| Literature DB >> 35812515 |
Ru Liu1, Rashid Menhas2, Jianhui Dai3, Zulkaif Ahmed Saqib4, Xiang Peng3,1.
Abstract
Background: Physical activity is an essential need of the human body that helps improve the physical fitness of an individual and creates a positive impact on overall wellbeing. Smartphone applications play an essential role in providing several benefits to consumers by offering various capabilities in terms of health and fitness.COVID-19 preventive measures shut down public places, and people cannot go to the gym and parks for physical activity. Smart applications for physical activity are an effective way to keep active while staying at home. Objective: The objective of the present study was to assess the mediating role of the e-platforms physical activity among the Chinese people in China during the COVID-19 lockdown. Method: The participants in this study were Chinese citizens living in home isolation during the early stages of the epidemic in China. The primary data was collected via an online survey using a convenience sample strategy in accordance with the study purpose. The collected data were cleaned by using the SPSS-25 statistical software. SmartPLS 3.0 software was used to investigate the suggested study framework utilizing the structural equation modeling technique.Entities:
Keywords: COVID-19; lockdown; mediating role; physical activity; smart applications
Mesh:
Year: 2022 PMID: 35812515 PMCID: PMC9257108 DOI: 10.3389/fpubh.2022.852311
Source DB: PubMed Journal: Front Public Health ISSN: 2296-2565
Data quality check distribution.
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| The questionnaires sent to the study participants | 5,600 (100.0%) |
| Questionnaire responded by the study participants | 5,450 (97.32%) |
| Questionnaire not responded | 150 (2.68%) |
| Incomplete excluded questionnaire | 99 (1.77%) |
| The questionnaire included in the final data analysis | 5,351 (95.55%) |
Demographic distribution of the study population (N-5,351).
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| Gender | Male | 2,626 | 49.1% |
| Female | 2,725 | 50.9% | |
| Age | 20–29 | 2,848 | 53.22% |
| 30–39 | 1,376 | 25.70% | |
| 40–49 | 527 | 9.8% | |
| 50–59 | 444 | 8.3% | |
| 60+ | 156 | 2.9% | |
| Education | College graduate | 1,677 | 31.34% |
| University graduate | 2,272 | 42.46% | |
| Others | 1,402 | 26.20% | |
| Occupation | Government employee | 628 | 11.7% |
| Private companies employee | 2,287 | 42.7% | |
| Self-employed | 897 | 16.8% | |
| Retired | 39 | 0.7% | |
| Student | 1,062 | 19.8% | |
| No occupation | 48 | 1.0% | |
| Others | 390 | 7.3% | |
| Marital status | Married | 3,347 | 62.5% |
| Unmarried (single) | 1871 | 35% | |
| Others | 133 | 2.5% |
Results for measurement of the conceptual framework (N-5,351).
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| Covid-19 preventive measures | CPM1 | 0.693 | 0.011 | 61.91 | 1.35 | 0.834 | 0.502 | 0.755 |
| CPM2 | 0.741 | 0.010 | 76.82 | 1.44 | ||||
| CPM3 | 0.702 | 0.015 | 48.03 | 1.48 | ||||
| CPM4 | 0.751 | 0.008 | 96.74 | 1.34 | ||||
| CPM5 | 0.651 | 0.014 | 46.39 | 1.31 | ||||
| Fitness app | FA1 | 0.942 | 0.002 | 428.01 | 4.79 | 0.953 | 0.837 | 0.935 |
| FA2 | 0.908 | 0.003 | 288.14 | 3.40 | ||||
| FA3 | 0.900 | 0.003 | 258.00 | 3.15 | ||||
| FA4 | 0.909 | 0.003 | 286.20 | 3.42 | ||||
| Live-streaming workout classes | LSWc1 | 0.900 | 0.003 | 280.56 | 3.04 | 0.945 | 0.812 | 0.923 |
| LSWc2 | 0.901 | 0.003 | 269.34 | 3.06 | ||||
| LSWc3 | 0.899 | 0.003 | 283.87 | 3.02 | ||||
| LSWc4 | 0.905 | 0.003 | 300.14 | 3.18 | ||||
| Physical activity | PA1 | 0.897 | 0.003 | 260.08 | 2.97 | 0.946 | 0.814 | 0.924 |
| PA2 | 0.904 | 0.003 | 302.55 | 3.16 | ||||
| PA3 | 0.903 | 0.003 | 299.11 | 3.14 | ||||
| PA4 | 0.905 | 0.003 | 302.62 | 3.18 | ||||
| Virtual reality fitness | VRF1 | 0.906 | 0.003 | 285.14 | 3.24 | 0.950 | 0.826 | 0.930 |
| VRF2 | 0.912 | 0.003 | 311.83 | 3.39 | ||||
| VRF3 | 0.910 | 0.003 | 306.61 | 3.33 | ||||
| VRF4 | 0.907 | 0.003 | 294.12 | 3.27 |
Constructs discriminant validity by following Fornell-Larcker Criteria.
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| Covid-19 preventive measures |
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| Fitness apps | 0.497 |
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| Live streaming workout classes | 0.498 | 0.888 |
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| Physical activity | 0.498 | 0.884 | 0.879 |
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| Virtual reality fitness | 0.471 | 0.872 | 0.886 | 0.887 |
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Bold values denote correlations (off-diagonal elements) and square root of the AVEs (diagonal elements).
Model fit summary.
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| SRMR | 0.043 |
| d_ULS | 0.418 |
| d_G | 0.184 |
| Chi-Square (χ2), | 5,479.225 |
| NFI | 0.947 |
SRMR, standardized-root-mean-square-residual; d_ULS, unweighted least squares discrepancy; d_G, geodesic discrepancy; NFI, normed fit index.
Structural results for proposed hypotheses.
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| H1 = CPM -> FA | 0.497 | 43.068 | 0.001 | Confirmed |
| H2 = CPM -> LSWc | 0.498 | 41.078 | 0.001 | Confirmed |
| H3 = CPM -> VRF | 0.471 | 39.103 | 0.001 | Confirmed |
| H4 = CPM -> PA | 0.468 | 42.633 | 0.001 | Confirmed |
| H5 = FA -> PA | 0.341 | 14.357 | 0.001 | Confirmed |
| H6 = LSWc -> PA | 0.251 | 11.212 | 0.001 | Confirmed |
| H7 = VRF -> PA | 0.367 | 16.032 | 0.001 | Confirmed |
| H8 = CPM -> FA -> PA | 0.170 | 13.750 | 0.001 | Confirmed |
| H9 = CPM -> LSWc -> PA | 0.125 | 10.604 | 0.001 | Confirmed |
| H10 = CPM -> VRF -> PA | 0.173 | 14.842 | 0.001 | Confirmed |
CPM, COVID-19 Preventive Measures; FA, Fitness Apps; LWSc, Live streaming workout classes; VRF, Virtual reality fitness.