Evaluation of the Impact of Adaptive Goal Setting on Engagement Levels of Government Staff With a Gamified mHealth Tool (BSAK19)

March 7, 2022 updated by: Eindhoven University of Technology

Evaluating the Impact of Adaptive Personalized Goal Setting on Engagement Levels of Government Staff With a Gamified mHealth Tool: Study Protocol for a 2-Month Randomized Controlled Trial

Background: Although the health benefits of physical activity are well established, it remains challenging for people to adopt a more active lifestyle. Mobile health (mHealth) interventions can be effective tools to promote physical activity and reduce sedentary behavior. Promising results have been obtained by using gamification techniques as behavior change strategies, especially when they were tailored toward an individual's preferences and goals; yet, it remains unclear how goals could be personalized to effectively promote health behaviors.

Objective: In this study, the investigators aim to evaluate the impact of personalized goal setting in the context of gamified mHealth interventions. The investigators hypothesize that interventions suggesting health goals that are tailored based on end users' (self-reported) current and desired capabilities will be more engaging than interventions with generic goals.

Methods: The study was designed as a 2-arm randomized intervention trial. Participants were recruited among staff members of Noorderkempen governmental organization. They participated in an 8-week digital health promotion campaign that was especially designed to promote walks, bike rides, and sports sessions. Using an mHealth app, participants could track their performance on two social leaderboards: a leaderboard displaying the individual scores of participants and a leaderboard displaying the average scores per organizational department. The mHealth app also provided a news feed that showed when other participants had scored points. Points could be collected by performing any of the 6 assigned tasks (eg, walk for at least 2000 m). The level of complexity of 3 of these 6 tasks was updated every 2 weeks by changing either the suggested task intensity or the suggested frequency of the task. The 2 intervention arms-with participants randomly assigned-consisted of a personalized treatment that tailored the complexity parameters based on participants' self-reported capabilities and goals and a control treatment where the complexity parameters were set generically based on national guidelines. Measures were collected from the mHealth app as well as from intake and posttest surveys and analyzed using hierarchical linear models.

Note: Eindhoven University of Technology is not an official GCP sponsor. Hence, this study is not a medical clinical trial.

Study Overview

Status

Completed

Conditions

Intervention / Treatment

Study Type

Interventional

Enrollment (Actual)

176

Phase

  • 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

  • Belgium
      • Wuustwezel, Belgium
        • Noorderkempen governmental organization

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

Ages Eligible for Study

  • Child
  • Adult
  • Older Adult

Accepts Healthy Volunteers

Yes

Genders Eligible for Study

All

Description

Inclusion Criteria:

  • Employee of Noorderkempen governmental organization

Exclusion Criteria:

  • None

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: None (Open Label)

Arms and Interventions

Participant Group / Arm
Intervention / Treatment
Placebo Comparator: Control: one-size-fits-all

The study was designed as a 2-arm randomized intervention trial. The experimental setup was centered around setting the complexity parameters (ie, the X values) of the 3 dynamic tasks. In particular, the parameters to determine were as follows: (1) the minimum distance of a longer walk, (2) the minimum distance of a longer bike ride, and (3) the maximum number of rewarded sports sessions (and consequently the number of rewarded points per sports session).

For the control group, the parameter values of the dynamic tasks were based on national guidelines.
Behavioral: GameBus (mHealth app)
Using the mHealth app GameBus, participants could track their performance on 2 social leaderboards: a leaderboard displaying the individual scores of participants and a leaderboard displaying the average scores per department. To score points on these leaderboards, a participant was given a set of 6 tasks that, upon completion, were rewarded with points. In this study, 3/6 tasks were either updated generically (for the control group) or personalized (for the treatment group). By means of the mobile app, users could manually register that they had performed a task. Alternatively, users could use an activity tracker to automatically track their efforts. The activity trackers that were supported included Google Fit, Strava, and a GPS-based activity tracker. Finally, GameBus provided a set of features for social support: a newsfeed showed when other participants had scored points, and participants could like and comment on each other's healthy achievements as well as chat with each other.
Active Comparator: Treatment: personalized

The study was designed as a 2-arm randomized intervention trial. The experimental setup was centered around setting the complexity parameters (ie, the X values) of the 3 dynamic tasks. In particular, the parameters to determine were as follows: (1) the minimum distance of a longer walk, (2) the minimum distance of a longer bike ride, and (3) the maximum number of rewarded sports sessions (and consequently the number of rewarded points per sports session).

For the treatment group, these parameters were tailored to the users' self-reported capabilities and health goals.
Behavioral: GameBus (mHealth app)
Using the mHealth app GameBus, participants could track their performance on 2 social leaderboards: a leaderboard displaying the individual scores of participants and a leaderboard displaying the average scores per department. To score points on these leaderboards, a participant was given a set of 6 tasks that, upon completion, were rewarded with points. In this study, 3/6 tasks were either updated generically (for the control group) or personalized (for the treatment group). By means of the mobile app, users could manually register that they had performed a task. Alternatively, users could use an activity tracker to automatically track their efforts. The activity trackers that were supported included Google Fit, Strava, and a GPS-based activity tracker. Finally, GameBus provided a set of features for social support: a newsfeed showed when other participants had scored points, and participants could like and comment on each other's healthy achievements as well as chat with each other.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Passive user engagement
Time Frame: one week.
Number of days participants visited in the app.
one week.
Active user engagement
Time Frame: one week.
Number of health-related activities participants visited in the app.
one week.

Collaborators and Investigators

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

Sponsor

Eindhoven University of Technology

Investigators

  • Principal Investigator: Pieter Van Gorp, Dr., Eindhoven University of Technology

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)

October 14, 2019

Primary Completion (Actual)

December 16, 2019

Study Completion (Actual)

December 16, 2019

Study Registration Dates

First Submitted

February 22, 2022

First Submitted That Met QC Criteria

March 2, 2022

First Posted (Actual)

March 3, 2022

Study Record Updates

Last Update Posted (Actual)

March 22, 2022

Last Update Submitted That Met QC Criteria

March 7, 2022

Last Verified

March 1, 2022

More Information

Terms related to this study

Keywords

Other Study ID Numbers

  • BSAK19

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

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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