PM -> VrF ->PhyH | −0.129 | −0.166 | −0.098 | 7.407 | 0.000 | Significant | Partial |
| C-19 P -> VrF -> PsyH | 0.000 | −0.002 | 0.000 | 0.639 | 0.523 | Not Significant | No Mediation |
| PM -> VrF-> PsyH | 0.007 | −0.006 | 0.021 | 1.077 | 0.282 | Not Significant | No Mediation |
Abbreviations: C-19 P, COVID-19 pandemic; PM, preventive measures; OaW, overall well-being; PE, physical exercise; PhyH, physical health; VrF, virtual reality fitness; PsyH, psychological health.
Figure 3Bootstrapping results with inner model t-values.