Digital inequalities in health information seeking behaviors and experiences in the age of web 2.0: A population-based study in Hong Kong.

BACKGROUND: Inequalities in health information seeking behaviors (HISBs) using mass media and internet websites (web 1.0) are well documented. Little is known about web 2.0 such as social networking sites (SNS) and instant messaging (IM) and experiences of HISBs.
METHODS: We surveyed representative Hong Kong Chinese adults (N = 10143, 54.9% female; 72.3% aged 25-64 years) on frequency of HISBs using traditional sources, internet websites, SNS (e.g., Facebook, Twitter), and IM (e.g., WhatsApp, WeChat) and experiences measured using Information Seeking Experience Scale. Adjusted prevalence ratios (aPRs) for HISBs and experiences by sociodemographic and health-related characteristics were yielded using multivariable Poisson regression with robust variance estimators. aPRs for experiences by HISBs using internet websites, SNS, and IM adjusting for sociodemographic and health-related characteristics were also yielded.
RESULTS: Being female, higher educational attainment, not smoking, and being physically active were associated with HISBs using any source (all P<0.05). Older age had decreased aPRs for HISBs using traditional sources (P for trend = 0.03), internet websites (P for trend<0.001), and SNS (P for trend<0.001) but not for IM (aged 45-64 years: aPR = 1.48, 95% CI 1.07, 2.03). Lower educational attainment and income were associated with negative experiences including feelings of effort and difficulties in understanding the information (all P for trend<0.05). Older age had increased aPRs for difficulties in understanding the information (P for trend = 0.003). Compared with internet websites, HISBs using IM was associated with feelings of frustration (aPR = 1.39, 95% CI 1.08, 1.79), difficulties in understanding the information (aPR = 1.36, 95% CI 1.12, 1.65), and quality concern (aPR = 1.20, 95% CI 1.08, 1.32).
CONCLUSIONS: We identified correlates of web-based health information seeking and experiences in Hong Kong Chinese adults. Providing greater access to and improved information environment of web 2.0 to the target groups may help address digital inequalities.

Introduction

Health information seeking behaviors (HISBs) using mass media and internet websites (web 1.0) are prevalent and positively associated with health knowledge, self-rated health, and disease prevention and management [1-3]. Disparities existed in which the older age, low socioeconomic status (SES), and racial/ethnic minorities had fewer HISBs due to limited access to information and communication technologies (ICTs) [4, 5]. Recent web 2.0 such as social networking sites (SNS; e.g., Facebook, Twitter) and instant messaging (IM; e.g., WhatsApp, WeChat) are increasingly accessible to the population regardless of demographics and have potential to reduce the access barrier [6]. The interactive and participative web 2.0 can facilitate HISBs through increased health information exchange, collaborations in health issues, and social support [7]. For example, patients can share their experiences with healthcare providers, people with a similar medical issue, friends, or family members using IM [8]. WeChat group chat was one of the primary means of seeking health information in a national survey in China [9]. Other functions of IM can include online appointment scheduling and online medical consultation. The already developed web 2.0 applications lessen financial and human resource costs, allowing cost-effective public health campaigns and interventions to reach more people [10]. Our randomized controlled trials supported the efficacy of IM chat with health counselors for smokers in smoking cessation [11] and SNS group discussion for ex-smokers in relapse prevention [12].

Despite the reducing physical barrier, the plethora and varying quality of web-based health information may induce a second-level inequality in experiences of HISBs [13]. Most web-based sources require higher school level or greater reading ability that the disadvantaged groups are lacking [14]. Frustration from the sheer volume of the information and efforts of seeking in the older and low SES group were reported in our qualitative interview [15]. Similar negative experiences were reported by the urban poor in an intervention study providing free internet access and technology support [16]. Quality concern has also been raised along with the spread of health misinformation on web 2.0 due to low rigor in monitoring and filtering contents [17]. People with lower SES were found to have limited confidence to distinguish between high- and low-quality web-based information [18], more unwillingness in further HISBs, and poorer health outcomes [19, 20].

We aimed to quantify the digital inequalities in web-based HISBs and experiences in Chinese adults in Hong Kong, the most developed city in China but with a widening wealth gap (2016 Gini coefficient 0.539) [21]. Internet connection and smartphone ownership are increasing particularly in the older population [22]. Information seeking has been one of the most commonly cited purposes among internet users [22]. Despite the penetration of ICTs, traditional mass media such as newspaper/magazine, television, and radio were the most common for HISBs in our previous analyses from 2009 to 2012 [5]. We therefore examined sociodemographic and health-related correlates of HISBs using traditional sources (i.e., television, radio, newspaper, magazine), internet websites, SNS, and IM and web-based health information experiences. We also compared the experiences by HISBs using internet websites, SNS, and IM.

Materials and methods

Design and participants

The Hong Kong Family and Health Information Trends Survey (FHInTS) is a periodic territory-wide telephone survey on the general public’s behaviors and views regarding information use, individual and family well-being, and health communication, under the project named “FAMILY: A Jockey Club Initiative for a Harmonious Society”. The target population was Cantonese-speaking Hong Kong residents aged 18 years or above. We have conducted five waves of FHInTS since 2009, and the details have been reported elsewhere [5].

The present study was part of the fifth wave of FHInTS that included two phases of the fieldwork. We conducted the phase 1 survey from April to July 2016 and the phase 2 survey from February to May 2017. As we used the same battery of instruments in phase 1 and 2 surveys, datasets were combined to improve the sample size. Each phase used the dual-frame probability-based telephone survey method. Landline and mobile telephone numbers were randomly generated using known prefixes assigned to telecommunication service providers under the Numbering Plan provided by the Government Office of the Communications Authority. Invalid numbers were removed according to the computer and manual dialing records. Telephone numbers of respondents from previous waves were also filtered. For the landline survey, once a household was successfully reached, an eligible family member whose next birthday was the closest to the interview day was invited for the survey. No second-level sampling was used in the mobile survey. All telephone interviews were conducted by trained interviewers of the Public Opinion Program (POP) at the University of Hong Kong. All data were collected by interviewers using a Web-based Computer Assisted Telephone Interview (Web-CATI) system invented in-house by the research team, which allowed real-time data capture and consolidation.

We successfully interviewed 10143 respondents (5080 in the phase 1, response rate = 73.7%, landline: n = 4038, mobile: n = 1042; 5063 in the phase 2, response rate = 68.9%, landline: n = 4054, mobile: n = 1009). The landline random subsets and mobile sample answered questions on web-based health information seeking experiences (n = 6062).

Measures

Health information seeking behaviors (HISBs)

Frequency of HISBs was asked as “How often have you searched for health information in the past 12 months from sources including traditional sources (i.e., television/radio/newspaper/magazine), internet websites, SNS (e.g., Facebook, Twitter), and IM (e.g., WhatsApp, WeChat)?” Responses included at least once a week, 1–3 times in a month, once in several months, seldom, or never. The frequencies were dichotomized into at least once a week/1–3 times in a month/once in several months and seldom/never (reference) due to the non-normal distributions.

Web-based HISBs experiences

Experiences of web-based HISBs were measured using the Information Seeking Experience (ISEE) Scale [23]. Skill barrier was measured using the widely used three items, as “It took a lot of effort to get the information you needed;” “You felt frustrated during your search for the information;” and “The information you found was too hard to understand.” The mental barrier was measured using the single item, as “You were concerned about the quality of the information.” Responses scored on a Likert scale from 1 = very much agree to 4 = very much disagree. Agreement with ISEE items was dichotomized into very much agree/somewhat agree and somewhat disagree/very much disagree (reference) [23-25].

Sociodemographic characteristics

Sociodemographic characteristics included sex, age, marital status, employment status, educational attainment, and monthly household income. We used educational attainment (primary or below, secondary, or tertiary), employment status (in-paid employed, unemployed, retired, housekeeper, or full-time student), and monthly household income (≤ HK$ 9999, 10000–19999, 20000–29999, 30000–39999, ≥ 40000, or unstable/refused) (median household income was HK$ 25000 in Hong Kong in 2016) as indicators of SES [2, 5].

Health-related characteristics

Lifestyle characteristics included smoking status (never, ex-smoker, or current smoker), alcohol drinking (never, ex-drinker, occasional drinker, less than once a month, 1–3 days/month, or 1 day/week or more), and frequency of moderate physical activity (none, 1–3 days/week, or 4 days/week or more). History of doctor-diagnosed chronic diseases (e.g., cardiovascular diseases, respiratory diseases, liver diseases, allergies, and others) was dichotomized into none and any. Depression symptoms were measured using the two-item Patient Health Questionnaire (PHQ-2) that has two DSM-IV diagnostic core criteria for major depression disorder [26]. Each item scores on a Likert scale from 0 = not at all to 3 = nearly every day, with a total score of ≥ 3 indicating possible presence of a depression disorder [26]. The Chinese version of PHQ-2 has been validated in Hong Kong [27]. Cronbach’s alpha was 0.72 in the present sample.

Statistical analyses

All data were weighted according to sex, age, and educational attainment distributions of the Hong Kong general population. Survey phases and frames were accounted for survey design effects. Missing data were handled by available case analyses as there were minimal missing values for all variables (< 0.25%). Adjusted prevalence ratios (aPRs) for HISBs and experiences by sociodemographic and health-related characteristics were yielded using multivariable Poisson regression with robust variance estimators. aPRs for experiences by different web-based sources adjusting for sociodemographic and health-related characteristics were yielded in respondents who exclusively used internet websites, SNS, or IM (at least once a week/1–3 times in a month/once in several months), whereas those seldom/never used the three sources or used multiple sources were excluded. The modified Poisson regression estimation of relative risk was used to avoid potential exaggeration because of high prevalence (> 10%) of frequent HISBs and negative experiences [28]. Note that log-binomial regression also estimates relative risk but is subject to narrower confidence intervals than they should be and convergence problems [28]. Stata’s “estat gof” command was used to yield goodness-of-fit statistics and the “nbreg” command was used to check the equi-dispersion assumption of Poisson regression. All Poisson regression models were supported as all goodness-of-fit chi-squared tests and tests of dispersion were found not statistically significant (all P = 1.00).

As secondary analysis, multinomial logistic regression was used to examine sociodemographic and health-related correlates of preferred web-based sources: SNS, IM, and internet websites (reference outcome) (S3 File). To test the robustness of results of Poisson regression, ordered logistic regression was used by treating agreement with web-based health information seeking experiences as an ordinal variable (1 = very much disagree, 2 = somewhat disagree, 3 = somewhat agree, 4 = very much agree) (S4 File). All analyses were conducted using Stata 15.1 (StataCorp LP, College Station, TX, USA). A two-sided P-value of < 0.05 was considered statistically significant.

Ethics

The Institutional Review Board of the University of Hong Kong/Hospital Authority Hong Kong West Cluster approved this study (UW 09–324). Verbal informed consent of all respondents was documented using the Web-CATI system under close supervision. Telephone interviews were tape-recorded for quality checking with respondents’ consent. Records were then erased six months after completing the survey.

Results

The weighted sample (N = 10143) was 54.9% female, and 72.3% were aged 25–64 years (Table 1). Over three quarters (76.4%) had attained secondary or tertiary education. Over half (55.6%) had a monthly household income of HK$ 20000 or higher. Few reported smoking currently (10.8%) or drinking alcohol 1 day/week or more (9.9%), whereas over half (57.4%) were physically inactive. Less than a third (31.9%) had diagnosed chronic diseases, and 8.3% screened positive for depression symptoms.

Table 1

Unweighted and weighted n (%) for sociodemographic, lifestyle, physical, and mental health-related characteristics (N = 10143).

	Unweighted	Weighted
Sex
Male	4121 (40.6)	4571 (45.1)
Female	6022 (59.4)	5572 (54.9)
Age, years
18–24	1245 (12.3)	943 (9.3)
25–44	2124 (20.9)	3594 (35.4)
45–64	3620 (35.7)	3744 (36.9)
≥65	3154 (31.1)	1863 (18.4)
Marital status
Never married	2566 (25.3)	2789 (27.5)
Divorced/separated/widowed	1332 (13.1)	1049 (10.3)
Cohabitated/ married	6245 (61.6)	6305 (62.2)
Educational attainment
Primary or below	2065 (20.4)	2400 (23.7)
Secondary	4255 (42.0)	4878 (48.1)
Tertiary	3823 (37.7)	2866 (28.3)
Employment status
In-paid employment	4181 (41.2)	5179 (51.1)
Unemployment	347 (3.4)	501 (4.9)
Retired	3303 (32.6)	2216 (21.9)
Housekeeper	1541 (15.2)	1646 (16.2)
Full-time student	771 (7.6)	600 (5.9)
Monthly household income (HK $) b
≤9999	2093 (20.6)	1682 (16.6)
10000–19999	1490 (14.7)	1710 (16.9)
20000–29999	1547 (15.3)	1798 (17.7)
30000–39999	1171 (11.5)	1252 (12.4)
≥40000	2646 (26.1)	2584 (25.5)
Unsteady/refused to answer	1196 (11.8)	1116 (11.0)
Smoking Status
Never	8297 (81.8)	7933 (78.2)
Ex-smoker	1027 (10.1)	1114 (11.0)
Current smoker	817 (8.1)	1092 (10.8)
Alcohol drinking
Never	4908 (48.4)	4726 (46.6)
Ex-drinker	507 (5.0)	490 (4.8)
Occasional drinker	3128 (30.9)	3195 (31.5)
1–3 days/month	685 (6.8)	725 (7.2)
1 day/week or more	911 (9.0)	1003 (9.9)
Moderate physical activity
None	5770 (56.9)	5819 (57.4)
1–3 days/week	2221 (21.9)	2268 (22.4)
4 days/week	2146 (21.2)	2047 (20.2)
Diagnosed chronic diseases
No	6333 (62.4)	6908 (68.1)
Yes	3810 (37.6)	3235 (31.9)
Screening for depression symptoms
Negative (PHQ-2<3)	9390 (92.7)	9289 (91.7)
Positive (PHQ-2≥3)	740 (7.3)	842 (8.3)

PHQ-2, Patient Health Questionnaire-2 Item, range 0–6.

a Weighted by sex, age, and educational attainment according to Hong Kong Census.

b US $1 = HK $7.8.

Of all respondents (N = 10143), over one third (36.9%) sought health information (at least once a week/1–3 times in a month/once in several months) using internet websites, followed by traditional sources (i.e., television/radio/newspaper/magazine; 35.4%), SNS (17.9%), and IM (12.9%) (Table 2). Prevalence of HISBs using all four sources increased from phase 1 to phase 2 (all P < 0.001). Nearly three quarters (74.4%) agreed that they were concerned about the quality of the information. Nearly half agreed that it took a lot of effort to get the information needed (46.4%) and the information found was too hard to understand (45.0%). Less than a third (29.8%) agreed that they felt frustrated during the search for the information. Prevalence of agreeing that they were concerned about the quality (72.3% to 76.3%, P = 0.03) and that the information found was too hard to understand (40.6% to 49.0%, P < 0.001) increased from phase 1 to phase 2.

Table 2

Weighted n (%) for health information seeking behavior and web-based health information seeking experiences by survey phases.

	Total	Phase 1	Phase 2	P
Health information seeking behaviors (at least once a week/1–3 times in a month/once in several months) b
Traditional sources (television, radio, newspaper, and magazine) (n = 10141)	3585 (35.4)	1658 (32.6)	1927 (38.1)	<0.001
Internet websites (n = 10138)	3739 (36.9)	1771 (34.9)	1968 (38.9)	<0.001
Social networking sites (n = 10140)	1810 (17.9)	818 (16.1)	992 (19.6)	<0.001
Instant messaging (n = 10140)	1304 (12.9)	581 (11.4)	723 (14.3)	<0.001
Web-based health information seeking experiences (very much agree/somewhat agree) c
It took a lot of effort to get the information you needed (n = 3530)	1638 (46.4)	771 (45.6)	866 (47.2)	0.45
You felt frustrated during your search for the information (n = 3506)	1049 (29.8)	491 (29.1)	555 (30.5)	0.50
The information you found was too hard to understand (n = 3560)	1601 (45.0)	694 (40.6)	907 (49.0)	<0.001
You were concerned about the quality of the information (n = 3546)	2637 (74.4)	1233 (72.3)	1404 (76.3)	0.03

a Weighted by sex, age, educational attainment according to Hong Kong Census.

b Frequency of health information seeking behavior was treated as a dummy variable (1 = “at least once a week/1–3 times in a month/once in several months” vs 0 = “seldom/never”).

c Agreement with web-based health information seeking experiences was treated as a dummy variable (1 = “very much agree/somewhat agree” vs 0 = “somewhat disagree/very much disagree”).

Being female was associated with HISBs using any source (all P < 0.001) (Table 3). Younger age was associated with HISBs using traditional sources (P for trend = 0.03), internet websites (P for trend < 0.001), and SNS (P for trend < 0.001), whereas the age group of 45–64 years was associated with HISBs using IM (adjusted prevalence ratio [aPR] = 1.48, 95% CI 1.07, 2.03). The older group was more likely to seek health information using IM compared with internet websites (S3 File). Being cohabitated or married was associated with HISBs using IM (aPR = 1.46, 95% CI 1.23, 1.73). Higher educational attainment was associated with HISBs using any source (all P for trend < 0.001), and stronger associations were observed for internet websites (Secondary education: aPR = 4.19, 95% CI 3.39, 5.18; Tertiary education: aPR = 6.38, 95% CI 5.15, 7.91). Higher monthly household income was associated with HISBs using traditional sources (P for trend = 0.003), internet websites (P for trend < 0.001), and IM (P for trend = 0.02), but no association was observed for SNS (P for trend = 0.08). Not smoking and being physically active (i.e., moderate physical activity ˃ 1 day/week) were associated with HISBs using any source.

Table 3

Adjusted associations of sociodemographic and health-related characteristics with health information seeking behaviors using traditional sources, internet websites, social networking sites, and instant messaging (N = 10143).

	Adjusted prevalence ratios (95% CI)
	Traditional sources (television, radio, newspaper, and magazine)	Internet websites	Social networking sites	Instant messaging
Sex
Male	1	1	1	1
Female	1.29 (1.22, 1.36)***	1.16 (1.11, 1.22)***	1.29 (1.17, 1.41)***	1.45 (1.29, 1.62)***
Age, years
18–24	1	1	1	1
25–44	1.01 (0.89, 1.14)	0.98 (0.93, 1.06)	1.13 (0.95, 1.34)	1.11 (0.81, 1.52)
45–64	0.98 (0.86, 1.11)	0.76 (0.69, 0.84)***	0.77 (0.64, 0.94)**	1.48 (1.07, 2.03)*
≥65	0.87 (0.74, 1.02)	0.34 (0.29, 0.41)***	0.40 (0.30, 0.54)***	1.01 (0.70, 1.46)
P for trend	0.03	<0.001	<0.001	0.64
Marital status
Never married	1	1	1	1
Divorced/separated/widowed	0.92 (0.81, 1.04)	0.78 (0.67, 0.90)**	0.69 (0.53, 0.89)**	1.10 (0.86, 1.41)
Cohabitated/married	1.07 (0.99, 1.16)	0.98 (0.92, 1.04)	0.95 (0.84, 1.07)	1.46 (1.23, 1.73)***
Educational attainment
Primary or below	1	1	1	1
Secondary	1.74 (1.58, 1.93)***	4.19 (3.39, 5.18)***	3.10 (2.37, 4.05)***	2.39 (1.95, 2.92)***
Tertiary	2.08 (1.87, 2.31)***	6.38 (5.15, 7.91)***	3.72 (2.82, 4.91)***	2.61 (2.09, 3.25)***
P for trend	<0.001	<0.001	<0.001	<0.001
Employment status
In-paid employed	1	1	1	1
Unemployed	1.02 (0.88, 1.18)	0.97 (0.86, 1.11)	0.89 (0.69, 1.14)	0.83 (0.59, 1.16)
Retired	1.12 (1.03, 1.23)**	0.97 (0.87, 1.07)	0.87 (0.72, 1.04)	1.13 (0.96, 1.33)
Housekeeper	1.05 (0.97, 1.14)	1.01 (0.93, 1.10)	0.94 (0.82, 1.09)	1.00 (0.86, 1.17)
Full-time student	0.96 (0.84, 1.10)	0.95 (0.87, 1.04)	1.16 (0.97, 1.40)	0.93 (0.65, 1.33)
Monthly household income (HK $) c
≤9999	1	1	1	1
10000–19999	1.10 (0.99, 1.21)	1.10 (0.97, 1.25)	0.98 (0.80, 1.20)	1.31 (1.07, 1.61)**
20000–29999	1.15 (1.04, 1.27)**	1.15 (1.02, 1.31)*	1.15 (0.94, 1.40)	1.46 (1.19, 1.80)***
30000–39999	1.10 (0.99, 1.23)	1.30 (1.15, 1.47)***	1.09 (0.88, 1.33)	1.39 (1.11, 1.73)**
≥40000	1.17 (1.06, 1.29)**	1.31 (1.16, 1.47)***	1.15 (0.95, 1.39)	1.35 (1.11, 1.66)**
P for trend	0.003	<0.001	0.08	0.02
Unstable or refused	1.00 (0.90, 1.12)	1.06 (0.93, 1.21)	0.98 (0.79, 1.21)	1.14 (0.91, 1.42)
Pseudo R-square	0.02	0.13	0.09	0.04
Smoking Status
Never	1	1	1	1
Ex-smoker	0.96 (0.88, 1.06)	1.12 (1.02, 1.22)*	1.24 (1.07, 1.45)**	1.02 (0.85, 1.23)
Current smoker	0.84 (0.75, 0.93)**	0.89 (0.81, 0.99)*	0.86 (0.72, 1.03)	0.79 (0.63, 0.99)*
Pseudo R-square	0.02	0.13	0.09	0.04
Alcohol drinking
Never	1	1	1	1
Ex-drinker	0.98 (0.85, 1.12)	1.02 (0.87, 1.20)	1.10 (0.85, 1.43)	0.95 (0.72, 1.25)
Occasional drinker	1.01 (0.95, 1.07)	1.11 (1.05, 1.17)***	1.07 (0.97, 1.19)	1.03 (0.91, 1.15)
Less than once a month	1.15 (1.04, 1.26)**	1.20 (1.11, 1.29)***	1.32 (1.15, 1.53)***	1.20 (0.98, 1.45)
1 day/week or more	1.02 (0.93, 1.12)	1.05 (0.97, 1.15)	1.10 (0.94, 1.28)	1.10 (0.92, 1.33)
Pseudo R-square	0.02	0.13	0.09	0.04
Moderate physical activity
None	1	1	1	1
1–3 days/week	1.31 (1.23, 1.39)***	1.22 (1.16, 1.29)***	1.36 (1.23, 1.50)***	1.41 (1.25, 1.60)***
4 days/week or more	1.30 (1.20, 1.36)***	1.20 (1.13, 1.27)***	1.33 (1.19, 1.49)***	1.47 (1.31, 1.66)***
Pseudo R-square	0.02	0.13	0.09	0.04
Diagnosed chronic diseases
No	1	1	1	1
Yes	1.04 (0.98, 1.11)	0.99 (0.93, 1.05)	1.06 (0.95, 1.18)	1.03 (0.92, 1.15)
Pseudo R-square	0.03	0.14	0.09	0.04
Screening for depression symptoms
Negative (PHQ-2<3)	1	1	1	1
Positive (PHQ-2≥3)	0.91 (0.82, 1.01)	1.03 (0.95, 1.12)	1.03 (0.89, 1.20)	0.89 (0.72, 1.11)
Pseudo R-square	0.02	0.13	0.09	0.04

CI, Confidence Interval; PHQ-2, Patient Health Questionnaire-2 Item, range 0–6

*P<0.05

**P<0.01

***P<0.001.

a Adjusted for sex, age, marital status, educational attainment, employment status, monthly household income, survey phase, and survey frame.

b Frequency of health information seeking behavior was treated as a dummy variable (1 = “at least once a week/1–3 times in a month/once in several months” vs 0 = “seldom/never”).

c US $1 = HK $7.8.

Lower educational attainment was associated with skill barriers, including feelings of effort, frustration, and difficulties in understanding the information (all P for trend < 0.001) (Table 4). Higher household income had decreased aPRs for feelings of effort (P for trend = 0.001; ≥ HK $40000: aPR = 0.84, 95% CI 0.72, 0.98) and difficulties in understanding the information (P for trend = 0.02). Older respondents reported that the information found was too hard to understand (P for trend = 0.003) but were less concerned about the quality (P for trend = 0.02). The robustness of results was supported using ordered logistic regression (S4 File).

Table 4

Adjusted associations of sociodemographic and health-related characteristics with web-based health information seeking experiences.

	Adjusted prevalence ratios (95% CI)
	It took a lot of effort to get the information you needed (n = 3530)	You felt frustrated during your search for the information (n = 3506)	The information you found was too hard to understand (n = 3560)	You were concerned about the quality of the information (n = 3546)
Sex
Male	1	1	1	1
Female	0.92 (0.85, 0.99)*	0.96 (0.85, 1.07)	0.96 (0.88, 1.04)	0.99 (0.95, 1.04)
Age, years
18–24	1	1	1	1
25–44	1.05 (0.89, 0.99)	1.01 (0.79, 1.29)	1.24 (1.03, 1.50)*	1.12 (0.94, 1.10)
45–64	1.09 (0.91, 1.30)	1.18 (0.90, 1.54)	1.36 (1.12, 1.66)**	0.95 (0.87, 1.04)
≥65	1.11 (0.88, 1.38)	1.08 (0.78, 1.49)	1.40 (1.10, 1.78)**	0.88 (0.77, 1.10)
P for trend	0.31	0.20	0.003	0.02
Marital status
Never married	1	1	1	1
Divorced/ separated/ widowed	0.95 (0.79, 1.15)	1.10 (0.85, 1.42)	0.97 (0.81, 1.16)	1.00 (0.89, 1.11)
Cohabitated/ married	1.02 (0.91, 1.13)	1.12 (0.95, 1.32)	0.98 (0.87, 1.09)	0.96 (0.91, 1.02)
Educational attainment
Primary or below	1	1	1	1
Secondary	0.76 (0.67, 0.88)***	0.70 (0.58, 0.84)***	0.71 (0.63, 0.80)***	1.05 (0.93, 1.20)
Tertiary	0.68 (0.59, 0.80)***	0.59 (0.48, 0.73)***	0.61 (0.53, 0.70)***	1.07 (0.94, 1.22)
P for trend	<0.001	<0.001	<0.001	0.31
Employment status
In-paid employed	1	1	1	1
Unemployed	0.97 (0.78, 1.22)	1.08 (0.79, 1.47)	0.93 (0.87, 1.18)	1.01 (0.90, 1.14)
Retired	1.02 (0.89, 1.17)	1.40 (1.18, 1.67)***	1.01 (0.88, 1.15)	1.09 (1.01, 1.19)
Housekeeper	1.13 (0.999, 1.27)	1.27 (1.07, 1.51)**	1.13 (0.91, 1.17)	1.04 (0.97, 1.12)
Full-time student	0.84 (0.69, 1.01)	0.80 (0.60, 1.07)	0.96 (0.78, 1.19)	1.06 (0.98, 1.15)
Monthly household income (HK $) c
≤9999	1	1	1	1
10000–19999	1.10 (0.87, 1.18)	1.13 (0.91, 1.39)	1.01 (0.87, 1.18)	1.01 (0.92, 1.12)
20000–29999	0.99 (0.85, 1.16)	1.03 (0.83, 1.28)	0.97 (0.84, 1.13)	1.07 (0.98, 1.17)
30000–39999	0.96 (0.82, 1.13)	1.04 (0.84, 1.30)	0.93 (0.79, 1.08)	1.03 (0.94, 1.13)
≥40000	0.84 (0.72, 0.98)*	0.90 (0.73, 1.10)	0.88 (0.76, 1.02)	1.02 (0.89, 1.09)
P for trend	0.001	0.06	0.02	0.99
Unstable or refused	0.92 (0.77, 1.09)	1.06 (0.84, 1.34)	1.05 (0.89, 1.23)	0.98 (0.89, 1.09)
Pseudo R-square	0.01	0.02	0.01	0.002
Smoking Status
Never	1	1	1	1
Ex-smoker	0.97 (0.84, 1.11)	0.87 (0.71, 1.07)	0.99 (0.86, 1.15)	0.97 (0.90, 1.06)
Current smoker	1.05 (0.92, 1.19)	1.03 (0.85, 1.24)	1.10 (0.96, 1.25)	1.04 (0.97, 1.11)
Pseudo R-square	0.01	0.02	0.01	0.002
Alcohol drinking
Never	1	1	1	1
Ex-drinker	0.98 (0.79, 1.21)	0.92 (0.68, 1.23)	0.84 (0.65, 1.09)	0.90 (0.77, 1.05)
Occasional drinker	0.91 (0.83, 0.99)*	0.94 (0.83, 1.06)	1.05 (0.97, 1.15)	1.02 (0.97, 1.06)
1–3 days/month	0.96 (0.83, 1.09)	0.92 (0.76, 1.13)	1.08 (0.95, 1.24)	1.06 (0.99, 1.13)
1 day/week or more	0.89 (0.78, 1.02)	0.88 (0.73, 1.06)	1.02 (0.89, 1.16)	0.97 (0.90, 1.05)
Pseudo R-square	0.01	0.02	0.02	0.002
Moderate physical activity
None	1	1	1	1
1–3 days/week	1.05 (0.96, 1.15)	1.00 (0.88, 1.14)	1.00 (0.91, 1.10)	1.00 (0.95, 1.04)
4 days/week	1.04 (0.95, 1.14)	0.98 (0.85, 1.11)	1.00 (0.91, 1.10)	1.00 (0.94, 1.04)
Pseudo R-square	0.01	0.02	0.01	0.002
Diagnosed chronic diseases
No	1	1	1	1
Yes	0.99 (0.91, 1.08)	1.07 (0.95, 1.21)	1.02 (0.93, 1.12)	1.02 (0.97, 1.18)
Pseudo R-square	0.01	0.02	0.01	0.002
Screening for depression symptoms
Negative (PHQ-2<3)	1	1	1	1
Positive (PHQ-2≥3)	1.09 (0.96, 1.25)	1.17 (0.98, 1.39)	1.07 (0.93, 1.23)	1.04 (0.97, 1.11)
Pseudo R-square	0.01	0.02	0.01	0.002

CI, Confidence Interval; PHQ-2, Patient Health Questionnaire-2 Item, range 0–6

*P<0.05

**P<0.01

***P<0.001.

a Adjusted for sex, age, marital status, educational attainment, employment status, monthly household income, survey phase, and survey frame.

b Agreement with web-based health information seeking experiences was treated as a dummy variable (1 = “very much agree/somewhat agree” vs 0 = “somewhat disagree/very much disagree”).

c US $1 = HK $7.8.

Quality concern was the most common negative web-based health information seeking experiences across different sources (74.5%–82.1%) (Table 5). Compared with internet websites, HISBs using IM was associated with feelings of frustration (aPR = 1.39, 95% CI 1.08, 1.79), difficulties in understanding the information (aPR = 1.36, 95% CI 1.12, 1.65), and being concerned about the qualities (aPR = 1.20, 95% CI 1.08, 1.32)

Table 5

Weighted n (%) and adjusted prevalence ratios (aPRs) for web-based health information seeking experiences by different sources.

	It took a lot of effort to get the information you needed			You felt frustrated during your search for the information			The information you found was too hard to understand			You were concerned about the quality of the information
	Disagree, n (%)	Agree, n (%)	aPR (95% CI)	Disagree, n (%)	Agree, n (%)	aPR (95% CI)	Disagree, n (%)	Agree, n (%)	aPR (95% CI)	Disagree, n (%)	Agree, n (%)	aPR (95% CI)
Internet websites	646 (56.3)	502 (43.7)	1	864 (75.4)	282 (24.6)	1	695 (60.5)	453 (39.5)	1	291 (25.4)	853 (74.6)	1
Social networking sites	56 (66.8)	28 (33.2)	0.95 (0.71, 1.27)	55 (62.9)	32 (37.1)	1.51 (1.11, 2.05)**	45 (52.0)	42 (48.0)	1.24 (0.96, 1.60)	22 (25.5)	65 (74.5)	1.01 (0.88, 1.16)
Instant messaging	41 (40.0)	62 (60.0)	1.19 (0.98, 1.44)	50 (48.4)	53 (51.6)	1.39 (1.08, 1.79)*	38 (34.9)	70 (65.1)	1.36 (1.12, 1.65)**	19 (18.0)	88 (82.1)	1.20 (1.08, 1.32)***
Pseudo R-square	-	-	0.01	-	-	0.03	-	-	0.01	-	-	0.004

CI, Confidence Interval

*P<0.05

**P<0.01

***P<0.001.

a Weighted by sex, age, educational attainment according to Hong Kong Census.

b Adjusted for sociodemographic and health-related characteristics, survey phase, and survey frame.

c Agreement with web-based health information seeking experiences was treated as a dummy variable (Agree: 1 = “very much agree/somewhat agree” vs Disagree: 0 = “somewhat disagree/very much disagree”). Respondents reporting seldom/never used the three sources or used multiple sources were excluded.

Discussion

The widespread web 2.0 has been a prevalent source for HISBs among ICTs users (range 30.1% in Hong Kong–35.7% in the United States) [6, 29]. Our study firstly extended the investigation to the general population and showed that the prevalence rates of frequent HISBs using web 2.0 ranged from 12.8%–17.9%. Traditional mass media particularly newspapers/magazines and televisions were the most prevalent sources for HISBs from 2009–2012 but have been replaced by internet websites in the present analyses from 2016–2017 [5]. This shift can be attributable to recent increasing internet connection (from 72.9% in 2012 to 89.4% in 2017) and smartphone use (from 61.1% in 2012 to 88.6% in 2017) in Hong Kong general population [22]. Similar findings of web-based sources as the most prevalent were observed in a national-wide survey in the United States [30, 31].

Women tended to be more health-conscious and are often caregivers, and hence being more motivated to seek health information [32]. This was supported by our findings that being female was associated with HISBs using any source. Compared with other SES indicators such as employment status and income, educational attainment was strongly associated with HISBs using any source. Education may provide people with higher health literacy defined as knowledge, skills, and confidence to access, process, and use health information [4, 14]. Health literacy has shown associations with HISBs using multiple sources from health professionals, family and friends, and mass media to web-based sources [33]. A deepening divide for those with lower educational attainment in HISBs using internet websites (web 1.0) was observed in our study. Web-based health information may require additional ICTs training, social support, time, and ICTs literacy that the disadvantaged are lacking [34].

Not smoking and being physically active were associated with HISBs using any source. The finding supported HISBs as a proactive approach for health promotion as posited in the health and wellness model [35]. Similar findings were shown in our previous study indicating that more health application possession in people who were physically active to log health records and track health measures (e.g., blood pressure and heart rate) [36]. Reverse causation is possible, as frequent health information seeking can provide behavioral change strategies, reinforcement of a psychological commitment, and social support to engaging in healthier behaviors such as quitting smoking and frequent vigorous physical activity [37].

Compared with internet websites (web 1.0), IM appeared to reduce digital inequalities in HISBs in older people in our study. Similar result that age was not a significant predictor of HISBs using web 2.0 was found in the United States [6]. Mobile phone for web 2.0 has higher penetration rate than personal computers for web 1.0 among the elderly in Hong Kong [22], possibly due to low-cost internet access and wide coverage of public free WiFi services (~ 51943 hotspots in 2017) [38]. Text messaging-based IM could be more popular due to the low requirements for technology skills. IM has been found as a feasible and effective intervention modality in promoting healthy behaviors in older people [39]. Given the continuous and expanding penetration, IM could be promising health communication channels to reach populations across sociodemographic characteristics [6]. Another explanation is that IM with a more interactive and user-centered environment increases the participation of the disadvantaged groups and hence facilitating HISBs [10]. Middle-aged quitters of smoking perceived benefits from emotional and informational support through participating in IM peer discussion groups in our relapse prevention trial [40].

Our findings go beyond the physical barrier by showing that people with lower educational attainment and income had more skill barriers, including a lot of effort and frustration during the search and difficulties in understanding the web-based health information. This confirmed the SES disparities in experiences of web-based HISBs identified in our qualitative interview [15] and studies including patient populations only [41, 42]. The findings supported the “Inverse Care Law” [43], which suggests that the disadvantaged groups are most in need of healthcare but may benefit less from health-related ICTs. Notably, the decline of health and ICTs literacy with age might explain the greater difficulties in understanding the information in the older respondents [14]. Quality concern was the most common (74.4%) negative experience in our study. Such mental barrier may be due to the spread of health misinformation on web-based sources that allow the anonymity of content generator and disseminator and low rigor in monitoring and fact-checking [15]. However, older respondents were found to have less quality concern about the information. One possible explanation is that the elderly may have lower ICTs literacy associated with less exposure to and knowledge of ICTs, which may lead to credulity in web-based sources compared with those with better literacy [6]. Community-based interventions, such as collaborative learning and increased social support, may improve people’s confidence in dealing with the web-based information [44]. Healthcare professionals could leverage online platforms to disseminate evidence-based content, correct misinformation, and build trust with the communities. Technology companies can implement mechanisms for vetting and validating the credibility of information. For example, Twitter has now used labels and warning messages to add context and instructions on some Tweets containing disputed or misleading information [45].

IM was associated with more negative experiences among the three web-based sources. Frustration and difficulties in understanding may be attributable to the lower readability of health information on IM as IM applications are designed with shorter text, smaller font size, and more crowded visual presentation than internet websites. Health information on IM may be from a small and closer social network not involving healthcare professionals. Nearly 70% of respondents were concerned about health information on WeChat in a national-wide survey in China [9]. Healthcare professionals can use WhatsApp Business or WeChat Official Account for delivering quality health information to the public.

The study had some limitations. The cross-sectional data restricted the inference of temporal sequence between health-related characteristics and HISBs and experiences. Prospective and intervention studies are warranted to investigate the causal relations. All data were self-reported, which were subject to recall bias and social desirability bias. Ecological momentary assessments of smoking and alcohol drinking behaviors and objective measurements of physical activity can be used in future studies. We examined general health information seeking and experiences. Future studies are needed to differentiate the purpose, such as health promotion, disease prevention, treatment, or management. The study sample was from the general Chinese population in Hong Kong, one of the most urbanized and developed cities in China. The generalizability to rural and underdeveloped Chinese communities outside Hong Kong is unclear. However, our findings might foresee the digital inequalities in web-based HISBs and experiences in places with improving cyber-infrastructure and increasing penetration of web 2.0.

Conclusions

We identified correlates of web-based health information seeking and experiences in Hong Kong Chinese adults. Providing greater access to and improved information environment of web 2.0 to the target groups may help address digital inequalities.

Chinese version of the Information Seeking Experience (ISEE) Scale.

(PDF)

Click here for additional data file.

Chinese version of the two-item Patient Health Questionnaire (PHQ-2).

(PDF)

Click here for additional data file.

Adjusted associations of sociodemographic and health-related characteristics with health information seeking behaviors using social networking sites, and instant messaging compared with internet websites.

(DOCX)

Click here for additional data file.

(DOCX)

Click here for additional data file.

13 Jul 2020

PONE-D-20-19346

Digital inequalities in health information seeking behaviors and experiences in the age of web 2.0: a population-based study in Hong Kong

PLOS ONE

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15 Jul 2020

We are grateful to editors’ thoughtful comments. We have now revised the manuscript and provided point-to-point responses. Modifications in the text were also quoted in the responses below.

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Response 1: Thank you for providing the helpful templates. We have now revised the manuscript format to meet PLOS ONE’s style requirements.

Comment 2: Please address the following:

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Response 2:

- Thank you. We have now uploaded the Chinese version of the Information Seeking Experience (ISEE) Scale and the two-item Patient Health Questionnaire (PHQ-2) as Supporting Information 1 and Supporting Information 2, respectively. Details about other measures were shown in the Methods section.

- As suggested, in the ethics statement in the revised methods section and online submission information, we have now stated that “Verbal informed consent of all respondents was documented using the Web-CATI system under close supervision.”

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Response 3: In the revised cover letter, we have now stated that “The data underlying the findings of this study are restricted by the Institutional Review Board of the University of Hong Kong/Hospital Authority Hong Kong West, who approved the participant consent. Data requests can be sent to the FAMILY project (FAMILY: A Jockey Club Initiative for a Harmonious Society), G/F, Patrick Manson Building, 7 Sassoon Road, Pok Fu Lam, Hong Kong. Email: jcfamily@hku.hk.”

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27 Jul 2020

PONE-D-20-19346R1

Digital inequalities in health information seeking behaviors and experiences in the age of web 2.0: a population-based study in Hong Kong

PLOS ONE

Dear Dr. Wang,

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Ho Ting Wong, PhD

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PLOS ONE

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I find that you have clarified that the data is restricted by the IRB, and provided a contact method for data request. Could you also explain the restrictions in detail (e.g., data contain potentially identifying or sensitive patient information) in the statement as if what is described in the PLOS' data policy webpage.

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28 Jul 2020

Comment 1: I find that you have clarified that the data is restricted by the IRB, and provided a contact method for data request. Could you also explain the restrictions in detail (e.g., data contain potentially identifying or sensitive patient information) in the statement as if what is described in the PLOS' data policy webpage.

https://journals.plos.org/plosone/s/data-availability#loc-acceptable-data-sharing-methods

Response 1: Thank you. We have now explained the restrictions in detail in the statement, as “The data underlying the findings of this study are restricted by the Institutional Review Board of the University of Hong Kong/Hospital Authority Hong Kong West, who approved the participant consent. Data contain potentially identifying information including direct identifiers (contact information) and indirect identifiers (location, occupation, income, etc.), which cannot be publicly shared in accordance with participant consent. Data requests can be sent to the FAMILY project (FAMILY: A Jockey Club Initiative for a Harmonious Society), G/F, Patrick Manson Building, 7 Sassoon Road, Pok Fu Lam, Hong Kong. Email: jcfamily@hku.hk”

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24 Nov 2020

PONE-D-20-19346R2

Digital inequalities in health information seeking behaviors and experiences in the age of web 2.0: a population-based study in Hong Kong

PLOS ONE

Dear Dr. Wang,

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PLOS ONE

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Reviewer #1: Yes

Reviewer #2: No

Reviewer #3: Yes

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5. Is the manuscript presented in an intelligible fashion and written in standard English?

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6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: This is a second review of this submission

The questions raised by the original reviewer have been answered properly. As a new reviewer in this process, I am in no position to raise new questions at this stage. On this basis, I recommend this submission as “accept”

Reviewer #2: Comments:

1. Some sentences in the conclusion (p.3 and p.24) are very confusing and inconsistent with your statistics results. Your results find that “lower educational attainment and income were associated with negative experiences regarding web-based seeking skills”, which suggests a digital divide between disadvantaged and advantaged groups. This is an important finding in this research, but it has contradicted the first sentence “Web 2.0 can reduce the physical barrier to HISBs and be promising health communication channels to reach the elderly and low-income groups” in the very beginning of the conclusion. That sentence could be a very brief interlude to the main conclusion, but it is inconsistent with the results. I suggest to drop or to re-write that inconsistent statement.

2. Regarding Table 3, the author said “lower household income is associated with skill barriers” (p. 17). However, the adjusted prevalence ratios on Table 3 does not strongly support this argument. The author lists four web skill barriers measures. About their adjusted prevalence ratios, two are increasing with income levels and two are decreasing with income levels. The statement could be better revised as follows: the highest income group is associated with less skill barriers since the highest group has the lowest adjusted prevalence ratios on the three measures for sure.

3. Do you have only binomial answers (Y/N) to web experience questions? If not, then Poisson regression might not be the best statistic model. You might need to try Cox or logistic regression to take account of ordinal or categorical dependent variables.

4. Please provide goodness of fit statistics and perform diagnostic tests about your Poisson regressions. And briefly explain how you choose Poisson over log-binomial.

Suggestions:

1. Web 2.0 is a very general and old idea. Nowadays, we need more precise and detailed discussions about the web. For example, “Web information” in this research includes information from websites, social networks, and instant message. Characteristics of the three information sources are very different; the differences may lead to very divergent web behaviors and web experiences. It would be very meaningful to see the comparisons among the three groups regarding their web-based health information seeking experiences. In other words, I am interested in the relative risk among the three sources.

2. It is also important to ask whether the sociodemographic and health-related characteristics are associated with the choice among three different sources: websites, social networks and IM. Table 2 implies that the older groups (45-64; >64) and higher income groups (>= 10000) may rely more on IM. These results are worth further discussion since this paper is talking about “digital inequality”. However, you have better to run a regression regarding the three groups, rather than to compare the coefficients among three different regressions.

Reviewer #3: 1. The authors chose four sources of health information, including the traditional source, internet websites, SNS (Facebook, Twitter), and IM (Wechat, WhatsApp). The authors are expected to justify the use of IM (Wechat, whatsapp), since they are not apps from health-care agencies. Although the authors gave examples of the use of IM chat for the smoking cessation. It may merely serve research purposes.

2. The research question seems too broad. For example, it targets the general health information without specifying the purpose the information seeking, such as disease prevention, self-monitoring of symptoms, health promotion, etc.

3. The authors conducted two phases survey. Is there any trend in the two phases survey?

4. The results showed that around 75% of the participants were concerned about the quality of the information. Thus, besides the access, confidence in the information plays an essential role in influencing people’s behavior. The authors are suggested to elaborate on this point in the Discussion section, for example, how to improve the quality of the information and how to influence people’s behavior.

5. The authors provided an overall picture of the information-seeking experience, such as concern about the quality of the information, frustration during the search, hard to understand the information, etc. It would be interesting to have a comparison among the different sources of HISB. For example, how many people complained the quality of information from traditional sources/internet websites/SNS/IM?

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

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Reviewer #1: Yes: Chih-yuan Wang

Reviewer #2: No

Reviewer #3: No

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6 Jan 2021

We thank Reviewers for the constructive comments. We have now provided point-to-point responses as below, with new texts in the revised manuscript quoted. The response letter containing Tables is also attached for easy viewing.

Reviewer 1

Comment 1 (C1). This is a second review of this submission. The questions raised by the original reviewer have been answered properly. As a new reviewer in this process, I am in no position to raise new questions at this stage. On this basis, I recommend this submission as “accept”.

Response 1 (R1). Thank you for your comments.

Reviewer 2

C1. Some sentences in the conclusion (p.3 and p.24) are very confusing and inconsistent with your statistics results. Your results find that “lower educational attainment and income were associated with negative experiences regarding web-based seeking skills”, which suggests a digital divide between disadvantaged and advantaged groups. This is an important finding in this research, but it has contradicted the first sentence “Web 2.0 can reduce the physical barrier to HISBs and be promising health communication channels to reach the elderly and low-income groups” in the very beginning of the conclusion. That sentence could be a very brief interlude to the main conclusion, but it is inconsistent with the results. I suggest to drop or to re-write that inconsistent statement.

R1. We have now re-written the Conclusion as follows: “We identified correlates of web-based health information seeking and experiences in Hong Kong Chinese adults. Providing greater access to and improved information environment of web 2.0 to the target groups may help address digital inequalities.”

C2. Regarding Table 3, the author said “lower household income is associated with skill barriers” (p. 17). However, the adjusted prevalence ratios on Table 3 does not strongly support this argument. The author lists four web skill barriers measures. About their adjusted prevalence ratios, two are increasing with income levels and two are decreasing with income levels. The statement could be better revised as follows: the highest income group is associated with less skill barriers since the highest group has the lowest adjusted prevalence ratios on the three measures for sure.

R2. We originally stated that lower household income was associated with skill barriers because of inverse associations of income with feelings of effort and difficulties in understanding the information. We have now modified the statement in Results as follows: “Higher household income had decreased aPRs for feelings of effort (P for trend = 0.001; ≥ HK $40000: aPR = 0.84, 95% CI 0.72, 0.98) and difficulties in understanding the information (P for trend = 0.02).”

C3. Do you have only binomial answers (Y/N) to web experience questions? If not, Poisson regression might not be the best statistic model. You might need to try Cox or logistic regression to take account of ordinal or categorical dependent variables.

R3. The dichotomized responses to experiences (1 = very much agree/somewhat agree vs 0 = somewhat disagree/very much disagree) were consistent with studies using the same questions [1-3]. Poisson regression models fitted reasonably well from results of goodness-of-fit chi-squared tests and tests of equi-dispersion assumption (Please see R4 below).

We have now tested the robustness of our results using ordered logistic regression for ordinal responses: 1= very much disagree, 2 = somewhat disagree, 3 = somewhat agree, 4 = very much agree (Response Table 1). Similar associations are found between ordered logistic regression and Poisson regression. For example, lower educational attainment was associated with skill barriers, including feelings of effort, frustration, and difficulties in understanding the information.

C4. Please provide goodness of fit statistics and perform diagnostic tests about your Poisson regressions. And briefly explain how you choose Poisson over log-binomial.

R4. The contents have been added in Methods as follows: “Note that log-binomial regression also estimates relative risk but is subject to narrower confidence intervals than they should be and convergence problems [4]. Stata’s “estat gof” command was used to yield goodness-of-fit statistics and the “nbreg” command was used to check the equi-dispersion assumption of Poisson regression. All Poisson regression models were supported as all goodness-of-fit chi-squared tests and tests of dispersion were found not statistically significant (all P = 1.00).”

Suggestions:

C5. Web 2.0 is a very general and old idea. Nowadays, we need more precise and detailed discussions about the web. For example, “Web information” in this research includes information from websites, social networks, and instant message. Characteristics of the three information sources are very different; the differences may lead to very divergent web behaviors and web experiences. It would be very meaningful to see the comparisons among the three groups regarding their web-based health information seeking experiences. In other words, I am interested in the relative risk among the three sources.

R5. We agree and have now compared experiences by the three different web-based sources in Methods as follows: “aPRs for experiences by different web-based sources adjusting for sociodemographic and health-related characteristics were yielded in respondents who exclusively used internet websites, SNS, or IM (at least once a week/1–3 times in a month/once in several months), whereas those seldom/never used the three sources or used multiple sources were excluded.” We have added Results as follows: “Quality concern was the most common negative web-based health information seeking experiences across different sources (74.5%–82.1%) (Table 5). Compared with internet websites, HISBs using IM was associated with feelings of frustration (aPR = 1.39, 95% CI 1.08, 1.79), difficulties in understanding the information (aPR = 1.36, 95% CI 1.12, 1.65), and being concerned about the qualities (aPR = 1.20, 95% CI 1.08, 1.32).” We have added Discussion as follows: “IM was associated with more negative experiences among the three web-based sources. Frustration and difficulties in understanding may be attributable to the lower readability of health information on IM as IM applications are designed with shorter text, smaller font size, and more crowded visual presentation than internet websites. Health information on IM may be from a small and closer social network not involving healthcare professionals. Nearly 70% of respondents were concerned about health information on WeChat in a national-wide survey in China [5]. Healthcare professionals can use WhatsApp Business or WeChat Official Account for delivering quality health information to the public.”

C6. It is also important to ask whether the sociodemographic and health-related characteristics are associated with the choice among three different sources: websites, social networks and IM. Table 2 implies that the older groups (45-64; >64) and higher income groups (>= 10000) may rely more on IM. These results are worth further discussion since this paper is talking about “digital inequality”. However, you have better to run a regression regarding the three groups, rather than to compare the coefficients among three different regressions.

R6. We have now run one regression model and added results as supporting information in S3. File. Specifically, a multinomial logistic regression has been used to examine sociodemographic and health-related correlates of preferred web-based sources: SNS, IM, and internet websites (reference outcome). S3. File shows that older group was more likely to seek health information using IM compared with internet websites, which complemented our findings that IM may reduce digital inequalities for the older people in the original Table 2 (now Table 3). We have now enriched our statement in Discussion as follows: “Compared with internet websites (web 1.0), IM appeared to reduce digital inequalities in HISBs in older people in our study. Similar result that age was not a significant predictor of HISBs using web 2.0 was found in the United States [6]. Mobile phone for web 2.0 has higher penetration rate than personal computers for web 1.0 among the elderly in Hong Kong [7], possibly due to low-cost internet access and wide coverage of public free WiFi services (~ 51943 hotspots in 2017) [8]. Text messaging-based IM could be more popular due to the low requirements for technology skills. IM has been found as a feasible and effective intervention modality in promoting healthy behaviors in older people [9]…”

Reviewer 3

C1. The authors chose four sources of health information, including the traditional source, internet websites, SNS (Facebook, Twitter), and IM (WeChat, WhatsApp). The authors are expected to justify the use of IM (WeChat, WhatsApp), since they are not apps from health-care agencies. Although the authors gave examples of the use of IM chat for the smoking cessation. It may merely serve research purposes.

R1. We have now elaborated on IM use in Introduction as follows: “For example, patients can share their experiences with healthcare providers, people with a similar medical issue, friends, or family members using IM [10]. WeChat group chat was one of the primary means of seeking health information in a national survey in China [5]. Other functions of IM can include online appointment scheduling and online medical consultation.”

C2. The research question seems too broad. For example, it targets the general health information without specifying the purpose the information seeking, such as disease prevention, self-monitoring of symptoms, health promotion, etc.

R2. The use of general health information was consistent with studies in the general population including ours [11] and others in the Western setting [12,13]. Nevertheless, we agree on the importance of purpose and have acknowledged the limitation in Discussion as follows: “We examined general health information seeking and experiences. Future studies are needed to differentiate the purpose, such as health promotion, disease prevention, treatment, or management.”

C3. The authors conducted two phases survey. Is there any trend in the two phases survey?

R3. Yes and we have now added a table on the trend in the two phases (Table 2) and described in Results as follows: “Prevalence of HISBs using all four sources increased from phase 1 to phase 2 (all P < 0.001).” and “Prevalence of agreeing that they were concerned about the quality (72.3% to 76.3%, P = 0.03) and that the information found was too hard to understand (40.6% to 49.0%, P < 0.001) increased from phase 1 to phase 2.”

C4. The results showed that around 75% of the participants were concerned about the quality of the information. Thus, besides the access, confidence in the information plays an essential role in influencing people’s behavior. The authors are suggested to elaborate on this point in the Discussion section, for example, how to improve the quality of the information and how to influence people’s behavior.

R4. The point has now been elaborated in Discussion as follows: “Quality concern was the most common (74.4%) negative experience in our study. Such mental barrier may be due to the spread of health misinformation on web-based sources that allow the anonymity of content generator and disseminator and low rigor in monitoring and fact-checking [14] …Community-based interventions, such as collaborative learning and increased social support, may improve people’s confidence in dealing with the web-based information [15]. Healthcare professionals could leverage online platforms to disseminate evidence-based content, correct misinformation, and build trust with the communities. Technology companies can implement mechanisms for vetting and validating the credibility of information. For example, Twitter has now used labels and warning messages to add context and instructions on some Tweets containing disputed or misleading information [16].”

C5. The authors provided an overall picture of the information-seeking experience, such as concern about the quality of the information, frustration during the search, hard to understand the information, etc. It would be interesting to have a comparison among the different sources of HISB. For example, how many people complained the quality of information from traditional sources/internet websites/SNS/IM?

R5. We agree and have now added a table on comparing experiences by different sources (Table 5). Please see R5 to Reviewer 2 above.

References

1. Vanderpool RC, Kornfeld J, Rutten LF, Squiers L. Cancer information-seeking experiences: the implications of Hispanic ethnicity and Spanish language. Journal of Cancer Education. 2009;24:141. doi:10.1080/08858190902854772

2. Arora NK, Hesse BW, Rimer BK, Viswanath K, Clayman ML, Croyle RT. Frustrated and confused: the American public rates its cancer-related information-seeking experiences. Journal of General Internal Medicine. 2008;23: 223–228. doi:10.1007/s11606-007-0406-y

3. Kim K, Lustria MLA, Burke D, Kwon N. Predictors of cancer information overload: findings from a national survey. Information research. 2007;12: 12–4.

4. Zou G. A modified poisson regression approach to prospective studies with binary data. American Journal of Epidemiology. 2004;159: 702–706. doi:10.1093/aje/kwh090

5. Zhang X, Wen D, Liang J, Lei J. How the public uses social media wechat to obtain health information in china: a survey study. BMC Medical Informatics and Decision Making. 2017;17. doi:10.1186/s12911-017-0470-0

6. Tennant B, Stellefson M, Dodd V, Chaney B, Chaney D, Paige S, et al. eHealth literacy and web 2.0 health information seeking behaviors among baby boomers and older adults. Journal of Medical Internet Research. 2015;17: e70. doi:10.2196/jmir.3992

7. Census and Statistics Department. Thematic Household Survey Report No. 69 - Personal computer and Internet penetration. 2020. Available: https://www.ogcio.gov.hk/en/about_us/facts/doc/householdreport2020_69.pdf

8. Office of the Communications Authority. Public Wi-Fi Services. 2019. Available: https://www.ofca.gov.hk/mobile/en/data_statistics/data_statistics/wifi/index.html

9. Kwan RY, Lee D, Lee PH, Tse M, Cheung DS, Thiamwong L, et al. Effects of an mHealth brisk walking intervention on increasing physical activity in older people with cognitive frailty: pilot randomized controlled trial. JMIR Mhealth Uhealth. 2020;8:e16596.. doi:10.2196/16596

10. Iftikhar R, Abaalkhail B. Health-seeking influence reflected by online health-related messages received on social media: cross-sectional survey. Journal of Medical Internet Research. 2017;19: e382. doi:10.2196/jmir.5989

11. Wang MP, Viswanath K, Lam TH, Wang X, Chan SS. Social determinants of health information seeking among Chinese Adults in Hong Kong. PloS one. 2013;8: e73049. doi:10.1371/journal.pone.0073049

12. Jacobs W, Amuta AO, Jeon KC. Health information seeking in the digital age: an analysis of health information seeking behavior among US adults. Cogent Social Sciences. 2017;3. doi:10.1080/23311886.2017.1302785

13. Cutrona SL, Mazor KM, Agunwamba AA, Valluri S, Wilson PM, Sadasivam RS, et al. Health information brokers in the general population: an analysis of the Health Information National Trends Survey 2013-2014. Journal of Medical Internet Research. 2016;18: e123. doi:10.2196/jmir.5447

14. Chu JT, Wang MP, Shen C, Viswanath K, Lam TH, Chan SSC. How, when and why people seek health information online: qualitative study in Hong Kong. Interactive Journal of Medical Research. 2017;6: e24. doi:10.2196/ijmr.7000

15. de Wit L, Fenenga C, Giammarchi C, di Furia L, Hutter I, de Winter A, et al. Community-based initiatives improving critical health literacy: a systematic review and meta-synthesis of qualitative evidence. BMC Public Health. 2017;18: 40. doi:10.1186/s12889-017-4570-7

16. Roth Y, Pickles N. Updating our approach to misleading information. Available: https://blog.twitter.com/en_us/topics/product/2020/updating-our-approach-to-misleading-information.html

Submitted filename: Response_HISB_20210106.docx

Click here for additional data file.

16 Feb 2021

PONE-D-20-19346R3

Digital inequalities in health information seeking behaviors and experiences in the age of web 2.0: a population-based study in Hong Kong

PLOS ONE

Dear Dr. Wang,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

==============================

Academic Editor's comments

Tables: For the table with Poisson regression analysis, please also provide the corresponding Pseudo R-square for reference. This is for readers to have a better understanding of the models.

Ethics section: Please also provide the approval reference number.

Others: Please add the data provision statement you provided as an author note at the end of your manuscript. Therefore,

“The data underlying the findings of this study are restricted by the Institutional Review Board of the University of Hong Kong/Hospital Authority Hong Kong West, who approved the participant consent. Data contain potentially identifying information including direct identifiers (contact information) and indirect identifiers (location, occupation, income, etc.), which cannot be publicly shared in accordance with participant consent. Data requests can be sent to the FAMILY project (FAMILY: A Jockey Club Initiative for a Harmonious Society), G/F, Patrick Manson Building, 7 Sassoon Road, Pok Fu Lam, Hong Kong. Email: jcfamily@hku.hk”

==============================

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Academic Editor

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #2: All comments have been addressed

Reviewer #3: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #2: Yes

Reviewer #3: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #2: Yes

Reviewer #3: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #2: Yes

Reviewer #3: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #2: Yes

Reviewer #3: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #2: The author fully addressed reviewers' previous comments and I am impressed. The paper becomes logically complete and the results looks more robust. The paper has more clear discussions on the digital inequality such as the difference among websites, social networks and IM users. I only have a few suggestions before it gets published:

1. The author's reply about the model selection between Poisson and Logistic is good. It will be good if author add a short footnote about his model selection and the robustness of his Poisson result in the paper.

2. This research conduct two-stage survey. Could the author briefly explain the reason to do two-stage?

3. It will be better if the findings can echo some theory of human behavior in the public health field.

Reviewer #3: This is an interesting study. The questions have been answered properly. I recommend this submission as “accept”.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

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Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #2: Yes: Yu-Hsi Liu

Reviewer #3: No

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

17 Feb 2021

Academic Editor's comments

Comment 1 (C1). Tables: For the table with Poisson regression analysis, please also provide the corresponding Pseudo R-square for reference. This is for readers to have a better understanding of the models.

Response 1 (R1). We have now added Pseudo R-squares in Tables 3, 4 and 5.

C2. Ethics section: Please also provide the approval reference number.

R2. The approval reference number (UW 09-324) has now been provided in Ethics section.

C3. Others: Please add the data provision statement you provided as an author note at the end of your manuscript. Therefore, “The data underlying the findings of this study are restricted by the Institutional Review Board of the University of Hong Kong/Hospital Authority Hong Kong West, who approved the participant consent. Data contain potentially identifying information including direct identifiers (contact information) and indirect identifiers (location, occupation, income, etc.), which cannot be publicly shared in accordance with participant consent. Data requests can be sent to the FAMILY project (FAMILY: A Jockey Club Initiative for a Harmonious Society), G/F, Patrick Manson Building, 7 Sassoon Road, Pok Fu Lam, Hong Kong. Email: jcfamily@hku.hk”

R3. We have now added the above data provision statement at the end of the manuscript.

Reviewers' comments

Reviewer #2: The author fully addressed reviewers' previous comments and I am impressed. The paper becomes logically complete and the results looks more robust. The paper has more clear discussions on the digital inequality such as the difference among websites, social networks and IM users. I only have a few suggestions before it gets published:

C1. The author's reply about the model selection between Poisson and Logistic is good. It will be good if author add a short footnote about his model selection and the robustness of his Poisson result in the paper.

R1. We notice that footnote is not permitted by PLOS ONE and have added the following: “To test the robustness of results of Poisson regression, ordered logistic regression was used by treating agreement with web-based health information seeking experiences as an ordinal variable (1=very much disagree, 2=somewhat disagree, 3=somewhat agree, 4=very much agree) (S4. File).” in Statistical analyses and “The robustness of results was supported using ordered logistic regression (S4. File).” in Results. Table presenting logistic regression result has been attached as supporting information S4. File.

C2. This research conduct two-stage survey. Could the author briefly explain the reason to do two-stage?

R2. We have now stated in Design and participants as follows: “As we used the same battery of instruments in phase 1 and 2 surveys, datasets were combined to improve the sample size.”

C3. It will be better if the findings can echo some theory of human behavior in the public health field.

R3. We have now stated in Discussion as follows: “The findings supported the “Inverse Care Law” [1], which suggests that the disadvantaged groups are most in need of healthcare but may benefit less from health-related ICTs.”

Reference

1. Hart JT. The inverse care law. The Lancet. 1971;297(7696):405–412.

Submitted filename: Response_20210218.docx

Click here for additional data file.

18 Mar 2021

Digital inequalities in health information seeking behaviors and experiences in the age of web 2.0: a population-based study in Hong Kong

PONE-D-20-19346R4

Dear Dr. Wang,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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Ho Ting Wong, PhD

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

22 Mar 2021

PONE-D-20-19346R4

Digital inequalities in health information seeking behaviors and experiences in the age of web 2.0: a population-based study in Hong Kong

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