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Completed

NCT Number: NCT04505124

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

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.

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Key information

Age range

18 year and older

Sex eligibility

All sexes

Study type

Interventional

Phase

Not applicable

Primary location

University of St. Gallen

Sankt Gallen, 9000, Switzerland

About this study

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:

  • To understand if avatar-based applications are more effective in promoting physical activity and improving food purchasing behavior compared to conventional tracking applications.
  • To understand if providing individualized shopping tips promotes self-efficacy.
  • To understand if providing consequential health behavior feedback increases behavior- related control over future health (outcome expectancy).
  • To understand if avatar-based applications increase intrinsic motivation compared to conventional health-tracking applications.
  • 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.

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

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

Treatment and study plan

FutureMe App

Behavioral

The FutureMe app provides visual and consequential feedback through a future-self avatar, meaning that the avatar changes its body shape and some additional characteristics based on the participants' activity and food purchasing behaviors. The app tracks participants physical activity behavior (steps) by means of their smartphone (integration to GoogleFit and AppleHealth) and motivates them to increase their step counts. The app also connects to participants' grocery loyalty cards to evaluate their food shopping behavior leveraging the Nutri-score concept. The app motivates participants to improve their food purchases through concrete shopping tips provided in-app.

Conventional TrackingApp

Behavioral

The Control Conventional Tracking app provides numeric and factual feedback through conventional data-dashboards. The app tracks participants physical activity behavior (steps) by means of their smartphone (integration to GoogleFit and AppleHealth) and motivates them to increase their step counts. The app also connects to participants' grocery loyalty cards to evaluate their food shopping behavior leveraging the Nutri-score concept. The app motivates participants to improve their food purchases through concrete shopping tips provided in-app.

Primary outcomes

  1. Physical Activity

    Time frame: 12 weeks

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

  2. Nutri-Score

    Time frame: 12 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.

Secondary outcomes

  1. Food Purchasing Behavior - Salt

    Time frame: 12 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.

  2. Food Purchasing Behavior - Proteins

    Time frame: Continuous 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.

  3. Food Purchasing Behavior - Fibers

    Time frame: Continuous 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.

  4. Food Purchasing Behavior - Saturated Fats

    Time frame: Continuous 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.

  5. User Engagement 1

    Time frame: 12 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.

  6. User Engagement 2

    Time frame: 12 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.

  7. Food Purchasing Behavior - Fruit & Vegetable Purchases

    Time frame: 12 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.

  8. Food Purchasing Behavior - Sugar (excluding Fructose & Lactose)

    Time frame: 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.

  9. Motivational Self-Efficacy

    Time frame: 12 weeks

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

  10. Recovery Self-Efficacy

    Time frame: 12 weeks

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

  11. Perceived behavior-related control over future health

    Time frame: 12 weeks

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

  12. Autonomous Motivation

    Time frame: 12 weeks

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

  13. Controlled Motivation

    Time frame: 12 weeks

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

Sponsors and collaborators

Lead sponsor

Annette Mönninghoff

Other

Collaborators

  • ETH Zurich
  • Helsana Zusatzversicherungen AG

Registry information

Official study title

The Effect of a Future-Self Avatar mHealth Intervention on Physical Activity and Nutrition: the FutureMe Randomized Controlled Trial

Important dates

Study start
2020
Primary completion
2021
Study completion
2021
First posted
Aug 10, 2020
Registry last updated
May 6, 2021

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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