Mobile Health Apps: Improving Usability for Older Adult Users

Abstract

With smartphone use among older populations on the rise, older adults have increased access to health-focused mobile apps. Despite their potential benefits for managing health, currently no guidelines exist for designing these apps specifically for older adult users. We evaluated the usability of one medication management app and two congestive heart failure management apps using cognitive walkthroughs, heuristic analysis, and user testing. We identified design issues that may affect usability for older users, including poor navigation, small button sizes, and inadequate data visualizations. We provide guidelines for developers of health apps to facilitate use by older adults.

Keywords

designing for older adults usability mHealth test and evaluation design guidelines health management

An abundance of mobile health (mHealth) apps that track health indicators are available for download from online app stores (IMS Institute for Healthcare Informatics, 2015). Older adults are a key population who can benefit from mHealth as these apps address various general health issues (e.g., medication management) and condition-focused health issues (e.g., osteoarthritis). mHealth apps may mitigate burdens associated with managing complex health and treatment plans, especially for older adults experiencing age-related declines in cognitive abilities (Mitzner, McBride, Barg-Walkow, & Rogers, 2013).

To be effective for use by an older population, mHealth apps must suit older adult characteristics. Although resources for designing older adult technologies are available (e.g., Czaja, Boot, Charness, & Rogers, 2019; Rogers & Fisk, 2010; Rogers, Stronge, & Fisk, 2005), few guidelines exist for older adult app design (Matthew-Maich et al., 2016), especially within the mHealth domain (Wildenbos, Peute, & Jaspers, 2018).

Evaluations of existing mHealth apps have highlighted issues that may limit usability by an older adult population. Perceptual issues, such as small font or screen sizes (Gao, Zhou, Liu, Wang, & Bowers, 2017) or poor use of color (Wildenbos et al., 2015), are noted in existing mHealth apps (see also McBride, Tsai, Knott, & Rogers, 2011). Some apps provide few options to correct errors (Gao et al., 2017) or fail to provide clear instructions (Grindrod, Li, & Gates, 2014). Others provide too many functions (Isaković, Sedlar, Volk, & Bešter, 2016) or unnecessary steps (Cornet, Daley, Srinivas, & Holden, 2017) for older adult users. A recent review of older adult mHealth app use (Wildenbos et al., 2018) found that age-related declines in cognition, perception, and physical abilities, along with low motivation, may all reduce older adults’ abilities to use these apps.

Building from these studies, we assessed the usability of existing mHealth apps for older adults and developed guidelines to support mHealth app design for older users. We identified and evaluated one commonly downloaded medication reminder app (Medisafe Pill Reminder [Medisafe Inc.]) and two popular congestive heart failure (CHF) management apps (Heart Failure Health Storylines [HFHS; Self Care Catalysts] and Heart Partner [Novartis Pharmaceuticals Corporation]) using heuristic evaluations, cognitive walkthroughs, and user testing. Here, we focus on the general findings of these studies. For a detailed description of the methods, see Morey, Barg-Walkow, and Rogers (2017) and Stuck, Chong, Mitzner, and Rogers (2017).

Cognitive Walkthrough and Heuristic Analysis

For each app, two different researchers conducted a cognitive walkthrough of typical app tasks (e.g., data entry), along with a heuristic analysis using the 10 design heuristics of Nielsen and colleagues (Nielsen, 1994; Nielsen & Molich, 1990). Evaluations were conducted via different platforms (iPhone 6 for the medication management app; iPad Air 2 for the CHF apps). Table 1 presents themes that emerged from these evaluations.

Table 1.

Design Issues Identified in Each mHealth App

	Usability Heuristic^a	Medisafe Pill Reminder	Heart Partner	Heart Failure Health Storylines
Visibility	• Aesthetic and minimalist design	• Poor color contrast • Small font	• Poor color contrast (e.g., gray text on a light blue background [contrast ratio = ~1.02]) • In some areas, text overlays other text—very difficult to read	• Poor color contrast (e.g., white text on light gray background [contrast ratio = ~1.49]) • Home screen houses many icons (requires scrolling to see many of the icons) • Low Sodium Guidelines page is very cluttered with small text
Consistency	• Consistency and standards	• Inconsistencies in submission of information creates confusion when navigating	• Inconsistencies in button sizes	• Inconsistencies with buttons for saving data (e.g., “Done” vs. “Finish”)
Navigation	• Flexibility and efficiency of use	• Lack of a home screen • Inconsistency in use of back arrow or alternative method to exit screen	• No clear “Back” button • Difficulty locating where to enter some types of data	• Lack of scroll bars indicating how far down a page the user is • Profile button does not look like a typical button
Data entry	• Flexibility and efficiency of use; error prevention	• Data entry process is time-consuming, and mistakes in entry could lead to medication adherence error • Buttons are very small, making input difficult	• Few/unclear directions for data entry • Data loss is easy if data are not entered and saved correctly • Easy to accidentally override previously entered data • Buttons are very small, making input difficult	• Data entry processes are unclear/lengthy • Limited options when entering some types of data
Reflection of real world	• Match between system and the real world	• Medfriend is an uncommon term and is poorly defined within app		• Circle of Support is an uncommon term and is poorly defined within the app
Privacy		• Users could unintentionally share private health information• App provides no option to double check privacy		• Users can only log in and enter data when connected to Internet
Help information and error recovery	• User freedom and control; help and documentation; help users recognize, diagnose, and recover from errors		• No general Help Pages• No directions for recovering from errors	• The Help Guide is extensive, though difficult to find• No instructions about how to enter sodium intake, develop an exercise program, and so on

Note. The issues presented here represent only some of the usability issues we identified using the heuristic analyses and cognitive walkthroughs.

Usability heuristics as stated by Nielsen (1994) and Nielsen and Molich (1990); not all the design issues we identified related directly to a specific usability heuristic.

Usability Testing

We conducted user testing of the CHF apps with 6 older adults (4 females; M_Age = 72 years, SD_Age = 3.29) recruited from the Human Factors and Aging Laboratory Participant Registry at Georgia Institute of Technology. All participants reported using a least one mobile app within the last 3 months. Our use case was someone newly diagnosed with CHF who was attempting to use the app; thus, participants were not required to have a CHF diagnosis but were familiar with smartphone apps in general.

Participants engaged in a variety of activities: familiarization with the apps, a think-aloud protocol for a specified set of within-app tasks, a semi-structured interview discussing app preferences and opinions, and an app comparison. Sessions were audio recorded and transcribed.

We used a thematic analysis to identify broad themes and categorize the interview data and think-aloud sessions. Usability issues and selected comments appear in Table 2.

Table 2.

User-Identified Design Issues and Comments Regarding the Congestive Heart Failure Management Apps

	Heart Partner		Heart Failure Health Storylines
	Issues	User Comments	Issues	User Comments
Navigation	Difficulty navigating between screensScrolling issuesConfusion over where to go to enter new data	“Are we missing a bar on the right side of the screen to show more information is below? I’m having to guess and guessing is bad.”	Difficulty navigating between screensDifficulty understanding actions to perform a task	“[it is] confusing [working out] where to go to add the different things you need to add”“I would need someone to show me” (when asked to navigate about the app)
Data entry	Issues with entering new dataUnclear processes for saving data		Issues with entering new dataUnclear processes for saving dataEntering data requires many steps	“for me to understand it, it would have to be more simpler”“make [the app processes] easier for a senior citizen to understand”“I guess I’m on the right screen, I just don’t know what to do”“cumbersome”
Help information and error recovery	Issues with deleting and editing previously entered data	“At this point, I would pick up the tablet and throw it against the wall”	No directions for how to recover from errors
Data visualizations	Difficulty understanding graphical information	“it is not clear to me”“I wouldn’t know how to do that” (when asked to view weight data graphically)	Difficulty understanding graphical informationGraphs contain too many elements (e.g., grid lines, multiple data points, different factors).	“I don’t understand [the graph]”

Guidelines for Improving mHealth App Usability

Our mixed-methods study identified several design issues within the mHealth apps that may limit usability by older adults. Based on our findings, we provide a list of guidelines for facilitating mHealth app usability for older users. Table 3 provides a summary list of the design guidelines, detailed herein.

Table 3.

Guidelines for Designing mHealth Apps for Older Adult Users

General Guideline	Specific Recommendations
Increase button and text sizes	Design text and buttons to be usable on the smallest device the app will be used on (e.g., make it usable for smartphone users).Use a font size of at least size 30 point for critical text and at least 20 point for secondary text. If in doubt, opt for a larger font size. Only use font sizes smaller than 20 point sparingly.Use large round or square buttons/icons of at least 15 mm in diameter. Avoid narrow/rectangular buttons as these can be difficult to engage with.Increase spacing between buttons/icons to minimize errors with button selection.
Use color effectively	Keep app themes high in color contrast (e.g., avoid using gray text on white or light-colored backgrounds). Following the WCAG guidelines (World Wide Web Consortium, 2016), aim for a text-to-background contrast ratio of at least 4.5:1 for smaller text and at least 3:1 for larger text (at least 18-point regular font).Opt for bold colors over pale or fluorescent colors.Use color as a tool to categorize different groups of options/icons (e.g., use red to represent physician information and blue to represent symptom tracking information).
Ensure within-app consistency	Keep all button sizes and labels consistent throughout the app.
Simplify within-app navigation	Use a clear home screen that the user can easily navigate to if they get lost in the app or want to use a different function. Make sure the home screen is easily identifiable and able to be located with ease from any screen/function (e.g., centered at bottom of each screen).Limit the number of key functions available from the home screen to 12 or fewer (for tablets) and 6 or fewer (for smartphones). Ensure all key functions are visible on the home screen without the need to scroll.Eliminate scroll bars where possible.Avoid overloading the user with too many options/functions/customizable features. Too much information may lead to incorrect use or failure to use.
Streamline data entry process	Remove unnecessary options/dialog boxes/customizable features from data entry processes.Keep the number of steps required to enter data to a minimum (ideally, fewer than three steps).Include auto-save functions where possible.
Enhance and simplify data visualizations	Use visualizations where necessary but keep to a minimum and make as simple as possible.Avoid overcrowding visualizations with multiple factors or data labels. Where possible, present no more than three factors on any one graph/chart.
Improve access to help information	Provide users with a Help Guide that is easy to identify and access. Ideally, help information should be accessible from the home screen or another central location within the app.Provide step-by-step instructions for all extra options/functions and explain what each option is for/what they do (e.g., Medisafe Pill Reminder’s Medfriend function). Consider using pop-up hints or a help bubble for the first time a user performs a new task. Direct users to a point of contact (e.g., email address/phone help line) if requiring additional help.
Improve user privacy and offline access	Explicitly state what information is private and what information is/can be made public.Ensure any data sharing with family/friends/physicians is appropriately set up so users do not accidentally share private information.Allow users to customize what information is shared. Provide explicit step-by-step instructions for how to change privacy/sharing settings. Before users change their sharing settings, include an explicit reminder/confirmation screen asking whether users are sure they wish to increase/decrease their privacy settings.Clearly define unfamiliar terms/functions (e.g., Medisafe’s Medfriend) that may lead users to accidentally share personal information.Ensure users can log on to the app and enter/edit/save data in the absence of an Internet connection.
Modify the level of app functionality	Set the default setting for the app to the basic functionality. Include the ability to switch on any additional/advanced options for users who desire more complex functionality.

Note. This table is a list of guidelines that may assist the design of mHealth apps for older adult users. We recognize that this list is not exhaustive and encourage others to expand on this list.

Increase Button and Text Size

A common issue in the apps were very small font and button sizes that could reduce usability for users with low or declining vision. Participants found Heart Partner’s larger fonts and less cluttered screens to be easier to read than those of HFHS. Heart Partner is less text-heavy and uses large fonts, some appearing at approximately size 40 point on the iPad. In contrast, HFHS includes some text as small as 10-point font, even when displayed on an iPad. We recommend minimizing the amount of text and using font sizes that are at least 30 point for critical text and at least 20 point for secondary text.

HFHS’s home screen was generally preferred by participants over that of Heart Partner, partly due to the large clear icons for each function. HFHS’s home screen shows a matrix of large square icons that each take up between one-third and one-half of the screen depending on the device (see Figure 1a for HFHS’s home screen appearing on an iPhone). Each icon is clearly distinguished from one another by its color and label, making it simple to locate different functions. We encourage creating similar home screens to that of HFHS, using large and clearly separated icons. With regards to buttons, Heart Partner’s large 15 mm round (±) buttons to adjust weight data were more manageable to use than HFHS’s narrow rectangular buttons. We suggest ensuring icons and buttons are no smaller than 15 mm in diameter, and are either round or square rather than narrow or rectangular, when appearing on smaller mobile devices.

Figure 1.

iPhone screenshots of Heart Failure Health Storylines. (a) App home screen showing large, distinctly colored app icons and a centrally located home button to assist user navigation. (b) Visualizations showing multiple health factors over time were often misinterpreted or ignored by older adult participants. Pale color schemes with low color contrast may be problematic for any users but particularly older adults. Screenshots reproduced with permission.

Note, however, that an appropriate button or font size for a larger tablet device may be inappropriate if used on smaller mobile devices. Here, participants tested the two CHF apps on an iPad and yet still experienced difficulties reading text and engaging with buttons. These issues may be further inflated if apps are downloaded onto smaller devices. In general, if users are likely to use the app on smartphones, opt for a font/button size suitable for smaller devices.

Use Color Effectively

Common issues raised in all apps related to the color of information on app screens. To accommodate age-related declines in color vision (Ishihara, Ishihara, Nagamachi, Hiramatsu, & Osaki, 2001), apps should employ a high text-to-background contrast ratio. The Web Content Accessibility Guidelines (WCAG; World Wide Web Consortium, 2016) recommend a text-to-background contrast ratio of at least 4.5:1 for small text and at least 3:1 for text larger than 18-point regular font. In the current apps, pale-colored texts appeared on pale-colored backgrounds, such as light gray text on a light blue or white background (see Figures 1b and 2a), producing contrast ratios of around 1.5:1 or lower. These values are well below the WCAG recommendations and could be problematic for users with low or declining vision. We endorse the WCAG color contrast recommendation and encourage app designers to follow similar benchmarks when designing for older adults. For those new to color contrast, online tools, such as Verou’s (n.d.) contrast ratio calculator, provide easy methods for assessing text-to-background contrast ratio.

Figure 2.

iPhone X screenshots of Medisafe. (a) The Add Medicine function uses bright colors to distinguish different medications; however, small, pale gray text on a white background may be problematic for some users. (b) Options to share data with family members or physicians may be valuable for managing health. However, data-sharing options must come with clear instructions about who has access to this information, what information is being shared, and how to change sharing settings. Screenshots reproduced with permission.

Heart Partner employs a pale color scheme comprising white, pale blue, and turquoise, with predominantly pale gray text. In contrast, HFHS uses a bright color scheme that incorporates white text on a background of dark gray and dark blue along with dark yellow and red. Though some participants stated they liked the pale color scheme of Heart Partner, many of the participants found the high-contrast color scheme of HFHS easier to work with. One participant also commented that she appreciated that the HFHS colors were not fluorescent. Thus, the best color options for text appear to be those that have high text-to-background color contrast, such as black text on white background. Grays are best if they are dark hues, but light grays may be problematic and should be avoided.

A related issue relates to the colors used for specific icons. A few participants erroneously believed different icons on HFHS’s home screen were grouped by color. One participant commented that he liked the different icon colors but that he would need to learn what each color represented. Designers should select consistent colors for similar options to minimize user confusion and reduce the cognitive burden associated with filtering through multiple options. For instance, an app that categorizes all symptom icons as blue and all well-being icons as yellow may be more logical to users than an app that randomly selects a different color for each icon. However, if there are not intended categories for options, designers should select different colors for each icon to avoid confusion.

Ensure Within-App Consistency

We identified inconsistencies between button sizes and labels and inconsistencies in the processes for entering or editing information in the apps. In some situations, identical functions use different button names. In HFHS, “Done” and “Finish” buttons appear interchangeably. Similarly, in Medisafe Pill Reminder, different functions require different data entry processes. These inconsistencies may increase user frustration and confusion, leading to unsuccessful attempts to enter or edit data (Grindrod et al., 2014; McBride et al., 2011). To minimize user confusion, we suggest standardizing the colors, sizes, and language used for labeling buttons and icons.

Simplify Within-App Navigation

Poor navigation is a problem with all three apps, especially when locating the correct icon on the home screen. Given complex navigation may be a cognitive burden for users with reduced working memory capacity or spatial cognition, apps should support a linear task flow, have a clear, easy-to-find home screen, and should limit the number of functions available. Although HFHS has a clear and easily accessible home screen, by providing numerous app tools, each with multiple possible functions, several users struggled to navigate between different screens and select the intended functions. Moreover, HFHS presents more icons on the home screen than there is space to show them all. Thus, to find some of the necessary options, participants needed to scroll down to the appropriate function. Not all participants were able to locate the scroll bar, and several commented that they found the scroll bars frustrating. HFHS successfully shows 12 app icons for different functions (e.g., Daily Moods, Symptom Tracker) on the iPad version and shows 6 icons on the iPhone version without the need to scroll. We recommend limiting the number of critical functions/icons to 12 or fewer on tablet versions and to 6 or fewer on smartphone versions. Finally, we suggest ensuring all icons are clearly displayed on the app home screen.

Several participants experienced difficulties navigating back to the home screen after completing a task. Though all apps included a “Back” or “Exit” button to exit an option, in several cases, there was no clear, easy-to-find button that directed users back to the home screen. One participant suggested they would have preferred a home button located centrally at the bottom of each screen to ease navigation back to the home screen after completing a task. We recommend including a clear “Return to Home Screen” button on every screen to assist disoriented users and ease navigation to the next function.

Streamline Data Entry Processes

Data entry was problematic for the older adult participants when using the CHF management apps. Heart Partner provides users with few directions about entering data, and the processes required to do so are not necessarily intuitive. Consequently, none of the participants successfully entered a new medication as they all failed to notice the “Save” button and the app provided no auto-save functionality. For HFHS, data entry involves clicking through multiple options before saving the data, leading one user to describe the process as “cumbersome.” As user motivation plays a key role in whether older adults adopt health technologies (Heart & Kalderon, 2013; Wildenbos et al., 2018), app processes should avoid complex or unnecessary options that may reduce user motivation. Remove optional dialogue boxes and only include options requiring input of key information. To reduce the possibility of user confusion or rejection of the app, where possible, aim to use no more than three steps for entering data. Finally, reduce the possibility for completion errors by providing auto-save functionality and clear buttons for finalizing entries.

Enhance and Simplify Data Visualizations

Visualizations of data created problems in both CHF apps. Graphs and charts can be a valuable means of presenting health information (Alnosayan, Chatterjee, Alluhaidan, Lee, & Houston Feenstra, 2017; Ledesma, Al-Musawi, & Nieminen, 2016); however, in the current apps, ill-presented or complex visualizations were either interpreted incorrectly or were ignored. Although some users found the graphs helpful for viewing changes in information over time, few users could understand the information presented graphically, and many asked for clarity over what the graphs showed. Poorly labeled components on the tables and charts caused confusion, as did graphs displaying relationships between multiple health factors. Moreover, several participants struggled to understand minimalistic graphs representing changes in only one factor (e.g., blood pressure) over time (see Figure 1b for an example of a simple graph that confused users). Including multiple factors within one visualization may further increase user confusion and limit understanding. We suggest visualizations should be used only when they provide valuable, albeit simplified, information for the user. Graphs should be easy to understand, contain few graphical elements or data labels, and only display multiple health factors (preferably, no more than three) when absolutely necessary.

Improve Access to Help Information

Another major issue within the CHF apps was the poor access to help information. Heart Partner provides no general help pages to teach users how to use the app or recover from errors. HFHS provides detailed user instructions and help information; however, the help guide was difficult to find, even for the researchers. Apps should provide detailed help information that is clearly labeled and easily accessible from either the home screen or an alternative central location within the app. Apps should also provide simple step-by-step instructions the first time a user accesses a specific tool, such as providing hints or a help bubble when entering data for the first time. These instructions should explain what the specific tool does and walk the user through each step. The instructions should also clearly state whether the tool includes any optional functions and if so, should explain how to change these settings. Finally, users requiring more information may benefit from directly accessing an app help contact, such as an email address or phone number, to receive tailored assistance with using the app.

Improve User Privacy and Offline Access

Regarding user privacy, Medisafe Pill Reminder provides the user with the option to add a “Medfriend” (see Figure 2b); however, it does not explicitly state what personal information will be shared if this option is used. In addition, this function does not allow users to select specific information for sharing (e.g., specific medications); thus, users may unintentionally share more information than desired. mHealth apps should clearly state whether personal data can be shared with third parties (e.g., the user’s physician) and if so, should allow users to modify the information that will be shared. Apps should also provide explicit instructions about how to update sharing settings.

For effective use, apps should allow offline access. This ensures data entry and other important functions are not disrupted during times of poor or no online connection.

Modify the Level of App Functionality

Lastly, developers should allow users to modify the level of app functionality. Although simplifying apps by implementing the aforementioned guidelines will assist use by many older adults, more advanced users may desire a broader range of functionality. To accommodate users of a wide range of skill sets and experience, we suggest using simplified functionality as the default option and providing options to switch on more complex functionality via the app’s settings.

Conclusion

mHealth apps pose an attractive method for managing health outside of a doctor’s office. These apps may be especially useful for patients who live remotely or are unable to meet face to face with health professionals. As older adults increasingly adopt mobile devices, there is a need to ensure they can use mHealth apps effectively. Consistent with previous research, we identified a range of common design issues in existing mHealth apps that may limit usage or discourage adoption by older adults. Our current guidelines provide solutions to address older adults’ cognitive, perceptual, physical, and motivational needs. We encourage developers to consider these guidelines when designing mHealth apps to assist successful use by one of their primary target user populations.

mHealth Apps and the Older Adult User

Although smartphone use among older adults is on the rise (Anderson & Perrin, 2017), mHealth use is still less common among older than younger adults (Bender et al., 2014). Adequately designed apps that consider the specific needs and limitations of older adult users may reduce the burden associated with adopting new technologies.

Older adults experience perceptual, motor, and cognitive limitations that may influence the usability and use of apps (Czaja, Boot, Charness, & Rogers, 2019). Many older adults experience declines in visual perception (e.g., Harvey, 2003; Ishihara, Ishihara, Nagamachi, Hiramatsu, & Osaki, 2001). Thus, the size of text and images must be increased, and high-contrast combinations of bold colors should be chosen over pale or similar colors. Moreover, buttons in apps should have high text contrast for readability, must be large enough for older adults to accurately engage with, and require meaningful labels. Declines in auditory perception are also common among older adults (e.g., Schneider, Daneman, & Murphy, 2005). Thus, apps incorporating tones should ensure volumes can be easily adjusted to suit the user. Older populations may also experience declines in fine motor skills (e.g., Stöckel, Wunsch, & Hughes, 2017). App designers must consider the different buttons, scroll bars, or other touch-based functions to ensure they are large and usable by people with poor fine motor control (Caprani, O’Connor, & Gurrin, 2012). Cognitive limitations, such as reduced working memory and slower general processing speed, are also common among older adults (Ebaid, Crewther, MacCalman, Brown, & Crewther, 2017; Salthouse, 2009). To minimize user forgetting, apps should provide information reminders about particular events (e.g., medication reminders), and to reduce user confusion, apps should limit the amount of information presented.

Footnotes

This project was based on a collaborative research effort between the Human Factors and Aging Laboratory (director: Wendy A. Rogers; www.hfaging.org) and Aptima, Inc. (project PI: Sylvain Bruni; http://www.aptima.com/). We appreciate the input of Camilla Knott and Diane Miller. This research was also supported in part by a grant from the National Institutes of Health (National Institute on Aging) Grant P01 AG17211 under the auspices of the Center for Research and Education on Aging and Technology Enhancement (CREATE; ). Stephanie A. Morey acknowledges the support of a Flinders University Overseas Travelling Fellowship, a Bank of South Australia Travelling Award, an Amy Forwood Travelling Award, and an Australian Government Research Training Program Scholarship.

Stephanie A. Morey is a PhD candidate in psychology at Flinders University, South Australia, and a safety intelligence analyst in the Defence Aviation Safety Authority’s Defence Flight Safety Bureau in Canberra, Australian Capital Territory. She received her bachelor of psychology (honours) from Flinders University in 2014. Her research interests include basic perceptual and cognitive performance, system usability, and improving safety and efficiency in complex operational environments. ORCID iD: .

Rachel E. Stuck is a PhD student under Dr. Bruce N. Walker in the Sonification Lab at Georgia Institute of Technology. In 2018, Ms. Stuck received a master’s in psychology from Georgia Institute of Technology with Dr. Wendy A. Rogers as her advisor. Her current research focuses on understanding how perceptions of risk influences trust in automation and robots.

Amy W. Chong is a PhD student in developmental psychology at Cornell University. She holds a master’s of science degree in psychology (cognitive aging) from the Georgia Institute of Technology. Her research interests include aging, health, and decision making. She aims to better understand younger and older adults’ decision-making processes and apply empirical findings to help them make better health decisions.

Laura H. Barg-Walkow, PhD, is a human factors engineer in the department of Patient Safety at Children’s Hospital Colorado in Aurora, Colorado. She received her PhD in engineering psychology at the Georgia Institute of Technology in 2017. Laura works operationally in the hospital improving safety, satisfaction, and efficiency. Her interests include interruptions, workflow, and errors.

Tracy L. Mitzner is a senior research scientist. Her research focuses on understanding age-related changes and the potential of technology to support older adults, including those with impairments. Her research topics include older adults’ acceptance and usage of emerging technologies (mobile applications, health monitoring, telepresence, robotics), particularly for applications to facilitate social and physical wellness. She is a co-director of the National Institute on Disability, Independent Living, and Rehabilitation Research–funded Rehabilitation Engineering Research Center, Technologies to Support Successful Aging with Disability and is an investigator on the NIH-funded Center for Research and Education on Aging and Technology Enhancement.

Wendy A. Rogers, PhD, is Shahid and Ann Carlson Khan Professor of Applied Health Sciences in the Department of Kinesiology and Community Health at the University of Illinois Urbana-Champaign. She is the director of the Health Technology Education Program; the program director of CHART: Collaborations in Health, Aging, Research, and Technology; and the director of the Human Factors and Aging Laboratory. Her research interests include design for aging, technology acceptance, human-automation interaction, aging in place, human-robot interaction, and aging with disabilities.

References

Alnosayan

Chatterjee

Alluhaidan

Lee

Houston Feenstra

(2017). Design and usability of a heart failure mHealth system: A pilot study. JMIR Human Factors, 4(1), e9. doi:10.2196/humanfactors.6481

Anderson

Perrin

(2017). Tech adoption climbs among older adults. Retrieved from http://www.pewinternet.org/2017/05/17/technology-use-among-seniors/

Bender

M. S.

Choi

Arai

Paul

S. M.

Gonzalez

Fukuoka

(2014). Digital technology ownership, usage, and factors predicting downloading health apps among caucasian, filipino, korean, and latino americans: the digital link to health survey. JMIR MHealth and UHealth, 2(4), e43. doi:10.2196/mhealth.3710

Caprani

O’Connor

N. E.

Gurrin

(2012). Touch screens for the older user. Retrieved from https://www.intechopen.com/books/assistive-technologies/touch-screens-for-the-older-user

Cornet

V. P.

Daley

C. N.

Srinivas

Holden

R. J.

(2017). User-centered evaluations with older adults: Testing the usability of a mobile health system for heart failure self-management. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting (pp. 6–10). Santa Monica, CA: Human Factors and Ergonomics Society.

Czaja

S. J.

Boot

W. R.

Charness

Rogers

W. A.

(2019). Designing for older adults: Principles and creative human factors approaches (3rd ed.). Boca Raton, FL: CRC Press.

Ebaid

Crewther

S. G.

MacCalman

Brown

Crewther

D. P.

(2017). Cognitive processing speed across the lifespan: Beyond the influence of motor speed. Frontiers in Aging Neuroscience, 9, 62. doi:10.3389/fnagi.2017.00062

Gao

Zhou

Liu

Wang

Bowers

(2017). Mobile application for diabetes self-management in China: Do they fit for older adults? International Journal of Medical Informatics, 101, 68–74. doi:10.1016/J.IJMEDINF.2017.02.005

Grindrod

K. A.

Gates

(2014). Evaluating user perceptions of mobile medication management applications with older adults: A usability study. JMIR MHealth and UHealth, 2(1), e11. doi:10.2196/mhealth.3048

10.

Harvey

P. T.

(2003). Common eye diseases of elderly people: Identifying and treating causes of vision loss. Gerontology, 49(1), 1–11. doi:10.1159/000066507

11.

Heart

Kalderon

(2013). Older adults: Are they ready to adopt health-related ICT? International Journal of Medical Informatics, 82, e209–e231. doi:10.1016/j.ijmedinf.2011.03.002

12.

IMS Institute for Healthcare Informatics. (2015). Patient adoption of mHealth. Retrieved from www.theimsinstitute.org

13.

Isaković

Sedlar

Volk

Bešter

(2016). Usability pitfalls of diabetes mHealth apps for the elderly. Journal of Diabetes Research, 2016, 1–9. doi:10.1155/2016/1604609

14.

Ishihara

Nagamachi

Hiramatsu

Osaki

(2001). Age-related decline in color perception and difficulties with daily activities–measurement, questionnaire, optical and computer-graphics simulation studies. International Journal of Industrial Ergonomics, 28(3–4), 153–163. doi:10.1016/S0169-8141(01)00028-2

15.

Ledesma

Al-Musawi

Nieminen

(2016). Health figures: An open source JavaScript library for health data visualization. BMC Medical Informatics and Decision Making, 16(1), 38. doi:10.1186/s12911-016-0275-6

16.

Matthew-Maich

Harris

Ploeg

Markle-Reid

Valaitis

Ibrahim

. . . Isaacs

. (2016). Designing, implementing, and evaluating mobile health technologies for managing chronic conditions in older adults: A scoping review. JMIR MHealth and UHealth, 4(2), e29. doi:10.2196/mhealth.5127

17.

McBride

S. E.

Tsai

W.-C.

Knott

C. C.

Rogers

W. A.

(2011). Supporting the management of osteoarthritis pain: A needs analysis. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 55(1), 286–290. doi:10.1177/1071181311551059

18.

Mitzner

T. L.

McBride

S. E.

Barg-Walkow

L. H.

Rogers

W. A.

(2013). Self-management of wellness and illness in an aging population. Reviews of Human Factors and Ergonomics, 8(1), 277–333. doi:10.1177/1557234X13492979

19.

Morey

S. A.

Barg-Walkow

L. H.

Rogers

W. A.

(2017). Managing heart failure on the go: Usability issues with mHealth apps for older adults. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 61(1), 1–5. doi:10.1177/1541931213601496

20.

Nielsen

(1994). Heuristic evaluation. In Nielsen

Mack

R. L.

(Eds.), Usability inspection methods (pp. 25–64). New York, NY: John Wiley & Sons, Ltd.

21.

Nielsen

Molich

(1990). Heuristic evaluation of user interfaces. CHI ’90 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, (April), 249–256. doi:10.1145/97243.97281

22.

Rogers

W. A.

Fisk

A. D.

(2010). Toward a psychological science of advanced technology design for older adults. The Journals of Gerontology. Series B, Psychological Sciences and Social Sciences, 65(6), 645–653. doi:10.1093/geronb/gbq065

23.

Rogers

W. A.

Stronge

A. J.

Fisk

A. D.

(2005). Technology and aging. Reviews of Human Factors and Ergonomics, 1(1), 130–171. doi:10.1518/155723405783703028

24.

Salthouse

T. A.

(2009). When does age-related cognitive decline begin? Neurobiology of Aging, 30(4), 507–514. doi:10.1016/j.neurobiolaging.2008.09.023

25.

Schneider

B. A.

Daneman

Murphy

D. R.

(2005). Speech comprehension difficulties in older adults: Cognitive slowing or age-related changes in hearing? Psychology and Aging, 20(2), 261–271. doi:10.1037/0882-7974.20.2.261

26.

Stöckel

Wunsch

Hughes

C. M. L.

(2017). Age-related decline in anticipatory motor planning and its relation to cognitive and motor skill proficiency. Frontiers in Aging Neuroscience, 9, 283. doi:10.3389/fnagi.2017.00283

27.

Stuck

R. E.

Chong

A. W.

Mitzner

T. L.

Rogers

W. A.

(2017). Medication management apps: Usable by older adults? Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 61(1), 1141–1144. doi:10.1177/1541931213601769

28.

Verou

(n.d.). Contrast ratio: Easily calculate color contrast ratios. Retrieved from https://contrast-ratio.com/

29.

Wildenbos

G. A.

Peute

L. W.

Jaspers

M. W. M.

(2015). A framework for evaluating mHealth tools for older patients on usability. Studies in Health Technology and Informatics, 210, 783–787. doi:10.3233/978-1-61499-512-8-783

30.

Wildenbos

G. A.

Peute

Jaspers

(2018). Aging barriers influencing mobile health usability for older adults: A literature based framework (MOLD-US). International Journal of Medical Informatics, 114, 66–75. doi:10.1016/j.ijmedinf.2018.03.012

31.

World Wide Web Consortium. (2016). Contrast (minimum): Understanding success criterion 1.4.3. Retrieved from https://www.w3.org/TR/UNDERSTANDING-WCAG20/visual-audio-contrast-contrast.html