Original Paper
- Lucille Standaar1,2,3, BSc, MSc ;
- Lilian van Tuyl2, MBA, PhD ;
- Anita Suijkerbuijk1, PhD ;
- Anne Brabers2, PhD ;
- Roland Friele2,3, Prof Dr
1Department of Population Health and Health Services Research, Centre for Public Health, Healthcare and Society, National Institute for Public Health and the Environment, Bilthoven, Netherlands
2Department Organisation and Quality of Care, Netherlands Institute for Health Services Research, Utrecht, Netherlands
3Tranzo Scientific Center for Care and Wellbeing, Tilburg University, Tilburg, Netherlands
Corresponding Author:
Lucille Standaar, BSc, MSc
Department of Population Health and Health Services Research
Centre for Public Health, Healthcare and Society
National Institute for Public Health and the Environment
PO Box 1
Bilthoven, 3723 BA
Netherlands
Phone: 31 302743243
Fax:31 302744407
Email: lucille.standaar@rivm.nl
Abstract
Background: There is a growing concern that digital health care may exacerbate existing health disparities. Digital health care or eHealth encompasses the digital apps that are used in health care. Differences in access, use, and perceived benefits of digital technology among socioeconomic groups are commonly referred to as the digital divide. Current research shows that people in lower socioeconomic positions (SEPs) use eHealth less frequently.
Objective: This study aims to (1) investigate the association between SEP and eHealth access to, use of, and perceived benefit within the adult Dutch population and (2) evaluate disparities in eHealth access, use, and perceived benefit through three socioeconomic variables—education, standardized income, and the socioeconomic status of the neighborhood.
Methods: A secondary analysis was conducted on data from the Nivel Dutch Health Care Consumer Panel (response rate 57%, 849/1500), to assess access to, use of, and perceived benefits from eHealth. These data were collected to monitor eHealth developments in the Netherlands. eHealth was examined through two concepts: (1) eHealth in general and (2) websites, apps, and wearables. Results were stratified into 9 SEP populations based on 3 indicators—education, standardized income, and socioeconomic status level of the neighborhood. Logistic regression analyses were performed to evaluate whether the outcomes varied significantly across different SEP groups. Age was included as a covariate to control for confounding.
Results: This study confirms the association between eHealth and SEP and shows that low SEP respondents have less access (odds ratio [OR] 5.72, 95% CI 3.06-10.72) and use (OR 4.96, 95% CI 2.66-9.24) of eHealth compared to medium or high SEP respondents. Differences were most profound when stratifying for levels of education.
Conclusions: The access to and use of eHealth has a socioeconomic gradient and emphasizes that SEP indicators cannot be used interchangeably to assess eHealth access and use. The results underline the importance of activities and policies aimed at improving eHealth accessibility and usage among low SEP groups to mitigate disparities in health between different socioeconomic groups.
doi:10.2196/49585
Keywords
Introduction
Digital health care is expected to provide benefits for health care systems, providers, and patients and is considered a solution to address workforce shortages and rising health care costs [Rutten P, Pruim J, Zijl NV, Merckelbach S. Digitale zorg in Nederland. Digitale zorg in Nederland. 2020. URL: https://www.mckinsey.com/nl/our-insights/digitale-zorg-in-nederland [accessed 2023-05-05] 1-Dutch digitisation strategy 2.0. Ministery of Economic Affairs and Climate Policy. 2019. URL: https://www.nederlanddigitaal.nl/documenten/publicaties/2019/11/13/english-version-of-the-dutch-digitalisation-strategy-2.0 [accessed 2023-05-05] 4]. Moreover, digital health care is anticipated to enhance the quality of care, stimulate patient self-management, and improve health accessibility and equity [Rutten P, Pruim J, Zijl NV, Merckelbach S. Digitale zorg in Nederland. Digitale zorg in Nederland. 2020. URL: https://www.mckinsey.com/nl/our-insights/digitale-zorg-in-nederland [accessed 2023-05-05] 1-Dutch digitisation strategy 2.0. Ministery of Economic Affairs and Climate Policy. 2019. URL: https://www.nederlanddigitaal.nl/documenten/publicaties/2019/11/13/english-version-of-the-dutch-digitalisation-strategy-2.0 [accessed 2023-05-05] 4]. Digital health care or eHealth is defined as the digital apps that are used in health care. Health care policies focus on increased use of and dependency on the eHealth apps [Dutch digitisation strategy 2.0. Ministery of Economic Affairs and Climate Policy. 2019. URL: https://www.nederlanddigitaal.nl/documenten/publicaties/2019/11/13/english-version-of-the-dutch-digitalisation-strategy-2.0 [accessed 2023-05-05] 4-2030 digital compass: the European way for the digital decade. European Commission. 2021. URL: https://eufordigital.eu/library/2030-digital-compass-the-european-way-for-the-digital-decade/ [accessed 2022-02-01] 6]. Concerns have been raised that digital health care may not be equally accessible for all [Boers SN, Jongsma KR, Lucivero F, Aardoom J, Büchner FL, de Vries M, et al. SERIES: eHealth in primary care. Part 2: Exploring the ethical implications of its application in primary care practice. Eur J Gen Pract. Oct 30, 2019;26(1):26-32. [FREE Full text] [CrossRef] [Medline]7-Perez SL, Kravitz RL, Bell RA, Chan MS, Paterniti DA. Characterizing internet health information seeking strategies by socioeconomic status: a mixed methods approach. BMC Med Inform Decis Mak. Aug 09, 2016;16:107. [CrossRef] [Medline]13]. Populations with lower socioeconomic positions (SEPs) are more likely to encounter financial, skill, or cognitive barriers to accessing and using eHealth, such as limited access to devices, limited digital health skills, or limited ability to take the initiative in using eHealth [Boers SN, Jongsma KR, Lucivero F, Aardoom J, Büchner FL, de Vries M, et al. SERIES: eHealth in primary care. Part 2: Exploring the ethical implications of its application in primary care practice. Eur J Gen Pract. Oct 30, 2019;26(1):26-32. [FREE Full text] [CrossRef] [Medline]7,McAuley A. Digital health interventions: widening access or widening inequalities? Public Health. Dec 2014;128(12):1118-1120. [CrossRef] [Medline]11-Wong BLH, Maaß L, Vodden A, van Kessel R, Sorbello S, Buttigieg S, et al. European Public Health Association (EUPHA) Digital Health Section. The dawn of digital public health in Europe: implications for public health policy and practice. Lancet Reg Health Eur. 2022;14:100316. [FREE Full text] [CrossRef] [Medline]16]. Additionally, these populations experience challenges in comprehending and implementing health information and healthy behaviors in daily life [Raad VVES. Een eerlijke kans op gezond leven. Raad voor Volksgezondheid en Samenleving. 2021. URL: https://www.raadrvs.nl/documenten/publicaties/2021/04/07/een-eerlijke-kans-op-gezond-leven [accessed 2021-10-13] 17-Phelan JC, Link BG, Tehranifar P. Social conditions as fundamental causes of health inequalities: theory, evidence, and policy implications. J Health Soc Behav. 2010;51 Suppl:S28-S40. [CrossRef] [Medline]21].
The demand for health care services is often higher in low-SEP populations, as people with a low SEP endure more often from chronic illnesses [de Boer WIJ, Buskens E, Koning RH, Mierau JO. Neighborhood socioeconomic status and health care costs: a population-wide study in the Netherlands. Am J Public Health. 2019;109(6):927-933. [CrossRef] [Medline]18,Loef B, Meulman I, Herber GM, Kommer GJ, Koopmanschap MA, Kunst AE, et al. Socioeconomic differences in healthcare expenditure and utilization in the Netherlands. BMC Health Serv Res. 2021;21(1):643. [FREE Full text] [CrossRef] [Medline]19,Agerholm J, Bruce D, Ponce de Leon A, Burström B. Socioeconomic differences in healthcare utilization, with and without adjustment for need: an example from Stockholm, Sweden. Scand J Public Health. 2013;41(3):318-325. [CrossRef] [Medline]22,Lemstra M, Mackenbach J, Neudorf C, Nannapaneni U. High health care utilization and costs associated with lower socio-economic status: results from a linked dataset. Can J Public Health. 2009;100(3):180-183. [FREE Full text] [CrossRef] [Medline]23]. Research shows that low-SEP populations often have different views on health and the possible benefits of healthy behaviors compared to high-SEP populations [Mackenbach JP. The persistence of health inequalities in modern welfare states: the explanation of a paradox. Soc Sci Med. 2012;75(4):761-769. [CrossRef] [Medline]24-Singh-Manoux A, Marmot M. Role of socialization in explaining social inequalities in health. Soc Sci Med. 2005;60(9):2129-2133. [FREE Full text] [CrossRef] [Medline]27]. Studies find that low-SEP populations have less time, more stress, and limited financial capacities to implement healthy behaviors [Pampel FC, Krueger PM, Denney JT. Socioeconomic disparities in health behaviors. Annu Rev Sociol. 2010;36:349-370. [CrossRef] [Medline]26]. Next to this, it is theorized that health beliefs often find origin in the health beliefs of previous generations [Mackenbach JP. The persistence of health inequalities in modern welfare states: the explanation of a paradox. Soc Sci Med. 2012;75(4):761-769. [CrossRef] [Medline]24-Singh-Manoux A, Marmot M. Role of socialization in explaining social inequalities in health. Soc Sci Med. 2005;60(9):2129-2133. [FREE Full text] [CrossRef] [Medline]27]. In the context of low SEP, the expectation of a shorter life and the belief that their own behavior has limited influence on their longevity pose barriers to adhering to healthy behaviors [Petrovic D, de Mestral C, Bochud M, Bartley M, Kivimäki M, Vineis P, et al. The contribution of health behaviors to socioeconomic inequalities in health: a systematic review. Prev Med. 2018;113:15-31. [FREE Full text] [CrossRef] [Medline]20]. Therefore, the digitalization of health care could seriously impact the access and use of health care for those who need it most [Digital economy and society index (DESI) 2021: The Netherlands. European Commission. 2021. URL: https://ec.europa.eu/newsroom/dae/redirection/document/80493 [accessed 2022-02-01] 5,2030 digital compass: the European way for the digital decade. European Commission. 2021. URL: https://eufordigital.eu/library/2030-digital-compass-the-european-way-for-the-digital-decade/ [accessed 2022-02-01] 6,Perez SL, Kravitz RL, Bell RA, Chan MS, Paterniti DA. Characterizing internet health information seeking strategies by socioeconomic status: a mixed methods approach. BMC Med Inform Decis Mak. Aug 09, 2016;16:107. [CrossRef] [Medline]13,Weiss D, Rydland HT, Øversveen E, Jensen MR, Solhaug S, Krokstad S. Innovative technologies and social inequalities in health: a scoping review of the literature. PLoS One. 2018;13(4):e0195447. [FREE Full text] [CrossRef] [Medline]28-Hansen AH, Bradway M, Broz J, Claudi T, Henriksen Ø, Wangberg SC, et al. Inequalities in the use of eHealth between socioeconomic groups among patients with type 1 and type 2 diabetes: cross-sectional study. J Med Internet Res. 2019;21(5):e13615. [FREE Full text] [CrossRef] [Medline]31].
National digital connectivity and policies that stimulate the transition toward a digital health care system could improve the implementation and accessibility of eHealth. The European Union and its member states deploy policies to realize the digital transition of health care systems [Digital economy and society index (DESI) 2021: The Netherlands. European Commission. 2021. URL: https://ec.europa.eu/newsroom/dae/redirection/document/80493 [accessed 2022-02-01] 5,2030 digital compass: the European way for the digital decade. European Commission. 2021. URL: https://eufordigital.eu/library/2030-digital-compass-the-european-way-for-the-digital-decade/ [accessed 2022-02-01] 6]. Some states, such as the Netherlands, have been experimenting with digital health care for over a decade [Dutch digitisation strategy 2.0. Ministery of Economic Affairs and Climate Policy. 2019. URL: https://www.nederlanddigitaal.nl/documenten/publicaties/2019/11/13/english-version-of-the-dutch-digitalisation-strategy-2.0 [accessed 2023-05-05] 4,2030 digital compass: the European way for the digital decade. European Commission. 2021. URL: https://eufordigital.eu/library/2030-digital-compass-the-european-way-for-the-digital-decade/ [accessed 2022-02-01] 6]. Most Dutch households (98%) have fast broadband coverage (2020) and 88% of the Dutch population uses mobile broadband (2019), which indicates the use of a mobile phone or other device with mobile internet access [Digital economy and society index (DESI) 2021: The Netherlands. European Commission. 2021. URL: https://ec.europa.eu/newsroom/dae/redirection/document/80493 [accessed 2022-02-01] 5]. The level of connectivity in the Netherlands could facilitate the implementation of digital health care [Digital economy and society index (DESI) 2021: The Netherlands. European Commission. 2021. URL: https://ec.europa.eu/newsroom/dae/redirection/document/80493 [accessed 2022-02-01] 5].
Several countries have developed national eHealth monitoring programs to monitor the uptake and effects of eHealth among health care professionals and citizens [Verhellen A, Jewell C, Van Gils M, Jacobs A, Steenberghs E. Ehealthmonitor Citizens. 2019. URL: https://researchportal.vub.be/en/publications/ehealthmonitor-2019-citizens [accessed 2023-09-13] 32-Huygens MWJ, Voogdt-Pruis HR, Wouters M, Meurs MM, van Lettow B, Kleijweg C, et al. The uptake and use of telemonitoring in chronic care between 2014 and 2019: nationwide survey among patients and health care professionals in the Netherlands. J Med Internet Res. 2021;23(5):e24908. [FREE Full text] [CrossRef] [Medline]34]. From a citizen’s perspective, the monitoring programs focus on the use and evaluation of eHealth that involve citizen interaction [Verhellen A, Jewell C, Van Gils M, Jacobs A, Steenberghs E. Ehealthmonitor Citizens. 2019. URL: https://researchportal.vub.be/en/publications/ehealthmonitor-2019-citizens [accessed 2023-09-13] 32-Huygens MWJ, Voogdt-Pruis HR, Wouters M, Meurs MM, van Lettow B, Kleijweg C, et al. The uptake and use of telemonitoring in chronic care between 2014 and 2019: nationwide survey among patients and health care professionals in the Netherlands. J Med Internet Res. 2021;23(5):e24908. [FREE Full text] [CrossRef] [Medline]34]. This includes applications such as websites, apps, and wearables that citizens can use independently or involve eHealth tools that facilitate digital communication with a health care professional, such as video calls or messaging via patient portals [Verhellen A, Jewell C, Van Gils M, Jacobs A, Steenberghs E. Ehealthmonitor Citizens. 2019. URL: https://researchportal.vub.be/en/publications/ehealthmonitor-2019-citizens [accessed 2023-09-13] 32-Huygens MWJ, Voogdt-Pruis HR, Wouters M, Meurs MM, van Lettow B, Kleijweg C, et al. The uptake and use of telemonitoring in chronic care between 2014 and 2019: nationwide survey among patients and health care professionals in the Netherlands. J Med Internet Res. 2021;23(5):e24908. [FREE Full text] [CrossRef] [Medline]34]. This study is based on secondary data analysis of the Dutch eHealth monitoring program (2021) [van der VR, van DL, Standaar LMB, Wouters EJM, Suijkerbuijk AWM, van TL. E-healthmonitor 2021: stand van zaken digitale zorg. 2022. URL: https://www.rivm.nl/documenten/e-healthmonitor-2021-stand-van-zaken-digitale-zorg [accessed 2023-05-05] 29], which collects data about access, use, and perceived benefits from eHealth among Dutch citizens through questionnaires.
There is still limited understanding of the relationship between SEP and eHealth access, use, and perceived benefit and that understanding is generally limited to either access, use, or perceived benefit from eHealth, specific eHealth apps or specific subpopulations. Research showed the relation between SEP and the use of personal health records [Paccoud I, Baumann M, Le Bihan E, Pétré B, Breinbauer M, Böhme P, et al. Socioeconomic and behavioural factors associated with access to and use of personal health records. BMC Med Inform Decis Mak. 2021;21(1):18. [FREE Full text] [CrossRef] [Medline]35] and mobile apps [Bol N, Helberger N, Weert JCM. Differences in mobile health app use: a source of new digital inequalities? Inf Soc. 2018;34(3):183-193. [CrossRef]36]. Other research focuses on either the benefit from [Van Deursen AJM, Helsper EJ. The third-level digital divide: who benefits most from being online. In: Robinson, L, Cotten SR, Schulz J, Hale TM, Williams A, editors. Communication and Information Technologies Annual. Leeds, England. Emerald Group Publishing Limited; 2015:29-52.37] or the use of eHealth [Chagpar AB. Sociodemographic factors affecting telemedicine access: a population-based analysis. Surgery. 2022;171(3):793-798. [FREE Full text] [CrossRef] [Medline]38,Aldhahir AM, Alqahtani JS, Althobiani MA, Alghamdi SM, Alanazi AF, Alnaim N, et al. Current knowledge, satisfaction, and use of E-Health mobile application (Seha) among the general population of Saudi Arabia: a cross-sectional study. J Multidiscip Healthc. 2022;15:667-678. [FREE Full text] [CrossRef] [Medline]39] or specific patient groups such as cancer survivors [Moon Z, Zuchowski M, Moss-Morris R, Hunter MS, Norton S, Hughes LD. Disparities in access to mobile devices and e-health literacy among breast cancer survivors. Support Care Cancer. 2022;30(1):117-126. [FREE Full text] [CrossRef] [Medline]40,Melhem SJ, Nabhani-Gebara S, Kayyali R. Digital trends, digital literacy, and E-Health engagement predictors of breast and colorectal cancer survivors: a population-based cross-sectional survey. Int J Environ Res Public Health. 2023;20(2):1472. [FREE Full text] [CrossRef] [Medline]41], or citizens bound to specific locations [Özdemir R, Bektemur G, Keles E, Baydili KN. Internet use, e-Heath literacy, and associated factors in Istanbul, Turkey: a cross-sectional study. J Consumer Health Internet. 2023;27(1):1-11. [FREE Full text] [CrossRef]42]. To our knowledge, insight into differences in eHealth access, use, and perceived benefits and how different indicators for SEP display these differences within the Dutch general population aged 18 years and older are largely unknown. This study aims to assess differences in eHealth-related access, usage, and perceived benefits for different socioeconomic populations, based on education, standardized income, and socioeconomic status (SES) level of the neighborhood in the Netherlands. The findings of this study give insight into the disparities in access to, use of, and perceived benefit from eHealth in a highly connected country with an increasingly digitalized health care system. The results are insightful for other contexts that aim for or experience the same ambitions to transition to a digital health care system.
Methods
Panel
Data from the Dutch Health Care Consumer Panel (DHCCP) were used [Brabers AEM, de JJD. Nivel Consumentenpanel Gezondheidszorg Basisrapport met informatie over het panel 2022. NIVEL. 2022. URL: https://www.nivel.nl/nl/publicatie/nivel-consumentenpanel-gezondheidszorg-basisrapport-met-informatie-over-het-panel-2022 [accessed 2023-04-06] 43]. The DHCCP is a panel managed by Nivel (the Netherlands Institute for Health Services Research) and currently (as of September 2023) consists of approximately 11,500 panel members aged 18 years and older [Brabers AEM, de JJD. Nivel Consumentenpanel Gezondheidszorg Basisrapport met informatie over het panel 2022. NIVEL. 2022. URL: https://www.nivel.nl/nl/publicatie/nivel-consumentenpanel-gezondheidszorg-basisrapport-met-informatie-over-het-panel-2022 [accessed 2023-04-06] 43]. For this study, a study sample of 1.500 panel members was drawn by researchers from DHCCP. The study sample was representative of the Dutch population aged 18 years and older regarding age and sex [Brabers AEM, de JJD. Nivel Consumentenpanel Gezondheidszorg Basisrapport met informatie over het panel 2022. NIVEL. 2022. URL: https://www.nivel.nl/nl/publicatie/nivel-consumentenpanel-gezondheidszorg-basisrapport-met-informatie-over-het-panel-2022 [accessed 2023-04-06] 43]. Background characteristics of panel members, including their sex, age, level of education, net monthly income per household, and 4-digit postal codes were known. The panel was periodically renewed to ensure representative samples of the adult population in the Netherlands can be drawn. New panel members were recruited by buying an address file from an address supplier [Brabers AEM, de JJD. Nivel Consumentenpanel Gezondheidszorg Basisrapport met informatie over het panel 2022. NIVEL. 2022. URL: https://www.nivel.nl/nl/publicatie/nivel-consumentenpanel-gezondheidszorg-basisrapport-met-informatie-over-het-panel-2022 [accessed 2023-04-06] 43]. As a result, possible new members were sampled at random from the general population in the Netherlands [Brabers AEM, de JJD. Nivel Consumentenpanel Gezondheidszorg Basisrapport met informatie over het panel 2022. NIVEL. 2022. URL: https://www.nivel.nl/nl/publicatie/nivel-consumentenpanel-gezondheidszorg-basisrapport-met-informatie-over-het-panel-2022 [accessed 2023-04-06] 43]. The panel could only be joined through invitation. It was not possible for people to sign up on their own initiative [Brabers AEM, de JJD. Nivel Consumentenpanel Gezondheidszorg Basisrapport met informatie over het panel 2022. NIVEL. 2022. URL: https://www.nivel.nl/nl/publicatie/nivel-consumentenpanel-gezondheidszorg-basisrapport-met-informatie-over-het-panel-2022 [accessed 2023-04-06] 43]. Upon membership, panel members were informed of the purpose, scope, method, and use of the panel [Brabers AEM, de JJD. Nivel Consumentenpanel Gezondheidszorg Basisrapport met informatie over het panel 2022. NIVEL. 2022. URL: https://www.nivel.nl/nl/publicatie/nivel-consumentenpanel-gezondheidszorg-basisrapport-met-informatie-over-het-panel-2022 [accessed 2023-04-06] 43]. Based on this information, participants could give permission to participate in the panel [Brabers AEM, de JJD. Nivel Consumentenpanel Gezondheidszorg Basisrapport met informatie over het panel 2022. NIVEL. 2022. URL: https://www.nivel.nl/nl/publicatie/nivel-consumentenpanel-gezondheidszorg-basisrapport-met-informatie-over-het-panel-2022 [accessed 2023-04-06] 43].
Ethical Considerations
According to the Dutch legislation, neither obtaining informed consent nor approval by a medical ethics committee was obligatory for doing research within the DHCCP [Brabers AEM, de JJD. Nivel Consumentenpanel Gezondheidszorg Basisrapport met informatie over het panel 2022. NIVEL. 2022. URL: https://www.nivel.nl/nl/publicatie/nivel-consumentenpanel-gezondheidszorg-basisrapport-met-informatie-over-het-panel-2022 [accessed 2023-04-06] 43,Your research: is it subject to the WMO or not? Central Committee on Research Involving Human Subjects. URL: https://english.ccmo.nl/investigators/legal-framework-for-medical-scientific-research/your-research-is-it-subject-to-the-wmo-or-not [accessed 2024-06-13] 44]. Data analysis was conducted with pseudonymized data, according to the privacy regulations of the DHCCP, in compliance with the General Data Protection Regulation [Your research: is it subject to the WMO or not? Central Committee on Research Involving Human Subjects. URL: https://english.ccmo.nl/investigators/legal-framework-for-medical-scientific-research/your-research-is-it-subject-to-the-wmo-or-not [accessed 2024-06-13] 44]. The privacy of the panel members was protected. All data were carefully stored by Nivel [Brabers AEM, de JJD. Nivel Consumentenpanel Gezondheidszorg Basisrapport met informatie over het panel 2022. NIVEL. 2022. URL: https://www.nivel.nl/nl/publicatie/nivel-consumentenpanel-gezondheidszorg-basisrapport-met-informatie-over-het-panel-2022 [accessed 2023-04-06] 43]. Personal information such as addresses was stored separately from the data of the questionnaires [Brabers AEM, de JJD. Nivel Consumentenpanel Gezondheidszorg Basisrapport met informatie over het panel 2022. NIVEL. 2022. URL: https://www.nivel.nl/nl/publicatie/nivel-consumentenpanel-gezondheidszorg-basisrapport-met-informatie-over-het-panel-2022 [accessed 2023-04-06] 43]. The privacy of the panel members in the study sample was guaranteed by DHCCP [Brabers AEM, de JJD. Nivel Consumentenpanel Gezondheidszorg Basisrapport met informatie over het panel 2022. NIVEL. 2022. URL: https://www.nivel.nl/nl/publicatie/nivel-consumentenpanel-gezondheidszorg-basisrapport-met-informatie-over-het-panel-2022 [accessed 2023-04-06] 43]. The researcher (LS) who analyzed the data had no access to the personal information of the panel members [Brabers AEM, de JJD. Nivel Consumentenpanel Gezondheidszorg Basisrapport met informatie over het panel 2022. NIVEL. 2022. URL: https://www.nivel.nl/nl/publicatie/nivel-consumentenpanel-gezondheidszorg-basisrapport-met-informatie-over-het-panel-2022 [accessed 2023-04-06] 43]. A written or digital informed consent was obtained at the time of registration of a new member to the panel [Brabers AEM, de JJD. Nivel Consumentenpanel Gezondheidszorg Basisrapport met informatie over het panel 2022. NIVEL. 2022. URL: https://www.nivel.nl/nl/publicatie/nivel-consumentenpanel-gezondheidszorg-basisrapport-met-informatie-over-het-panel-2022 [accessed 2023-04-06] 43]. Panel members were asked to participate approximately 4 or 5 times per year [Brabers AEM, de JJD. Nivel Consumentenpanel Gezondheidszorg Basisrapport met informatie over het panel 2022. NIVEL. 2022. URL: https://www.nivel.nl/nl/publicatie/nivel-consumentenpanel-gezondheidszorg-basisrapport-met-informatie-over-het-panel-2022 [accessed 2023-04-06] 43]. Participation was voluntary.
Data Collection
Data on the population’s perspective on eHealth were collected via the DHCCP as part of a larger monitoring study into the perceptions, experiences, and usage of eHealth in the Netherlands [Brabers AEM, de JJD. Nivel Consumentenpanel Gezondheidszorg Basisrapport met informatie over het panel 2022. NIVEL. 2022. URL: https://www.nivel.nl/nl/publicatie/nivel-consumentenpanel-gezondheidszorg-basisrapport-met-informatie-over-het-panel-2022 [accessed 2023-04-06] 43]. A questionnaire was developed and reviewed by a team of representatives from the health care field in the Netherlands. The questionnaire was based on earlier distributed questionnaires of the monitoring study and was adjusted to reflect market developments [Huygens MWJ, Voogdt-Pruis HR, Wouters M, Meurs MM, van Lettow B, Kleijweg C, et al. The uptake and use of telemonitoring in chronic care between 2014 and 2019: nationwide survey among patients and health care professionals in the Netherlands. J Med Internet Res. 2021;23(5):e24908. [FREE Full text] [CrossRef] [Medline]34]. The questionnaire was distributed via email and post (according to the preferences of the panel members) in May 2021. A digital reminder was sent after 1, 2, and 3 weeks after the start of the questionnaire and 1 written reminder was sent after 2 weeks. Panel members had 4 weeks to respond.
Socioeconomic Position Indicators
Overview
The concept of SEP is complex, as it results from the interaction between individual, social, economic, cultural, and societal factors [Giddens A. The Constitution of Society: Outline of the Theory of Structuration. Berkeley. University of California Press; 1984. 45,Bourdieu P. Outline of a Theory of Practice. Cambridge. Cambridge University Press; 1977. 46]. In this study, 3 different operationalizations of SEP were used to study the digital divide in the context of eHealth—education as a historic starting point, standardized household income as a measure of current wealth, and SES level of the neighborhood to include environmental influence [Bourdieu P. Outline of a Theory of Practice. Cambridge. Cambridge University Press; 1977. 46-Vrooman J, Boelhouwer J, Iedema J, van der T. Summary contemporary inequality. The Netherlands Institute for Social Research. 2023. URL: https://english.scp.nl/publications/publications/2023/03/07/summary-contemporary-inequality [accessed 2023-05-09] 49].
Education
The education levels were defined as low (none, primary school, or prevocational education); medium (secondary or vocational education level 1, 2, 3, or 4); and high (professional higher education or university) [Opleidingsniveau. Statistics Netherlands. 2019. URL: https://www.cbs.nl/nl-nl/nieuws/2019/33/verschil-levensverwachting-hoog-en-laagopgeleid-groeit/opleidingsniveau [accessed 2022-01-27] 50].
Standardized Income
Standardized income was defined as the net monthly income of the household adjusted for number of household members. The net income was converted to the equivalent of the net income of a single adult household by using equivalence factors from Statistics Netherlands (the Dutch Institute for Population Statistics) [What is my spendable income? Statistics Netherlands. 2018. URL: https://www.cbs.nl/en-gb/background/2008/50/what-is-my-spendable-income- [accessed 2023-04-06] 51]. Some respondents acknowledged having children or other adults living in their household apart from their partner or children older than 18 years of age but did not specify the number. In the case of an unknown amount of children, 1.57 children were assumed. In case of an unknown amount of extra adults 1 extra adult was assumed. Assumptions were derived from the averages in Dutch households [Opleidingsniveau. Statistics Netherlands. 2019. URL: https://www.cbs.nl/nl-nl/nieuws/2019/33/verschil-levensverwachting-hoog-en-laagopgeleid-groeit/opleidingsniveau [accessed 2022-01-27] 50,Geboorte; kerncijfers. StatLine. 2022. URL: https://opendata.cbs.nl/statline/#/CBS/nl/dataset/37422ned/table [accessed 2022-02-20] 52,Ruim 140 duizend samenwonende volwassen kinderen met ouders op leeftijd. Centraal Bureau van de Statistiek. 2014. URL: https://www.cbs.nl/nl-nl/achtergrond/2014/27/ruim-140-duizend-samenwonende-volwassen-kinderen-met-ouders-op-leeftijd [accessed 2022-08-02] 53]. Information gathered from the panel members about their monthly net income was in ranges, and the mean of the range was taken as the monthly net income. Standardized income was divided into three categories (1) low (between €0 and €1659 [US $1718.56] per month; The used conversion rate was applicable on May 1, 2021), (2) medium (between €1660 [US $1719.59] and €2332 [US $2415.72] per month), and (3) high (more than €2332 [US $2415.72] per month). The categories were derived from the quartile distribution of the net income of the Dutch households (2020) [Income distribution (standardised income). Centraal Bureau van de Statistiek. 2019. URL: https://www.cbs.nl/en-gb/visualisations/income-distribution [accessed 2021-08-04] 54].
Socioeconomic Status Level of the Neighborhood
The SES level of the neighborhood of all respondents was determined using the Social Economic Status-Wealth Education Employment (SES-WOA) score (2019) from the Statistics Netherlands. The SES-WOA score was based on the wealth, educational status, and recent employment history of households in the neighborhood [Statusscore per wijk en buurt o.b.v. welvaart, opleidingsniveau en arbeid. Centraal Bureau van de Statistiek. 2022. URL: https://www.cbs.nl/nl-nl/achtergrond/2022/14/statusscore-per-wijk-en-buurt-o-b-v-welvaart-opleidingsniveau-en-arbeid [accessed 2022-08-01] 55,Sociaal-economische status; scores per wijk en buurt, regio-indeling 2021. Centraal Bureau van de Statistiek. 2022. URL: https://www.cbs.nl/nl-nl/cijfers/detail/85163NED [accessed 2022-08-01] 56]. The SES-WOA score was matched to the respondent by the 4-digit postal code. The neighborhoods of the respondents were categorized as (1) low (first tertile of SES score: –0.89 to 0.042), (2) medium (second tertile of SES score: 0.043-0.21), and (3) high (third tertile of SES score: 0.21-0.71). The average score of Dutch neighborhoods was 0.092 (SD 0.23; range –0.89 to 0.71) [Statusscore per wijk en buurt o.b.v. welvaart, opleidingsniveau en arbeid. Centraal Bureau van de Statistiek. 2022. URL: https://www.cbs.nl/nl-nl/achtergrond/2022/14/statusscore-per-wijk-en-buurt-o-b-v-welvaart-opleidingsniveau-en-arbeid [accessed 2022-08-01] 55,Sociaal-economische status; scores per wijk en buurt, regio-indeling 2021. Centraal Bureau van de Statistiek. 2022. URL: https://www.cbs.nl/nl-nl/cijfers/detail/85163NED [accessed 2022-08-01] 56].
eHealth and the Digital Divide
For the interpretation of the data, the digital divide model was used. The digital divide model published by van Dijk et al [Van Dijk J. The Digital Divide. Cambridge. Polity Press; 2020. 57] conceptualized that individuals’ SEPs influence the available resources to access, use, and benefit from new digital media. In this study, the digital media in focus was eHealth.
Two concepts of eHealth were examined: (1) eHealth in general and (2) websites, apps, and wearables. Items in the questionnaire that informed these 2 concepts were matched to the 3 levels of the digital divide model [Van Dijk J. The Digital Divide. Cambridge. Polity Press; 2020. 57], namely level 1—access, level 2—use, and level 3—perceived benefit. Operationalization of the variables measuring access, use, and perceived benefit for eHealth in general and websites, apps, and wearables can be found in Operationalization of access, use, and perceived benefit of eHealth in general and websites, apps, and wearables.Multimedia Appendix 1
First, eHealth in general was studied to gain insight into an overall interest toward digital apps in health care. The levels of digital divide that were studied are access and the perceived benefit. Access was measured by motivation. Here respondents were asked what their general thoughts are about digital apps used in health care. The perceived benefit was operationalized by measuring to what extent the respondents perceived themselves as making more conscious decisions about their health as a result of eHealth use.
Second, eHealth in terms of websites, apps, and wearables was studied to gain insight into the use of these specific eHealth tools to improve health or provide support in coping with a disease. The digital divide levels that were taken into account are access and use. Access was measured by motivation and physical access. Motivation was measured by asking the respondents if they have used or would like to use websites, apps, and wearables for their health. For physical access, the respondents were asked if they have access to an electronic device with internet.
The digital divide concept use was measured by barriers in use, diversity of use, and frequency of use. For the concept barriers in use, the respondents were asked if they experienced barriers in the use of websites, apps, and wearables. The use of websites, apps, and wearables was further operationalized in 2 variables—diversity of use and frequency of use. For these variables, the respondents were asked about 16 different websites, apps, or wearables if they used the app (once or more than once). Diversity entails the variety of websites, apps, and wearables used, while the frequency of use operationalizes the number of times (more than once) the apps were used.
Statistical Analysis
Descriptive analyses were used to describe the demographics and the outcome of the variables measuring the access, use, and perceived benefit for eHealth in general and websites, apps, and wearables. The variables measuring access, use, and perceived benefit were constructed by combining items from the original questionnaire. The operationalization of these variables can be found in Operationalization of access, use, and perceived benefit of eHealth in general and websites, apps, and wearables.Multimedia Appendix 1
The outcomes were stratified by the 3 variables of SEP—educational level, standardized income, and SES level of the neighborhood. The differences in eHealth access and usage between SEP populations were investigated using logistic regression analysis. Ordered logistic regression was used for testing the differences in perceived benefit between SEP populations. Age was included in the analysis to test for confounding, as age is associated with both health and familiarity and use of digital media [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. Apr 27, 2017;19(4):e136. [FREE Full text] [CrossRef] [Medline]10,Elena-Bucea A, Cruz-Jesus F, Oliveira T, Coelho PS. Assessing the role of age, education, gender and income on the digital divide: evidence for the European union. Inf Syst Front. 2020;23(4):1007-1021. [CrossRef]58,Reiners F, Sturm J, Bouw LJW, Wouters EJM. Sociodemographic factors influencing the use of eHealth in people with chronic diseases. Int J Environ Res Public Health. 2019;16(4):645. [FREE Full text] [CrossRef] [Medline]59]. The correlation between the independent variables was determined via the Spearman rank order correlation coefficient. Data analysis was conducted using the Stata Statistical Software release (version 16.1; StataCorp) [Stata statistical software: release 16. StataCorp. 2019. URL: https://www.stata-press.com/data/r16/ [accessed 2024-06-20]
60]. A P<.05 was considered statistically significant. Variables of physical access and diversity of use were not included in the logistic regression analysis because there were too few cases in outcome categories to meet the assumptions of the logistic regression analysis. The univariate outcomes are presented in Frequency table of the outcomes of access, use, and perceived benefit for eHealth in general and websites, apps, and wearables gathered via a questionnaire (2021).Multimedia Appendix 2
Results
Sample Characteristics
In total, 849 panel members responded to the questionnaire, resulting in a response rate of 56.6% (849/1500). Among the panel population of 1500, 8.9% (133/1500), 40% (600/1500), and 48.7% (731/1500) had a low, medium, and high level of education, respectively. Of these groups, 55.6% (74/133), 59.3% (356/600), and 54.3% (397/731) responded to the questionnaire. Regarding standardized income 35.8% (537/1500), 31.8% (477/1500), and 27.9 (419/1500) had low, medium, and high levels of standardized income, respectively. Of these groups, 57.2% (307/537), 56.4% (269/477), and 54.9% (230/269) had responded to the questionnaire. Finally, for SES-level of the neighborhood, 42.8% (642/1500), 32.6% (489/1500), and 23.1% (346/1500) had low, medium, and high levels of SES-level of the neighborhood in the panel population, respectively. Of these groups, 56.1% (360/642), 58.1% (284/489), and 55.2% (191/346) responded to the questionnaire.
The demographics of the study population can be found in Table 1. An overview of the study population stratified by education, standardized income, and SES level of the neighborhood can be found in Demographic description of the study population and the study population stratified by levels of education, standardized income, and SES level of the neighborhood. Frequency table of males and females aged above 18 in three age categories of the Dutch general population (2021) and the study population (2021).Multimedia Appendix 3
Multimedia Appendix 4
Total (n=849) | |||
Sex, n (%) | |||
Male | 413 (48.6) | ||
Female | 435 (51.2) | ||
Missing | 1 (0) | ||
Age (years) | |||
Mean (SD) | 54 (16.96) | ||
Range | 19-92 | ||
Average household size | |||
Mean (SD) | 2.29 (1.11) | ||
Range | 1-7 | ||
Number of households with children younger than 18 years, n (%) | |||
No | 604 (71.1) | ||
Yes | 235 (27.7) | ||
Missing | 10 (1.2) | ||
Number of households with children older than 18 years, n (%) | |||
No | 763 (89.9) | ||
Yes | 76 (9) | ||
Missing | 10 (1.2) | ||
Education | |||
Mean (SD) | 2.39 (0.65) | ||
Range | 1-3 | ||
Low, n (%) | 74 (8.7) | ||
Medium, n (%) | 356 (41.9) | ||
High, n (%) | 397 (46.8) | ||
Missing, n (%) | 22 (2.6) | ||
Standardized income | |||
Mean (SD) | 1.90 (0.81) | ||
Range | 1-3 | ||
Low, n (%) | 307 (36.2) | ||
Medium, n (%) | 269 (31.7) | ||
High, n (%) | 230 (27.1) | ||
Missing, n (%) | 43 (5.1) | ||
SESb level of the neighborhood | |||
Mean (SD) | 1.80 (0.79) | ||
Range | 1-3 | ||
Low, n (%) | 360 (42.4) | ||
Medium, n (%) | 284 (33.5) | ||
High, n (%) | 191 (22.5) | ||
Missing, n (%) | 14 (1.7) |
aEducation level—low (none, primary school or prevocational education); medium (secondary or vocational education level 1, 2, 3, or 4); and high (professional higher education or university). Standardized income was divided into 3 categories—low (between €0 and €1659 [US $1718.56] per month); medium (between €1660 [US $1719.59] and €2332 [US $2415.72] per month); and high (more than €2332 [US $2415.72] per month). The SES level of the neighborhood was determined using the Social Economic Status-Wealth Education Employment (SES-WOA) score (2019) from Statistics Netherlands. The SES-WOA score was based on the wealth, educational status, and recent employment history of households in the neighborhood [Statusscore per wijk en buurt o.b.v. welvaart, opleidingsniveau en arbeid. Centraal Bureau van de Statistiek. 2022. URL: https://www.cbs.nl/nl-nl/achtergrond/2022/14/statusscore-per-wijk-en-buurt-o-b-v-welvaart-opleidingsniveau-en-arbeid [accessed 2022-08-01] 55,Sociaal-economische status; scores per wijk en buurt, regio-indeling 2021. Centraal Bureau van de Statistiek. 2022. URL: https://www.cbs.nl/nl-nl/cijfers/detail/85163NED [accessed 2022-08-01] 56]. Categories: low (first tertile of SES score: –0.89 to 0.042); medium (second tertile of SES score: 0.043-0.21); and high (third tertile of SES score: 0.21-0.71). Not all values add up to 100% due to missing values.
bSES: socioeconomic status.
Results of the Relation Between the Digital Divide Levels and the Socioeconomic Position Indicators
Overview
An overview of the measured and analyzed variables can be found in Figure 1. The frequencies of these outcomes can be found in Frequency table of the outcomes of access, use, and perceived benefit for eHealth in general and websites, apps, and wearables gathered via a questionnaire (2021). Results from logistic (ordered) regression analysis.Multimedia Appendix 2
Multimedia Appendix 5
Total | Education | Standardized income | SES level of the neighborhood | ||||||||||||||||||||
Low | Medd | High | Low | Med | High | Low | Med | High | |||||||||||||||
Population, n | 849 | 74 | 356 | 397 | 307 | 269 | 230 | 360 | 284 | 191 | |||||||||||||
eHealth in general | |||||||||||||||||||||||
Access—motivation, n (%) | |||||||||||||||||||||||
No motivation | 386 (45.5) | 46 (62.2) | 177 (49.7) | 152 (38.3) | 153 (49.8) | 124 (46.1) | 90 (39.1) | 174 (48.3) | 141 (49.6) | 68 (35.6) | |||||||||||||
Motivation | 401 (47.2) | 21 (28.4) | 153 (43.0) | 218 (54.9) | 131 (42.7) | 127 (47.2) | 123 (53.5) | 157 (43.6) | 127 (44.7) | 107 (56) | |||||||||||||
Perceived benefit—perceived benefit, n (%) | |||||||||||||||||||||||
Totally disagree—disagree | 211 (24.9) | 17 (23.0) | 80 (22.4) | 108 (27.2) | 70 (22.8) | 68 (25.3) | 65 (28.3) | 85 (23.6) | 76 (26.7) | 46 (24.1) | |||||||||||||
Not agree nor disagree | 370 (43.6) | 33 (44.5) | 171 (48.0) | 159 (40.1) | 144 (46.9) | 119 (44.2) | 89 (38.7) | 158 (43.9) | 134 (47.2) | 75 (39.3) | |||||||||||||
Agree—totally agree | 205 (24.1) | 17 (23.0) | 81 (22.8) | 104 (26.2) | 69 (22.5) | 65 (24.2) | 61 (26.5) | 87 (24.2) | 61 (21.5) | 53 (27.7) | |||||||||||||
Websites, apps, and wearables | |||||||||||||||||||||||
Access—motivation, n (%) | |||||||||||||||||||||||
No motivation | 143 (16.8) | 35 (47.3) | 62 (17.4) | 41 (10.3) | 59 (19.2) | 43 (16.0) | 34 (14.8) | 65 (18.1) | 50 (17.6) | 23 (12.0) | |||||||||||||
Motivation | 636 (75.0) | 29 (39.2) | 265 (74.4) | 328 (82.6) | 221 (72.0) | 206 (76.6) | 178 (77.4) | 263 (73.1) | 215 (75.7) | 150 (78.5) | |||||||||||||
Use—barriers in use, n (%) | |||||||||||||||||||||||
Experienced barriers | 339 (40.0) | 29 (39.2) | 140 (39.3) | 159 (40.1) | 135 (44.0) | 109 (40.5) | 76 (33.0) | 145 (40.3) | 119 (41.9) | 71 (37.2) | |||||||||||||
No experienced barriers | 472 (55.6) | 39 (52.7) | 201 (56.5) | 223 (56.2) | 157 (51.1) | 149 (55.4) | 145 (63.0) | 197 (54.7) | 156 (54.9) | 110 (57.6) | |||||||||||||
Use—frequency of use, n (%) | |||||||||||||||||||||||
No frequent use | 131 (15.4) | 29 (39.2) | 60 (16.9) | 38 (9.6) | 59 (19.2) | 36 (13.4) | 29 (12.6) | 57 (15.8) | 47 (16.5) | 24 (12.6) | |||||||||||||
Frequent use | 662 (78.0) | 34 (45.9) | 274 (77.0) | 339 (85.4) | 222 (72.3) | 219 (81.4) | 189 (82.2) | 277 (76.9) | 226 (79.6) | 149 (78.0) |
aSES: socioeconomic status.
bThe variables physical access and diversity of use for websites, apps, and wearables were not presented in Table 2 because these variables could not meet the assumptions for logistic regression analysis. The frequencies of these outcomes can be found in Frequency table of the outcomes of access, use, and perceived benefit for eHealth in general and websites, apps, and wearables gathered via a questionnaire (2021).Multimedia Appendix 2
cNot all values add up to 100% due to missing values.
dMed: medium level.
Access and Motivation
For eHealth in general, as well as for websites, apps and wearables, differences in motivation were found between different levels of education. Differences in motivation were most profound between low or medium versus highly educated respondents in both eHealth in general (odds ratio [OR] 2.18, 95% CI 1.22-3.88) and websites, apps, and wearables (OR 5.72, 95% CI 3.06-10.72). Regarding standardized income, a difference in motivation for eHealth in general was found between high and low standardized incomes (OR 1.52, 95% CI 1.05-2.21). A significant difference in motivation between low (OR 1.74, 95% CI 1.19-2.55) and medium (OR 1.63, 95% CI 1.1-2.43) versus high SES level of the neighborhood was found for eHealth in general.
Use, Barriers in Use, and Experienced Barriers
High standardized income was associated with no experience of barriers in use in comparison to low and medium levels of standardized income. This implies that fewer respondents with a high standardized income experienced barriers while using eHealth websites, apps, and wearables (OR 1.60, 95% CI 1.11-2.31). The frequency of eHealth use also differed between respondents with a low, medium, or high level of education, with the most significant difference between high and low educational levels (OR 4.96, 95% CI 2.66-9.24). In terms of standardized income, there were significant differences between low and high (OR 1.76, 95% CI 1.06-2.91) and low and medium (OR 1.74, 95% CI 1.09-2.79) standardized income levels. High SEP respondents were more likely to frequently use an eHealth app, website, or wearable compared to medium or low SEP respondents.
Perceived Benefit
There were no significant differences found regarding the perceived benefits between low, medium, or high-SEP populations.
Discussion
Principal Findings
This study shows that low SEP respondents have less access to and use of eHealth compared to medium or high SEP respondents. The most significant digital divide observed in this study is related to educational background. The results of this paper contribute on a population level to previous findings that the access and use of eHealth has a socioeconomic gradient. Additionally, the results emphasize that SEP indicators cannot be used interchangeably to assess eHealth access and use.
The results of this study highlight that, across all 3 SEP indicators, the most substantial differences are found in access through motivation. Respondents from higher socioeconomic categories expressed greater motivation to use eHealth, including websites, apps, and wearables, in comparison to those from lower SEPs. Health equity researchers emphasize that comprehending how people perceive health and health care is a complex issue influenced by several societal, contextual, social, and biological individual factors. Therefore, a multidimensional and multicausal approach is necessary to comprehend these disparities [Mackenbach JP. The persistence of health inequalities in modern welfare states: the explanation of a paradox. Soc Sci Med. 2012;75(4):761-769. [CrossRef] [Medline]24,Øversveen E, Rydland HT, Bambra C, Eikemo TA. Rethinking the relationship between socio-economic status and health: making the case for sociological theory in health inequality research. Scand J Public Health. 2017;45(2):103-112. [CrossRef] [Medline]63,Eikemo TA, Øversveen E. Social inequalities in health: challenges, knowledge gaps, key debates and the need for new data. Scand J Public Health. 2019;47(6):593-597. [CrossRef] [Medline]64]. Weiss et al [Weiss D, Rydland HT, Øversveen E, Jensen MR, Solhaug S, Krokstad S. Innovative technologies and social inequalities in health: a scoping review of the literature. PLoS One. 2018;13(4):e0195447. [FREE Full text] [CrossRef] [Medline]28] discuss existing literature and multiple theories as to why differences in eHealth access and use exist between socioeconomic groups. The literature described that the social position of individuals and the influence of the context and organizations surrounding the individual play a role in whether individuals choose to consume digital health care or not [Weiss D, Rydland HT, Øversveen E, Jensen MR, Solhaug S, Krokstad S. Innovative technologies and social inequalities in health: a scoping review of the literature. PLoS One. 2018;13(4):e0195447. [FREE Full text] [CrossRef] [Medline]28]. Other literature emphasized that the diffusion of digital health care in society will decrease the digital divide gap as the low-SEP population is assumed to be the latest to adopt [Weiss D, Rydland HT, Øversveen E, Jensen MR, Solhaug S, Krokstad S. Innovative technologies and social inequalities in health: a scoping review of the literature. PLoS One. 2018;13(4):e0195447. [FREE Full text] [CrossRef] [Medline]28]. However, the role of health care organizations, social policies, and political decisions that impact individuals’ motivations toward eHealth has not been adequately studied. Current national Dutch policies concerning eHealth are focused on the development and interoperability of eHealth apps, the digital skills of health care professionals and the use of eHealth by older people at home, resulting in eHealth becoming an essential part of the health care system [Stimuleren gebruik e-health. Ministry of Public Health, Wellbeing and Sports. URL: https://www.rijksoverheid.nl/onderwerpen/e-health/overheid-stimuleert-e-health [accessed 2023-09-08] 65]. In countries where health care digitalization is progressing, it would be valuable to examine the potential influence of the government, health care organizations, and businesses on the motivation of low socioeconomic populations toward eHealth. Such research could offer valuable insights into the societal and policy changes required to make eHealth more appealing to low-SEP populations.
In this study, use was examined by studying the barriers in use and frequency of use of eHealth websites, apps, and wearables. The results demonstrated that respondents with a high standardized income level infrequently experienced barriers to using eHealth websites, apps, and wearables compared to lower standardized income levels. Previous studies show that highly educated people often have higher health literacy levels and digital skills and are more in contact with the digital world via their education or profession [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. Apr 27, 2017;19(4):e136. [FREE Full text] [CrossRef] [Medline]10,Hansen AH, Bradway M, Broz J, Claudi T, Henriksen Ø, Wangberg SC, et al. Inequalities in the use of eHealth between socioeconomic groups among patients with type 1 and type 2 diabetes: cross-sectional study. J Med Internet Res. 2019;21(5):e13615. [FREE Full text] [CrossRef] [Medline]31,Reiners F, Sturm J, Bouw LJW, Wouters EJM. Sociodemographic factors influencing the use of eHealth in people with chronic diseases. Int J Environ Res Public Health. 2019;16(4):645. [FREE Full text] [CrossRef] [Medline]59,Hiltz SR, Turoff M. Education goes digital: the evolution of online learning and the revolution in higher education. Commun ACM. 2005;48(10):59-64. [CrossRef]66-Vasilescu MD, Serban AC, Dimian GC, Aceleanu MI, Picatoste X. Digital divide, skills and perceptions on digitalisation in the European union-towards a smart labour market. PLoS One. 2020;15(4):e0232032. [FREE Full text] [CrossRef] [Medline]70]. Other studies show that in the development of eHealth new apps are often pilot-tested by highly educated respondents and, therefore, might be more tailored to the needs of highly educated individuals [Goedhart NS, Zuiderent-Jerak T, Woudstra J, Broerse JEW, Betten AW, Dedding C. Persistent inequitable design and implementation of patient portals for users at the margins. J Am Med Inform Assoc. Feb 15, 2021;28(2):276-283. [FREE Full text] [CrossRef] [Medline]9,Wagenaar KP, Hakim N, Broekhuizen BDL, Jaarsma T, Rutten FH, Hoes AW. Representativeness of participants in heart failure E-health trials: a report from the E-vita HF study. J Card Fail. 2017;23(1):88-89. [FREE Full text] [CrossRef] [Medline]71,Al-Dhahir I, Reijnders T, Faber JS, van den Berg-Emons RJ, Janssen VR, Kraaijenhagen RA, et al. The barriers and facilitators of eHealth-based lifestyle intervention programs for people with a low socioeconomic status: scoping review. J Med Internet Res. 2022;24(8):e34229. [FREE Full text] [CrossRef] [Medline]72]. In contrast, other studies point out the variety of health behaviors and health care use within SEP groups. De Boer et al [de Boer WIJ, Dekker LH, Koning RH, Navis GJ, Mierau JO. How are lifestyle factors associated with socioeconomic differences in health care costs? Evidence from full population data in the Netherlands. Prev Med. 2020;130:105929. [FREE Full text] [CrossRef] [Medline]73] show that low SEP groups have more health care costs but that healthy lifestyle behavior such as smoking or being member of a sports club are attributed greatly to the variety in health care use in each socioeconomic group. In the light of eHealth, Agachi et al [Agachi E, Bijmolt THA, Mierau JO, van Ittersum K. Adoption of the website and mobile app of a preventive health program across neighborhoods with different socioeconomic conditions in the Netherlands: longitudinal study. JMIR Hum Factors. 2022;9(1):e32112. [FREE Full text] [CrossRef] [Medline]74] showed that the user interface and the type of eHealth offered attributes to the use of eHealth between socioeconomic groups. Results revealed that for the same eHealth program, people living in a low socioeconomic neighborhood use the app-based tool more than people living high in a socioeconomic neighborhood. For the web-based version, results show the opposite emphasizing the importance of the user interface and the accessibility to digital devices, as is also theorized in the digital divide model [Vrooman J, Boelhouwer J, Iedema J, van der T. Summary contemporary inequality. The Netherlands Institute for Social Research. 2023. URL: https://english.scp.nl/publications/publications/2023/03/07/summary-contemporary-inequality [accessed 2023-05-09] 49,Agachi E, Bijmolt THA, Mierau JO, van Ittersum K. Adoption of the website and mobile app of a preventive health program across neighborhoods with different socioeconomic conditions in the Netherlands: longitudinal study. JMIR Hum Factors. 2022;9(1):e32112. [FREE Full text] [CrossRef] [Medline]74]. Both De Boer [de Boer WIJ, Dekker LH, Koning RH, Navis GJ, Mierau JO. How are lifestyle factors associated with socioeconomic differences in health care costs? Evidence from full population data in the Netherlands. Prev Med. 2020;130:105929. [FREE Full text] [CrossRef] [Medline]73] and Agachi [Agachi E, Bijmolt THA, Mierau JO, van Ittersum K. Adoption of the website and mobile app of a preventive health program across neighborhoods with different socioeconomic conditions in the Netherlands: longitudinal study. JMIR Hum Factors. 2022;9(1):e32112. [FREE Full text] [CrossRef] [Medline]74] show that behavior-related and technical-related factors play a role in the use of health care and eHealth. The results of this study and other studies showed that understanding and creating insight into the existence of and possible solutions for health disparities is dependent on multiple dimensions. Future research into how the socioeconomic gradient in eHealth access and use are associated with other behavioral and technical factors is important to create an in-depth understanding of disparities in eHealth access and use that can inspire research, policy, and practice.
Results pointed out that high education and high standardized income levels were associated with frequent use of websites, apps, and wearables. This is in accordance with previous research, which shows that a high level of education and income is found to be associated with more access to and use of eHealth [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. Jul 16, 2014;16(7):e172. [FREE Full text] [CrossRef] [Medline]12,Perez SL, Kravitz RL, Bell RA, Chan MS, Paterniti DA. Characterizing internet health information seeking strategies by socioeconomic status: a mixed methods approach. BMC Med Inform Decis Mak. Aug 09, 2016;16:107. [CrossRef] [Medline]13,Hansen AH, Bradway M, Broz J, Claudi T, Henriksen Ø, Wangberg SC, et al. Inequalities in the use of eHealth between socioeconomic groups among patients with type 1 and type 2 diabetes: cross-sectional study. J Med Internet Res. 2019;21(5):e13615. [FREE Full text] [CrossRef] [Medline]31]. Surprisingly, no difference was found in the perceived benefits (making more conscious decisions in health because of eHealth use) in any of the SEP indicators, although the frequency of use of websites, apps, and wearables was high (78%) and did show significant differences. This implies that respondents with more frequent use of websites, apps, and wearables had the same perceived benefit, namely, making more conscious decisions due to eHealth use, compared to respondents who have not used websites, apps, and wearables once. Evaluation studies show that high SEP respondents have better outcomes from eHealth use than low SEP respondents [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. Apr 27, 2017;19(4):e136. [FREE Full text] [CrossRef] [Medline]10,Western MJ, Armstrong MEG, Islam I, Morgan K, Jones UF, Kelson MJ. The effectiveness of digital interventions for increasing physical activity in individuals of low socioeconomic status: a systematic review and meta-analysis. Int J Behav Nutr Phys Act. 2021;18(1):148. [FREE Full text] [CrossRef] [Medline]30,Hansen AH, Bradway M, Broz J, Claudi T, Henriksen Ø, Wangberg SC, et al. Inequalities in the use of eHealth between socioeconomic groups among patients with type 1 and type 2 diabetes: cross-sectional study. J Med Internet Res. 2019;21(5):e13615. [FREE Full text] [CrossRef] [Medline]31,Reiners F, Sturm J, Bouw LJW, Wouters EJM. Sociodemographic factors influencing the use of eHealth in people with chronic diseases. Int J Environ Res Public Health. 2019;16(4):645. [FREE Full text] [CrossRef] [Medline]59]. The results of this study might indicate that in the real-life context, even though current eHealth apps might be more suitable for highly educated individuals, eHealth is not used appropriately or with similar discipline as in the clinical trial context.
Strengths and Limitations
The strengths of this study were the use of a large and representative sample of the Dutch population and the use of 3 SEP operationalizations to provide a broad insight into the effects of SEP on the digital divide. This study also has some limitations. Despite a large number of respondents, the skewed distribution of outcomes across SEP levels hampered the performance of multivariable logistic regression analysis [Akoglu H. User's guide to correlation coefficients. Turk J Emerg Med. 2018;18(3):91-93. [FREE Full text] [CrossRef] [Medline]62]. Next to this, the SEP indicators used are focused on social demographic and economic aspects of SEP. Cultural and other social aspects, such as social networks and cultural background, have not been taken into account.
The questionnaire used in the study was not designed to measure all the different aspects of the digital divide model and is a secondary analysis of the data gathered. Although the majority of digital divide levels could be well matched with questionnaire items, data on the second level digital divide for eHealth in general and the third digital divide level for websites, apps, and wearables was lacking. In some cases, with emphasis on the concept barriers in use for websites, apps, and wearables more in-depth insights into the digital divide levels would have been desirable to improve the validation of the findings. Van Dijk [Van Dijk J. The Digital Divide. Cambridge. Polity Press; 2020. 57] provides concepts to further define the digital divide levels. Access, the first level of digital divide, is described to entail the concepts of motivational access and physical access. Use, the second level of digital divide entails the concepts of digital skills and usage. Usage here encompasses both frequency and diversity of digital media use. Perceived benefit, the third level digital divide, is conceptualized by personal outcomes that are a result of the use of digital media [Van Dijk J. The Digital Divide. Cambridge. Polity Press; 2020. 57]. In this study, the concept barriers in use were used instead of digital skills, as experienced barriers are not limited to barriers formed by a lack of digital skills.
Additionally, for the first and the second level digital divide, the technical design and the information and communication technology of eHealth are of importance [Van Dijk J. The Digital Divide. Cambridge. Polity Press; 2020. 57]. The technical design and the information and communication technology imply factors such as accessibility, usability, mobility, quality, and accessibility of internet access and automation (self-learning devices or software tailored to serve the consumer better and automatically) of devices and apps. These factors are important to facilitate adherence and appropriate use of eHealth apps [Van Dijk J. The Digital Divide. Cambridge. Polity Press; 2020. 57]. The questionnaire provided no insight into these factors.
Conclusions
The results of this study revealed that differences in motivation for eHealth use are most profound between different socioeconomic populations in the Dutch society, in which low-educated people are likely to be disadvantaged. A successful transition toward digital health care is a social issue that is dependent on the motivation to use eHealth and specific apps. It is imperative that future studies within academia and within the health care field focus on the motivations and needs associated with digital health care, specifically for low-SEP populations. Research on the societal changes stemming from the digital health care transition and the technical and design studies of digital care apps in single-intervention studies are both vital for the realization of an inclusive and comprehensive digital health care system. If eHealth takes a predominant role in the Dutch health care system, it might affect access and use of health care for the citizens who need it the most. The results of this study underscore the importance of policies aimed at facilitating and supporting low-SEP populations in the use of eHealth to reduce differences in health.
Acknowledgments
The authors would like to thank E de Vries and L van Deursen for providing comments and suggestions. This study was funded by the Dutch Ministry of Health, Welfare, and Sport. The funders had no role in the study design, data collection and analysis, data interpretation, writing of the paper, or approval for publication.
Data Availability
The datasets generated and analyzed during this study are available upon reasonable request from Prof Judith D de Jong (j.dejong@nivel.nl), project leader of the Dutch Health Care Consumer Panel, or the panel’s secretary (conusmentenpanel@nivel.nl).
Conflicts of Interest
None to declare.
Multimedia Appendix 1
Operationalization of access, use, and perceived benefit of eHealth in general and websites, apps, and wearables.
DOCX File , 24 KBMultimedia Appendix 2
Frequency table of the outcomes of access, use, and perceived benefit for eHealth in general and websites, apps, and wearables gathered via a questionnaire (2021).
DOCX File , 24 KBMultimedia Appendix 3
Demographic description of the study population and the study population stratified by levels of education, standardized income, and SES level of the neighborhood.
DOCX File , 31 KBMultimedia Appendix 4
Frequency table of males and females aged above 18 in three age categories of the Dutch general population (2021) and the study population (2021).
DOCX File , 14 KBReferences
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Abbreviations
DHCCP: Dutch Health Care Consumer Panel |
OR: odds ratio |
SEP: socioeconomic position |
SES-WOA: Social Economic Status-Wealth Education Employment |
SES: socioeconomic status |
Edited by A Mavragani; submitted 03.06.23; peer-reviewed by J Mierau, H Traino, PhD, MPH, M Earl; comments to author 29.08.23; revised version received 02.10.23; accepted 21.07.24; published 07.01.25.
Copyright©Lucille Standaar, Lilian van Tuyl, Anita Suijkerbuijk, Anne Brabers, Roland Friele. Originally published in JMIR Formative Research (https://formative.jmir.org), 07.01.2025.
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