So the family caregiver program when I started my research back in July 2012 had 4,501 individuals
enrolled in it.
It now has well over 10,000 individuals enrolled in it.
The way that it works is the caregivers receive a stipend, approximately $1,500 per month,
to provide home health care support to veterans.
The program provides training and respite care and support to the caregivers.
The veterans in the program are all post 9/11 veterans who have sustained serious physical
or emotional illnesses.
It’s very difficult to reduce caregiver burden with interventions, particularly difficult
to reduce caregiver burden with technological interventions which is what this study did.
Over 800 individuals received the iPads with the mHealth apps on it and it was the first
study that we know of that looked at the factors that predict and drive use of the mHealth
apps.
Caregivers provide $450 billion of uncompensated care in the US.
They also keep people out of the hospital.
We in the VA intend to invest in mHealth technology but in order to do that we need to know what
works.
So the study design, I actually did two studies simultaneously.
One I’m going to call the Zarit burden study.
I asked individuals, ‘Do you want to participate in this pilot?’
Those people that said yes went to the treatment group, those people that said no went to the
control.
That’s why it’s a non-randomised control and then what I did was I measured the change
in their burden levels pre-intervention and post intervention and I compared the treatment
group to the control group seeing if there was a difference.
The second study was the mHealth Use study and that was a prospective cohort study which,
over three months, looked at those individuals that received the intervention, which again
is the iPad with mHealth apps on, and we looked at those that were exposed and tried to predict
the use.
So what was the intervention?
Well we took an iPad, we loaded it with mobile health apps, we purchased a data plan for
the caregivers, we gave them the iPad with the apps on it.
The intervention was only available to the study group, they couldn’t get them off
the app store.
The caregiver focus groups, we conducted caregiver focus groups, and they conceptualised the
apps that they wanted and then when we began to design the apps we actually brought them
back to the caregivers and they helped us design the apps.
So this is called user-centred design.
The help we provided to them was when we gave them the iPad we gave them a quick start guide,
we had a help desk to support them, we had a website with frequently asked questions
and we had a monthly newsletter.
So the data collection, the way that I did this was I…remember I told that the caregivers
go into the homes?
Well when they go into the homes they do a Zarit burden screening exam or assessment
and it’s four questions and it helps them quickly screen burden.
In many ways it was free from bias because these were things that were collected on a
regular ongoing basis unrelated to my study and so that made them unbiased as a measure
of burden.
For the mHealth study every app had back in analytics built in it so we knew…we had
a database we knew who used it, we knew how long they used it, we knew how many times
they used it, almost all, 95 per cent, of my population were females, the average age
was 40, 91 per cent were spouses.
So these are the key findings from my study.
The first thing was I was not able to find a statistically significant difference between
the control group and the treatment group with respect to their Zarit burden change
scores.
Neither one changed hardly at all.
There was less than a 1 per cent change in both the treatment and the control group,
but this, as I said, is somewhat consistent with other studies which have shown that it’s
difficult for technological interventions to reduce caregiver burden and it may be that
the three month study period was not sufficient to actually see a change in burden.
What I saw was in the group the control there was hardly any change in Zarit burden.
In the group that never used the apps, they had the iPad but they didn’t use them, there
was no change.
In the groups that used the apps very little there was a small change, a small increase
in burden, 0.13, very small.
For the medium use group they had a decrease in burden and yet for the high use group they
had an increase in burden and there’s a statistically significant difference between
the high use group and the control and between the high use group and the medium use group
and if it were a linear relationship I’d say it was a dose-response relationship but
it’s not and I just can’t explain it.
mHealth use study outcomes, this is to show you that the apps that were used the most,
at least once, was the notifications app - that’s the one that notified them about medication,
helped with medication adherence, the summary of care, which is the one that gave them access
to the electronic medical record and the prescription refill.
The care for caregivers was used a lot more than I thought it would be and the journal
one, the one that everybody wanted, they didn’t use.
So we’re working on making it better.
This is consistent with everything that we know about technology.
You will adopt it if it makes your life easy.
You will adopt it if it fits seamlessly into your life.
People don’t have copious amounts of spare time to just sit around and use apps because
we want them to.
They’ll use them if they make their life better.
If you were in tier three, which means you had a huge amount of responsibility in caregiving
or your veteran was receiving poly trauma care which they are complex patients to take
care of or they had a high amount of service connected disabilities that reduced the use
of the apps.
So to me that means that they simply don’t have time.
Caregivers who told us that they felt unprepared for caregiving and they had high strain that
increased the use.
So the more they needed them the more they used them.
If you were taking care of a veteran with a mental health diagnosis, not PTSD, it always
increased the use of the apps.
The caregiver and veteran demographics were important.
Increasing age decreased the use of the apps and computer skills were important.
The more you told us that you were adept at computers and had a propensity to adopt technology
the more likely it was that you would use the apps.
If you lived in a rural location it increased your use by 1.5 times.
This is a really important finding because most studies on web applications have shown
that if you live in a rural area you have less use and that’s usually attributed to
not having connectivity, but because we provided the data plan the people now in the rural
areas actually used the apps more.
So I want to thank the people at the Department of Veteran Affairs who helped and the people
at George Washington University who helped with this study.
Thank you.