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

NCT ID: NCT05264155

Last Updated: 2022-03-22

Basic Information

• Recruitment Status: COMPLETED
• Clinical Phase: NA
• Total Enrollment: 176 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2019-10-14
• Study Completion Date: 2019-12-16

Brief Summary

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.

Conditions

Lifestyle; Lifestyle, Healthy; Lifestyle, Sedentary; Lifestyle Risk Reduction

Study Design

• Allocation Method: RANDOMIZED
• Intervention Model: PARALLEL
• Primary Study Purpose: PREVENTION
• Blinding Strategy: NONE

Study Groups

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.

Group Type: PLACEBO_COMPARATOR

GameBus (mHealth app)

Intervention Type: BEHAVIORAL

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.

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.

Group Type: ACTIVE_COMPARATOR

GameBus (mHealth app)

Intervention Type: BEHAVIORAL

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.

Interventions

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.

Intervention Type: BEHAVIORAL

Eligibility Criteria

Inclusion Criteria

• Employee of Noorderkempen governmental organization

Exclusion Criteria

• None

• Eligible Sex: ALL
• Accepts Healthy Volunteers: Yes

Sponsors

Eindhoven University of Technology

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Principal Investigators

Pieter Van Gorp, Dr.

Role: PRINCIPAL_INVESTIGATOR

Eindhoven University of Technology

Locations

Noorderkempen governmental organization

Wuustwezel, , Belgium

Countries

Belgium

References

Nuijten R, Van Gorp P, Khanshan A, Le Blanc P, van den Berg P, Kemperman A, Simons M. Evaluating the Impact of Adaptive Personalized Goal Setting on Engagement Levels of Government Staff With a Gamified mHealth Tool: Results From a 2-Month Randomized Controlled Trial. JMIR Mhealth Uhealth. 2022 Mar 31;10(3):e28801. doi: 10.2196/28801.

Reference Type: DERIVED

PMID: 35357323 (View on PubMed)

Other Identifiers

BSAK19

Identifier Type: -

Identifier Source: org_study_id