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Clinical Trial Details — Status: Completed

Administrative data

NCT number NCT03384550
Other study ID # 001-Ally
Secondary ID
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
Health authority
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

Study Design


Related Conditions & MeSH terms


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
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