Clinical Trial Details
— Status: Active, not recruiting
Administrative data
| NCT number |
NCT06388317 |
| Other study ID # |
123632 |
| Secondary ID |
|
| Status |
Active, not recruiting |
| Phase |
N/A
|
| First received |
|
| Last updated |
|
| Start date |
January 1, 2023 |
| Est. completion date |
February 1, 2025 |
Study information
| Verified date |
April 2024 |
| Source |
Western University, Canada |
| Contact |
n/a |
| Is FDA regulated |
No |
| Health authority |
|
| Study type |
Interventional
|
Clinical Trial Summary
This 12-week study compares the effectiveness of personalized daily step goals generated by a
machine learning algorithm in the Sprout app versus fixed daily step goals of 10,000 steps
among adults. Participants will be recruited through the Sprout app, and after a 1-week
run-in period, they will be assigned to either the intervention or control group. The
intervention group will receive adaptive goals based on their historical step data, while the
control group will have a fixed goal. Both groups will receive financial incentives. This
study aims to inform future interventions measuring changes in daily steps and app engagement
levels (i.e., time spent on app, number of app opens) by studying how using financial
incentives and an adaptive goal-setting design can improve physical activity levels of app
users, informed by a machine learning algorithm.
Description:
The research team will partner with Telus Health and the Sprout app (iOS and Android
versions), which is designed to increase physical activity by allowing participants to track
their daily step counts and actively compare them to their daily goals. After downloading
Sprout, users can open the app and navigate through the landing and home page. On the home
page, the number of steps completed that day and the users daily step goal are shown.
Participants can click on two icons at the top of the home page. If the users click on the
left icon, the history page is displayed. The history page allows participants to track their
performance over the past week by showing their daily steps and daily goals on a color-coded
bar graph. The green bar indicates the accomplishment of achieving the step goal on the
corresponding day, and the red bar indicates failure to achieve the step goal on the
corresponding day. The built-in health chip in the iPhone and Android devices collects the
step data, and the accuracy of step counts collected by the iPhone and Android health chip
has been validated in a number of studies to have comparable accuracy to an ActiGraph. The
Sprout app first saves the step and goal data locally on the phone and then syncs with the
server every 10 min when the phone is active. The push notification for the app is also
activated (if activated by the user), and the standard iOS and Android push notification is
used. The push notification is visible in the landing page and in the recent notifications
tab on the phone.
Eligible participants will start a 1-week run-in period after downloading the app. The
purpose of the run-in period is to collect baseline daily steps, and assess if the
participant is able to comply with the requirements needed to regularly use the Sprout app.
During the run-in period, all participants in the control and the intervention groups will
receive identical sets of daily step goals for day 1 to day 7 as 3000, 3500, 4000, 4500,
5000, 5500, and 6000 steps, respectively. The machine learning algorithm will not be used to
compute step goals for participants in the intervention group during the run-in period.
Dynamically increasing step goals will be used in the run-in period to engage participants in
using the app regularly. In addition, all participants will receive a push notification at 8
AM that provides the day's step goal, and if the participant accomplishes the goal before 8
PM, then another push notification will be sent to congratulate that participant on reaching
their step goal for the day. The identical goals between the 2 groups during the run-in
period is used to establish a reference level of initial physical activity, which will be
used in the statistical analyses to compare the difference in daily steps between run-in and
12 weeks for the 2 groups. Data collected during the run-in period will be used by the
machine learning algorithm to compute step goals for the intervention period. This is a valid
approach because run-in data will be indicative of the preference of different participants.
All participants will have been placed into one of two groups. The allocation of app users to
groups will be implemented by Telus Health after the run-in period. After the 1-week run-in
period, participants in the control group will be provided with constant daily step goals
that were set to 10,000 steps per day through the Sprout app. Participants will receive a
push notification at 8:00 AM every day that provides that day's step goal (10,000 steps), and
if the participant achieves the goal before 8:00 PM, then another push notification will be
sent to congratulate the participant on reaching their step goal (of 10,000 steps) for the
day.
After the 1-week run-in period, participants in the intervention group will receive
adaptively personalized step goals through the Sprout app. The daily step goals will be
computed using machine learning on the complete history (past steps and goals) of the user.
Machine learning will be applied every day to reduce variance in future steps and goals.
Participants will receive a push notification at 8:00 AM every day that provides today's step
goal, and if the participant accomplishes the goal before 8:00 PM, then another push
notification will be sent to congratulate the participant on reaching their step goal for
that day. Machine learning will adaptively compute personalized step goals that are predicted
to maximize future physical activity for each participant based on all their past steps' data
and goals of each participant. Machine learning is applied to each participant individually,
and it consists of two main steps. The first step is to use all of the participant's data to
construct a quantitative model that predicts how many steps the participant will take in the
future, given a prescribed set of step goals, and an important aspect of the model is a
component that describes how achieving goals in the present can increase the likelihood of
achieving goals in the future. The second step is to use this quantitative model to select a
sequence of step goals that maximizes the predicted future number of steps. To make the
process of updating step goals adaptive, machine learning is applied each day (using all the
users' past data) to generate step goals for the coming day. Moreover, the step goals
computed by machine learning for the coming day are not constant but increase or decrease
based on the model prediction. The Sprout app will automatically track the participants' step
counts each day and will provide goals regardless of their level of engagement within the app
over the 12-week study period. As the data is being analyzed retrospectively, no "end of
study" letter will be provided to participants. However, upon registering for the studies
procedures, all users provided written informed consent for their data to be collected and
analyzed.
