| Literature DB >> 32391798 |
Lingling Gao1, Yiqun Gan2, Amanda Whittal1, Song Yan1, Sonia Lippke1.
Abstract
BACKGROUND: Work-life balance is associated with health behaviors. In the face of digitalization, understanding this link requires a theory-based investigation of problematic internet use and perceived stress, which are so far unknown.Entities:
Keywords: culture; exercise; healthy diet; healthy lifestyle; internet; work-life balance
Year: 2020 PMID: 32391798 PMCID: PMC7248799 DOI: 10.2196/16468
Source DB: PubMed Journal: J Med Internet Res ISSN: 1438-8871 Impact factor: 5.428
Descriptive statistics of main study variables in the 2 groups (N=877; group 1: residents in Germany and group 2: residents in China).
| Variables | Group 1, mean (SD) | Group 2, mean (SD) | Total | |||
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| Mean (SD) | Range |
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| Age (years) | 26.7 (12.5) | 32.5 (8.4) | 30.0 (10.8) | 17-65 | −8.24 (875) | <.001 |
| Problematic internet use | 41.8 (11.6) | 44.2 (11.9) | 43.2 (11.8) | 20-80 | −2.96 (875) | .003 |
| Perceived stress | 5.3 (1.8) | 4.6 (1.5) | 4.9 (1.6) | 2-10 | 6.32 (875) | <.001 |
| Health behaviors | 3.0 (1.1) | 2.9 (1.2) | 2.9 (1.2) | 0-5 | 2.44 (875) | .02 |
| Work-life balance | 16.5 (4.5) | 18.0 (4.1) | 17.4 (4.3) | 5-25 | −5.36 (875) | <.001 |
Pearson correlation of variables in 2 groups.
| Group 1a | Group 2b | |||||
| 1 | 2 | 3 | 4 | 5 | 6 | |
| 1. Genderc | N/Ac | −0.07 | −0.19 | 0.10 | 0.02 | −0.04 |
| 2. Age | −0.11 | N/A | −0.05 | −0.36 | −0.20 | 0.15 |
| 3. Health behaviors | −0.04 | −0.06 | N/A | −0.12 | −0.27 | 0.11 |
| 4. Problematic internet use | 0.15 | −0.40 | −0.16 | N/A | 0.32 | −0.22 |
| 5. Perceived stress | −0.02 | −0.23 | −0.14 | 0.35 | N/A | −0.32 |
| 6. Work-life balance | 0.01 | 0.16 | 0.19 | −0.27 | −0.45 | N/A |
aGroup 1: residents in Germany; correlations presented below the diagonal.
bGoup 2: residents in China; correlations presented above the diagonal.
cGender was dummy coded such that 1=female and 2=male. r≥.15, P=.01; 0.10≤ r≤.14, P=.05.
dN/A: not applicable.
Figure 1Serial mediation model and path coefficients predicting work-life balance. Path a1 → a2 → a3 is the full serial mediation path (1). Path a1d1 is the path from health behavior to work-life balance through problematic internet use. Path b1a3 is the path from health behavior to work-life balance through perceived stress.
Mediation effects of health behaviors on work-life balance.
| Predictors | Model 1a (work-life balance) | Model 2b (problematic internet use) | Model 3c (perceived stress) | Model 4d (work-life balance) | ||||
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| β (95% CI) | β (95% CI) | β (95% CI) | β (95% CI) | ||||
| Gendere | .04 (−0.10 to 0.18) | .53 | .14 (0.02 to 0.27) | .03 | −0.10 (−0.23 to 0.03) | .14 | .02 (−0.11 to 0.15) | .76 |
| Age (years) | .02 (0.01 to 0.03) | <.001 | −0.03 (−0.04 to −0.03) | <.001 | −0.03 (−0.03 to −0.02) | <.001 | .01 (0.001 to 0.01) | .02 |
| Health behaviors | .14 (0.08 to 0.21) | <.001 | −0.15 (−0.22 to −0.09) | <.001 | −0.21 (−0.28 to −0.15) | <.001 | .05 (−0.01 to 0.12) | .10 |
| Problematic internet use | N/Af | N/A | N/A | N/A | N/A | N/A | −0.08 (−0.15 to −0.01) | .02 |
| Perceived stress | N/A | N/A | N/A | N/A | N/A | N/A | −0.36 (−0.42 to −0.29) | <.001 |
aR=0.06, F3,873=17.73, P<.001.
bR=0.14, F3,873=47.55, P<.001.
cR=0.11, F3,873=36.83, P<.001.
dR=0.19, F5,871=39.78, P<.001.
eGender was dummy coded with 1=female and 2=male.
fN/A: not applicable.
Figure 2Multiple mediation model with standardized regression coefficients predicting work-life balance in both groups. G1, Group 1: residents in Germany; G2, Group 2: residents in China.
Mediated moderation effects of health behaviors on work-life balance.
| Predictors | Model 1a (work-life balance) | Model 2b (problematic internet use) | Model 3c (perceived stress) | Model 4d (work-life balance) | ||||
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| β (95% CI) | β (95% CI) | β (95% CI) | β (95% CI) | ||||
| Gendere | .03 (−0.10 to 0.17) | .63 | .14 (0.02 to 0.27) | .03 | −0.10 (−0.23 to 0.03) | .13 | .02 (−0.11 to 0.14) | .82 |
| Age (years) | .02 (0.01 to 0.02) | <.001 | −0.04 (−0.04 to 0.00) | <.001 | −0.02 (−0.03 to −0.02) | <.001 | .01 (−0.00 to 0.01) | .15 |
| Health behaviors | .15 (0.09 to 0.22) | <.001 | −0.15 (−0.21 to −0.08) | <.001 | −0.22 (−0.28 to −0.16) | <.001 | .07 (0.003 to 0.13) | .04 |
| Residence | .15 (0.08 to 0.21) | <.001 | .19 (0.13 to 0.25) | <.001 | −0.16 (−0.22 to −0.10) | <.001 | .11 (0.05 to 0.18) | <.001 |
| Problematic internet use | N/Af | N/A | N/A | N/A | N/A | N/A | −0.10 (−0.17 to −0.04) | .003 |
| Perceived stress | N/A | N/A | N/A | N/A | N/A | N/A | −0.34 (−0.40 to −0.27) | <.001 |
| Health behaviors × residence | −0.05 (−0.12 to 0.01) | .11 | −0.04 (−0.03 to 0.10) | .25 | −0.04 (−0.10 to 0.03) | .28 | −0.06 (−0.12 to 0.00) | .05 |
aR=0.08, F5,871=15.09, P<.001.
bR=0.18, F5,871=37.15, P<.001.
cR=0.14, F5,871=27.97, P<.001.
dR=0.20, F7,869=31.04, P<.001.
eGender was dummy coded with 1=female and 2=male.
fN/A: not applicable.
Figure 3Interaction of health behaviors and residence on work-life balance (Group 1: residents in Germany; Group 2: residents in China).