Changes in behavior, particularly in seniors can indicate an increased likelihood of a fall. These changes can be very subtle can take place over an extended period of time, which make them difficult to detect through human observation alone. If an elderly person is living alone, it is even more likely that these signs will be missed.
By using passively collected data from easy to install sensors in the home, Curo can identify those key signals associated with an increased likelihood of falls.
This information can be shared with family and caregivers in a timely manner, along with critical information about the cause of the decline. With this information, family members or caregivers can respond appropriately to reduce the risk of falls.
According to the World Health Organization (WHO) there are 4 areas which influence the likelihood of a fall:
2. Behavioral (Our area of focus)
3. Environmental (Secondary area of focus)
Furthermore, WHO have established a three-stage prevention model to lower the risks of falls:
1. Build awareness of the importance of fall prevention
2. Improve identification and assessment of risk factors. (Our area of focus)
3. Identify and implement realistic and effective intervention. (Secondary area of focus)
Curo will address this by:
Relating to “Improve identification and assessment of risk factors” (2) in the prevention model:
1. Determining (and defining exactly) which our data points have the strongest correlation with increased risk. Based on WHO, these are likely to include:
- Decline in movement (Physical activity)
- Medication usage
- Temperature in the home (Environmental)
- Food preparation (Healthy eating)
- Leaving the house (Social interaction)
2. Create a risk algorithm based on the occurrence (or otherwise) of the above behaviors (none, low, medium high).
1. None - no behaviors associated with increased risk of fall have been observed
2. Low - one of the behaviors has been observed
3. Medium - either; one of the behaviors more than once OR two distinct behaviors have been observed
4.High - combination of multiple behaviors and multiple frequencies of events have been observed
Relating to “Identify and implement realistic and effective intervention” (3) in the prevention model:
3. Inform / Alert caregiver and/or family of increased risk.
4. To ensure that we address the WHO’s need for “realistic & effective” intervention, we will not just alert caregivers, but also provide information on what behaviors specifically have to lead to the increased risk. Therefore allowing for more informed care decisions to take place.
5. Post intervention, Curo can monitor to determine success or failure of integration and care provision (i.e falls risk will decrease if successful)
Curo is an established health-tech startup who have spent the last two years developing this product to keep elderly people in their homes longer. The team of 7 people have a background in digital consulting, development, product management, and entrepreneurship and are based in Melbourne & Sydney Australia and Ann Arbor, Michigan.
There are a number of competitors in the senior health monitoring space. However, the balance of costs/usability and insight from each system varies greatly. The existing options typically have three shortcomings with Curo does not:
1) Fixed 'hardwired' systems. This makes them difficult to install (i.e requiring an electrician) & difficult to redeploy. Therefore competitors to Curo are typically found in facilities where the system can be left in place semi-permanently. Curo's major differentiator here is that the system can easily be installed by anyone in the home environment. The home environment is key, as this is where early prevention can best occur.
2) Hardware-focused companies. Unlike Curo's competitors, Curo makes use of existing off-the-shelf IoT sensors, making it hardware agnostic. This means as the technology changes/improves, Curo will continue to use modern equipment produced by the largest IoT companies in the world (i.e Samsung, Amazon, Belkin etc).
REFINEMENT PHASE - Kickoff Session
To kick off the Refinement Phase of the Challenge, we met up in the Curo offices in Melbourne. We went through the existing Curo product and brainstormed ways in which we could use the existing data collected by Curo to identify and reduce falls risk.
ORIGINAL USER JOURNEY
We decided to focus on family members using the product (Curo is aimed at both family members and professional care providers). The reason family members were chosen in this instance was to simplify the experience and allow us to prototype and test part of the solution quickly and easily.
DETERMINING WHAT TO PROTOTYPE
We went through our User Journey and used the "Determining What to Prototype" tool to help us prioritize what to prototype for this challenge. We began by looking at each step of the User Journey and asking "What is the Most Important Question to Answer" for each stage, followed by ideas on "How We Might Test It".
This lead us to focus on two key questions: "How might family members be informed of an increase in falls risk?" followed by "How might they react?" and "How would we want them to react?"
To test this we decided to mock up of a fall risk indicator on top of the existing Curo app. We decided this could then be shown and discussed with family members to ascertain how they might react and gauge how effective the falls risk indicator is at communicating the risk while not causing panic and distress.
PERSONAS AND REFINED USER JOURNEY
We refined our user journey and added two personas, John and his daughter Mary, to better explain the concept. These can be viewed in the slide deck below.
DEVELOPING A PROTOTYPE
Using the existing Curo app as a base, we mocked up two different versions of fall risk indicators, one in which falls risk was communicated more directly and in graduated stages and one with more general recommendations based on the severity of the falls risk. Our hypothesis was that by communicating falls risk on a scale of 1-4 for example, a family member might be more distressed than if that information was presented more as general recommendations (while still alerting them when an urgent intervention needed to be made.) This was one of the key assumptions we wished to test with the prototype.
Questions we want to test
1. How do family members react when seeing a change notification. How are they responding? Are we creating unnecessary stress? What do they think each notification means?
2. What is the best metric to communicate change?
Do you call it falls risk, or just notifying that something is changing?
What is the best language to communicate change?
How will we test this?
Put the prototype in front of someone. “If you saw this warning, what would this evoke for you? What would you want to do in response?”
We conducted tests with two participants in the Curo offices.
We took both versions of the prototype and presented it to the participants, asking them to talk through their thoughts as they interacted with each one.
Both participants drastically preferred prototype A. Overall they both found it much clearer and easier to understand.
Both participants during the testing told unprompted stories about grandparents who had recently passed away and expressed the wish that they had had Curo.
- In Rebecca’s case, they pro-actively removed the family member from their home (against will) because of concern specifically about a fall.
- In Elias’s case, they were unaware of the difficulties the family member was having in the lead up to passing away. Elias believed this ‘fall prevention feature’ that we are testing within Curo would have certainly helped them better understand what he was going through.
IMPACT OF TEST FINDINGS
Our internal discussions had focused largely on Prototype B. We had believed that a softer approach (i.e Education rather than alerts) would be more meaningful to the user. However, the testing showed a clear bias in favor of prototype A.
We had overestimated the ‘panic’ that might be caused by displaying a fall risk and underestimated users desire to get straight to the point (i.e there is a low risk of fall).
Although the user testing didn’t cover the recommendations, both confirmed the importance of making relevant recommendations after having made an assessment. For example “if meals are identified as a problem, what are some tips to help specifically with that”.