Can a Smartphone App That Includes a Chatbot-based Coaching and Incentives Increase Physical Activity in Healthy Adults?

Physical Activity Clinical Trial

Official title:

Investigating Different Intervention Components of a Smartphone App to Promote Physical Activity: The ALLY Micro-Randomized Trial

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT03384550
• Other study ID #: 001-Ally
• Status: Completed
• Phase: N/A
• First received: December 14, 2017
• Last updated: March 24, 2018
• Start date: October 24, 2017
• Est. completion date: January 31, 2018

Study information

• Verified date: December 2017
• Source: University of St.Gallen
• Contact: n/a
• Is FDA regulated: No
• Study type: Interventional

Clinical Trial Summary

The investigators conduct a micro-randomized trial to test main effects and moderators of three different intervention components of Ally, a mHealth intervention to promote physical activity that is offered to customers of a large Swiss health insurance. Interventions include the use of different incentive strategies, a weekly planning intervention and daily message prompts to support self-regulation. The Health Action Process Approach (HAPA) as well as principles from behavioral economics were used to guide the development of interventions. Further, sensor data is collected in order to enable prediction of latent contextual variables. These data can be used to build prediction models for the user's state of receptivity, i.e. points in time where the user is able and/or willing to receive, process and utilize the support provided. The results of this study enable the evidence-based development of a just-in-time adaptive intervention for physical activity.

Description:

Just-in-time adaptive interventions (JITAIs) have recently been proposed as framework for health interventions that exploit the potential of mobile health information and sensing technologies. By obtaining contextual information for example from smartphone sensors (e.g. location, time of day), a JITAI adapts the provision of interventions over time with the goal to deliver support when the person needs it most (state of vulnerability) and is most likely to be receptive (state of receptivity).

To facilitate the development of a JITAI for physical activity, the present study has the following objectives:

1. To quantify main effects and interactions of three intervention components of Ally, a mHealth intervention for physical activity.

2. To identify moderators for these intervention components to formulate evidence-based decision rules.

3. To train machine learning models that predict the user's state of receptivity

A micro-randomized trial design is used to meet the objectives of the study. Customers of a large Swiss health insurance company will use Ally over a 10-day baseline and a 6-week study period. During the baseline period, participants only have access to the dashboard of the app and no interventions are administered. During the intervention period, Ally provides daily personalized step goals and different interventions via an interactive chatbot interface based on the MobileCoach system (www.mobile-coach.eu). We investigate the following intervention components as between-subject or within-subject experimental factors during the intervention period: daily self-regulation coaching (two levels, within-subjects), a weekly planning intervention (3 levels, within-subjects) and different incentive strategies (3 levels, between-subjects).

Primary outcome will be the difference in achievement of the daily personalized step goal between intervention and control conditions for all intervention components. We expect all intervention components to increase the probability of goal achievement. Sensitivity analyses will be conducted for per protocol analysis and adjustment for covariates. Moderators of intervention components will be investigated exploratively.

To reach objective 3, we will collect a wide range of smartphone sensor data as well as usage logs of the Ally app throughout the study.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 274
• Est. completion date: January 31, 2018
• Est. primary completion date: December 17, 2017
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: 18 Years and older

Eligibility

Inclusion Criteria:

• Possession of iPhone (5s or newer) or Android smartphone (Android 4.0 or higher)

Exclusion Criteria:

• not enrolled in a complementary health insurance plan

• actively using an activity tracker or comparable smartphone app

• working night shifts

• presence of medical condition(s) that prohibit increased levels of physical activity

Related Conditions & MeSH terms

• Physical Activity

Intervention

Behavioral:
• Self-regulation coaching
Short (2-5 min.) dialogue with the digital coach who provides information relevant for behavioral self-regulation, such as a goal reminder, the distance between the current step count and the goal and strategies to increase daily steps. Participants are randomized to self-regulation coaching or control (no coaching) on a daily basis.
• Planning
A dialogue with the digital coach who prompts the participant to either formulate action plans (when and where the participant can go for a walk) or coping plans (strategies to respond to barriers for increasing daily steps) for the upcoming week. Participants are randomized on a weekly basis to action planning, coping planning or control (no planning).
• Financial Incentives
Participants receive CHF 1 ($1) for each day they meet a personalized adaptive step goal.
• Charity Incentives
Participants donate CHF 1 ($1) to a charity of choice for each day they meet a personalized adaptive step goal.

Locations

Country	Name	City	State
Switzerland	Center for Digital Health Interventions	Saint Gallen

Sponsors (2)

Lead Sponsor	Collaborator
University of St.Gallen	CSS health insurance

Country where clinical trial is conducted

Switzerland, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Daily goal achievement	Goal achievement will be assessed daily by comparing participants daily step count to the respective individualized step goal	seven weeks
Secondary	Steps per day	Steps will be measured daily via the GoogleFit or Apple Health application using the smartphone's built-in accelerometer	seven weeks
Secondary	Behavioral regulation in physical activity	Behavioral regulation in physical activity will be measured using the second version of the Behavioral Regulation in Exercise Questionnaire (BREQ-2). The BREQ-2 consists of five subscales which represent different forms of regulation along the extrinisc-intrinsic continuum of motivation. Because the external regulation subscale in the BREQ-2 exclusively relates to external regulation by other people, it is substituted by the corresponding sub-scale of the Situational Motivation Scale (SIMS). Scores will be reported for each subscale separately. Answers are given on a five-point likert scale with the endpoints 0-not true for me and 4-very true for me.; Scores range from 0 to 16 for the subscales amotivation, external regulation, identified regulation and intrinsic regulation and from 0-12 for the subscale introjected regulation with higher scores representing a stronger manifestation of the respective form of regulation.	seven weeks
Secondary	Engagement	App engagement is measured using the number and length of app launch sessions per day. An app launch session is defined as any interaction of the user with the Ally app, separated by five minutes between events. If a user left the app open and did not take action for 5 minutes or more, then the next interaction with the app counts as a new session.	seven weeks
Secondary	Non-usage attrition	Non-usage attrition is measured using the proportion of users that have stopped using the app for at least 7 days (i.e. there are >7 days between their last session and the end of the study,	seven weeks