Background: Although recent developments in mobile health have elevated the importance of how smartphones empower individuals to seek health information, research investigating this phenomenon in Asian countries has been rare.
Objective: The goal of our study was to provide a comprehensive profile of mobile health information seekers and to examine the individual- and country-level digital divide in Asia.
Methods: With survey data from 10 Asian countries (N=9086), we ran multilevel regression models to assess the effect of sociodemographic factors, technological factors, and country-level disparities on using smartphones to seek health information.
Results: Respondents who were women (β=.13, P<.001), parents (β=.16, P<.001), employed (β=.08, P=.002), of higher social status (β=.08, P<.001), and/or from countries with low health expenditures (β=.19, P=.02) were more likely to use smartphones to seek health information. In terms of technological factors, technology innovativeness (β=.10, P<.001) and frequency of smartphone use (β=.42, P<.001) were important factors of health information seeking, whereas the effect of online information quality was marginal (β=–.04, P<.001).
Conclusions: Among smartphone users in Asia, health information seeking varies according to individuals’ socioeconomic status, their innovativeness toward technology, and their frequency of smartphone use. Although smartphones widen the digital divide among individuals with different socioeconomic status, they also bridge the divide between countries with varying health expenditures. Smartphones appear to be a particularly useful complement to manage health in developing countries.
With the development of mobile technology and the spread of its use, smartphones have facilitated several positive health-related outcomes, including the increased provision of psychological interventions , improved access to health services, and reductions in various forms of social inequality [ ] in populations worldwide. In that context, mobile health (mHealth) refers to public health or medical practices that involve using mobile information communication technologies (ICTs) to seek out health information, communicate with health care professionals, and monitor personal health [ ]. Since mHealth provides individuals with ubiquitous access to timely health services at low cost, it can play a major role in bridging inequalities in digital access to health care services [ , ].
In particular, the act of seeking health-related information online has received substantial research attention, but mainly in western countries. For example, analyses of the data from several surveys on the topic administered in the United States and Germany [, ] have provided valuable insights into the determinants and outcomes of such information-seeking behavior in those populations. By contrast, research investigating such behavior in Asian countries has been sorely limited, especially with respect to health information seeking on smartphones [ ].
To address this gap in the literature, we designed this study to investigate the factors contributing to mHealth information seeking (MHIS) to profile such information seekers in Asia using data from 10 Asian countries. In effect, our comprehensive profiling stands to help service providers understand the mHealth market from the perspective of users. We also sought to examine the extent to which individual- and country-level digital divides exist in MHIS across various Asian countries. To the best of our knowledge, this project represents by far the largest multinational survey on smartphone users in Asia. Thus, its findings are expected to provide theoretical insights into both MHIS and its practical implications, all of which may support efforts to bridge the digital divide in Asia.
Profiling mHealth Information Seekers
The primary objective of this study was to investigate factors related to MHIS to ultimately profile such information seekers in Asia. Previous research has examined correlates of using mHealth that can be classified into four groups: consumers’ motivations and needs for health information [, ]; driving factors behind adopting mHealth according to theories about technology and health behavior (eg, technology acceptance model, diffusion of innovation theory, and comprehensive model of information seeking) [ , , ]; sociodemographic features of mHealth users, including their age, gender, education, and income [ - ]; and boundary conditions (eg, cultural context) of theoretical models applied in examining the use of smartphones for health-related purposes [ - ].
Zhao et al  recently performed a meta-analysis of 35 empirical studies on mHealth, the overall results of which diverged depending on the context studied and the characteristics of the sample. Their meta-analysis also revealed that the findings of most studies have been based on data from small samples in single-culture contexts, whereas few studies have used multinational data from diverse cultural contexts. Moreover, Wang et al [ ] found that technological factors were important yet neglected factors in determining individuals’ online health information–seeking behaviors. Thus, in response to these gaps, we profiled mHealth information seekers in Asia according to their sociodemographic characteristics as well as perceptions of technology.
Parallel to the rise of mHealth technologies, discussions about the quality of health information available online have also intensified. From the perspective of information systems, a high level of information quality—defined as the credibility and reliability of information in terms of relevance, sufficiency, accuracy, and timeliness —promotes the use of information systems [ ]. At the same time, studies have highlighted users’ concerns with the quality of health information available online, which could hinder online information seeking about health-related topics [ ]. Nevertheless, few studies have examined how information quality affects the behavior of MHIS [ ].
Against this trend, research has suggested that the adoption of new technology may be primarily driven by how individuals perceive its innovativeness . According to the diffusion of innovation theory, technology innovativeness, defined as the degree to which an individual perceives a technological device to be innovative and is thus willing to experiment with using it, is an important factor for adopting technology [ ]. Recently, technology innovativeness was also identified as a significant determinant of the intention to use mHealth [ ]. In light of these findings, we aimed to scrutinize the effect of online information quality and technology innovativeness on MHIS.
The Digital Divide and Seeking Health Information on Smartphones
The ability to seek health-related information online has been found to enable individuals to make informed decisions about their health, provide individuals in need with disease-related social support, and help patients adhere to their medication and treatment regimens [, ]. Nevertheless, extensive research has also revealed that not all individuals benefit equally from assessing and consuming health information online [ , , , ]. A critical reason for this inequality is the so-called “digital divide,” a phenomenon related not only to internet access but also to the existence of a gap between people who can and cannot effectively use new communication tools (eg, smartphones) or comprehend new information [ ]. Past findings have additionally suggested that smartphones can act either as a bridge or as a barrier for people in assessing health-related information, depending on their socioeconomic status (SES) [ , - ].
Thus, in a final contribution to the literature, we also tested the digital divide in MHIS at the individual as well as national levels in Asia. To date, scholars have suggested that the global digital divide has narrowed the most for mobile phone use, likely because many developing countries have simply stopped using fixed-line communication as their access to technology has advanced . Therefore, we expected that with smartphones, residents in less developed countries can easily access the internet and health information, which may in turn reduce the inequality in MHIS in those countries.
Procedure and Participants
For our sample, 9086 adults in 10 Asian countries (China, India, Indonesia, Thailand, the Philippines, Malaysia, South Korea, Japan, Vietnam, and Singapore) were recruited in 2016. In line with the procedures of cross-cultural research [, ], the data were collected from major cities in all countries using synchronous data collection between June 2016 and October 2016. The questionnaire developed for this purpose was translated into each country’s dominant language using standard translation and back-translation, after which it was distributed in each country through an online survey company. To participate, individuals had to have a smartphone and be between 18 and 55 years of age. The total sample comprised at least 800 respondents from each country who were evenly distributed by age and gender in order to fully capture the situation of each stratum in the population. Given these methods, the Institutional Review Board at Nanyang Technological University approved the study.
The participants were adult smartphone users from 10 Asian countries: mainland China (n=1238), India (n=1238), Indonesia (n=824), Thailand (n=821), the Philippines (n=843), Malaysia (n=837), South Korea (n=858), Japan (n=804), Vietnam (n=809), and Singapore (n=814). In total, 9086 smartphone users were recruited. Participants ranged in age from 18 to 55 years, and the majority were women. Nearly three-quarters of the participants had completed college, university, and/or graduate school, and more than half were married and had at least one child. Approximately 88% of participants were employed or self-employed. Detailed descriptive statistics of the sample are given in.
|Characteristic||All participants (N=9086)||Frequent seekersa (n=6508)|
|Gender, n (%)|
|Man||4716 (51.90)||3292 (50.58)|
|Woman||4370 (48.10)||3216 (49.42)|
|Age (years), mean (SD)||34.3 (9.11)||33.7 (8.80)|
|Marital status, n (%)|
|Married||5369 (59.09)||3889 (59.76)|
|Single, divorced, separated, or widowed||3717 (40.91)||2619 (40.24)|
|Have child(ren), n (%)|
|No children||3924 (43.19)||2526 (38.81)|
|At least one child||5162 (56.81)||3982 (61.19)|
|Level of education, n (%)|
|High school or less||2580 (28.40)||1686 (25.91)|
|College or university||5101 (56.14)||3755 (57.70)|
|Graduate school or more||1405 (15.46)||1067 (16.40)|
|Employment status, n (%)|
|Employed||7971 (87.73)||5808 (89.24)|
|Unemployed, homemaker, or retired||1115 (12.27)||700 (10.76)|
|Smartphone use, mean (SD)||4.87 (1.17)||5.16 (1.01)|
|Concern with online information quality, mean (SD)||5.15 (1.18)||5.23 (1.15)|
|Technology innovativeness, mean (SD)||4.42 (1.46)||4.68 (1.34)|
a“Frequent seekers” reported seeking health information on their smartphones at least a few times per month.
Participants reported how frequently they used their smartphones to seek information about health and medical issues on a 5-point scale (1=never, 2=rarely [ie, once per month], 3=sometimes [ie, a few times per month], 4=often [ie, a few times per week], 5=always [ie, daily]). This item was adopted to measure participants’ MHIS (mean 3.17, SD 1.23).
Participants reported their gender (0=man, 1=woman), age, marital status (1=married, 0=single, divorced, separated, or widowed), and parental status (1=have child(ren), 0=no children).
Level of education, employment status, and monthly household income were employed as indicators of objective SES . Participants were asked to report their level of education (0=high school or less, 1=college or university, 2=graduate school or more), employment status (0=unemployed, 1=employed), and monthly income. Given that income levels vary across countries, we standardized the reported monthly income within each country to enable comparisons and analyses across the 10 countries.
A scale for measuring subjective SES was employed , in which participants were asked to rate their perceptions of their SES on a 10-rung hierarchical scale. The bottom of the scale, where the score was 1, represented participants who perceived themselves as having the least wealth, the least education, and the least-respected jobs, or no job whatsoever, compared with those of others. The top of the scale, where the score was 10, represented participants who perceived themselves as having the most wealth, the most education, and the most respected jobs relative to others. Participants were asked to place an “X” on the rung of the scale that they believed best reflected their SES (mean 5.84, SD 1.78).
Technological factors included participants’ self-reported perceptions of technology and frequency of smartphone use. Items were adapted from previous studies that involved the measurement of similar concepts [, ].
Perceived online information quality was assessed on a 7-point Likert scale from 1 (strongly disagree) to 7 (strongly agree). Participants rated to what extent they agree with the following two items: “In general, there is less control over the quality of the content posted online” and “There is a lot of fake news online these days.” The answers were recoded such that a larger number indicated a higher perceived quality of information online (mean 5.16, SD 1.22; r=0.59).
Technology innovativeness was assessed on a 7-point Likert scale from 1 (strongly disagree) to 7 (strongly agree). Participants indicated their technology innovativeness on the following three items: “Your friends describe you as ‘into the latest technology’,” “You often purchase new technology before your friends,” and “You consider yourself technologically sophisticated” (mean 4.43, SD 1.40; Cronbach α=.91).
The frequency of smartphone use was determined according to participants reporting how often they used their smartphones for the following activities in the past year: (1) keeping up to date with news and information, (2) using social networking sites or apps, (3) using electronic banking, and (4) playing games or watching entertaining videos. The answers were recorded on a 5-point Likert scale where 1=never, 2=rarely (once a month), 3=sometimes (few times a month), 4=often (few times a week), and 5=always (daily). The four items were averaged on a scale of the frequency of smartphone use (mean 3.91, SD 0.78).
Country-Level Digital Divide
The country-level digital divide was measured using the ICT Development Index (IDI) from the International Telecommunication Union database of development indicators , which indicates the level of the development of ICT infrastructure in a given country. The IDI generates a score from 1 to 10, and of the countries sampled, India scored the lowest (ie, 3.03) and South Korea scored the highest (ie, 8.85). Economic inequality at the country level was measured using the Gini index from the database of World Bank Development Indicators [ ]. The Gini index generally ranges from 0% (perfect equality) to 100% (perfect inequality); of the countries sampled, the Philippines scored the highest (ie, 44.4%), whereas South Korea scored the lowest (ie, 31.6%). Health inequality at the country level was measured by each country’s current health expenditure (CHE) per capita based on the global health expenditure database maintained by the World Health Organization [ ], which is reported in purchasing power parity, ranging from 253 in India to 4563 in Japan.
Multilevel linear regression models were constructed to determine the effect of individual-level characteristics and country-level inequalities in MHIS, after which analyses were performed using the lme4 package in R, an open-source program for statistical analysis. The model-building process involved three steps: (1) a univariate analysis of each variable using appropriate statistical tests (eg,t test, analysis of variance, or Pearson correlation), (2) a model including any variables whose univariate test had a P value less than .25, and (3) a two-level linear model with variances specified at the individual level and country level. In this paper, the results of multilevel linear regression analysis are presented in terms of their standardized β coefficients and model statistics. A two-sided P value of less than .05 for all tests was considered to be statistically significant.
The sociodemographic characteristics of the participants are shown in. Of the 9086 smartphone users sampled, only 996 (10.96%) had never sought health-related information on their smartphones. By contrast, 71.63% (n=6508) reported seeking such information on their smartphones at least a few times per month. Other than seeking health-related information, most participants reported regularly using smartphones to keep themselves informed of the latest news and information (8650/9086, 95.20%), build and maintain social networks (8432/9086, 92.80%), use electronic banking (7578/9086, 83.40%), play video games (7196/9086, 79.20%), and watch entertaining videos (7196/9086, 79.20%). Generally, the participants considered themselves to be interested in technological innovation and new technology (mean 4.42, SD 1.46). Regarding their concerns over the quality of online information, more than half agreed (Likert scale>4) that there is less control over the quality of online content than content from other media outlets (6108/9086, 67.22%) and that there is a lot of fake news online (6766/9086, 74.47%).
In the univariate analyses, the frequency of using smartphones to find health information was significantly higher among participants who were women (t9085=4.00, P<.001), married (t9085=–3.25, P=.01), employed (t9085=–9.06, P<.001), parents (t9085=–16.83, P<.001), and/or had a high monthly household income level (r=0.06, P<.001) and a high level of education (F2,9084=58.0, P<.001). Moreover, the frequency of using smartphones to seek health information was positively associated with the level of subjective SES (r=0.29, P<.001) but was negatively associated with age (r=–0.09, P<.001).
In terms of technology-related factors, the frequency of using smartphones to search for health information was significantly and positively associated with the frequency of smartphone use in general (r=0.51, P<.001). Additionally, concern about online information quality (r=0.14, P<.001) was significantly and positively related to seeking health information on smartphones.
The results of the multilevel regression analysis substantially confirmed the findings of the univariate analyses (). For individual-level characteristics, individuals who frequently sought health-related information using their smartphones were more likely to be women, to be employed, to have at least one child, and to perceive themselves as having a high SES. At the same time, age, household income, and marital status were not significantly related to MHIS in the regression model. Frequencies of smartphone use and technology innovativeness were positively related to seeking health information. However, the relationship between concern over online information quality and MHIS became negative with the presence of other factors. Model statistics are summarized in .
|Variable||Model 1||Model 2||Model 3|
|β (SE)||P value||β (SE)||P value|
|Intercept||–.02 (.15)||.06 (.07)||.09||–.08 (.05)||.07|
|Man||—||–.13 (.02)||<.001||–.13 (.02)||<.001|
|Age||—||–.01 (.01)||.13||.01 (.01)||.11|
|Single, divorced, separated, or widowed||—||Reference||Reference||Reference||Reference|
|Married||—||-.004 (.02)||.42||–.004 (.02)||.41|
|High school or less||—||Reference||Reference||Reference||Reference|
|College or university||—||.02 (.03)||.14||.02 (.02)||.13|
|Graduate school or more||—||.08 (.02)||.004||.07 (.03)||.009|
|Monthly income||—||-.02 (.01)||.06||-.02 (.01)||.06|
|Subjective SESb||—||.08 (.01)||<.001||.08 (.01)||<.001|
|Employed||—||.08 (.02)||<.001||.08 (.02)||.002|
|Have at least one child||—||.16 (.02)||<.001||.16 (.02)||<.001|
|Concern with online information quality||—||–.04 (.01)||<.001||–.04 (.01)||<.001|
|Technology innovativeness||—||.11 (.01)||<.001||.10 (.01)||<.001|
|Frequency of smartphone use||—||.42 (.01)||<.001||.42 (.01)||<.001|
aNot included in model.
bSES: socioeconomic status.
cIDI: Information Communications Technology Development Index.
dCHE: current health expenditure per capita (purchasing power parity, 2017).
eGINI: Gini index (World Bank estimate).
|Model statistic||Model 1||Model 2||Model 3|
|Level 1 variance (SD)||0.88 (0.94)||0.66 (0.18)||0.66 (0.82)|
|Level 2 variance (SD)||0.15 (0.38)||0.03 (0.18)||0.02 (0.13)|
aN/A: not applicable.
presents the overall descriptive analysis at the country level. We found that the frequency of seeking health information using smartphones was the highest in Vietnam (mean 4.57, SD 1.83), followed by Indonesia (mean 4.31, SD 1.63), India (mean 4.06, SD 1.73), China (mean 4.00, SD 1.60), the Philippines (mean 3.96, SD 1.62), Thailand (mean 3.94, SD 1.72), Malaysia (mean 3.53, SD 1.68), South Korea (mean 3.48, SD 1.72), Singapore (mean 3.10, SD 1.54), and Japan (mean 2.29, SD 1.46). The distribution of overall smartphone use across countries showed a similar pattern to that of using such technology to seek health information. People in Vietnam (mean 5.05, SD 1.15), China (mean 5.03, SD 0.94), India (mean 4.90, SD 1.06), Thailand (mean 4.85, SD 1.05), Indonesia (mean 4.80, SD 1.00), and the Philippines (mean 4.76, SD 0.93) reported a higher frequency of using their smartphones for news and information, electronic banking, social networking, and entertainment than people in Malaysia (mean 4.44, SD 1.06), South Korea (mean 4.40, SD 1.08), Singapore (mean 4.18, SD 1.06), and Japan (mean 3.61, SD 1.20).
shows a scatterplot of the relationship between CHE, IDI, the Gini index, and MHIS among the 10 Asian countries. All three factors of inequality at the country level were highly related to information-seeking behavior. Countries with a low Gini index, low IDI, and low CHE were more likely to rely on smartphones as a source for their health information. However, the multilevel regression analysis ( ) showed that CHE was the only significant factor related to individuals’ MHIS. Economic inequality and IDI were not significantly associated with MHIS in the regression model.
|Country||Sample, n||MHISa (SD)||General smartphone use (SD)||COIQb (SD)||IDIc||CHEd||GINIe|
|China||1238||3.34 (1.11)||4.11 (0.67)||4.85 (1.12)||5.60||841.1||38.6|
|India||1238||3.39 (1.19)||4.09 (0.71)||5.36 (1.20)||3.03||253.3||35.7|
|Indonesia||824||3.58 (1.11)||4.03 (0.70)||5.31 (1.11)||4.33||367.9||38.1|
|Japan||804||2.09 (1.10)||3.11 (0.93)||4.28 (1.43)||8.43||4563||32.1|
|South Korea||858||2.97 (1.21)||3.68 (0.79)||4.94 (1.19)||8.85||2980||31.6|
|Malaysia||837||3.03 (1.18)||3.82 (0.74)||5.37 (1.06)||6.38||1139||41.0|
|Philippines||843||3.37 (1.12)||4.10 (0.64)||5.55 (1.10)||4.67||371.7||44.4|
|Singapore||814||2.75 (1.11)||3.64 (0.78)||5.23 (1.00)||8.05||4270||35.6|
|Thailand||821||3.29 (1.20)||4.10 (0.71)||5.35 (1.12)||5.67||670.9||36.5|
|Vietnam||809||3.72 (1.20)||4.18 (0.71)||5.46 (1.33)||4.43||375.6||35.5|
|All||9086||3.17 (1.23)||3.91 (0.80)||5.16 (1.22)||5.94||1583||36.9|
aMHIS: mobile health information seeking.
bCOIQ: concern with online information quality.
cIDI: Information Communications Technology Development Index.
dCHE: current health expenditure per capita (purchasing power parity, 2017).
eGINI: Gini index (World Bank estimate).
Understanding how individual- and country-level differences in MHIS are affected by socioeconomic conditions is important for developing and evaluating public health policy. Studies have suggested that the digital divide is a serious barrier that largely contributes to health inequality . In our study, we extended these findings by examining MHIS in Asia and the digital divide associated with such behavior at both the individual and country levels. Based on a sample of 9068 participants from 10 Asian countries, our results suggest that the act of seeking health information with smartphones varies according to individuals’ SES, perceptions of technology, and country of residence. It seems that smartphone technology widens the digital divide throughout the socioeconomic structure of society, such that individuals who are of higher education or subjective SES, married, parents, and/or employed are more likely to use smartphones to seek health information. However, smartphones also bridge the gap between countries to some extent. Individuals from countries with lower expenditures in health are more likely to use smartphones to seek health information. The multilevel digital divide documented in our study has practical implications for public health professionals.
Profiling Mobile Health Information Seekers in Asia
Being a woman has consistently predicted increased activity in seeking health information online [- ]. Women tend to use smartphones for obtaining health information more often than men owing to their higher engagement in health-related activities for themselves and their family members. In that light, our study extends past findings to the Asian context. Interestingly, although age has been emphasized as an important factor of social division in previous studies [ ], its effect was not significant in our regression models, possibly due to the nonlinear relationship between age and MHIS. Younger and older generations emerged as being more likely to use smartphones to seek health information than middle-aged individuals. This may reflect the fact that the younger generation is more familiar and comfortable with using smartphones to meet their everyday needs, including health-related needs, whereas the older generation has a strong motivation and need to seek health information from any source.
Our findings also highlight the important role of technological factors in explaining MHIS. The perceived innovativeness of technology was a primary factor for accepting new technology, including in relation to mHealth. Information quality surfaced as another factor related to seeking health information, because the information involved in these practices is highly personal and thus sensitive. However, previous studies on mHealth have not sufficiently investigated the role of these technological factors . Future studies aiming for a comprehensive understanding of MHIS should thus include the factors of technology innovativeness and information quality.
Individual-Level Digital Divide
Our study confirmed that individuals who are of higher education or social status, married, parents, and/or employed are more likely to use smartphones to seek health information. Therefore, our results extend previous findings to the Asian context. The digital divide in health refers to inequalities not only in internet access, mobile technology, and social media but also in the ability to comprehend the information found online. Although access to the internet or smartphones is now ubiquitous in most Asian countries, a second level of socioeconomic inequalities such as different levels of education and SES affect individuals’ ability to seek and comprehend online information [, ]. Therefore, future studies should focus on this second level of the digital divide and its influence on mHealth across various social groups. Public health efforts attempting to leverage the power of mobile technology should also adopt different strategies to avoid inequalities across social structures. For instance, online communication–based interventions should better investigate and address issues pertaining to eHealth literacy, including the ability to seek, find, understand, and appraise health information from electronic sources, so as to reduce inequalities in communication across different socioeconomic groups.
Country-Level Digital Divide
At the country level, we found that participants from countries that spend less on health per capita were more likely to rely on smartphones as a source for health information. This result stresses that smartphones in developing countries, including China, India, the Philippines, Indonesia, Vietnam, and Thailand, may function as a tool for managing daily activities, including seeking health information. Considering that these Asian countries have a relatively high mobile internet penetration rate , our results provide evidence that smartphones act as tools that can bridge health inequalities between countries. In developing countries where health or digital resources are limited, mobile technology may also help individuals to access information about health as well as manage their health. Whereas previous studies investigating the digital divide in access to health information and technology have focused on the socioeconomic characteristics, internet access, and information literacy of individuals, our study examined the digital divide at both the individual and country levels. Therefore, this study adds a new dimension for understanding the digital divide in mHealth.
Strengths and Limitations
The strengths of our study include its large sample, and the collection of data on individuals’ smartphone use and perceptions in several Asian countries. A previous meta-analysis suggested that studies about information-seeking behavior with mobile technology in Asia with large samples have been lacking . Thus, our study has filled this gap through administering by far the largest multinational survey on smartphone users in Asia. Another strength was that two levels of the digital divide were examined, which furnishes considerable knowledge about the digital divide in MHIS.
Nevertheless, the limitations of our study should be noted when generalizing the findings. First, our sample was not representative. Our participants were smartphone owners who live in urban areas in each country sampled. Despite variance in our participants’ SES, their relatively high SES may limit the generalizability of the findings. Second, we only examined the role of socioeconomic factors and technology perceptions on MHIS. Other psychological-related factors (eg, attitude, self-efficacy, perceived risk, worry, and anxiety) should also be taken into consideration in future studies with the aim of forming a comprehensive understanding of such phenomena . Finally, information quality has drawn great attention from both academia and industry because of prevalent misinformation online [ ]. Although our study found a marginal effect of information quality, future studies aiming to gain a nuanced understanding of such a phenomenon could examine the multidimensional nature of information quality or the reciprocal relationship between perceived online information quality and information seeking.
Among smartphone users in Asia, seeking health information on mobile devices varies according to the users’ SES, perceptions of technology and information, and their governments’ health expenditures, but not in accordance with the ICT divide or economic inequality at the country level. These findings suggest that although smartphones represent a readily available source of health information, they can also create inequalities in the access to health information among different socioeconomic classes of society. At the same time, the findings imply that smartphones are widely accepted as a tool for daily activities and communication in developing areas in Asia. In that light, mobile technology appears to be a particularly useful complement for the management of health in developing countries.
The study was supported by the Institute on Asian Consumer Insight funded jointly by the Singapore Economic Development Board and Nanyang Technological University.
Conflicts of Interest
- Watts S, Mackenzie A, Thomas C, Griskaitis A, Mewton L, Williams A, et al. CBT for depression: a pilot RCT comparing mobile phone vs. computer. BMC Psychiatry 2013 Feb 07;13:49 [FREE Full text] [CrossRef] [Medline]
- Brown K, Campbell SW, Ling R. Mobile phones bridging the digital divide for teens in the US? Future Internet 2011 May 13;3(2):144-158. [CrossRef]
- Xia Q, Sifah EB, Asamoah KO, Gao J, Du X, Guizani M. MeDShare: trust-less medical data sharing among cloud service providers via blockchain. IEEE Access 2017;5:14757-14767. [CrossRef]
- Fox G, Connolly R. Mobile health technology adoption across generations: narrowing the digital divide. Info Systems J 2018 Jan 29;28(6):995-1019. [CrossRef]
- Feng Y, Xie W. Digital divide 2.0: the role of social networking sites in seeking health information online from a longitudinal perspective. J Health Commun 2015 Aug 13;20(1):60-68. [CrossRef] [Medline]
- Kontos E, Blake KD, Chou WYS, Prestin A. Predictors of eHealth usage: insights on the digital divide from the Health Information National Trends Survey 2012. J Med Internet Res 2014 Jul 16;16(7):e172 [FREE Full text] [CrossRef] [Medline]
- Neter E, Brainin E. eHealth literacy: extending the digital divide to the realm of health information. J Med Internet Res 2012 Jan 27;14(1):e19 [FREE Full text] [CrossRef] [Medline]
- Wang X, Shi J, Kong H. Online health information seeking: a review and meta-analysis. Health Commun 2021 Sep;36(10):1163-1175. [CrossRef] [Medline]
- Pian W, Song S, Zhang Y. Consumer health information needs: A systematic review of measures. Inf Process Manag 2020 Mar;57(2):102077. [CrossRef]
- Chi J, Pian W, Zhang S. Consumer health information needs: a systematic review of instrument development. Inf Process Manag 2020 Nov;57(6):102376. [CrossRef]
- Zhao Y, Ni Q, Zhou R. What factors influence the mobile health service adoption? A meta-analysis and the moderating role of age. Int J Inf Manag 2018 Dec;43:342-350. [CrossRef]
- Wang X, Lee KM. The paradox of technology innovativeness and risk perceptions – A profile of Asian smartphone users. Telemat Inform 2020 Aug;51:101415. [CrossRef]
- Sun Y, Wang N, Guo X, Peng Z. Understanding the acceptance of mobile health services: a comparison and integration of alternative models. J Electron Commer Res 2013;14:183-200.
- Bhuyan SS, Lu N, Chandak A, Kim H, Wyant D, Bhatt J, et al. Use of mobile health applications for health-seeking behavior among US adults. J Med Syst 2016 Jun;40(6):153. [CrossRef] [Medline]
- Chae J. A comprehensive profile of those who have health-related apps. Health Educ Behav 2018 Aug 20;45(4):591-598. [CrossRef] [Medline]
- Krebs P, Duncan DT. Health app use among US mobile phone owners: a national survey. JMIR Mhealth Uhealth 2015 Nov 04;3(4):e101 [FREE Full text] [CrossRef] [Medline]
- Deng Z, Liu S. Understanding consumer health information-seeking behavior from the perspective of the risk perception attitude framework and social support in mobile social media websites. Int J Med Inform 2017 Sep;105:98-109. [CrossRef] [Medline]
- Dwivedi YK, Shareef MA, Simintiras AC, Lal B, Weerakkody V. A generalised adoption model for services: A cross-country comparison of mobile health (m-health). Gov Inf Quart 2016 Jan;33(1):174-187. [CrossRef]
- Guo X, Han X, Zhang X, Dang Y, Chen C. Investigating m-Health acceptance from a protection motivation theory perspective: gender and age differences. Telemed J E Health 2015 Aug;21(8):661-669. [CrossRef] [Medline]
- Lee YW, Strong DM, Kahn BK, Wang RY. AIMQ: a methodology for information quality assessment. Inf Manage 2002 Dec;40(2):133-146. [CrossRef]
- DeLone WH, McLean ER. Information systems success: the quest for the dependent variable. Inf Syst Res 1992 Mar;3(1):60-95. [CrossRef]
- Miller LMS, Bell RA. Online health information seeking: the influence of age, information trustworthiness, and search challenges. J Aging Health 2012 Apr;24(3):525-541. [CrossRef] [Medline]
- Agarwal R, Prasad J. A conceptual and operational definition of personal innovativeness in the domain of information technology. Inf Syst Res 1998 Jun;9(2):204-215. [CrossRef]
- Xu H, Luo XR, Carroll JM, Rosson MB. The personalization privacy paradox: An exploratory study of decision making process for location-aware marketing. Decision Support Syst 2011 Apr;51(1):42-52. [CrossRef]
- Rai A, Chen L, Pye J, Baird A. Understanding determinants of consumer mobile health usage intentions, assimilation, and channel preferences. J Med Internet Res 2013 Aug 02;15(8):e149 [FREE Full text] [CrossRef] [Medline]
- Renahy E, Parizot I, Chauvin P. Health information seeking on the internet: a double divide? Results from a representative survey in the Paris metropolitan area, France, 2005-2006. BMC Public Health 2008 Feb 21;8:69 [FREE Full text] [CrossRef] [Medline]
- Scheerder A, van Deursen A, van Dijk J. Determinants of internet skills, uses and outcomes. A systematic review of the second- and third-level digital divide. Telemat Inform 2017 Dec;34(8):1607-1624. [CrossRef]
- Bodie GD, Dutta MJ. Understanding health literacy for strategic health marketing: eHealth literacy, health disparities, and the digital divide. Health Mark Q 2008;25(1-2):175-203. [CrossRef] [Medline]
- Bert F, Giacometti M, Gualano MR, Siliquini R. Smartphones and health promotion: a review of the evidence. J Med Syst 2014 Jan;38(1):9995. [CrossRef] [Medline]
- Graetz I, Huang J, Brand RJ, Hsu J, Yamin CK, Reed ME. Bridging the digital divide: mobile access to personal health records among patients with diabetes. Am J Manag Care 2018 Jan;24(1):43-48 [FREE Full text] [Medline]
- Tsetsi E, Rains SA. Smartphone internet access and use: extending the digital divide and usage gap. Mobile Media Commun 2017 Jun 13;5(3):239-255. [CrossRef]
- Free C, Phillips G, Felix L, Galli L, Patel V, Edwards P. The effectiveness of M-health technologies for improving health and health services: a systematic review protocol. BMC Res Notes 2010 Oct 06;3:250 [FREE Full text] [CrossRef] [Medline]
- Ember C, Ember M. Cross-cultural research. In: Bernard HR, editor. Handbook of methods in cultural anthropology. Walnut Creek, CA: AltaMira Press; 1998:647-687.
- Lopez-Fernandez O, Kuss DJ, Romo L, Morvan Y, Kern L, Graziani P, et al. Self-reported dependence on mobile phones in young adults: A European cross-cultural empirical survey. J Behav Addict 2017 Jun 01;6(2):168-177 [FREE Full text] [CrossRef] [Medline]
- Doshi T, Smalls BL, Williams JS, Wolfman TE, Egede LE. Socioeconomic status and cardiovascular risk control in adults with diabetes. Am J Med Sci 2016 Jul;352(1):36-44 [FREE Full text] [CrossRef] [Medline]
- Operario D, Adler NE, Williams DR. Subjective social status: reliability and predictive utility for global health. Psychol Health 2007 Feb;19(2):237-246. [CrossRef]
- Limayem M, Khalifa M, Frini A. What makes consumers buy from internet? A longitudinal study of online shopping. IEEE Trans Syst Man Cybern A 2017;30(4):421-432. [CrossRef]
- The future of truth and misinformation online. Pew Research Center. 2017. URL: http://www.pewinternet.org/2017/10/19/the-future-of-truth-and-misinformation-online/ [accessed 2020-10-24]
- ICT Development Index 2017. International Telecommunication Union (ITU). URL: https://www.itu.int/net4/ITU-D/idi/ [accessed 2021-12-30]
- Indicators. The World Bank Data. URL: https://data.worldbank.org/indicator [accessed 2021-12-30]
- Global health expenditure database. World Health Organization. URL: https://apps.who.int/nha/database [accessed 2021-12-30]
- Latulippe K, Hamel C, Giroux D. Social health inequalities and eHealth: a literature review with qualitative synthesis of theoretical and empirical studies. J Med Internet Res 2017 Apr 27;19(4):e136 [FREE Full text] [CrossRef] [Medline]
- Newzoo Global Mobile Market Report. Newzoo. 2019. URL: https://newzoo.com/insights/trend-reports/newzoo-global-mobile-market-report-2019-light-version/ [accessed 2021-10-24]
- Chen L, Wang X, Peng T. Nature and diffusion of gynecologic cancer-related misinformation on social media: analysis of tweets. J Med Internet Res 2018 Oct 16;20(10):e11515 [FREE Full text] [CrossRef] [Medline]
|CHE: current health expenditure|
|ICT: information communication technology|
|IDI: Information Communication Technology Development Index|
|mHealth: mobile health|
|MHIS: mobile health information seeking|
|SES: socioeconomic status|
Edited by R Kukafka; submitted 03.09.20; peer-reviewed by W Pian, H Kim; comments to author 02.11.20; revised version received 31.12.20; accepted 21.06.21; published 13.01.22Copyright
©Xiaohui Wang, Jingyuan Shi, Kwan Min Lee. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 13.01.2022.
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