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Internet use is an important means of accessing health-related information. Identifying the associations between internet use and health outcomes could provide insight into strategies for improving public health among middle-aged and older adults (45 years and up).
This study aimed to examine the relationship between internet use and health outcomes in middle-aged and older adults.
Data were obtained from the 2018 China Health and Retirement Longitudinal Study. Physical, mental, and subjective health were assessed using the Activities of Daily Living (ADL) Scale, the 10-item Center for Epidemiologic Studies Depression Scale, and the 3-level Self-Rated Health Scale, respectively. The chi-square test and rank sum test were used to explore whether internet use was associated with health status. A multivariate logistic regression model was used to determine this association further after controlling for the confounding factors.
Overall, 13% (1752/13,474) of the participants used the internet. Regression analyses revealed that the prevalence of depression (odds ratio [OR] 0.59, 95% CI 0.52-0.68;
Internet use had a positive effect on the physical and mental health of middle-aged and older adults who participated in this study. However, the internet usage rate remains low among older Chinese people. Therefore, the internet penetration rate should be a priority.
The global population is aging at an unprecedented rate. Between 2020 and 2030, the proportion of the global population aged 60 years and above is estimated to increase by 34% [
Additionally, various social factors can affect health [
However, fewer studies have presented different perspectives on internet usage and health among middle-aged and older adults. There are substantial variations in the information acquired from limited-income countries and high-income countries [
CHARLS began in 2011 [
Prior to the survey, each participant was given written informed consent specifying the purpose of this study. Ethical approval for all the CHARLS waves was granted by the institutional review board of Peking University (IRB00001052-11015). The survey was also anonymous, and the answers were protected by privacy law.
Health describes more than the mere integrity of the physical body [
In this study, internet use was considered the core explanatory variable, with 1 representing internet access and 0 otherwise. After reviewing the literature, we organized the control variables according to the following demographic characteristics: sex (male=1, female=2), age (continuous variable), marital status (married=1, unmarried=0), education (below middle school=1, high school and vocational training=2, above high school=3), chronic diseases (yes=1, no=0), health insurance (yes=1, no=0), household expenditure per capita (continuous variable), place of residence (rural=1, urban=0), number of children (continuous variable), and interaction with friends (yes=1, no=0). The household expenditure per capita was added to “1” and then logged. For marital status, “unmarried” included separated, divorced, widowed, and never married options.
Stata version 14.0 software (StataCorp), was used for all statistical analyses;
As shown in
Description and univariate analysis results for study participants (N=13,474)a,b,c.
Variable | All | Unrestricted | Restricted | ||
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<.001 | |
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Yes | 1752 (13) | 1654 (14.94) | 98 (4.07) |
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No | 11,722 (87) | 9415 (85.06) | 2307 (95.93) |
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<.001 | |
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Male | 6526 (48.43) | 5619 (50.76) | 907 (37.71) |
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Female | 6948 (51.57) | 5450 (49.24) | 1498 (62.29) |
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Age (years), mean (SD) | 61.50 (9.30) | 60.54 (9.01) | 65.93 (9.32) | <.001 | |
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<.001 | |
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Married | 11,737 (87.11) | 9844 (88.93) | 1893 (78.71) |
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Unmarried | 1737 (12.89) | 1225 (11.07) | 512 (21.29) |
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<.001 | |
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Below middle school | 11,783 (87.45) | 9521 (86.01) | 2262 (94.05) |
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High school and vocational training | 1447 (10.74) | 1315 (11.88) | 132 (5.49) |
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Above high school | 244 (1.81) | 233 (2.10) | 11 (0.46) |
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<.001 | |
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Yes | 5915 (43.90) | 4493 (40.59) | 1422 (59.13) |
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No | 7559 (56.10) | 6576 (59.41) | 983 (40.87) |
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<.001 | |
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Yes | 11,127 (82.58) | 9042 (81.69) | 2085 (86.69) |
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No | 2347 (17.42) | 2027 (18.31) | 320 (13.31) |
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Household expenditure per capita, mean (SD) | 9.34 (0.97) | 9.37 (0.96) | 9.19 (1.04) | <.001 | |
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<.001 | |
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Urban | 5038 (37.39) | 4328 (39.10) | 710 (29.52) |
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Rural | 8436 (62.61) | 6741 (60.90) | 1695 (70.48) |
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Number of children, mean (SD) | 2.54 (1.30) | 2.43 (1.23) | 3.04 (1.45) | <.001 | |
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<.001 | |
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Yes | 4628 (34.35) | 3900 (35.23) | 728 (30.27) |
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No | 8846 (65.65) | 7169 (64.77) | 1677 (69.73) |
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aRegarding the statistical description of variables, we used mean and SD for continuous variables and frequency (n) and percentage for categorical variables.
bPhysical health was classified into 2 types: restricted (having difficulty in any item of activities of daily living [ADL]) and unrestricted (having no difficulty with ADL).
cThe internet usage rate was 20.98% (1057/5038) in urban and 8.24% (695/8436) in rural areas.
Description and univariate analysis results for study participants (N=13,474)a.
Variable | All | No depression | Depression |
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<.001 |
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Yes | 1752 (13) | 1414 (15.79) | 338 (7.48) |
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No | 11,722 (87) | 7540 (84.21) | 4182 (92.52) |
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<.001 |
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Male | 6526 (48.43) | 4791 (53.51) | 1735 (38.38) |
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Female | 6948 (51.57) | 4163 (46.49) | 2785 (61.62) |
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Age (years), mean (SD) | 61.50 (9.30) | 61.28 (9.33) | 61.95 (9.23) | <.001 |
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<.001 |
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Married | 11,737 (87.11) | 7971 (89.02) | 3766 (83.32) |
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Unmarried | 1737 (12.89) | 983 (10.98) | 754 (16.68) |
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<.001 |
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Below middle school | 11,783 (87.45) | 7595 (84.82) | 4188 (92.65) |
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High school and vocational training | 1447 (10.74) | 1147 (12.81) | 300 (6.64) |
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Above high school | 244 (1.81) | 212 (2.37) | 32 (0.71) |
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<.001 |
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Yes | 5915 (43.90) | 3534 (39.47) | 2381 (52.68) |
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No | 7559 (56.10) | 5420 (60.53) | 2139 (47.32) |
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<.001 |
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Yes | 11,127 (82.58) | 7148 (79.83) | 3979 (88.03) |
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No | 2347 (17.42) | 1806 (20.17) | 541 (11.97) |
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Household expenditure per capita, mean (SD) | 9.34 (0.97) | 9.38 (0.96) | 9.25 (0.99) | <.001 |
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<.001 |
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Urban | 5038 (37.39) | 3687 (41.18) | 1351 (29.89) |
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Rural | 8436 (62.61) | 5267 (58.82) | 3169 (70.11) |
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Number of children, mean (SD) | 2.54 (1.30) | 2.47 (1.28) | 2.68 (1.31) | <.001 |
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<.001 |
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Yes | 4628 (34.35) | 3156 (35.25) | 1472 (32.57) |
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No | 8846 (65.65) | 5798 (64.75) | 3048 (67.43) |
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aRegarding the statistical description of variables, we used mean and SD for continuous variables and frequency (n) and percentage for categorical variables.
Description and univariate analysis results for study participants (N=13,474)a,b.
Variable | All | Positive | General | Negative | ||||||||
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<.001 | |||||||
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Yes | 1752 (13) | 622 (19.35) | 904 (13.56) | 226 (6.29) |
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No | 11,722 (87) | 2592 (80.65) | 5765 (86.44) | 3365 (93.71) |
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<.001 | |||||||
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Male | 6526 (48.43) | 1741 (54.17) | 3232 (48.46) | 1553 (43.25) |
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Female | 6948 (51.57) | 1473 (45.83) | 3437 (51.54) | 2038 (56.75) |
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Age (years), mean (SD) | 61.50 (9.30) | 60.33 (9.13) | 61.09 (9.24) | 63.32 (9.31) | <.001 | |||||||
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<.001 | |||||||
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Married | 11,737 (87.11) | 2841 (88.39) | 5905 (88.54) | 2991 (83.29) |
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Unmarried | 1737 (12.89) | 373 (11.61) | 764 (11.46) | 600 (16.71) |
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<.001 | |||||||
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Below middle school | 11,783 (87.45) | 2628 (81.77) | 5822 (87.30) | 3333 (92.82) |
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High school and vocational training | 1447 (10.74) | 479 (14.90) | 734 (11.01) | 234 (6.52) |
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Above high school | 244 (1.81) | 107 (3.33) | 113 (1.69) | 24 (0.67) |
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<.001 | |||||||
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Yes | 5915 (43.90) | 891 (27.72) | 2784 (41.75) | 2240 (62.38) |
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No | 7559 (56.10) | 2323 (72.28) | 3885 (58.25) | 1351 (37.62) |
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<.001 | |||||||
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Yes | 11,127 (82.58) | 2515 (78.25) | 5456 (81.81) | 3156 (87.89) |
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No | 2347 (17.42) | 699 (21.75) | 1213 (18.19) | 435 (12.11) |
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Household expenditure per capita, mean (SD) | 9.34 (0.97) | 9.41 (1) | 9.35 (0.96) | 9.24 (0.97) | <.001 | |||||||
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<.001 | |||||||
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Urban | 5038 (37.39) | 1383 (43.03) | 2605 (39.06) | 1050 (29.24) |
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Rural | 8436 (62.61) | 1831 (56.97) | 4064 (60.94) | 2541 (70.76) |
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Number of children, mean (SD) | 2.54 (1.30) | 2.38 (1.21) | 2.48 (1.27) | 2.80 (1.38) | <.001 | |||||||
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<.001 | |||||||
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Yes | 4628 (34.35) | 1249 (38.86) | 2324 (34.85) | 1055 (29.38) |
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No | 8846 (65.65) | 1965 (61.14) | 4345 (65.15) | 2536 (70.62) |
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aRegarding the statistical description of variables, we used mean and SD for continuous variables and frequency (n) and percentage for categorical variables.
bSelf-reported health was categorized into 3 types: positive (very good or good), fair, and negative (poor or very poor).
Regression results for ADLa, depressionb, and subjective self-rated healthc status among study participants (N=13,474).
Variable | ADL | Depression | Self-rated health | |||||
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ORd (95% CI) | OR (95% CI) | OR (95% CI) | |||||
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Yes (ref: no) | 0.48 (0.39-0.60) | <.001 | 0.59 (0.52-0.68) | <.001 | 0.68 (0.61-0.76) | <.001 | |
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Female (ref: male) | 1.71 (1.55-1.88) | <.001 | 1.72 (1.60-1.86) | <.001 | 1.30 (1.22-1.39) | <.001 | |
Age | 1.05 (1.04-1.06) | <.001 | 0.99 (0.99-1.00) | .075 | 1.01 (1-1.02) | <.001 | ||
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Married (ref: unmarried) | 0.79 (0.69-0.90) | <.001 | 0.68 (0.61-0.77) | <.001 | 0.91 (0.82-1.01) | .078 | |
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High school and vocational training (ref: below middle school) | 0.77 (0.63-0.94) | .012 | 0.73 (0.63-0.84) | <.001 | 0.78 (0.70-0.87) | <.001 | |
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Above high school (ref: below middle school) | 0.43 (0.23-0.80) | .007 | 0.61 (0.41-0.90) | .013 | 0.63 (0.48-0.81) | <.001 | |
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Yes (ref: no) | 2.12 (1.93-2.33) | <.001 | 1.74 (1.61-1.87) | <.001 | 2.68 (2.51-2.87) | <.001 | |
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Yes (ref: no) | 1.11 (0.95-1.29) | .181 | 1.26 (1.12-1.42) | <.001 | 1.18 (1.07-1.30) | .001 | |
Household expenditure per capita | 0.97 (0.93-1.02) | .18 | 0.97 (0.93-1.01) | .139 | 0.99 (0.96-1.03) | .755 | ||
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Rural (ref: urban) | 1.33 (1.20-1.49) | <.001 | 1.40 (1.29-1.52) | <.001 | 1.30 (1.21-1.40) | <.001 | |
Number of children | 1.10 (1.06-1.15) | <.001 | 1.05 (1.01-1.08) | .008 | 1.05 (1.02-1.08) | .001 | ||
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Yes (ref: No) | 0.88 (0.80-0.98) | .015 | 0.94 (0.87-1.02) | .149 | 0.81 (0.76-0.87) | <.001 |
aADL: activities of daily living. Reporting difficulty with ADL (ref: no).
bDepression (ref: no).
cSelf-rated health (ref: reporting positive self-reported health).
dOR: odds ratio.
The logistic regression was also conducted to identify an association between several factors and health (
Similarly, having more children was positively associated with ADL performance (OR 1.10, 95% CI 1.06-1.15;
Results for urban versus rural subgroups (N=13,474).
Variable | ADLa | Depressionb | Self-rated healthc | |||||||||||||||
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Urban | Rural | Urban | Rural | Urban | Rural | ||||||||||||
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ORd (95 % CI) | OR (95% CI) | OR (95 %CI) | OR (95% CI) | OR (95% CI) | OR (95% CI) | ||||||||||||
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Yes (ref: no) | 0.44 (0.32-0.61) | <.001 | 0.55 (0.41-0.75) | <.001 | 0.69 (0.57-0.84) | <.001 | 0.52 (0.43-0.63) | <.001 | 0.70 (0.61-0.81) | <.001 | 0.67 (0.57-0.78) | <.001 | |||||
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Female (ref: male) | 1.40 (1.18-1.67) | <.001 | 1.89 (1.68-2.13) | <.001 | 1.60 (1.40-1.83) | <.001 | 1.79 (1.63-1.97) | <.001 | 1.24 (1.11-1.38) | <.001 | 1.34 (1.23-1.46) | <.001 | |||||
Age | 1.05 (1.04-1.06) | <.001 | 1.05 (1.04-1.06) | <.001 | 0.99 (0.98-0.99) | .003 | 1.00 (0.99-1.01) | .920 | 1.01 (1-1.02) | .002 | 1.02 (1.01-1.02) | <.001 | ||||||
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Married (ref: unmarried) | 0.91 (0.72-1.16) | .454 | 0.74 (0.63-0.87) | <.001 | 0.65 (0.54-0.80) | <.001 | 0.71 (0.62-0.81) | <.001 | 0.84 (0.70-0.99) | .044 | 0.96 (0.84-1.09) | .486 | |||||
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High school and vocational training (ref: below middle school) | 0.89 (0.67-1.18) | .428 | 0.72 (0.54-0.96) | .024 | 0.82 (0.67-1.01) | .061 | 0.65 (0.53-0.80) | <.001 | 0.77 (0.66-0.90) | .001 | 0.79 (0.67-0.93) | .004 | |||||
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Above high school (ref: below middle school) | 0.42 (0.20-0.88) | .033 | 0.73 (0.27-1.96) | .530 | 0.60 (0.39-0.93) | .024 | 0.79 (0.31-2.03) | .630 | 0.64 (0.48-0.85) | .002 | 0.46 (0.22-0.96) | .038 | |||||
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Yes (ref: no) | 2.01 (1.70-2.39) | <.001 | 2.18 (1.95-2.45) | <.001 | 1.64 (1.44-1.86) | <.001 | 1.79 (1.63-1.96) | <.001 | 2.43 (2.18-2.72) | <.001 | 2.83 (2.60-3.09) | <.001 | |||||
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Yes (ref: no) | 1.33 (1.07-1.65) | .009 | 0.88 (0.71-1.08) | .224 | 1.26 (1.07-1.49) | .005 | 1.19 (0.99-1.43) | .063 | 1.12 (0.98-1.28) | .097 | 1.22 (1.05-1.43) | .012 | |||||
Household expenditure per capita | 0.99 (0.90-1.09) | .847 | 0.97 (0.92-1.03) | .375 | 0.99 (0.92-1.08) | .978 | 0.97 (0.92-1.01) | .147 | 1.01 (0.95-1.08) | .714 | 0.99 (0.95-1.03) | .578 | ||||||
Number of children | 1.16 (1.09-1.24) | <.001 | 1.07 (1.02-1.12) | .009 | 1.18 (1.11-1.25) | <.001 | 0.99 (0.95-1.03) | .499 | 1.12 (1.07-1.18) | <.001 | 1.02 (0.98-1.06) | 0.332 | ||||||
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Yes (ref: no) | 0.82 (0.69-0.99) | .040 | 0.91 (0.81-1.03) | .126 | 0.78 (0.68-0.90) | .001 | 1.04 (0.94-1.14) | .478 | 0.75 (0.67-0.84) | <.001 | 0.84 (0.77-0.92) | <.001 |
aADL: activities of daily living. Reporting difficulty with ADL (ref: no).
bDepression (ref: no).
cSelf-rated health (ref: reporting positive self-reported health).
dOR: odds ratio.
Internet use has a significant positive association with the health of middle-aged and older adults. This could be attributed to the vast resources of the internet that have laid the foundation for guiding people toward a healthy lifestyle. With the improvement in living standards and increase in age, people pay more attention to their own health and are more inclined to search for health-related information when required. There are abundant health resources on the internet, and people can search for relevant information in real time using their cell phones or other digital devices [
However, China started utilizing the internet later than Western countries [
Middle-aged and older adults with restricted mobility and financial burdens are faced not only with the daunting task of maintaining a normal life but also with enormous barriers to social interaction, which makes them prone to depression [
Self-rated health is an individual’s subjective evaluation and comprehensive assessment based on their actual health status. Related studies have shown that pain is associated with self-rated health [
Influenced by the urban-rural dual structure, there is a large gap in the internet usage rate between rural and urban areas (8.24% vs 20.98%, respectively) in this study. Influential factors include physical geography, regional development policies, and misallocation of medical resources, which affect various health outcomes [
Our findings revealed that female participants had poorer health than male participants. Generally, with respect to individual biological factors, women suffer greater morbidity, particularly late in life, and have worse self-rated health [
The more children in a family, the greater the expenditure. According to the resource dilution theory, this is detrimental to the health of individuals to have limited access to resources [
In addition, internet use may have a greater impact on the middle-aged population than on the older adult population. It may be that middle-aged people are more receptive to and understanding of information and are therefore better able to use the internet to improve their health [
This study has some limitations. Due to the large number of missing values removed for the main variables “depression” and “self-rated health” and to keep the sample size uniform, 31.3% (4228/13,474) of individuals from the 2018 CHARLS were excluded for missing data. Therefore, the representativeness may be limited due to the reduction of the sample size for analysis. The potential bias caused by sample selection may underestimate the association between internet use and health outcomes in middle-aged and older populations.
This study has further limitations. First, we aimed to incorporate more factors that may influence internet use and health. However, other variables may affect health and depression that were not included. Second, this was a cross-sectional study, and we could not explore the causal relationship between internet use and health. Third, it did not include the pathways by which internet use affects health. In the future, we plan to conduct further research through mechanistic exploration and heterogeneity analyses.
This study examined the association between internet use and the physical, mental, and subjective self-rated health status among middle-aged and older adults and found that internet use has a positive effect on health status. The internet, a powerfully open and inclusive tool, is an important milestone in the development of human society. However, the massive usage of internet technology has largely amplified the age-related digital divide [
First, future internet use should consider the accessibility of tools and develop access to appropriate programs for older adults. The government, communities, and enterprises should pay attention to the health of middle-aged and older adults, strengthen cooperation, and work together to promote aging-friendly internet applications. Second, they should guide middle-aged and older adults who are willing to use the internet to become familiar with it. On the one hand, the confidence of middle-aged and older adults in using the internet can be enhanced by offering training courses on cell phones and computers in the community. Another method could be to have younger people volunteer to go and teach or help older people in their homes, which would equally have benefits.
activities of daily living
10-item Center for Epidemiologic Studies Depression Scale
China Health and Retirement Longitudinal Study
odds ratio
The authors would like to thank the Shandong University School of Public Health and all participants for making this study possible. This work was supported by the National Natural Science Foundation of China (72274108), the Natural Science Foundation of Shandong Province (ZR2022MG003), the Shandong Social Science Foundation (21CCXJ04), the Humanities and Social Science Foundation of the Ministry of Education of China (21YJC630060), and the National Natural Science Foundation of China (71673170, 71303137).
The data sets generated and/or analyzed during this study are available from the China Health and Retirement Longitudinal Study (CHARLS) data repository.
None declared.