Med 2.0 Digital Divide & mHealth by Kelly Grindrod

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Slide Notes

Med 2.0 Digital Divide & mHealth

Published on Nov 19, 2015

To make an impact in healthcare, we need to build and test mHealth tools with the biggest consumers of healthcare. Yet, our ability to adopt technology is influenced by age, income and education. This presentation discusses why older adults are an important mHealth demographic and why we should build to their abilities and needs.

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PRESENTATION OUTLINE

Bridging the Digital Health Divide

Helping Older Users with mHealth

Discussion

mHealth vs. mHealthcare
Adoption curve
Tips & tricks

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Healthcare covers us from birth to death.

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Healthcare covers us from birth to death.

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Canada: Who is using health care dollars?

A minority of Ontarians are using the majority of healthcare dollars. These are our "high impact" users.

Source: Wodchis W et al. The concentration of healthcare spending: little ado (yet) about much money. Presented at CAHSPR 2012 at https://www.longwoods.com/articles/images/The_Concentration_of_Healthcare_S...

US: Who is using health care dollars?

The numbers are similar in the US, with high impact users responsible for almost half of healthcare spending. A key difference may be in the number of uninsured high impact users in the US (vs the universal health system in Ontario).

Source: Stanton & Rutherford. The high concentration of US healthcare expenditures. AHRQ 2006.

mHealth has been covering us from birth until illness

Often patients with multiple conditions, complex care needs and people who "don't feel their best." mHealth needs to be designed for this group.

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Of 43,000 mHealth apps

And there are many, many, many mobile health apps on the market.

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Directly related to health & treatment

However, only 1 in 3 of those apps directly related to health or treatment. Most apps actually focus on wellness.

Source: http://www.imshealth.com/portal/site/imshealth/menuitem.762a961826aad98f53c...

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Simply provide instruction.

Of the health apps that focus on health and treatment, the vast majority simply provide users with information or instructions.

Source: http://www.imshealth.com/portal/site/imshealth/menuitem.762a961826aad98f53c...

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Collect health data.

Only 1 in 5 collect health data. What we're missing are those key apps that collect health data and turn that data into useful instructions or information.
Source: http://www.imshealth.com/portal/site/imshealth/menuitem.762a961826aad98f53c...

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Today's mHealth is for the (relatively) healthy.

In other words, apps are built for people who only need information and who don't actually need to learn how to manage a condition.

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And the (relatively) wealthy.

Further, in order to use mHealth you need to be able to afford it. If an app is designed only for the Apple iOS system, then users will need to be able to afford an iPhone or iPad. This may be significantly more expensive than the low cost Android devices.

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What about the high impact users?

So, why aren't we building for our high impact users? Are they too hard to build for? Are we trying? How are the 5% of heavy healthcare users difference from everyone else?

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Rogers's Adoption Curve (%)

A nice way to start thinking about this is with Roger's adoption curve. We often concentrate on getting the innovators and early adopters to adoptnew technologies.

Source: Rogers, E. Diffusion of Innovations, 5th Edition. Simon and Schuster: 2003 ISBN 978-0-7432-5823-4.

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Rogers's Adoption Curve (%)

This is just the same curve with different names. In other words, our earliest adopters are venturers and leaders whereas our later adopters are skeptics and traditionalists.

Source: Rogers, E. Diffusion of Innovations, 5th Edition. Simon and Schuster: 2003 ISBN 978-0-7432-5823-4.

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Skeptics/Late Adopters

Wait until most others adopt
Average social status
Limited financial resources
Contact with adopters
No opinion leadership

This describes a lot of our high impact healthcare users.

Source: Rogers, E. Diffusion of Innovations, 5th Edition. Simon and Schuster: 2003 ISBN 978-0-7432-5823-4.

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Traditionalists/Laggards

Last to adopt
Lower social status
Limited financial resources
Less contact with adopters
Older

So does this.

Source: Rogers, E. Diffusion of Innovations, 5th Edition. Simon and Schuster: 2003 ISBN 978-0-7432-5823-4.

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Late adopters are half the audience...

The problem with writing off those late adopters and laggards is that they are half the population and likely include our patients who need the most care.

Source: Rogers, E. Diffusion of Innovations, 5th Edition. Simon and Schuster: 2003 ISBN 978-0-7432-5823-4.

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“Getting the attention of late adopters can present an enormous challenge.” http://www.uxmatters.com

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Untitled Slide

Roger's model has been criticized as being more explanatory than predictive. Comparatively, Azjen and Fishbein's technology acceptance model is more predictive. It tells us that users will adopt and use something when they think it is useful to them, when they find it easy to use.

Ajzen I & Fishbein M. Understanding attitudes and predicting social behavior, Englewood Cliffs, NJ: Prentice-Hall, 1980.

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Self-efficacy: one's belief in one's own ability to complete tasks and reach goals. (Bandura)

The work on self-efficacy by Stanford's Albert Bandura also tells us to concentrate on helping users believe in their ability to complete a task and reach a goal.

Bandura, A. (1994). Self-efficacy. In V. S. Ramachaudran (Ed.), Encyclopedia of human behavior (Vol. 4, pp. 71-81). New York: Academic Press. (Reprinted in H. Friedman [Ed.], Encyclopedia of mental health. San Diego: Academic Press, 1998).

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Who is your population?

If you are planning to build something new, you should start by asking yourself some key questions.

1. Start with, who is your population? No really, who are they? How old are they? What is their health like? Where do they live? Do they have internet? Are you making any assumptions about them? Have you challenged your assumptions by talking with some people in your target population (and I don't mean your friends or family...think about that guy sitting next to you at the coffee shop).

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What is your intervention?

2. Next, think about your intervention. What does it cover? What are you trying to achieve? How will it work? What kind of technology will you use?

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Where are they on the curve?

3. When you think of your population and you think of your intervention, how much has your population adopted the basic technology platform. For an app, for example, how many people in your target population have an smartphone or tablet? If it's online, do they have internet access? Do they access the internet at home or in a public space like a library or school or cafe?

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What would be useful to them?

4. Now you need to define "useful". Ask them what they are struggling with. Does you intervention fill a gap? Address a need? Make life easier? Also, watch for the "me me me" trap--don't just design something that will be useful. We often see this when a health professional wants someone to collect blood sugar or blood pressure data. If the user doesn't understand the data, a data log isn't very useful to them.

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What would be usable to them?

5. Once you have a useful idea that fits your population, think about what it most usable to your population. Is the font big enough? Are the buttons big enough? Are the sounds loud enough?

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How can you increase
self-efficacy?

6. Finally, think about how you can use your tool to help people build their own self-efficacy. That may also mean that you have to do some extra work outside the tool to help users learn to use something and feel like they can do it.

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The digital divide.

We don't all adopt technology in the same way.

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AGE
INCOME
EDUCATION

We are divided by age, income and gender.

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StatsCan Data (2009)

Canadian data shows that the people who are online tend to be younger and wealthier. The people who are not online (a minority of the population who fits into Roger's "laggard" category) tend to be older, have a less income and be less educated.

McConnaughey J et al. Online and on point: broadband usage in Canada and in the US. J Information Policy 2013: 123-157. http://jip.vmhost.psu.edu/ojs/index.php/jip/article/download/120/78

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Canada Internet Use Survey (2012)

More recent Canadian data supports this. People in higher income brackets are more likely to have internet access.

Statistics Canada. Table 358-0152 - Canadian Internet use survey, Internet use, by age group and household income for Canada, provinces and census metropolitan areas (CMAs), occasional (percent), CANSIM (database). (accessed: 2014-11-14)

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Canada Internet Use Survey (2012)

People with more education are more likely to have internet access.

Statistics Canada. Table 358-0152 - Canadian Internet use survey, Internet use, by age group and household income for Canada, provinces and census metropolitan areas (CMAs), occasional (percent), CANSIM (database). (accessed: 2014-11-14)

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Canada Internet Use Survey (2012)

In other words, almost 100% of young people who have a high income are online but only 1 in 4 older adults who have a low income are online.

That's the digital divide.

Statistics Canada. Table 358-0152 - Canadian Internet use survey, Internet use, by age group and household income for Canada, provinces and census metropolitan areas (CMAs), occasional (percent), CANSIM (database). (accessed: 2014-11-14)

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US Census Internet Use (2013)

The US follows the same trends.

http://www.census.gov/content/dam/Census/library/publications/2014/acs/acs-...

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US Census Internet Use (2013)

The US follows the same trends.

http://www.census.gov/content/dam/Census/library/publications/2014/acs/acs-...

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Desktop Ownership

So think of those assumptions you make about your users. For example, did you know that two thirds of seniors have a desktop computer? If you build something that can work with a smartphone or a desktop, you can reach many more people in your target demographic.

Statistics Canada. Table 358-0152 - Canadian Internet use survey, Internet use, by age group and household income for Canada, provinces and census metropolitan areas (CMAs), occasional (percent), CANSIM (database). (accessed: 2014-11-14)

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Handheld Ownership

Similarly, even though older adults are less likely to own a smartphone or tablet, a good one third of people over age 65 do own one. That's a lot of people.

Statistics Canada. Table 358-0152 - Canadian Internet use survey, Internet use, by age group and household income for Canada, provinces and census metropolitan areas (CMAs), occasional (percent), CANSIM (database). (accessed: 2014-11-14)

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Age is not the only explanatory variable for adoption or non-adoption.

(Loos 2012)

What we need to remember is that being old is not the only reason for non-adoption.

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Our individual differences increase as we age.

(Loos 2012)

Loos calls this a "fanning out". Like the autumn leaves, which all started out as green but became yellow, orange, red, burgundy, purple and brown in the autumn months, we too change in our later years. That means we are more different when we are alike. We have different incomes, social supports, physical abilities and cognitive abilities.

Source: http://dare.uva.nl/document/506413

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Age-related physical changes affect our ability to adopt new technologies.

The age related physical changes that affect our ability to adopt technology include things like hearing, sight, touch, walking, cognition (thinking, memory, understanding, learning).

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Our income also affects our willingness to buy-in.

We may be early adopters when we are working and have a disposable income. When we stop working, we may become non-adopters simply because we are now living on a limited income or a pension.

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Study 1:
Medication management apps

This is a study we did with older to explore medication management apps. The biggest lesson we learned here was that med management apps were not being designed for people who actually use meds. This was the first time we really understood the disconnect between developers and users.

Grindrod K, Li M, Gates A. Evaluating User Perceptions of Mobile Medication Management Applications With Older Adults: A Usability Study. JMIR mHealth uHealth 2014; 2(1). Jan-Mar. http://mhealth.jmir.org/2014/1/e11/

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Study 2: ClereMed

Here's a study where we became the developer and our biggest lesson learned was that it is really difficult to solve a patient's problem by targeting a health professional's behaviour. We designed a tool to help health professionals identify people who could not read or understand labels. When we tested it, no health professionals wanted to use it. By comparison, patients thought it was a great idea. We were disconnected from our users. We designed for the patient and ignored the health professional user.

Grindrod KA, Gates A, Dolovich L, Slavcev R, Drimmie R, Aghaei B, Poon C, Khan S, Leat SJ
ClereMed: Lessons Learned From a Pilot Study of a Mobile Screening Tool to Identify and Support Adults Who Have Difficulty With Medication Labels
JMIR mHealth uHealth 2014;2(3):e35 http://mhealth.jmir.org/2014/3/e35/

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Study 3: Activity Trackers

In our most recent study, which was presented as a poster at Med 2.0, we asked older adults to try out some new activity trackers. Our big lesson learned here was that the challenge was not in getting someone to adopt a fancy new or emerging technology. It was getting them to adopt something designed, advertised and positioned for younger users.

Unpublished (Nov 2014). See poster from Medicine 2.o (Maui): http://www.slideshare.net/grindrod/wearables-poster-nov-2014

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Lesson 1: Don't worry, you
won't break it!

So, I hope you'll consider building or trialling mHealthcare apps and tools with high impact health care users. Here are some tips that can help:

1. People who are new to technology don't automatically learn by trial and error. You need to teach them how.To promote learning by trial and error say this again, and again, and again, and again...

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Unless you...

But also tell them how they can break something. In other words, be careful with liquids, washing machines, coffee and ceramic tile.

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Lesson 2:
Just play Angry Birds

2. Make learning fun by having a few games in your back pocket to help people familiarize themselves with a touchscreen. We like Angry Birds, Candy Crush and Temple Run.

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Or Candy Crush

This was the favourite of an 85yo participant...

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Or Temple Run

This was the favourite of a 62yo participant...

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Lesson 3:
Instructions manuals are not out of style.

3. If there isn't a clear instruction manual, make your own. Record a YouTube video of you setting up the device and getting it to work.

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Be prepared to instruct.

If you have a new technology, make a nice, slow video that can be watched at home. Include the things that can go wrong (e.g., look what happens when I insert the battery upside down).

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And offer extra support.

If you are planning a study, budget for some extra help to support your participants with the technology. An undergraduate volunteer or graduate research assistant is a great resource.

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Lesson 4:
Visible + Audible = Usable.

4. Before you do anything, make sure your user group can both see and hear your device.

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Know this by testing with actual users.

If you can't find your users, ask a health professional. A pharmacist, physician, nurse or physiotherapist could probably help you find some people in your target demographic.

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Lesson 5:
Ask your users what they want.

5. And finally, know your users. As in talk to them. No really, go find some people in your target population and take them out for coffee and just run ideas by them. You'd be surprised what you learn.

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Do they want another data collection tool?

Many mHealth tools focus on data collection, but many older adults have simple strategies for tracking data (e.g., paper calendars for tracking blood sugar, microsoft word for keeping a list of medications). What they often need is a way to interpret they data they have already collected.

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To make an impact, we need mHealth to be mHealthcare.

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Case Study:
Wearable Activity Trackers

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Sedentary Behaviour

Watching TV
Computer work
Driving

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Guidelines recommend 150 minutes of moderate to vigorous physical activity per week.

http://www.who.int/dietphysicalactivity/factsheet_recommendations/en/

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And muscle/strength building exercise 2x/week.

http://www.who.int/dietphysicalactivity/factsheet_recommendations/en/

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Two thirds of adults 60+ are sedentary

Balde et al. J Aging Phys Act 2003;11(1):90.
Barnes et al. NCHS Data Brief 2012;(86):1–8.

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Step Goals

PEdometer + Individual Goal + feedback

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Pedometer + individual goal + feedback = 2000 more steps/d.
Bravata. JAMA 2007; 298(19): 2296.

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No difference between a goal the patient sets and 10,000 steps/day.

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Walking Clubs

pedometer + social interaction

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Biggest effect when it lasts >6mos or when both genders are invited. Kassavou et al. Int J Behav Nutr Phys Act 2013; 10:18.

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Websites & Apps

Pedometer + online program

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An online walking community can keep patients engaged longer. Richardson et al. JMIR 2010; 12(4): Oct-Dec.

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In the first quarter of 2014, 2.7 million fitness bands were shipped worldwide. (Canalys)

http://www.canalys.com/newsroom/fitbit-accounted-nearly-half-global-wearabl...

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Wearable activity trackers should be a natural fit with older adults.

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Research on activity trackers has been limited to accuracy.

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Fitbit Zip & BodyMedia FIT are accurate.

Welk GJ1, Blair SN, et. al. A comparative evaluation of three accelerometry-based physical activity monitors. Med Sci Sports Exerc. 2000 Sep;32(9 Suppl):S489-97.
Van Remoortel H, Giavedoni S, et.al. Validity of activity monitors in health and chronic disease: a systematic review. Int J Behav Nutr Phys Act. 2012 Jul 9;9:84. doi: 10.1186/1479-5868-9-84.
Van Remoortel H, Raste Y, Louvaris Z, et. al. Validity of six activity monitors in chronic obstructive pulmonary disease: a comparison with indirect calorimetry. PLoS One. 2012;7(6):e39198. doi: 10.1371/journal.pone.0039198. Epub 2012 Jun 20.
Lee JM, Kim Y, Welk GJ. Validity of consumer-based physical activity monitors. Med Sci Sports Exerc. 2014 Sep;46(9):1840-8. doi: 10.1249/MSS.0000000000000287.

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Trackers are not accurate when there's a slow, abnormal or shuffling gate.

Lauritzen J,Munoz A, Luis J, Civit A. The usefulness of activity trackers in elderly with reduced mobility: a case study. JOURNAL???
Schmalzried TP, Szuszczewicz ES, Northfield MR, Aki- zuki KH, Frankel RE, Belcher G and Amstutz HC. Quan- titative assessment of walking activity after total hip or knee replacement. J Bone Joint Surg 1998: 80-A: 54–9.
Kochersberger G, McConnell E, Kuchibhatla MN, and Pieper C. The reliability, validity, and stability of a meas- ure of physical activity in the elderly. Arch Phys Med Rehabil 1996: 77: 793–5.
Macko RF, Haeuber E, Shaughnessy M, Coleman KL, Boone DA, Smith GV and Silver KH. Microprocessor based ambulatory activity monitoring in stroke patients. Med Sci Sports Exerc 2002: 34: 394–9.

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