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

NCT05264155 | Completed

• 1 other identifier: BSAK19
• Study Type: interventional
• Target: 176
• Locations: 1 country
• Sites: 1

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

Keywords

Promoting Healthier Lifestyles; mHealth

Outcome Measures

Primary Outcomes (2)

• Passive user engagement

  Number of days participants visited in the app.

  one week.

• Active user engagement

  Number of health-related activities participants visited in the app.

  one week.

Study Arms (2)

Control: one-size-fits-all

PLACEBO COMPARATOR

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)

Treatment: personalized

ACTIVE COMPARATOR

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)

Interventions

GameBus (mHealth app) 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.

Control: one-size-fits-all; Treatment: personalized

Eligibility Criteria

• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)

You may qualify if:

• Employee of Noorderkempen governmental organization

You may not qualify if:

• None

Sponsors & Collaborators

• Eindhoven University of Technology: lead

Study Sites (1)

Noorderkempen governmental organization

Wuustwezel, Belgium

Location

Related Publications (1)

• 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.

  PMID: 35357323 DERIVED

MeSH Terms

Conditions

Sedentary Behavior; Risk Reduction Behavior

Condition Hierarchy (Ancestors)

Behavior

Study Officials

• Pieter Van Gorp, Dr.

  Eindhoven University of Technology

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: NONE
• Purpose: PREVENTION
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: February 22, 2022
• First Posted: March 3, 2022
• Study Start: October 14, 2019
• Primary Completion: December 16, 2019
• Study Completion: December 16, 2019
• Last Updated: March 22, 2022

Record last verified: 2022-03

Data Sharing

• IPD Sharing: Will not share

Locations

BE (1)