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The Effect of an mHealth Intervention on Physical Activity and Nutrition: the FutureMe Trial

Not Applicable
Completed
Conditions
Healthy
Interventions
Behavioral: Conventional TrackingApp
Behavioral: FutureMe App
Registration Number
NCT04505124
Lead Sponsor
Annette Mönninghoff
Brief Summary

This study is a randomized controlled trial (RCT) which investigates the effect of a Future-Self Avatar intervention (FutureMe App) on physical activity (PA) and nutrition. The Health Action Process Approach (HAPA) and principles from consumer behavior theory were used to guide the development of the intervention.

The study investigates the impact of avatar-based interventions on PA and food purchasing behavior and aims to understand if avatars can help increase the stand-alone effectiveness of mHealth interventions.

Detailed Description

Consumer behavior is a key determinant for chronic disease risk. Mobile health (mHealth) technologies are promising in addressing the rise in risky lifestyle behaviors, as they can be leveraged in large population samples without high human resource or monetary requirements. However, research shows that mHealth technologies are less effective when used stand-alone, meaning without intervention components that require human to human interaction. Leveraging virtual reality in mHealth applications could help increase their stand-alone effectiveness.

Building on behavioral biases, and the health-action-process approach (HAPA), this trial investigates the use of a future-self avatar smartphone intervention (FutureMe app) on consumers' physical activity and food purchasing behavior. A 12-week field experiment aims to show that avatar-based health applications can support behavior change towards more active lifestyles and healthier food choices.

The FutureMe trial has the following objectives:

1. To understand if avatar-based applications are more effective in promoting physical activity and improving food purchasing behavior compared to conventional tracking applications.

2. To understand if providing individualized shopping tips promotes self-efficacy.

3. To understand if providing consequential health behavior feedback increases behavior- related control over future health (outcome expectancy).

4. To understand if avatar-based applications increase intrinsic motivation compared to conventional health-tracking applications.

5. To understand if self-efficacy, outcome expectancy, user engagement or specific types of motivation moderate the effect on PA and foor purchasing.

The study participants recruitment process is supported by a large Swiss health insurance company. The insurer only provides access to potential study participants and is not involved in the design or execution of the study. The insurer has no access to participant study data. Participants are randomized into two groups and either receive the innovative FutureMe intervention or a control intervention consisting of a more conventional nutrition and physical activity tracking app (numeric feedback). Participants will download the respective apps to their personal mobile phone.

Step counts and food purchasing data is collected continuously throughout the trial. The respective psychological constructs (see outcome overview) are collected at baseline and after 12 weeks (end of intervention) via an online questionnaire.

The results of this study enable the evidence-based development of scalable interventions for sustainable physical activity and nutrition behavior change and advance the understanding of the psychological processes behind health behavior change.

Recruitment & Eligibility

Status
COMPLETED
Sex
All
Target Recruitment
95
Inclusion Criteria
  • Living in Switzerland
  • German speaking
  • Participating in at least one grocery loyalty program (Migros Cumulus and/or Coop SuperCard)
  • Apple or Android smartphone
  • Healthy (self-declaration)

Exclusion Criteria

  • <18 years
  • Increasing PA or adjusting nutrition creates health risk (e.g. diabetic)
  • Not living in Switzerland
  • Not German speaking
  • Not using a grocery loyalty card
Exclusion Criteria

Not provided

Study & Design

Study Type
INTERVENTIONAL
Study Design
PARALLEL
Arm && Interventions
GroupInterventionDescription
ControlConventional TrackingAppParticipants use the a control app for 12 weeks. The control app has the following functionality: * Tracking of PA (steps) and nutrition (purchasing behavior at food retailers) * Feedback on health behaviors represented through conventional dashboards (numeric \& text feedback) * Individualized shopping tipps
FutureMeFutureMe AppParticipants use the FutureMe app for 12 weeks. The app has the following functionality: * Opportunity to personalize one's avatar * Tracking of PA (steps) and nutrition (purchasing behavior at food retailers) * Feedback on health behaviors represented through a Future-self avatar (consequential and visual feedback) * Individualized shopping tipps
Primary Outcome Measures
NameTimeMethod
Nutri-Score12 weeks

Nutri-Score calculated based on total food purchases; Minimum Value: -15 (A=Very Good), Maximum Value: 40 (E=Very Bad). Nutriscore will be measured by shopping basket, continuously over 12 weeks.

Physical Activity12 weeks

Steps will be measured daily via the GoogleFit or Apple Health application using the Smartphone's built-in accelerometer.

Secondary Outcome Measures
NameTimeMethod
User Engagement 112 weeks from beginning to end of intervention

Number of app openings during 12 week intervention period. App openings will be measured daily directly via tracking mechanisms in the app.

Food Purchasing Behavior - Sugar (excluding Fructose & Lactose)12 weeks

Sugar in grams (g) per 100g food purchases (based on loyalty card data). Sugar purchases will be measured by shopping basket, continuously over 12 weeks.

Autonomous Motivation12 weeks

Treatment Self-Regulation Questionnaire (TSRQ); Minimum Value: 1 (totally disagree), Maximum Value: 7 (totally agree)

Recovery Self-Efficacy12 weeks

Recovery Self-Efficacy scale adjusted from Schwarzer et al. 2007; Minimum Value: 1 (totally disagree), Maximum Value: 7 (totally agree)

Food Purchasing Behavior - ProteinsContinuous measurement during study (12 weeks)

Proteins in grams (g) per 100g food purchases (based on loyalty card data). Protein purchases will be measured by shopping basket, continuously over 12 weeks.

Food Purchasing Behavior - Fruit & Vegetable Purchases12 weeks

Fruits and Vegetables in grams (g) per 100g food purchases (based on loyalty card data). Fruit and Vegetable purchases will be measured by shopping basket, continuously over 12 weeks.

Food Purchasing Behavior - Salt12 weeks

Salt in grams (g) per 100g food purchases (based on loyalty card data). Salt purchases will be measured by shopping basket, continuously over 12 weeks.

Food Purchasing Behavior - Saturated FatsContinuous measurement during study (12 weeks)

Saturated Fats in grams (g) per 100g food purchases (based on loyalty card data). Saturated fat purchases will be measured by shopping basket, continuously over 12 weeks.

User Engagement 212 weeks from beginning to end of intervention

Time spent in app measured in seconds during 12 week intervention period. Time spent in app will be measured daily directly via tracking mechanisms in the app.

Perceived behavior-related control over future health12 weeks

Perceived behavior-related control scale adjusted from Renner and Schwarzer, 2005; Minimum Value: 1 (totally disagree), Maximum Value: 7 (totally agree)

Food Purchasing Behavior - FibersContinuous measurement during study (12 weeks)

Fibers in grams (g) per 100g food purchases (based on loyalty card data). Fiber purchases will be measured by shopping basket, continuously over 12 weeks.

Motivational Self-Efficacy12 weeks

Motivational Self-Efficacy scale adjusted from Schwarzer et al. 2007; Minimum Value: 1 (totally disagree), Maximum Value: 7 (totally agree)

Controlled Motivation12 weeks

Treatment Self-Regulation Questionnaire (TSRQ); Minimum Value: 1 (totally disagree), Maximum Value: 7 (totally agree)

Trial Locations

Locations (1)

University of St. Gallen

🇨🇭

Saint Gallen, Switzerland

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