A Mobile App to Increase Physical Activity in Students

June 22, 2020 updated by: University of California, Berkeley

An mHealth App Using Adaptive Learning to Increase Physical Activity in University Students

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.

Study Overview

Status

Status

Indicates the current recruitment status or the expanded access status.
Completed

Conditions

Conditions

The disease, disorder, syndrome, illness, or injury that is being studied. On ClinicalTrials.gov, conditions may also include other health-related issues, such as lifespan, quality of life, and health risks.

Intervention / Treatment

Intervention / Treatment

A process or action that is the focus of a clinical study. Interventions include drugs, medical devices, procedures, vaccines, and other products that are either investigational or already available. Interventions can also include noninvasive approaches, such as education or modifying diet and exercise.

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.

Study Type

Study Type

Describes the nature of a clinical study. Study types include interventional studies (also called clinical trials), observational studies (including patient registries), and expanded access.
Interventional

Enrollment (Actual)

Enrollment

The number of participants in a clinical study. The "anticipated" enrollment is the target number of participants that the researchers need for the study.
103

Phase

Phase

The stage of a clinical trial studying a drug or biological product, based on definitions developed by the U.S. Food and Drug Administration (FDA). The phase is based on the study's objective, the number of participants, and other characteristics. There are five phases: Early Phase 1 (formerly listed as Phase 0), Phase 1, Phase 2, Phase 3, and Phase 4. Not Applicable is used to describe trials without FDA-defined phases, including trials of devices or behavioral interventions.
  • Not Applicable

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

  • United States
    • California
      • Berkeley, California, United States, 94709
        • Caroline Figueroa

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Eligibility Criteria

The key requirements that people who want to participate in a clinical study must meet or the characteristics they must have. Eligibility criteria consist of both inclusion criteria (which are required for a person to participate in the study) and exclusion criteria (which prevent a person from participating). Types of eligibility criteria include whether a study accepts healthy volunteers, has age or age group requirements, or is limited by sex.

Ages Eligible for Study

18 years to 65 years (Adult, Older Adult)

Accepts Healthy Volunteers

Yes

Genders Eligible for Study

All

Description

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.

-

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

  • Primary Purpose: Prevention
  • Allocation: Randomized
  • Interventional Model: Parallel Assignment
  • Masking: Single

Arms and Interventions

Participant Group / Arm

Participant Group / Arm

A group or subgroup of participants in a clinical trial that receives a specific intervention/treatment, or no intervention, according to the trial's protocol.
Intervention / Treatment

Intervention / Treatment

A process or action that is the focus of a clinical study. Interventions include drugs, medical devices, procedures, vaccines, and other products that are either investigational or already available. Interventions can also include noninvasive approaches, such as education or modifying diet and exercise.
Active Comparator: Uniform random
In this arm the types of messages were sent out randomly, i.e. with a uniform random distribution.
Behavioral: Uniform random message delivery
The uniform random intervention group receives feedback and motivational messages chosen from the messaging banks with equal probabilities.
Experimental: Reinforcement learning
In this arm the types of messages were chosen by a reinforcement learning algorithm. The decision about which message to send was based on several contextual variables, including data for the pedometer app, and consecutive days since messages from different categories were sent.
Behavioral: Reinforcement learning message delivery
The adaptive intervention group receives messages chosen from the messaging banks by a reinforcement learning algorithm.

What is the study measuring?

Primary Outcome Measures

Primary Outcome Measures

In a clinical study's protocol, the planned outcome measure that is the most important for evaluating the effect of an intervention/treatment. Most clinical studies have one primary outcome measure, but some have more than one.
Outcome Measure
Measure Description
Time Frame
Steps (measured by phone pedometer)
Time Frame: 24 hours (measured for a period of 6 weeks)
Change in daily step counts (today's steps count minus yesterday's steps count)
24 hours (measured for a period of 6 weeks)
Steps (measured by phone pedometer)
Time Frame: Change from baseline to 6 week follow-up
Mean change in daily step counts during the course of the study
Change from baseline to 6 week follow-up

Secondary Outcome Measures

Secondary Outcome Measures

In a clinical study's protocol, a planned outcome measure that is not as important as the primary outcome measure for evaluating the effect of an intervention but is still of interest. Most clinical studies have more than one secondary outcome measure.
Outcome Measure
Measure Description
Time Frame
Depression scores
Time Frame: Change from baseline to 6 week follow-up
Patient Health Questionnaire 9 item (PHQ-9). The PHQ-9 has scores from 0 to 27. Higher scores mean a worse outcome.
Change from baseline to 6 week follow-up
Anxiety scores
Time Frame: Change from baseline to 6 week follow-up
General Anxiety Disorder 7 item (GAD-7). The GAD-7 has scores from 0 to 21. Higher scores mean a worse outcome.
Change from baseline to 6 week follow-up
Behavioral Activation
Time Frame: Change from baseline to 6 week follow-up
Behavioral Activation for Depression Scale - Short Form (BADS-SF). The BADS-SF has scores from 0-54. Higher scores mean better outcomes.
Change from baseline to 6 week follow-up

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Sponsor

The organization or person who initiates the study and who has authority and control over the study.
University of California, Berkeley

Investigators

Investigators

A researcher involved in a clinical study. Related terms include site principal investigator, site sub-investigator, study chair, study director, and study principal investigator.
  • Principal Investigator: Adrian Aguilera, PhD, University of California, Berkeley

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

Study Start

The actual date on which the first participant was enrolled in a clinical study. The "anticipated" study start date is the date that the researchers think will be the study start date.
September 12, 2019

Primary Completion (Actual)

Primary Completion

The date on which the last participant in a clinical study was examined or received an intervention to collect final data for the primary outcome measure. Whether the clinical study ended according to the protocol or was terminated does not affect this date. For clinical studies with more than one primary outcome measure with different completion dates, this term refers to the date on which data collection is completed for all the primary outcome measures. The "anticipated" primary completion date is the date that the researchers think will be the primary completion date for the study.
December 10, 2019

Study Completion (Actual)

Study Completion

The date on which the last participant in a clinical study was examined or received an intervention/treatment to collect final data for the primary outcome measures, secondary outcome measures, and adverse events (that is, the last participant's last visit). The "anticipated" study completion date is the date that the researchers think will be the study completion date.
December 20, 2019

Study Registration Dates

First Submitted

First Submitted

The date on which the study sponsor or investigator first submitted a study record to ClinicalTrials.gov. There is typically a delay of a few days between the first submitted date and the record's availability on ClinicalTrials.gov (the first posted date).
June 17, 2020

First Submitted That Met QC Criteria

First Submitted That Met QC Criteria

The date on which the study sponsor or investigator first submits a study record that is consistent with National Library of Medicine (NLM) quality control (QC) review criteria. The sponsor or investigator may need to revise and submit a study record one or more times before NLM's QC review criteria are met. It is the responsibility of the sponsor or investigator to ensure that the study record is consistent with the NLM QC review criteria.
June 17, 2020

First Posted (Actual)

First Posted

The date on which the study record was first available on ClinicalTrials.gov after National Library of Medicine (NLM) quality control (QC) review has concluded. There is typically a delay of a few days between the date the study sponsor or investigator submitted the study record and the first posted date.
June 19, 2020

Study Record Updates

Last Update Posted (Actual)

Last Update Posted

The most recent date on which changes to a study record were made available on ClinicalTrials.gov. There may be a delay between when the changes were submitted to ClinicalTrials.gov by the study's sponsor or investigator (the last update submitted date) and the last update posted date.
June 24, 2020

Last Update Submitted That Met QC Criteria

Last Update Submitted That Met QC Criteria

The most recent date on which the study sponsor or investigator submitted changes to a study record that are consistent with National Library of Medicine (NLM) quality control (QC) review criteria. It is the responsibility of the sponsor or investigator to ensure that the study record is consistent with the NLM QC review criteria.
June 22, 2020

Last Verified

Last Verified

The most recent date on which the study sponsor or investigator confirmed the information about a clinical study on ClinicalTrials.gov as accurate and current. If a study with a recruitment status of recruiting; not yet recruiting; or active, not recruiting has not been confirmed within the past 2 years, the study's recruitment status is shown as unknown.
June 1, 2020

More Information

Terms related to this study

Other Study ID Numbers

Other Study ID Numbers

Identifiers or ID numbers other than the NCT number that are assigned to a clinical study by the study's sponsor, funders, or others. These numbers may include unique identifiers from other trial registries and National Institutes of Health grant numbers.
  • 2019-04-12118

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

YES

IPD Plan Description

Individual participant data that underlies the results reported in the articles will be made available to researchers on request after deidentification.

IPD Sharing Time Frame

After publication of the data, no end date

IPD Sharing Access Criteria

Anyone with a methodologically sound proposal.

IPD Sharing Supporting Information Type

  • STUDY_PROTOCOL
  • SAP
  • ANALYTIC_CODE

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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