Clinical trial NCT04440553: A Mobile App to Increase Physical Activity in Students
Official title: An mHealth App Using Adaptive Learning to Increase Physical Activity in University Students
Study type: INTERVENTIONAL
Overall status: COMPLETED
Phase: NA
Conditions: Mobile Health
Physical Activity
Exercise
Mood
Machine Learning
Lead sponsor: University of California, Berkeley
Sponsor class: OTHER
First posted: 2020-06-19
Enrollment: 103
Sex: ALL
Minimum age (years): 18
Maximum age (years): 65
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Allocation: RANDOMIZED
Intervention model: PARALLEL
Intervention model description: Participants were randomized to a uniform random group, or a reinforcement learning group. Within these groups, participants received the same types of text-messages, but the delivery schedule differed.
Primary purpose: PREVENTION

Brief summary: Background: Insufficient physical activity is one of the leading risk factors of death worldwide. Behavioral treatments delivered via smartphone apps, hold great promise for helping people engage in healthy behaviors including becoming more physically active. However, similar to 'face-to-face' treatments, effects typically do not seem to be sustained over longer periods of time.

Methods: the investigators developed a smartphone application that uses different types of motivational and feedback text-messaging to motivate individuals to increase physical activity. Here, participants are randomized to either receive messages by a uniform random distribution (n=50), or chosen by a reinforcement learning algorithm (n=50), which learns from daily participant data to personalize the frequency and type of motivation of messages.

Objectives: In the current study, the investigators examine this application in undergraduate and graduate students at the University of California, Berkeley. The investigators compare whether participants in the uniform random or adaptive group have higher increases in steps during the study. The investigators also examine the effect of the different types of messages on step counts. Further the investigators assess the influence of patient characteristics, such as socio-demographic, psychological questionnaire scores and baseline physical activity on the effect of the adaptive arm and effectiveness of the messages. Finally, the investigators assess participant qualitative feedback on the text-messaging program, through feedback provided via questionnaires, text-message and phone interviews.

Detailed description: The investigators developed a smartphone application, the DIAMANTE app, that uses machine learning to generate adaptive text messages, learning from daily participant data to personalize the frequency and type of motivation of messages. In the current study, the investigators will compare this application in undergraduate and graduate students at the University of Berkeley, to text-messaging chosen randomly. This study will provide insight into the effectiveness of this smartphone application for increasing physical activity in university students. Further, it will provide preliminary knowledge on the working mechanisms and variables that moderate the effectiveness of the intervention.

This study is characterized by a factorial design with a total of 3 factors representing Motivational Messages (M), Feedback Messages (F) and the Time Frame (T) when the message was sent, of 4, 5 and 4 levels each, respectively. One level of M and F corresponded to a control treatment, i.e., no message sent. Each participant received one different combination of M, F and T every day.

Both the adaptive and uniform random group will receive the same types of messages: feedback (4 active categories plus no message) and motivation (3 active categories plus no message). However, the message categories, timing and frequency will be optimized by a reinforcement learning algorithm in the adaptive group, and will be delivered with equal probabilities in the uniform random group (following a uniform random distribution).

For the reinforcement learner group, the algorithm training data consists of the historical data of all participants (contextual variables), which include which messages were sent previously and within which time periods, and select clinical/demographic data (such as age, day of the week and depression scores) to improve prediction abilities. Subsequently, the message is chosen based on the predicted effectiveness of messages, combined with a sampling method. As such, it frequently picks out from the most rewarding messages and occasionally explores the messages with uncertainty in their reward.

The aims of this study are:

1. to assess if participants in the reinforcement learning policy show a greater increase in daily steps after six week follow-up, than participants receiving messages with a uniform random distribution
2. to assess if sociodemographic, baseline physical activity behavior/attitudes and psychological factors influence the effect of the adaptive intervention.
3. to assess which messages are most beneficial in increasing physical activity.

Eligibility criteria: Inclusion Criteria: We will include currently enrolled undergraduate and graduate students ages 18 to 65.

-

Exclusion Criteria: Students that do not have a smartphone, are not able to exercise due to disability, or have plans to leave the country during the 6 week study will be excluded.

-

Primary outcomes:
measure: Steps (measured by phone pedometer)
timeFrame: 24 hours (measured for a period of 6 weeks)
measure: Steps (measured by phone pedometer)
timeFrame: Change from baseline to 6 week follow-up
Secondary outcomes:
measure: Depression scores
timeFrame: Change from baseline to 6 week follow-up
measure: Anxiety scores
timeFrame: Change from baseline to 6 week follow-up
measure: Behavioral Activation
timeFrame: Change from baseline to 6 week follow-up
Other outcomes:
Locations:
city: Berkeley
country: United States
facility: Caroline Figueroa
state: California
Study officials:
affiliation: University of California, Berkeley
name: Adrian Aguilera, PhD
role: PRINCIPAL_INVESTIGATOR
References:
citation: Figueroa CA, Deliu N, Chakraborty B, Modiri A, Xu J, Aggarwal J, Jay Williams J, Lyles C, Aguilera A. Daily Motivational Text Messages to Promote Physical Activity in University Students: Results From a Microrandomized Trial. Ann Behav Med. 2022 Feb 11;56(2):212-218. doi: 10.1093/abm/kaab028.