544047

research-article2014

JHI0010.1177/1460458214544047Health Informatics Journal X(X)Zettel-Watson and Tsukerman

Article

Adoption of online health management tools among healthy older adults: An exploratory study

Health Informatics Journal 2016, Vol. 22(2) 171­–183 © The Author(s) 2014 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav DOI: 10.1177/1460458214544047 jhi.sagepub.com

Laura Zettel-Watson and Dmitry Tsukerman California State University, Fullerton, USA

Abstract As the population ages and chronic diseases abound, overburdened healthcare systems will increasingly require individuals to manage their own health. Online health management tools, quickly increasing in popularity, have the potential to diminish or even replace in-person contact with health professionals, but overall efficacy and usage trends are unknown. The current study explored perceptions and usage patterns among users of online health management tools, and identified barriers and barrier-breakers among nonusers. An online survey was completed by 169 computer users (aged 50+). Analyses revealed that a sizable minority (37%) of participants use online health management tools and most users (89%) are satisfied with these tools, but a limited range of tools are being used and usage occurs in relatively limited domains. Improved awareness and education for online health management tools could enhance people’s abilities to remain at home as they age, reducing the financial burden on formal assistance programs.

Keywords consumer health information, data security and confidentiality, ehealth, electronic health records, health information on the Web, information and knowledge management

Introduction In the United States, 80 percent of people aged 65 and older live with at least one chronic disease, and many older adults have multiple chronic conditions.1 In the United States, chronic diseases such as hypertension, diabetes, and heart disease are responsible for about 75 percent of the US$2.7 trillion spent on healthcare each year, costing nearly US$2 trillion annually.2,3 It is estimated that over 37 million Baby Boomers (born 1946–1964) will be managing more than one chronic disease by 2030.4 The American healthcare system is not equipped to handle the projected number of patients or their healthcare needs or costs. Individuals increasingly will be called upon to assist in Corresponding author: Laura Zettel-Watson, Department of Psychology, California State University, Fullerton, P.O. Box 6846, Fullerton, CA 92834-6846, USA. Email: [email protected]

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managing their own healthcare needs. The emergence of online health management tools has provided consumers with a low-cost and accessible means to take control of their own health. Online health management tools are web-based programs that assist users with organizing their health records, communicating with their doctors, monitoring their health trends, making treatment decisions, and sharing their health information with healthcare providers and loved ones.5 Proprietary tools are usually offered through the websites of hospitals, insurance companies, or physicians, and a number of free tools are available from various sources (e.g. Microsoft HealthVault). Considering that online health management tools are already in existence and increasing in popularity,6 it should be expected that such Internet-based technologies will continue to thrive as the number of older adults embracing technology grows. As of December 2012, 77 percent of adults aged 50 to 64 were using the Internet, as were 54 percent of adults over 65.7 Moreover, 32 percent of adults aged 50–64 years, and 12 percent of adults over age 65 years, reported owning a smart phone.8 These numbers illustrate the growing interest in and usage of technology among older adults. Predicting the acceptance of online health management tools has become a popular theme in the healthcare literature, though much of this literature features acceptance models for physicians, nurses, and other medical personnel.9,10 However, the focus is expanding to understanding the acceptance of home users of this technology.11 Given the rising prevalence of chronic conditions, the aging of our population, and the importance of adequate healthcare for all, it is essential to understand the nature and usage of health service provision alternatives such as online health management tools. The primary objective of the present study was to explore perceptions and usage patterns among users of online health management tools, and to identify among non-users reasons related to not using online tools (i.e. barriers). This inquiry utilized data that have been collected from 169 members of a university-affiliated chapter of the Osher Lifelong Learning Institute (OLLI; described in detail below). Participants were over the age of 50 (M = 70.8) and regular users of computer and Internet technology, defined here as possessing an active email account and having the ability to complete an online survey. We collected data on usage patterns, needs, wants, and barriers to using online health management tools to better understand the usage of online health management tools.

Methods Participants and procedures Participants were recruited from a university-affiliated center for lifelong learning. The OLLI is a nationwide network of lifelong learning programs associated with over 100 universities in the United States.12 Members, individuals over 50, join OLLI organizations in search of intellectual stimulation through educational courses and social interactions. Compared to the average older American, OLLI members tend to be more educated and have higher incomes. In our sample, most had college educations or advanced degrees and the median household income was nearly US$80,000 per year. Individuals with higher socioeconomic status (SES) are more likely to be early adopters of technology due to having the resources to try new things and the ability to absorb any risks or costs associated with failure.13 Computer-using OLLI members therefore were targeted as participants in the current study because their characteristics suggest their behaviors may be indicators of trends in technology use among older adults.14,15 A total of 169 OLLI members (about 30% of those targeted) participated. To be included in the study, participants needed to be over the age of 50 and a competent computer user (i.e. active email user with the ability to complete an online survey). The computer-user requirement was included to prevent lack of computer familiarity from impacting the results.

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An online survey was created using Google Spreadsheet. Online data collection was chosen as it (1) uses the same medium as the technology of interest and (2) has support as a viable method of data collection.16,17 After receiving approval from the university’s Institutional Review Board and the OLLI chapter’s board of directors, the survey was launched using an email message sent to all OLLI members with an email account. A second reminder was sent out a week later. The online survey was closed after 4 weeks. The survey data set was then converted into SPSS for analysis. Although the original survey included three versions of the questionnaire (depending on usage of online health management tools: current users, former users, and non-users), only three participants were classified as former users; they were removed from analysis due to small sample size. Thus, this report includes data from 166 current users and non-users.

Measures Demographics.  All participants answered questions regarding demographic factors (e.g. gender, age, race/ethnicity, marital status, education, income, employment). Participants provided information on perceived health status, health conditions (yes/no to 28 medical diagnoses), and healthcare utilization (i.e. frequency of visits to various providers in the past year). Two published scales also were included. The first, the Perceived Health Competence Scale,18 is an 8-item scale used to assess health self-efficacy. The second, the Multidimensional Health Locus of Control scale19 (Form B), is an 18-item scale with subscales reflecting three dimensions of health locus of control: internal (Cronbach’s alpha = .72), chance (Cronbach’s alpha = .66), and powerful others (Cronbach’s alpha = .64). All participants answered the question, “Have you ever used any online health management tool to store your medical records or to manage your health?” Those answering “Yes, and I continue to use such a tool” were directed to a series of questions for Current Users. Those answering “No” were directed to a section for Non-Users. Questions in both of these sections were researcher-generated. Current users.  Current users were asked closed- and open-ended questions assessing: type of tool used, purpose of use, length of use, factors influencing initial and continued use, perceived usefulness and ease of use, whether access is given to others, and perceptions/attitudes toward the technology. The content of the individual items can be viewed in Tables 2 to 4. Non-users.  Non-users were asked about the reasons contributing to their decisions not to use online tools. They responded to a series of questions regarding their reasons for not using these tools and factors that might encourage them to try such tools. Specific items can be found in Table 5.

Results Sample demographic characteristics Of the 166 participants, 102 (61%) were female. Participants ranged in age from 50 to 87 (M = 70.8 years; standard deviation (SD) = 8.0), and they were primarily Caucasian (89%), married (64.5%), and retired (82.5%). In terms of (former) occupation, 26 percent reported working in education, 10 percent in healthcare, and 10 percent in engineering. Most participants were highly educated (83.7% had at least a Bachelor’s degree) and median household income was high (just under US$80,000/year). Nearly all (93.8%) described their current health as good or better. All but one participant had Internet access at home and 95 percent of those with access reported frequent

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use. Thus, this was a high-SES, computer-savvy group of middle-aged and older adults, typifying OLLI membership.

Comparing users and non-users T-tests and chi-square analyses were conducted to investigate whether online health management tool users (37%) and non-users (62%) differed on demographic factors. In addition to being much more likely to keep their health records online, users were significantly more likely than non-users to be female, younger, and report a higher quality of life. See Table 1 for specific comparisons.

Users’ usage patterns and perceptions Among those who use online health management tools (see Table 2), the majority use tools provided by their doctor’s office or hospital or their insurance company. Length of use varied widely, from just over a month to over a decade, but the average length of use was just over 3 years. Most users report using the tool(s) for communication or records storage. Many users have given access to their physicians or spouses. As Table 2 shows, the online tools are used most frequently by participants to check their lab results, store records, be informed of health changes, or to manage chronic disease(s). The users are deriving a number of benefits from the tools as evidenced by benefits they experienced upon first use and continue to experience (see Table 3). Most find that the tools are useful to them, allow them to better manage their health, allow them to keep all their records in one place, and give them confidence that they are in control of their own health. Interestingly, no participants reported their doctor’s recommended use of the tool as being important now, although most cite this as important when they first adopted the technology. In terms of the perceptions they hold, as shown in Table 4, the majority of online health management tool users find the tools convenient, time saving, useful for the management of their health, and helpful for protection against lost records. Users reported relative ease of experience across a variety of uses, especially setting up their accounts, moving around in their accounts, and inputting their health history from paper records. When considering the impact the tools have had on their lives (Table 4), most users believe that the tools enhanced their communication with their physicians and enhanced their abilities to manage their health. They appear satisfied, find the tools easy to use, will continue using the tools, and are somewhat likely to recommend the tools to others. In terms of how the tools might be improved, analysis of an open-ended response item revealed that a frequently mentioned area for improvement was the desire to see an increase in the available functions of these tools. Suggested improvements included the desire to schedule appointments, to involve usage beyond viewing of test/lab results, and to include health information beyond basic health history. Although the majority of users (82.3%) believe they will never stop using the tools, the remaining users thought they might stop using if their doctors stop offering the tool, they become concerned with lost data or their privacy, they find the tool hard to use, or they no longer have a need for the tool (see Table 4).

Non-users’ barriers and barrier-breakers When asked for reasons why they were not using an online health management tool, nearly half of the non-users reported that they had never heard of them (see Table 5). Other prominent factors included their doctors not offering an online service and not knowing what benefits the tools

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Zettel-Watson and Tsukerman Table 1.  Demographic information by user status. Variables

Non-users, n = 104

Users, n = 62



%

%

Gender (% female) Age Ethnicity:  Caucasian   Asian/Pacific Islander  Hispanic/Latino  Other Marital status  Married  Widowed  Divorced   Never married Last grade completed (16 = bachelor’s degree) Income for the last year (8 = US$70,000 to US$79,999; 9 = US$80,000 to US$89,999) No. of children No. of children co-residing Co-residing with:   Spouse/life partner  Alone   Unrelated individual  Child  Relative Employment status:  Retired   Working part time  Homemaker   Looking for work   Working full time   On temporary leave   Permanently disabled  Other Covered by health insurance (% yes) Format of records:   Hard copy   Online/hard copy   Don’t keep   Online @doctors   Online @insurance Living will (% yes) Perceived healthb Quality of lifeb

55.9

M (SD)

p-valuea (* = significant) M (SD)

72.6



16.3 (1.5)

16.4 (1.3)

.032* .004* .093         .060         .636

8.2 (3.1)

8.5 (2.7)

.563

2.3 (1.4) 0.2 (0.4)

2.2 (1.4) 0.1 (0.3)

.580 .348 .235           .235                 .171 .000*           .118 .502 .003*

72.2 (7.6)

68.5 (8.0)

94 2.0 3.0 1.0

88.5 6.6 0 4.9

58.3 23.2 12.6 5.8

75.8 9.7 12.9 1.6

61.2 31.1 1.0 5.8 1.0

71.0 21.0 .0 3.2 4.8

82.7 4.8 3.8 3.8 1.0 1.0 .0 1.0 97.0

82.3 11.3 1.6 .0 3.2 .0 1.6 .0 100.0

52.4 8.7 32.0 6.8 .0 76.9

9.7 51.6 3.2 21.0 14.5 86.9 3.7 (.9) 4.1 (.8)

3.8 (.8) 4.5 (.6)

(Continued)

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Table 1. (Continued) Variables

Non-users, n = 104

Users, n = 62



%

%

No. of health conditions Health locus of control:   Internal locus   Chance locus   Powerful others locus Health self-efficacy

M (SD)

p-valuea (* = significant) M (SD)

2.7 (1.9)

2.7 (1.9)

21.0 (3.4) 14.3 (3.9) 19.2 (3.5) 31.0 (4.1)

21.3 (3.2) 13.6 (3.1) 18.3 (3.5) 31.1 (3.7)

  .895   .667 .218 .102 .908

SD: standard deviation. aBased on t-tests for continuous variables and chi-square analyses for nominal variables. b5 = excellent, 4 = very good, 3 = good, 2 = fair, 1 = poor.

provide. Some do not see the need or are concerned about Internet privacy/security. Only a few mentioned not having someone to show them how to use it or that they thought it seemed hard to use. The non-users rated several factors as potential barrier-breakers (see Table 5). These are factors that would be important if these participants were to be encouraged to try an online health management tool. Most critical was an assurance that their privacy would not be violated, closely followed by assurances that their information would not be lost and that their identity would not be stolen.

Discussion Users vs non-users More than a third (37%) of participants in the study reported using online health management tools. When comparing the users to the non-users, only a few demographic differences were found. Specifically, users were more likely than non-users to be younger, female, and married. Despite a general assumption that education is a relevant factor in adopting technology,20 education was not related to user status in the current sample. This may be due to the sample itself; OLLI members tend to be highly educated, resulting in very little educational variability. Also of interest, there were no differences in perceived health or number of health conditions between the two groups. Furthermore, users did not exhibit a higher level of internal control tendencies in terms of their health, nor did they possess a higher level of health self-efficacy (i.e. belief in ability to manage one’s health). These findings suggest that health status is not a major factor in the utilization of online technologies. Of course, health status may play more of a role in online health management tool utilization among individuals with a greater health management need.21

Users’ perceptions and usage patterns The satisfaction rate for users in this sample is strikingly high; nearly 9 in 10 current users are satisfied with the tools they are using. This could be a selection effect, where only those who are satisfied would continue to use the tools. However, the high satisfaction rate may be legitimate, given

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Zettel-Watson and Tsukerman Table 2.  Users’ usage patterns. Variables

%

Tool(s) used (%yes)  Doctor’s/hospital’s 64.5   Insurance company’s 46.8   Google Health 4.8   Mayo Clinic Embody Health 3.2   Weight Watchers Online 1.6   AHA Heart Profiler 1.6   USDA MyPyramid Tracker 1.6 Length of use (months) Currently using tool for (% yes)  Communication 85.5   Health records storage 74.2   Cholesterol monitoring 17.7 14.5   Blood pressure monitoring   Physical activity management 11.3   Glucose monitoring 9.7   Dietary program 8.1   Stress management 4.8 Allow access to (%yes)  Physician 74.2  Spouse 51.6   No one 29.0  Pharmacist 19.4 9.7   Adult children Frequency of use (if tool permits; 1 = never, 2 = when in need, 3 = regularly/ongoing)   Check lab results   Store all records in one place   Inform self of changes in my health   Manage chronic disease   New doctor use   Make appointments   Print records for new doctor   Connect w/other programs   Access for family   Upload data from a monitoring device   Import insurance statements   Print forms

M (SD)               38.8 (41.9)                           2.7 (0.6) 2.6 (0.6) 2.5 (0.5) 2.4 (0.7) 2.2 (0.6) 2.1 (0.5) 2.0 (0.6) 2.0 (0.8) 1.9 (0.6) 1.6 (0.7) 1.4 (0.7) 1.3 (0.6)

SD: standard deviation.

that out of the 169 computer-users surveyed, only 3 (1.7%) were classified as former users. High user satisfaction with health management tools is consistent with the literature.22,23 Approximately two-thirds of participants are using their doctors’ sites and most (84%) cite their doctors as being “very important” in their initial decision to adopt an online tool. This is consistent with previous studies showing that positive relationships with healthcare providers are linked with personal health record adoption.24 However, other research suggests the reverse, where positive provider—patient relationships have been associated with lower likelihoods of

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Table 3.  Benefits to users. Reasons for using/continuing to use tool

Not applicable %

Important NOW %

Useful to me Manage health better Records in one place Gives confidence of being in control of health Helps care team coordinate care Overview of changes Share data w/doctor Prevents unnecessary tests Monitor health trend Prevents medical accidents Don’t worry when away Track vital signs Share data w/loved ones Spouse recommends it Need records while away Adult child recommends it Relative/friend recommends it Doctor recommends it

3.3 9.4 8.6 17.6

3.3

21.2 21.4 25.5 29.1 30.8 35.3 32.1 37.7 56.3 73.5 73.5 95.8 97.9 15.8

Important AT BEGINNING % 1.6 1.7

2.0

3.8 3.8 2.1 4.1 6.1 2.1 84.2

Important THEN and NOW % 91.8 90.6 89.7 80.4 78.8 78.69 74.5 70.9 65.4 64.7 64.2 62.3 41.7 22.4 20.4 2.1 2.1  

SD: standard deviation.

personal health record adoption.25 In the current study, doctors’ early influences on patients’ usage appear significant, but no participants reported their doctors’ recommended use of the tool as being important now, suggesting that the tools themselves are what keep people using them. This finding coincides with previous findings that hands-on experience with online health tools improves users’ perceptions of the tools.26 Overall, users appear satisfied with the aspects of the tools they are using, but as a group, they are not using these tools to their fullest capabilities. Why these tools remain underutilized is unclear. Open-ended responses revealed a desire to see an increase in available functions, suggesting that users may not be fully aware of what is already available, or that the tools really are simpler than users would like. Kim and colleagues27 found that users of online personal health records use some categories of available tools more than others, and even vary in their usage patterns within each category. The authors speculated that some tool components may have a better user interface than others or that users may simply lack an understanding and proper recall of certain health information necessary for utilizing particular functions. Additional research is needed to investigate these possibilities. Many users have granted access to their online health information to their doctors and spouses, but fewer than 10 percent of users have granted access to an adult child. This discrepancy is contrary to expectation, but this very healthy and very independent group of aging adults may not yet rely on their adult children for care-giving. In a survey study of more than 18,000 veterans, more than half (62%) were willing to share their personal health records with a spouse or partner, but much fewer (23%) would grant access to a child; interestingly, older age, but not health status, was associated with a greater willingness to share access to personal health records.28

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Zettel-Watson and Tsukerman Table 4.  Users’ perceptions of tools. Variables

%

M (SD)

Value of tool: (%yes)  Convenient   90.3   Time saving 80.6     Useful for health management 75.8     Protection against lost records 69.4    Portable 43.5     Useful for sharing data 37.1     Sense of control over own data 35.5   Ease of experience (if applicable; 1 = very difficult, 2 = somewhat difficult, 3 = somewhat easy, 4 = very easy)   Set up account 3.7 (0.6)   Move around in account 3.6 (0.6)   Input health history from paper records 3.6 (1.0)   Import insurance statements 2.8 (1.3)   Upload data from device 3.0 (1.1)   Interpret data 3.4 (1.0)   Search health history 3.5 (0.8)   Print out health history 3.5 (0.8)   Permit others to access 3.5 (0.9) Impact/perceptions of the tool (0 = not at all, 1 = not much, 2 = neutral, 3 = somewhat, 4 = a great deal)   It changed the way health records are managed 2.6 (1.3)   Needed to change previous habits to use this tool 1.7 (1.2)   Ease of use 3.3 (1.0)   Meets health record needs 3.3 (1.0)   Enhances communication w/physician 3.5 (0.9)   Enhances ability to manage health 3.1 (0.9)   Improves confidence in chronic disease management 2.3 (1.6)   Satisfied w/tool 3.6 (0.7)   Likely to continue using tool 3.8 (0.5)   Likely to recommend tool to others 3.2 (1.3)   Frustrated w/tool 1.2 (1.2)   Tool improved quality of life 2.4 (1.2) Factors that would cause you to stop using   No, will never stop 82.3     Yes would stop if … 16.1   Stop using if (0 = not critical at all, 1 = somewhat critical, 2 = very critical)   Doctor stops offering 1.7 (0.7)   Concerned w/lost data 1.2 (0.9)   Concerned w/privacy 1.2 (0.8)   Hard to use 1.1 (0.7)   No need for tool anymore 1.0 (0.9) SD: standard deviation.

Barriers to usage The biggest barriers to using online health management tools for non-users appear to be a simple lack of knowledge (as opposed to active avoidance) and a concern for internet security and/or

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Table 5.  Non-users’ barriers and barrier-breakers. Variable

%

M (SD)

Reasons for not using an online tool: (% yes)   Never heard of it 46.2     Doctor doesn’t offer it 40.4     Don’t know what benefits it provides 39.4     Don’t see the need 26.0     Concerned about privacy/security 25.0    Other   14.4   No one to teach me how to use it 6.7   2.9     Seems hard to use Encouraged to try online tool if (0 = not important, 1 = somewhat important, 2 = very important)   Assured privacy not violated 1.7 (0.7)   Assured information won’t be lost 1.6 (0.7)   No worry about identity theft 1.6 (0.7)   Inform doctor of health 1.5 (0.7)   Gives a complete record and overview of health changes 1.4 (0.7)   Prevents medical accidents 1.4 (0.7)   Want to share data w/doctor 1.4 (0.7)   Made easy to use 1.4 (0.7)   Convinced it was useful 1.4 (0.7)   Helps care team coordinate 1.3 (0.8)   Doctor recommends it 1.3 (0.8)   Need records in one place 1.3 (0.8)   Helps manage health better 1.3 (0.7)   Helps track vital signs 1.2 (0.8)   Need to monitor health trend 1.1 (0.8)   Gives confidence that I am in control 1.1 (0.8)   Needed portable records when on road 1.0 (0.8)   Inform family/caregiver 1.0 (0.7)   Free classes to show me 0.9 (0.9)   Had help to set up tool 0.9 (0.8)   Have someone I trust to teach me 0.7 (0.8)   Can afford computer/Internet at home 0.7 (0.8)   Want to share date w/loved one 0.7 (0.8)  Other 0.6 (0.8)   Family recommends it 0.6 (0.7)   Relative recommends it 0.4 (0.6)   Friend recommends it 0.4 (0.5) SD: standard deviation.

privacy. The former can be addressed through education and exposure. One study11 found that social influence indirectly affected adoption through its effect on perceived usefulness, but the source of influence (i.e. friend, family, physician) was not explored. Given that 84.2 percent of users in the current study reported that their doctors’ recommendations influenced their initial use, it is likely that professional influences outweigh informal influences, but further research is

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necessary to confirm this. The second major concern, Internet security, will require a bit more effort, but security measures and assurances on the part of the service providers may go a long way. Much of the healthcare-specific literature on technology acceptance supports the increasing acceptance of online health information technology by the elderly29,30 and is consistent with the patterns of usage and perceptions found in this study. Here, a vast majority of users endorsed the tools as being useful to them (91.8%), as well as relatively easy to use. For non-users, barriers included not knowing the benefits of the tools (39.4%) and not seeing the need for these tools (26%), which seem to reflect a lack of perceived usefulness. Non-users’ concerns with the tools being difficult to use were minimal (2.9%). Similarly, several studies with health information technology found perceived usefulness to be a greater predictor of technology acceptance and satisfaction than was perceived ease of use.11,31,32 People are primarily drawn to a technology for how it functionally meets their needs, with usability being a secondary factor.33

Study limitations As a result of drawing from OLLI membership, the current sample was quite homogeneous (i.e. high SES and education levels, careers with technology exposure) and not representative of all older adults. Although this reduces external validity, OLLI members tend to fit the profile of early adopters of innovative technology13 and their behaviors may be indicative of budding trends in technology use among older adults. The modest sample size (N = 166) of this study, being further broken down into users and non-users for analyses, may have adversely affected statistical power. The questionnaire, aside from the inclusion of a few published scales, was primarily researcher-driven; reliability and validity of the questions have not been determined. Furthermore, although not intentional, an innovation bias may be present in the form of an underlying assumption that online health management tool usage is beneficial. Finally, this study explored usage patterns for a relatively limited domain of available health technology, and findings may not generalize to other online tools available to the aging population, or to those currently in development.

Conclusion The current investigation has provided an overview of the usage patterns of online health management tools among an educated sample of older adults. A sizable proportion (37%) of participants use online health management tools and most users (89%) are satisfied with them. However, few tools are being used across relatively limited domains. Most non-users seem open to using this technology, but they need more information on existence and capabilities, as well as internet security assurances. As the population ages and the number of chronic conditions increases, the impact of online health management tools on the future of healthcare could be enormous. If individuals can be enabled to monitor their own care from home while their physicians and loved ones monitored it electronically (and from afar), this could enhance people’s abilities to remain in their own homes as they age. This type of assistive technology could ultimately reduce the financial burden on the state by reducing reliance on assisted-living and skilled-nursing institutions (which are already in short supply in many parts of the United States) and on formal assistance programs such as Medicaid and Medicare. Finally, by gaining an understanding of what users want and need, we can assist program planners in developing suitable education/training programs for online health management tools, aid designers with product/software design, and lend empirical information to planners of electronic healthcare policy.

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Acknowledgements The authors wish to thank Echo Chang, Karen Wong, Sang June Oh, Pauline Abbott, and Joseph Weber for their contributions to the survey’s content, and Renee Davis, William Moreno, Christian Perrodin, and the Osher Lifelong Learning Institute at California State University, Fullerton, for their assistance with data collection.

Declaration of Conflicting Interests The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This project was funded by the Beverly Miller University Assistive Technology User Research Project Grant.

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Adoption of online health management tools among healthy older adults: An exploratory study.

As the population ages and chronic diseases abound, overburdened healthcare systems will increasingly require individuals to manage their own health. ...
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