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Although previous studies have shown that a high level of health literacy can improve patients’ ability to engage in health-related shared decision-making (SDM) and improve their quality of life, few studies have investigated the role of eHealth literacy in improving patient satisfaction with SDM (SSDM) and well-being.
This study aims to assess the relationship between patients’ eHealth literacy and their socioeconomic determinants and to investigate the association between patients’ eHealth literacy and their SSDM and well-being.
The data used in this study were obtained from a multicenter cross-sectional survey in China. The eHealth Literacy Scale (eHEALS) and Investigating Choice Experiments Capability Measure for Adults were used to measure patients’ eHealth literacy and capability well-being, respectively. The SSDM was assessed by using a self-administered questionnaire. The Kruskal-Wallis one-way analysis of variance and Wilcoxon signed-rank test were used to compare the differences in the eHEALS, SSDM, and Investigating Choice Experiments Capability Measure for Adults scores of patients with varying background characteristics. Ordinary least square regression models were used to assess the relationship among eHealth literacy, SSDM, and well-being adjusted by patients’ background characteristics.
A total of 569 patients completed the questionnaire. Patients who were male, were highly educated, were childless, were fully employed, were without chronic conditions, and indicated no depressive disorder reported a higher mean score on the eHEALS. Younger patients (SSDM≥61 years=88.6 vs SSDM16-30 years=84.2) tended to show higher SSDM. Patients who were rural residents and were well paid were more likely to report good capability well-being. Patients who had a higher SSDM and better capability well-being reported a significantly higher level of eHealth literacy than those who had lower SSDM and poorer capability well-being. The regression models showed a positive relationship between eHealth literacy and both SSDM (
This study showed that patients with a high level of eHealth literacy are more likely to experience optimal SDM and improved capability well-being. However, patients’ depressive status may alter the relationship between eHealth literacy and SSDM.
eHealth literacy refers to the acquisition and use of web-based information and communication technology to make appropriate health decisions [
The internet provides a convenient way to approach health-related information to the public; however, a low level of eHealth literacy may lead, in contrast, to serious harm [
Recently, shared decision-making (SDM) has been reported to be an effective way to improve trust in patient–doctor relationships, reduce negative emotions, and promote patients’ well-being [
As reported, there are more than 980 million internet users in China, accounting for more than 20% of the users worldwide [
This study aims to (1) assess the relationship between patients’ eHealth literacy and their socioeconomic status (SES) and (2) investigate the association between patients’ eHealth literacy and their SSDM and well-being.
The data used in this study were obtained from a multicenter cross-sectional survey that investigated patients’ attitudes toward patient-centered care (PCC) in Guangdong province, China, from November 2019 to January 2020. Patients were recruited from the inpatient departments of 8 hospitals from 5 cities (Guangzhou, Shenzhen, Zhanjiang, Meizhou, and Shaoguan). All patients from the target hospitals were invited to participate in the survey during the survey period. The inclusion criteria were as follows: (1) being aged ≥18 years, (2) being able to read and speak Chinese, (3) having no cognitive impairment, and (4) being able to provide informed consent. With the assistance of ward nurses, all eligible patients were invited to participate in the survey. The patients who agreed to participate in the survey and provided written informed consent were asked to complete a structured questionnaire that included questions about their demographic characteristics, SES, health conditions, well-being, use of health services, lifestyle, and attitudes toward PCC. A convenience sample of 569 patients (569/800, 71.1% response rate) successfully completed the questionnaire and provided valid responses. The study protocol and informed consent were approved by the institutional review board of the Second Affiliated Hospital of Guangzhou Medical University (reference ID: 2019-ks-28).
The eHealth Literacy Scale (eHEALS) was used to measure consumers’ combined knowledge, comfort, and perceived skills at finding, evaluating, and applying eHealth information to manage health problems [
The Investigating Choice Experiments Capability Measure for Adults (ICECAP-A) is a generic and preference-based instrument that evaluates an individual’s capability well-being [
Patient SSDM was assessed using a self-administered questionnaire. It was developed based on our previous patient engagement framework [
The Patient Health Questionnaire-2 was used to assess whether patients experienced depressed mood over the past 2 weeks. An individual with a score of 3 or above (range: 0-6) was recognized as someone with a depressive disorder [
Patients’ background characteristics (section 1: demographics; section 2: SES; section 3: lifestyle; and section 4: health status) were presented with the mean and SD of the eHEALS, SSDM, and ICECAP-A sum scores. The Kruskal-Wallis one-way analysis of variance (multiple groups) and Wilcoxon signed-rank test (2 groups) were used to compare the differences in the eHEALS, SSDM, and ICECAP-A sum scores of patients with different background characteristics. The Wilcoxon signed-rank test was also used to assess the relationship between level of eHealth literacy and SSDM and well-being. Patients’ level of eHealth literacy was recategorized into high (≥30) and low (<30) on the basis of the median of the original eHEALS sum score. In addition, patients’ depressive status was considered in the analysis of the relationship between 3 measures. Three ordinary least square multivariate regression models were developed to assess the relationships between measures adjusted by patients’ background characteristics. In the first model, the dependent variable was eHealth literacy, and the independent variables were SSDM, well-being, and patients’ background characteristics. In the second model, SSDM was the dependent variable, and the independent variables were eHealth literacy and patients’ background characteristics. In the third model, the dependent variable was capability well-being, and the independent variables were eHealth literacy and patients’ background characteristics. The objective of the first model was to assess the relationship between patients’ eHealth literacy and their socioeconomic determinants, whereas the other two models assessed how patients’ eHealth literacy can predict the changes in their SSDM and well-being, after adjusting for background characteristics. The Bland-Altman (B-A) plot was used to assess the agreement between three measures. The mean scores of the eHEALS, SSDM, and ICECAP-A were plotted on the x-axis, and the differences between them was plotted on the y-axis. The observations clustered evenly around a horizontal line representing y=0, reflecting good agreement between the measures [
Patients’ characteristics and scores of the eHEALSa, SSDMb, and ICECAP-Ac.
|
Patients, n (%) | eHEALS | SSDM | ICECAP-A | ||||||||||||||||
|
|
Value, mean (SD) | Value, mean (SD) | Value, mean (SD) | ||||||||||||||||
Overall | 569 (100) | 66.4 (21.2) | —e | 85.7 (17.0) | — | 77.5 (15.8) | — | |||||||||||||
|
.02 |
|
.55 |
|
.14 | |||||||||||||||
|
Female | 281 (49.4) | 64.3 (21.4) |
|
85.3 (17.5) |
|
79.8 (16.2) |
|
||||||||||||
|
Male | 288 (50.6) | 68.5 (21) |
|
86.1 (16.6) |
|
83.6 (15.3) |
|
||||||||||||
|
.003 |
|
.03 |
|
.85 | |||||||||||||||
|
16-30 | 106 (18.6) | 71.7 (19.9) |
|
84.2 (18.8) |
|
77.9 (15) |
|
||||||||||||
|
31-40 | 132 (23.2) | 69.6 (18.2) |
|
84.1 (16.3) |
|
78.1 (15.7) |
|
||||||||||||
|
41-50 | 116 (20.4) | 66.0 (19.4) |
|
84.4 (17.5) |
|
78.4 (15.3) |
|
||||||||||||
|
51-60 | 106 (18.6) | 63.7 (22.4) |
|
87.5 (18.3) |
|
76.8 (15.4) |
|
||||||||||||
|
≥61 | 109 (19.2) | 60.5 (24.7) |
|
88.6 (13.9) |
|
76.3 (17.6) |
|
||||||||||||
|
.01 |
|
.40 |
|
<.001 | |||||||||||||||
|
No or primary | 90 (15.8) | 62.9 (25.2) |
|
87.8 (15) |
|
70.9 (19.7) |
|
||||||||||||
|
Secondary | 215 (37.8) | 64.0 (22.1) |
|
84.8 (18.4) |
|
75.6 (16.1) |
|
||||||||||||
|
Tertiary or above | 264 (46.4) | 69.6 (18.5) |
|
85.7 (16.5) |
|
81.3 (12.9) |
|
||||||||||||
|
.03 |
|
.70 |
|
.92 | |||||||||||||||
|
Single | 95 (16.7) | 72.5 (17.9) |
|
84.5 (17.5) |
|
78.7 (13.6) |
|
||||||||||||
|
Married | 446 (78.4) | 65.1 (21.8) |
|
85.8 (17) |
|
77.3 (15.9) |
|
||||||||||||
|
Divorced, widow, or widower | 28 (4.9) | 67.1 (19.9) |
|
87.0 (16.3) |
|
75.8 (20.3) |
|
||||||||||||
|
.86 |
|
.35 |
|
.03 | |||||||||||||||
|
Rural | 279 (49.1) | 66.5 (20.3) |
|
86.5 (16.3) |
|
79.3 (14.1) |
|
||||||||||||
|
urban | 290 (50.9) | 66.3 (22.2) |
|
84.8 (17.7) |
|
75.7 (17.2) |
|
||||||||||||
|
.02 |
|
.52 |
|
.05 | |||||||||||||||
|
0 | 104 (18.3) | 72.7 (17.8) |
|
84 (18.5) |
|
78.9 (14.3) |
|
||||||||||||
|
1 | 170 (29.9) | 66.4 (21.3) |
|
86.7 (15) |
|
79.5 (15.3) |
|
||||||||||||
|
2 | 202 (35.5) | 66.5 (20.4) |
|
85.4 (16.4) |
|
75.7 (16.3) |
|
||||||||||||
|
≥3 | 93 (16.3) | 59.4 (24.3) |
|
86.4 (20.0) |
|
76.1 (16.7) |
|
||||||||||||
|
.42 |
|
.31 |
|
.48 | |||||||||||||||
|
No | 414 (72.8) | 65.8 (21.7) |
|
85.4 (17.1) |
|
77.1 (16.2) |
|
||||||||||||
|
Yes | 155 (27.2) | 68 (20.0) |
|
86.4 (16.9) |
|
78.5 (14.7) |
|
||||||||||||
|
.86 |
|
.41 |
|
.98 | |||||||||||||||
|
Live with family or others | 512 (89.9) | 66.4 (21.4) |
|
85.6 (16.9) |
|
77.4 (16) |
|
||||||||||||
|
Live alone | 57 (10.1) | 66.6 (20.4) |
|
86.2 (18.6) |
|
78.3 (13.8) |
|
||||||||||||
|
.005 |
|
.11 |
|
.29 | |||||||||||||||
|
Employed | 394 (69.2) | 68.5 (20.2) |
|
85.1 (17.1) |
|
78.1 (15.1) |
|
||||||||||||
|
Nonemployed | 175 (30.8) | 61.7 (22.8) |
|
87.1 (16.9) |
|
76.1 (17.3) |
|
||||||||||||
|
.55 |
|
.62 |
|
<.001 | |||||||||||||||
|
≤1800 (270) | 155 (27.2) | 64.7 (22.6) |
|
84.9 (18.4) |
|
73.5 (18.1) |
|
||||||||||||
|
1801-3800 (270.15-570) | 146 (25.7) | 65.5 (22.9) |
|
87.4 (15.4) |
|
76.6 (15.8) |
|
||||||||||||
|
3801-6400 (570.15-960) | 127 (22.3) | 68.7 (18.2) |
|
85.5 (16.3) |
|
78.6 (14.0) |
|
||||||||||||
|
≥6401 (960.15) | 141 (24.8) | 67.3 (20.3) |
|
84.9 (17.7) |
|
81.9 (13.4) |
|
||||||||||||
|
.97 |
|
.98 |
|
.93 | |||||||||||||||
|
FHSf | 30 (5.3) | 68.3 (18.9) |
|
87.3 (14.9) |
|
81.1 (11.7) |
|
||||||||||||
|
UEBMIg | 258 (45.3) | 67 (20.3) |
|
85.3 (17.2) |
|
78.3 (14.3) |
|
||||||||||||
|
URBMIh | 132 (23.2) | 65.6 (21.8) |
|
85.8 (18.4) |
|
75.8 (18.7) |
|
||||||||||||
|
NRCMSi | 131 (23) | 65.7 (22.9) |
|
85.7 (16.4) |
|
76.9 (16.0) |
|
||||||||||||
|
No | 18 (3.2) | 67.2 (23.4) |
|
87.7 (12.5) |
|
77.2 (16.6) |
|
||||||||||||
|
.79 |
|
.03 |
|
.03 | |||||||||||||||
|
Normal | 242 (42.5) | 66.5 (21.9) |
|
83.7 (18.4) |
|
75.7 (17.1) |
|
||||||||||||
|
Abnormal | 327 (57.5) | 66.3 (20.8) |
|
87.1 (15.8) |
|
78.9 (14.6) |
|
||||||||||||
|
.04 |
|
.17 |
|
.71 | |||||||||||||||
|
No | 408 (71.7) | 70.2 (21.0) |
|
84.9 (17.3) |
|
77.7 (15.5) |
|
||||||||||||
|
Sometimes | 83 (14.5) | 69 (21.2) |
|
85.6 (12.0) |
|
76.9 (18.3) |
|
||||||||||||
|
Everyday | 78 (13.8) | 65.2 (21.9) |
|
89.7 (19.4) |
|
76.9 (14.3) |
|
||||||||||||
|
.20 |
|
.49 |
|
.23 | |||||||||||||||
|
Few | 87 (15.3) | 65.4 (21.1) |
|
84.8 (20.2) |
|
74.4 (18.0) |
|
||||||||||||
|
Sometimes | 310 (54.5) | 66.4 (20.8) |
|
85.1 (16.9) |
|
77.8 (15.0) |
|
||||||||||||
|
Everyday | 172 (30.2) | 70.2 (21.9) |
|
87.2 (15.5) |
|
78.6 (15.1) |
|
||||||||||||
|
.009 |
|
.28 |
|
.004 | |||||||||||||||
|
Never | 151 (26.5) | 63.2 (23.7) |
|
84.3 (17.8) |
|
73.2 (19.0) |
|
||||||||||||
|
Sometimes | 321 (56.4) | 68.8 (20.2) |
|
86.1 (16.3) |
|
78.6 (14.0) |
|
||||||||||||
|
Always | 97 (17.1) | 63.5 (19.6) |
|
86.5 (18.2) |
|
80.7 (14.6) |
|
||||||||||||
|
.008 |
|
.56 |
|
.003 | |||||||||||||||
|
No | 278 (48.9) | 68.5 (20.9) |
|
85 (17.6) |
|
79.3 (15.5) |
|
||||||||||||
|
Yes | 291 (51.1) | 64.4 (21.4) |
|
86.3 (16.4) |
|
75.7 (15.9) |
|
||||||||||||
|
.04 |
|
.07 |
|
<.001 | |||||||||||||||
|
No | 410 (72.1) | 67.6 (20.9) |
|
86.3 (16.8) |
|
80.6 (13.8) |
|
||||||||||||
|
Yes | 159 (27.9) | 63.3 (21.9) |
|
84 (17.5) |
|
69.4 (17.7) |
|
||||||||||||
|
.50 |
|
.11 |
|
.08 | |||||||||||||||
|
Severe threat to life | 113 (19.9) | 66.6 (22.1) |
|
88 (16.3) |
|
73.9 (17.3) |
|
||||||||||||
|
Moderate threat to life | 113 (19.9) | 64 (22.1) |
|
83.6 (18.4) |
|
77.7 (15.4) |
|
||||||||||||
|
Mild threat to life | 136 (23.9) | 65.6 (19.4) |
|
85.6 (15.5) |
|
78.1 (14.2) |
|
||||||||||||
|
No threat to life | 207 (36.3) | 68.2 (21.4) |
|
85.6 (17.5) |
|
78.9 (16.0) |
|
aeHEALS: eHealth Literacy Scale.
bSSDM: satisfaction with shared decision-making.
cICECAP-A: Investigating Choice Experiments Capability Measure for Adults.
d
eNot available.
fFHS: free health care scheme.
gUEBMI: urban employee basic medical insurance.
hURBMI: urban resident basic medical insurance.
iNRCMS: new rural cooperative medical care system.
jBMI: normal: 18.5≤BMI<23; abnormal: BMI<18.5 or BMI≥23.
Satisfaction with SDMa and well-being in different groups of eHealth literacy and stratified by patients’ depressive disorder and chronic condition status.
|
Satisfaction with SDM | Well-being | |
|
|||
|
High eHealth literacy, mean (SD) | 88.7 (14.7) | 81.1 (14.7) |
|
Low eHealth literacy, mean (SD) | 82.4 (18.7) | 73.7 (16.1) |
|
<.001 | <.001 | |
|
|||
|
High eHealth literacy, mean (SD) | 86.2(16.2) | 73.3(18) |
|
Low eHealth literacy, mean (SD) | 82.2 (18.4) | 66.1 (16.8) |
|
.10 | .004 | |
|
|||
|
High eHealth literacy, mean (SD) | 89.5 (14.2) | 83.6 (12.4) |
|
Low eHealth literacy, mean (SD) | 82.5 (18.9) | 77.1 (14.6) |
|
<.001 | <.001 | |
|
|||
|
High eHealth literacy, mean (SD) | 88.4 (14.7) | 82.6 (14.4) |
|
Low eHealth literacy, mean (SD) | 80.5 (20.1) | 75 (15.8) |
|
<.001 | <.001 | |
|
|||
|
High eHealth literacy, mean (SD) | 89 (14.8) | 79.1 (14.8) |
|
Low eHealth literacy, mean (SD) | 83.9 (17.5) | 72.7 (16.4) |
|
<.001 | <.001 |
aSDM: shared decision-making.
b
The results of multivariate regression models showed that there was a significant and positive relationship between eHealth literacy and SSDM and well-being after adjusting for patients’ background characteristics (
Regression analysis of eHealth literacy and satisfaction with shared decision-making (SSDM) and well-beinga.
Variables | |||||||
|
Model 1 (DVb=eHEALSc) | Model 2 (DV=SSDM) | Model 3 (DV=ICECAP-Ad) | ||||
eHealth literacy | —e | — | .17 (0.11 to 0.24) | <.001 | .15 (0.09 to 0.21) | <.001 | |
Satisfaction in SDM | .22 (0.12 to 0.32) | <.001 | — | — | — | — | |
Well-being | .26 (0.14 to 0.38) | <.001 | — | — | — | — | |
Sex (male) | 2.99 (−1.11 to 7.1) | .15 | −1.58 (−5.03 to 1.87) | .37 | .29 (−2.67 to 3.25) | .85 | |
|
|||||||
|
31-40 | −2.05 (−8.87 to 4.78) | .56 | .68 (−5.03 to 6.4) | .81 | 2.58 (−2.32 to 7.49) | .30 |
|
41-50 | −4.96 (−12.24 to 2.33) | .18 | .58 (−5.52 to 6.67) | .85 | 4.98 (−0.25 to 10.22) | .06 |
|
51-60 | −6.37 (−14.15 to 1.41) | .11 | 4.46 (−2.05 to 10.97) | .18 | 5.2 (−0.39 to 10.79) | .07 |
|
≥61 | −7.91 (−16.35 to 0.53) | .07 | 5.97 (−1.09 to 13.03) | .1 | 5.88 (−0.18 to 11.94) | .06 |
|
|||||||
|
Secondary | −2.78 (−8.35 to 2.78) | .33 | −2.67 (−7.31 to 1.97) | .26 | 4.51 (0.53 to 8.49) | .03 |
|
Tertiary or above | −.7 (−7.23 to 5.83) | .83 | −.91 (−6.33 to 4.5) | .74 | 8.34 (3.7 to 12.99) | .004 |
|
|||||||
|
Married | −2.63 (−12.44 to 7.17) | .60 | −2.26 (−10.48 to 5.97) | .59 | −.35 (−7.41 to 6.71) | .92 |
|
Divorced, widow, or widower | 3.57 (−8.77 to 15.91) | .57 | −4.92 (−15.26 to 5.42) | .35 | .94 (−7.94 to 9.81) | .84 |
Urban resident | −1.8 (−6.18 to 2.59) | .42 | −.3 (−3.98 to 3.38) | .87 | −.07 (−3.23 to 3.09) | .97 | |
|
|||||||
|
1 | −2.84 (−13.15 to 7.47) | .59 | 4.02 (−4.62 to 12.66) | .36 | .01 (−7.41 to 7.43) | .99 |
|
2 | −1.06 (−11.56 to 9.43) | .84 | 2.77 (−6.03 to 11.56) | .54 | −2.25 (−9.79 to 5.3) | .56 |
|
≥3 | −6.89 (−18.22 to 4.43) | .23 | 2.87 (−6.63 to 12.38) | .55 | 1.29 (−6.87 to 9.45) | .76 |
Caregiver (yes) | .1 (−3.8 to 4.16) | .93 | 1.33 (−2 to 4.67) | .43 | −.04 (−2.9 to 2.82) | .98 | |
Live alone | −6.82 (−13.28 to −0.36) | .04 | 2.91 (−2.52 to 8.34) | .29 | 1.35 (−3.31 to 6.02) | .57 | |
Nonemployed | −4.55 (−9.02 to −0.08) | .04 | 1.43 (−2.33 to 5.19) | .45 | .35 (−2.88 to 3.57) | .83 | |
|
|||||||
|
1801-3800 (270.15-570) | −1.34 (−6.23 to 3.54) | .59 | 2.05 (−2.04 to 6.15) | .32 | 1.94 (−1.58 to 5.45) | .28 |
|
3801-6400 (570.15-960) | −1.32 (−6.9 to 4.25) | .64 | 1.13 (−3.54 to 5.8) | .63 | 2.43 (−1.58 to 6.44) | .23 |
|
≥6401 (960.15) | −4.74 (−10.69 to 1.21) | .12 | .47 (−4.51 to 5.45) | .85 | 4.84 (0.56 to 9.11) | .03 |
|
|||||||
|
Urban employee basic medical insurance | −3.03 (−10.82 to 4.76) | .45 | −2.17 (−8.7 to 4.35) | .51 | −2.48 (−8.08 to 3.13) | .39 |
|
Urban resident basic medical insurance | −2.21 (−10.47 to 6.06) | .60 | −1.53 (−8.47 to 5.4) | .66 | −2.07 (−8.02 to 3.88) | .50 |
|
New rural cooperative medical care system | −2.12 (−10.91 to 6.67) | .64 | −1.91 (−9.28 to 5.46) | .61 | −.02 (−6.35 to 6.31) | .99 |
|
No | −4.95 (−17.27 to 7.38) | .43 | .66 (−9.68 to 11) | .90 | 2.05 (−6.82 to 10.93) | .65 |
BMI (abnormal) | −2.29 (−5.78 to 1.19) | .20 | 3.17 (0.26 to 6.08) | .03 | 2.38 (−0.12 to 4.87) | .06 | |
|
|||||||
|
Sometimes | 2.56 (−2.83 to 7.96) | .35 | 6.04 (1.54 to 10.54) | .01 | .01 (−3.85 to 3.87) | .99 |
|
Everyday | 2.84 (−2.8 to 8.48) | .32 | 2.13 (−2.6 to 6.86) | .38 | −1.49 (−5.55 to 2.57) | .47 |
|
|||||||
|
Sometimes | −6.28 (−11.19 to −1.38) | .01 | .79 (−3.35 to 4.92) | .71 | 2.37 (−1.18 to 5.91) | .19 |
|
Everyday | −2.45 (−7.92 to 3.01) | .38 | 2.15 (−2.42 to 6.73) | .36 | 2.61 (−1.31 to 6.54) | .19 |
|
|||||||
|
Sometimes | 4.13 (0.02 to 8.24) | .04 | 1.43 (−2.01 to 4.87) | .41 | 3.7 (0.74 to 6.65) | .01 |
|
Always | −1.94 (−7.36 to 3.47) | .48 | 1.89 (−2.63 to 6.4) | .41 | 4.9 (1.03 to 8.77) | .01 |
Chronic condition (yes) | .07 (−3.6 to 3.75) | .97 | 1.12 (−1.95 to 4.19) | .47 | −2.5 (−5.14 to 0.13) | .06 | |
Depressive disorder (yes) | −1.6 (−5.62 to 2.42) | .43 | −1.94 (−5.2 to 1.32) | .24 | −8.55 (−11.35 to −5.75) | <.001 | |
Moderate threat to life | −2.84 (−8.24 to 2.55) | .30 | −3.41 (−7.92 to 1.1) | .14 | 2.71 (−1.16 to 6.58) | .17 | |
Mild threat to life | −2.62 (−7.93 to 2.69) | .33 | −1.76 (−6.21 to 2.69) | .44 | .64 (−3.18 to 4.47) | .74 | |
No threat to life | −.62 (−5.66 to 4.42) | .81 | −1.34 (−5.57 to 2.89) | .53 | .1 (−3.53 to 3.73) | .96 |
aReference: female, 16-30 years, no or primary education, single, rural resident, no child, no caregiver, live with family or others, income ≤ Chinese ¥1800 (US $270), free health care scheme insurance, normal BMI, no smoking, few healthy diets, no exercise, no chronic conditions, no depressive disorder, and severe threat to life.
bDV: dependent variable.
ceHEALS: eHealth Literacy Scale.
dICECAP-A: Investigating Choice Experiments Capability Measure for Adults.
eNot available.
Although the B-A plot shows a wide limit of agreement interval between the three measures, systematic differences were detected. A good agreement was observed in patients who reported a high level of eHealth literacy, SSDM, and well-being; however, patients who reported a low level of eHealth literacy, SSDM, and well-being were more likely to show less consistent results across the measures, indicating low agreement (
Agreement between scores of the eHEALS, satisfaction with SDM, and ICECAP-A. eHEALS: eHealth Literacy Scale; ICECAP-A: Investigating Choice Experiments Capability Measure for Adults; SDM: shared decision-making.
This study extended the findings of previous studies by demonstrating a statistically significant association between eHealth literacy and SSDM and capability well-being in a sample of Chinese patients. However, when patients reported a low level of eHealth literacy, its association with SSDM and well-being turned to weak and inconsistent. Our findings suggested that providing training to improve patients’ eHealth literacy may be a useful way to strengthen their ability to search and use web-based health and health care information to improve their activity in clinical decision-making and well-being. However, although the internet carries a vast range of information resources and services to help people manage their health, we noticed that disparities in using the internet are persistent in people with low SES (unemployed status and unhealthy lifestyle) and, therefore, affect their potential to maintain and improve eHealth literacy and limit their ability to navigate the health care system. In addition, there seemed to be a negative relationship between patients’ mental health status and their use of internet-based knowledge and skills to improve SSDM. However, further research is needed to support this finding, as it has not been studied extensively.
Our results firstly exhibited that there is a positive relationship between eHealth literacy and Chinese patients’ SSDM, which is in line with the findings of previous studies. For example, an Iranian study indicated that eHealth literacy is positively associated with SDM and patient communication patterns in patients with multiple myeloma [
Studies examining the relationship between patients’ well-being and eHealth-related interventions have recently been explored. For example, Villani et al [
The results of bivariate analysis indicated that patients with high SES and healthy lifestyle are more likely to indicate a high level of eHealth literacy; however, the multivariable regression analysis showed a different picture. This is consistent with the mixed findings of the relationship between individuals’ socioeconomic determinants and their level of eHealth literacy, as reported in previous studies. For example, Lwin et al [
When patients reported having depressive disorders, the difference in SSDM between those with high and low eHealth literacy was statistically insignificant. This is not inconsistent with previous findings in those patients with good skills in searching, assessing, and correctly using web-based health care information may lead to decreased levels of hospitalization-related mental disorders and improve their long-term quality of life and well-being [
It is important to address the limitations of this study. First, this was a cross-sectional study; thus, no causal relationships could be concluded. Second, all the respondents were recruited from inpatient departments in hospitals; the issue of a single information source may affect the validity of our findings. In addition, compared with the data from the 2019 Guangdong census, our respondents were slightly older and comprised a higher proportion of rural residents (
According to the findings of this study, patients with a high level of eHealth literacy were more likely to experience an optimal SDM and improved capability well-being. This suggests that the implementation of interventions to strengthen patients’ eHealth literacy could improve their optimal use of health care services and the efficiency of the health and social care system. In addition, univariable analysis demonstrated that patients with low SES showed insufficient eHealth literacy, which may affect their ability to buffer against the negative impacts of an adverse event on their health. It is important for policy makers to understand the facilitators and barriers to improve patients’ eHealth literacy and to develop strategies to enhance their health behaviors and health outcomes. Moreover, the effects of patients’ mental health status on the relationship between eHealth literacy and SSDM require further investigation.
Results of the confirmatory factor analysis of the satisfaction with shared decision-making.
Correlation between the satisfaction with shared decision-making and 9-item Shared Decision-Making Questionnaire.
eHEALS scores stratified by items of the ICECAP-A and satisfaction with SDM. eHEALS: eHealth Literacy Scale; ICECAP-A: Investigating Choice Experiments Capability Measure for Adults; SDM: shared decision-making. *<italic>P</italic><.05; **<italic>P</italic><.01; ***<italic>P</italic><.001
Comparisons between the sample and Guangdong general population.
Bland-Altman
eHealth Literacy Scale
Investigating Choice Experiments Capability Measure for Adults
shared decision-making
socioeconomic status
satisfaction with shared decision-making
This study was funded by a grant from the Guangdong Basic and Applied Basic Research Foundation (Ref ID: 2021A1515011973).
RHX was involved in the study conceptualization and design, data analysis and interpretation, software use, the writing of the original draft, review, and editing. LMZ was involved in software use, visualization, writing, review, and editing. ELYW was involved in study concept and design, supervision, writing, review, and editing. DW was involved in the study concept and design process, the provision of study materials or patients, the collection and assembly of data, supervision, writing, review, and editing.
None declared.