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Sociodemographic and health-related factors are often investigated for their association with the active use of electronic health (eHealth). The importance of such factors has been found to vary, depending on the purpose or means of eHealth and the target user groups. Pakistanis are one of the biggest immigrant groups in the Oslo area, Norway. Due to an especially high risk of developing type 2 diabetes (T2D) among this population, knowledge about their use of eHealth for T2D self-management and prevention (self-care) will be valuable for both understanding this vulnerable group and for developing effective eHealth services.
The aim of this study was to examine how commonly were the nine types of eHealth for T2D self-care being used among our target group, the first-generation Pakistani immigrants living in the Oslo area. The nine types of eHealth use are divided into three broad categories based on their purpose: information seeking, communication, and active self-care. We also aimed to investigate how sociodemographic factors, as well as self-assessment of health status and digital skills are associated with the use of eHealth in this group.
A survey was carried out in the form of individual structured interviews from September 2015 to January 2016 (N=176). For this study, dichotomous data about whether or not an informant had used each of the nine types of eHealth in the last 12 months and the total number of positive answers were used as dependent variables in a regression analysis. The independent variables were age, gender, total years of education, digital skills (represented by frequency of asking for help when using information and communication technology [ICT]), and self-assessment of health status. Principal component analyses were applied to make categories of independent variables to avoid multicollinearity.
Principal component analysis yielded three components:
In our sample, knowledge, as a composite measure of education and digital skills, was found to be the main factor associated with eHealth use regarding T2D self-care. Enhancing digital skills would encourage and support more active use of eHealth for T2D self-care.
In the last decade, we have seen a rapid development of accessible information and communication technology (ICT). The cost of accessing the Internet has decreased, especially mobile broadband, and there has been an increase in the variety of services and products for personal self-care. Commensurate with this trend, the use of electronic health (eHealth) has become a general practice in many developed countries. Purposes for using eHealth for self-care include, for example, seeking related information [
A large number of studies have explored how different factors are associated with target users’ eHealth use for self-care, both in general and with focus on chronic diseases [
We reviewed relevant literature that were published within the last 5 years and that analyzed data obtained in 2010 or later. We limited ourselves to this time frame for two reasons: one is the rapid evolution of mobile phone technology in the years preceding 2010, and the other is the time it would have taken until published literature reflected such changes. Moreover, some studies have found that the factors influencing the use of eHealth changed over time. By comparing data from 2005, 2007, and 2012, Bjunowska-Fedak [
Studies investigating Web-based health information–seeking and associating factors.
Author (year) | Description of eHealtha use | Education level | Age (years) | Female |
Kalantzi et al, 2015 [ |
Using the Internet as an important source for information about diabetes | (+)b | (−)c | NSd |
Lee et al, 2012 [ |
Using the Internet to seek health or medical information | (+) | (−) | Nonee |
Mesch et al, 2012 [ |
Frequency of searching for health information on the Internet | (−) College, graduate school | (−) | (+) |
Gonzalez et al, 2016 [ |
Health information–seeking behavior in the last 12 months | (+) | (−) >65 | (+) |
Wangberg et al, 2015 [ |
Experience in reading about diet and exercise on the Web | (+) | None | (+) |
Manierre et al, 2015 [ |
(Among Internet users) Experience in looking for health information on the Internet for self or someone else in the past 12 months | None | None | (+) |
Lee et al, 2014 [ |
Experience in either of the following in the last 12 months: Participating in an online support group for people with similar health or medical issues Using email or the Internet to communicate with a doctor or doctor's office Using the Internet to look up health or medical information |
(+) | NS | NS |
Kontos et al, 2014 [ |
Using the Internet to download health-related information to a mobile device in the last 12 months | (+) College degree or more versus some college | NS | NS |
Using the Internet to look for health or medical information for self in the last 12 months | NS | (−) | NS | |
AlGhamdi et al, 2015 [ |
Using the Internet to search for health-related information | (+) | Inconsistent (see |
(+) |
Bjunowska-Fedak et al, 2015 [ |
Using the Internet to obtain information about health or illness | (+) | (−) Sample age above 60 | NS |
Bjunowska-Fedak, 2015 [ |
Using the Internet to get information about health or illness at least once a year (including using interactive Internet health services) | NS | (−) | (+) |
Duplaga et al, 2013 [ |
Declaration of the Internet as one of main sources of health-related information | (+) | (−) | NS |
Beck et al, 2014 [ |
Having used the Internet to look for information or advice about health during the past 12 months | None | (+) Sample age 15-30 | Conditionally (+) those with psychological distress, being pregnant, or having a child or more |
Nölke et al, 2015 [ |
Using the Internet to search for information on medical or health issues | (+) On the basis of social class index comprising of “educational qualification,” “occupational status,” and “household net income” | NS | (+) |
aeHealth: electronic health.
b(+) indicates positive association with eHealth use (
c(−) indicates negative association with eHealth use (
dNS: not significant; no significant association with eHealth use.
eNone: the factor was not investigated in a study.
Studies investigating Web-based communication with experts or peers about health and associating factors.
Author (year) | Description of eHealtha use | Education level | Age (years) | Female |
Mesch et al, 2012 [ |
Frequency of participating in Internet forums about health issues or sent an email to a physician or a nurse | NSb | NS | NS |
Wangberg et al, 2015 [ |
Experience in asking questions about exercise or diet to experts | (−)c | Noned | NS |
Experience in posting a status about exercise or diet on a social networking site | (+)e | None | (−) | |
Experience in sharing exercise or diet data with others online | NS | None | (+) | |
Experience in discussing exercise or diet with peers | NS | None | NS | |
Kontos et al, 2014 [ |
Experience of using email or the Internet to communicate with a doctor or doctor's office in the last 12 months | (+) College degree or more versus high school degree or less | (−) Age between 18-34 versus >65 | (+) |
Experience of participating in an online support group for people with a similar health or medical issue in the last 12 months | NS | NS | (+) | |
Experience of visiting a social networking site to read and share about medical topics in the last 12 months | Inconsistent | (−) Age between 18-34 versus >65; and age between 35-49 versus >65 | NS | |
Tennant et al, 2015 [ |
Having used the Internet for any of the following reasons to locate or share health information in last 12 months: (1) participated in a Web-based support group, (2) used a social networking site such as Facebook, Twitter, or LinkedIn, or (3) wrote in a Web-based diary or blog | (+) 4 years of college or more versus less than high school | NS (sample: age >50) | (+) |
Thackeray et al, 2013 [ |
Using a social networking site (SNS) for health-related activities | NS | (−) | (+) |
aeHealth: electronic health.
bNS: not significant; no significant association with eHealth use.
c(−) indicates negative association with eHealth use (
dthe factor was not investigated in a study.
e(+) indicates positive association with eHealth use (
Most of the studies concentrate on experiences of Web-based health information search. The number of studies investigating Web-based communication or use of Web applications is still limited. In general, having a high education, being young, and being female are positively associated with eHealth use in many of the studies listed. However, a few studies ([
In addition to the three factors above, health-related factors and socioeconomic status are also commonly examined for their association with eHealth use. Examples of health-related factors include subjective measure of being in good health [
Studies investigating the use of mobile apps or Web applications for active self-care and associating factors.
Author (year) | Description of eHealtha use | Education level | Age (years) | Female |
Krebs et al, 2015 [ |
Experience of having ever downloaded an “app” to track anything related to a user's own health | (+)b (Ref less than high school) | (−)c | NSd |
Bender et al, 2014 [ |
Experience of downloading health apps | (+) Ref high school | (−) | NS |
Wangberg et al, 2015 [ |
Using Internet- or mobile-based programs to support health behavior | NS | Nonee | (+) |
Keeping a Web-based exercise or diet journal | (+) | None | (+) | |
Kontos et al, 2014 [ |
Experience of using the Internet to keep track of personal health information in the last 12 months | (+) College degree or more versus high school degree or less | NS | (+) |
Experience of using a website to help with diet, weight, or physical activity in the last 12 months | (+) | (−) | NS |
aeHealth: electronic health.
b(+) indicates positive association with eHealth use (
c(−) indicates negative association with eHealth use (
dNS: not significant; no significant association with eHealth use.
eNone: the factor was not investigated in a study.
Interestingly, few studies have investigated the relationship between factors relevant to digital skills and eHealth use. Lee et al [
Norway is one of the most highly digitalized countries in Europe. As of 2016, 96% of the population has access to the Internet, and 75% have basic digital skills [
Pakistani immigrants are one of the large immigrant groups in the Oslo area [
From a larger survey among first-generation Pakistani immigrants in Oslo, this paper focuses on results regarding the use of eHealth for self-care of T2D depending on its purpose and means, which are categorized as follows: (A) For seeking T2D-relevant information: (a) by using search engines that require input of search terms, (b) on specific websites or by email subscriptions that can be navigated by only scrolling and clicking, or (c) by searching for software programs on personal computers, or apps on a mobile phone or a tablet (mobile apps) that could be used as a look-up tool; (B) For communicating or consulting about T2D self-care: (d) by using ICT in general for closed conversation with a few specific acquaintances such as voice or video or text communication, (e) via SNS, (f) on portals for peer communication, or (g) by online consulting with experts in diabetes; (C) For active decision making on T2D self-care (h) by using Web applications for or mobile apps tracking health information such as diet, physical activities, weight, blood glucose level, and so on, or (i) by using Web applications or mobile apps to assess one’s own health status with regard to T2D.
The objective of this paper is to answer the following research questions: (1) How common is eHealth use for T2D self-care among first-generation immigrants from Pakistan in the Oslo area? and (2) To what extent are education, age, gender, digital skill, and self-assessed health associated with the use of eHealth for self-care of T2D in this population?
We carried out the survey from September 2015 to January 2016. Ethical approval was given to the project protocol by Norwegian Social Science Data Services in June 2015 (project number: 43549). We employed purposive sampling for the recruitment of informants. Reflecting the results from our pilot, the following inclusion criteria were set, as shown in the survey protocol paper [
On the basis of the recommendations on recruitment in immigrant populations [
The survey employed individual structured interviews. The research assistants, who are fluent in speaking Urdu, interviewed the informants and recorded their verbal responses. The protocol of the whole survey, as well as the entire set of questions used in the survey, can be found elsewhere [
We chose to analyze the following variables to be able to compare eHealth use in our survey sample with relevant studies shown in
Demographic variables include being a female, age group by a range of birth year, and the total years of education from Pakistan and Norway. The decision to use age group by a range of birth year rather than exact age was made to avoid a potential risk that, because of the relatively small sample size, an informant could be identified by a combination of the answers provided to some questions in the survey [
In Pakistan, primary education lasts for 5 years, starting from the age of 5. This is followed by a 3-year junior secondary education after which there is 2-year secondary school and then 2-year higher secondary school before undergraduate level [
Self-assessment of health status was obtained by a multiple-choice question with answer alternatives being “Excellent (5),” “Very good (4),” “Good (3),” “Fair (2),” “Going up and down (1),” and “Poor (0)” based on a question used in [
Lack of digital skills were captured by a question related to the frequency of asking for assistance when using ICT devices, with answer alternatives being “Always (4),” “Often (3),” “Sometimes (2),” “Seldom (1),” and “Never (0).” In the analysis, these two variables were treated as continuous data as they present ordinal categorical data with more than five categories [
As dependent variables, we used dichotomous answers to the nine questions asking about whether or not informants had used eHealth for self-care of T2D in the last 12 months, depending on the purpose and means described in the Objective subsection. We also used a total number of positive answers to these nine questions as a variable showing how a wide variety of eHealth activities an informant was engaged with for self-care of T2D.
Logistic regressions were used to assess associations between experience with each purpose of eHealth use (dependent variable) and the independent variables. The variety of eHealth use can be interpreted as a count variable, therefore, we resorted to Poisson regression.
Although we were interested in digital skills as a separate factor from education level, it is probable that they are correlated to each other. Given the result of a report about digital skills of immigrants with a Pakistani background in Norway, it is also probable that the age and gender would be also highly correlated to digital skills [
The majority of the informants answered that their health status, in general, was good or better. One who answered “going up and down” mentioned explicitly that the condition was due to pregnancy.
Forty-four informants (25.0%, 44/176) reported that they need assistance in the use of ICT tools always or often. On the other hand, 68 informants (38.6%, 68/176) expressed they did not need any assistance in the use of ICT tools.
Regarding eHealth use for information seeking, 63 informants (35.8%, 63/176) reported use of portals and similar sources that required only simple operations such as scrolling and clicking (b). On the other hand, no more than 35 informants (19.9%, 35/176) reported the use of a search engine that requires text input (a). Only 8 informants (4.5%, 8/176) had searched the Internet for mobile apps or software programs on personal computers for look-up of relevant information to T2D self-care (c).
Eighty-four informants (47.7%, 84/176) had used ICT for closed communication with acquaintances regarding self-care of T2D (d), and 58 informants (33.0%, 58/176) had used SNS for communication with others about self-care of T2D (e). However, only 9 informants reported using portals for peer communication in the context of T2D self-care (f). There was only 1 informant who had asked about issues relevant to T2D self-care to an expert on the Web (g). Mobile apps and Web applications for active decision making on T2D self-care were not very popular. Twenty-five informants (14.2%, 25/176) reported the use of mobile apps or Web applications for keeping track of health information (h), 38 informants (21.6%, 38/176) used mobile apps or Web applications for self-assessment of health status (i), and 41 informants (23.2%, 41/176) had never used ICT for T2D self-care. Among the 46 informants (26.1%, 46/176) who had used only one type of eHealth in the context of T2D self-care, 31 informants (17.6%, 31/176) answered that they were using ICT for closed communication with acquaintances (d). No informant had experience of eight or more types of eHealth for T2D self-care.
The second principal component (PC2) is strongly related to health status (positive value) and to some extent to age (negative value). The self-assessment of health status and age were negatively correlated according to Pearson correlation test that was separately applied to these variables (correlation=−.32,
The third principal component (PC3) is related to
Descriptive characteristics of the survey informants (N=176).
Variables | Informants, n (%) | |||
Male | 42 (23.9) | |||
Female | 134 (76.1) | |||
1981–1990 | 54 (30.7) | |||
1971-1980 | 61 (34.7) | |||
1956-1970 | 61 (34.7) | |||
0 years | 14 (8.0) | |||
5 years | 13 (7.4) | |||
<10 years | 17 (9.7) | |||
<12 years | 33 (18.8) | |||
<14 years | 39 (22.2) | |||
14 years or more | 55 (31.3) | |||
Excellent (5) | 11 (6.3) | |||
Very good (4) | 27 (15.3) | |||
Good (3) | 70 (39.8) | |||
Fair (2) | 37 (21.0) | |||
Going up and down (1) | 19 (10.8) | |||
Poor (0) | 12 (6.8) | |||
Always (4) | 18 (10.2) | |||
Often (3) | 26 (14.8) | |||
Sometimes (2) | 51 (29.0) | |||
Seldom (1) | 12 (6.8) | |||
Never (0) | 68 (38.6) | |||
(a) By using search engines that require input of search terms | 35 (19.9) | |||
(b) On specific websites or by mail subscriptions that can be navigated by only scrolling and clicking | 63 (35.8) | |||
(c) By searching for software programs on personal computers or applications on mobile phone or tablet (mobile apps) that could be used as a look-up tool | 8 (4.5) | |||
(d) By using ICT in general for closed conversation with a few specific acquaintances | 84 (47.7) | |||
(e) By social networking sites | 58 (33.0) | |||
(f) On portals for peer communication | 9 (5.1) | |||
(g) By online consulting with experts in diabetes | 1 (0.6) | |||
(h) Keeping track of health information | 25 (14.2) | |||
(i) Self-assessment of health status | 38 (21.6) | |||
8 or more | 0 (0.0) | |||
7 | 2 (1.1) | |||
6 | 5 (2.8) | |||
5 | 7 (4.0) | |||
4 | 9 (5.1) | |||
3 | 28 (15.9) | |||
2 | 38 (21.6) | |||
1 | 46 (26.1) | |||
0 | 41 (23.3) |
aICT: information and communication technology.
beHealth: electronic health.
cT2D: type 2 diabetes.
Computed principal components (PCs).
Variables | PC1a | PC2 | PC3 |
Being a female | −.24 | −.15 | .87 |
Age | −.41 | −.58 | −.49 |
Total years of education | .85 | .13 | −.08 |
Self-assessment of health status | .17 | .90 | −.21 |
Frequency for asking help when using ICTb | −.82 | −.23 | .15 |
aPC: principal component.
bICT: information and communication technology.
The
Result of regression analyses.
Variables | Estimate | Standard error | z value | |||
Intercept | −1.415 | 0.194 | −7.299 | <.001 | ||
Knowledge | 0.282 | 0.207 | 1.358 | .18 | ||
Health | 0.002 | 0.192 | 0.011 | >.99 | ||
Gender | −0.137 | 0.181 | −0.754 | .45 | ||
Intercept | −0.615 | 0.164 | −3.750 | <.001 | ||
Knowledge | 0.489 | 0.179 | 2.734 | .006 | ||
Health | 0.093 | 0.163 | 0.571 | .57 | ||
Gender | −0.082 | 0.157 | −0.522 | .60 | ||
Intercept | −3.955 | 0.706 | −5.602 | <.001 | ||
Knowledge | 1.298 | 0.650 | 1.996 | .046 | ||
Health | 0.518 | 0.440 | 1.177 | .24 | ||
Gender | 0.745 | 0.517 | 1.441 | .15 | ||
Intercept | −0.084 | 0.158 | −0.531 | .60 | ||
Knowledge | −0.375 | 0.160 | −2.341 | .02 | ||
Health | −0.400 | 0.163 | −2.454 | .01 | ||
Gender | 0.231 | 0.159 | 1.450 | .15 | ||
Intercept | −0.766 | 0.171 | −4.487 | <.001 | ||
Knowledge | 0.597 | 0.191 | 3.120 | .002 | ||
Health | 0.068 | 0.168 | 0.406 | .69 | ||
Gender | −0.015 | 0.161 | −0.092 | .93 | ||
Intercept | −3.329 | 0.481 | −6.928 | <.001 | ||
Knowledge | 0.988 | 0.500 | 1.976 | .048 | ||
Health | −0.132 | 0.361 | −0.364 | .72 | ||
Gender | 0.298 | 0.378 | 0.788 | .43 | ||
Intercept | −1.640 | 0.249 | −6.597 | <.001 | ||
Knowledge | 1.165 | 0.289 | 4.036 | <.001 | ||
Health | 0.312 | 0.206 | 1.515 | .13 | ||
Gender | 0.096 | 0.189 | 0.509 | .61 | ||
Intercept | −2.309 | 0.334 | −6.922 | <.001 | ||
Knowledge | 1.257 | 0.365 | 3.447 | <.001 | ||
Health | 0.487 | 0.251 | 1.942 | .05 | ||
Gender | 0.039 | 0.218 | 0.181 | .86 | ||
Intercept | 0.566 | 0.582 | 9.725 | <.001 | ||
Knowledge | 0.290 | 0.063 | 4.644 | <.001 | ||
Health | 0.024 | 0.057 | 0.420 | .68 | ||
Gender | 0.046 | 0.055 | 0.823 | .41 |
aICT: information and communication technology.
bT2D: type 2 diabetes.
This study targeted first-generation immigrants from Pakistan living in the Oslo area and examined their use of various types of eHealth in the context of T2D self-care. As there has not been any data showing eHealth use by this target population, this study increased our understanding of one of the biggest minority groups in Norway. Wilson et al [
The finding that nearly half of the survey sample has used ICT for closed communication with acquaintances about T2D self-care implies that T2D self-care is not a rarity among the target population. This is reasonable, considering the high prevalence of diabetes among the target population [
To the authors’ best knowledge, there is no other similar study investigating eHealth use concerning T2D self-care among the ethnic Norwegian population. The closest is the study by Wangberg et al [
As shown in
The principal component
In this study, the principal component
Due to the existing privacy and security regulation, use of the middle year of each range of age group as the representative year of birth was the best possible solution within the choices we could make. However, the results might have changed slightly if we instead could have used the actual age of each informant in the analyses.
The study sample had uneven gender balance, which may not reflect the gender balance of the population that fulfills our inclusion criteria. The 2 research assistants reported several cases where they failed to recruit male informants because of them falling outside one of the inclusion criteria or their unwillingness to participate. The cases included immigration to Norway at the age of 16 or 17 years, which caused exclusion because of one of the inclusion criteria. We set an inclusion criterion that informants had immigrated to Norway after age of 18 years because of the age authorized as a legal adult in Norway. In Pakistan, age at completion of higher secondary education can be 17 years. If our inclusion criterion regarding the age at immigration had been 16 years, we might have been able to include more male informants. The other cases are negative attitudes toward being asked about personal health, engagement in night-shift work and sleeping during the daytime, and a negative reaction to interaction with the female research assistants. For future similar studies, using male research assistants may also help to ensure recruiting male informants. Due to the limited budget of the project and cost ineffectiveness of purposeful sampling, we needed to focus on recruiting as many informants as possible, instead of focusing on including more males. Gender was controlled for in the regression analysis to account for the imbalance.
In a survey about digital skills of immigrant groups in Norway [
This study adds to the knowledge about the use of eHealth for T2D self-care among first-generation Pakistani immigrants living in the Oslo area, especially those who are interested in and capable of T2D self-care.
Complete summary of studies shown in
electronic health
information and communication technology
not significant
principal component
social network service
type 1 diabetes
type 2 diabetes
This research was funded by the Department of Computer Science, the Faculty of Technology, Design and Art, Oslo and Akershus University College of Applied Sciences in December 2014. The authors are grateful to Monica Morris and Anica Munir for serving as research assistants and are thankful to Tulpesh Patel, the associate professor at the the Department of Computer Science, the Faculty of Technology, Design and Art, Oslo and Akershus University College of Applied Sciences for critical comments on writing and proofreading. Last but not least, the authors thank the reviewers for their constructive comments.
NT conceived this study and drafted the study design and the first version of the manuscript. HLH designed and conducted the statistical analyses and wrote the relevant part of the draft. All the other 3 authors contributed to further development of the study design and in finalizing the manuscript by giving comments to all versions of the manuscript draft. All authors read and approved the final manuscript.
None declared.