<clinical_study>
  <!-- This xml conforms to an XML Schema at:
    https://clinicaltrials.gov/ct2/html/images/info/public.xsd -->
  <required_header>
    <download_date>ClinicalTrials.gov processed this data on September 20, 2023</download_date>
    <link_text>Link to the current ClinicalTrials.gov record.</link_text>
    <url>https://clinicaltrials.gov/ct2/show/NCT04505124</url>
  </required_header>
  <id_info>
    <org_study_id>FSA2020</org_study_id>
    <nct_id>NCT04505124</nct_id>
  </id_info>
  <brief_title>The Effect of an mHealth Intervention on Physical Activity and Nutrition: the FutureMe Trial</brief_title>
  <official_title>The Effect of a Future-Self Avatar mHealth Intervention on Physical Activity and Nutrition: the FutureMe Randomized Controlled Trial</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>Annette Mönninghoff</agency>
      <agency_class>Other</agency_class>
    </lead_sponsor>
    <collaborator>
      <agency>Helsana Zusatzversicherungen AG</agency>
      <agency_class>Other</agency_class>
    </collaborator>
    <collaborator>
      <agency>ETH Zurich</agency>
      <agency_class>Other</agency_class>
    </collaborator>
  </sponsors>
  <source>University of St.Gallen</source>
  <oversight_info>
    <has_dmc>No</has_dmc>
    <is_fda_regulated_drug>No</is_fda_regulated_drug>
    <is_fda_regulated_device>No</is_fda_regulated_device>
  </oversight_info>
  <brief_summary>
    <textblock>
      This study is a randomized controlled trial (RCT) which investigates the effect of a&#xD;
      Future-Self Avatar intervention (FutureMe App) on physical activity (PA) and nutrition. The&#xD;
      Health Action Process Approach (HAPA) and principles from consumer behavior theory were used&#xD;
      to guide the development of the intervention.&#xD;
&#xD;
      The study investigates the impact of avatar-based interventions on PA and food purchasing&#xD;
      behavior and aims to understand if avatars can help increase the stand-alone effectiveness of&#xD;
      mHealth interventions.&#xD;
    </textblock>
  </brief_summary>
  <detailed_description>
    <textblock>
      Consumer behavior is a key determinant for chronic disease risk. Mobile health (mHealth)&#xD;
      technologies are promising in addressing the rise in risky lifestyle behaviors, as they can&#xD;
      be leveraged in large population samples without high human resource or monetary&#xD;
      requirements. However, research shows that mHealth technologies are less effective when used&#xD;
      stand-alone, meaning without intervention components that require human to human interaction.&#xD;
      Leveraging virtual reality in mHealth applications could help increase their stand-alone&#xD;
      effectiveness.&#xD;
&#xD;
      Building on behavioral biases, and the health-action-process approach (HAPA), this trial&#xD;
      investigates the use of a future-self avatar smartphone intervention (FutureMe app) on&#xD;
      consumers' physical activity and food purchasing behavior. A 12-week field experiment aims to&#xD;
      show that avatar-based health applications can support behavior change towards more active&#xD;
      lifestyles and healthier food choices.&#xD;
&#xD;
      The FutureMe trial has the following objectives:&#xD;
&#xD;
        1. To understand if avatar-based applications are more effective in promoting physical&#xD;
           activity and improving food purchasing behavior compared to conventional tracking&#xD;
           applications.&#xD;
&#xD;
        2. To understand if providing individualized shopping tips promotes self-efficacy.&#xD;
&#xD;
        3. To understand if providing consequential health behavior feedback increases behavior-&#xD;
           related control over future health (outcome expectancy).&#xD;
&#xD;
        4. To understand if avatar-based applications increase intrinsic motivation compared to&#xD;
           conventional health-tracking applications.&#xD;
&#xD;
        5. To understand if self-efficacy, outcome expectancy, user engagement or specific types of&#xD;
           motivation moderate the effect on PA and foor purchasing.&#xD;
&#xD;
      The study participants recruitment process is supported by a large Swiss health insurance&#xD;
      company. The insurer only provides access to potential study participants and is not involved&#xD;
      in the design or execution of the study. The insurer has no access to participant study data.&#xD;
      Participants are randomized into two groups and either receive the innovative FutureMe&#xD;
      intervention or a control intervention consisting of a more conventional nutrition and&#xD;
      physical activity tracking app (numeric feedback). Participants will download the respective&#xD;
      apps to their personal mobile phone.&#xD;
&#xD;
      Step counts and food purchasing data is collected continuously throughout the trial. The&#xD;
      respective psychological constructs (see outcome overview) are collected at baseline and&#xD;
      after 12 weeks (end of intervention) via an online questionnaire.&#xD;
&#xD;
      The results of this study enable the evidence-based development of scalable interventions for&#xD;
      sustainable physical activity and nutrition behavior change and advance the understanding of&#xD;
      the psychological processes behind health behavior change.&#xD;
    </textblock>
  </detailed_description>
  <overall_status>Completed</overall_status>
  <start_date type="Actual">November 17, 2020</start_date>
  <completion_date type="Actual">April 30, 2021</completion_date>
  <primary_completion_date type="Actual">March 9, 2021</primary_completion_date>
  <phase>N/A</phase>
  <study_type>Interventional</study_type>
  <has_expanded_access>No</has_expanded_access>
  <study_design_info>
    <allocation>Randomized</allocation>
    <intervention_model>Parallel Assignment</intervention_model>
    <primary_purpose>Prevention</primary_purpose>
    <masking>Quadruple (Participant, Care Provider, Investigator, Outcomes Assessor)</masking>
    <masking_description>Participants are blinded. Investigators are also blinded. The intervention is delivered through a mobile phone application (care provider) and automatically and objectively assessed (through sensor and purchasing data).&#xD;
Some constructs are assessed via self-reported questionnaire at baseline, 12 weeks and during the 6 month voluntary follow-up (Treatment Self-Regulation Questionnaire (TSRQ); International Physical Activity Questionnaire (IPAQ short-form); Perceived competence maintaining a healthy diet (Williams, Deci &amp; Ryan); Perceived behavior-related control (Renner &amp; Schwarzer)).</masking_description>
  </study_design_info>
  <primary_outcome>
    <measure>Physical Activity</measure>
    <time_frame>12 weeks</time_frame>
    <description>Steps will be measured daily via the GoogleFit or Apple Health application using the Smartphone's built-in accelerometer.</description>
  </primary_outcome>
  <primary_outcome>
    <measure>Nutri-Score</measure>
    <time_frame>12 weeks</time_frame>
    <description>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.</description>
  </primary_outcome>
  <secondary_outcome>
    <measure>Food Purchasing Behavior - Salt</measure>
    <time_frame>12 weeks</time_frame>
    <description>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.</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Food Purchasing Behavior - Proteins</measure>
    <time_frame>Continuous measurement during study (12 weeks)</time_frame>
    <description>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.</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Food Purchasing Behavior - Fibers</measure>
    <time_frame>Continuous measurement during study (12 weeks)</time_frame>
    <description>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.</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Food Purchasing Behavior - Saturated Fats</measure>
    <time_frame>Continuous measurement during study (12 weeks)</time_frame>
    <description>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.</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>User Engagement 1</measure>
    <time_frame>12 weeks from beginning to end of intervention</time_frame>
    <description>Number of app openings during 12 week intervention period. App openings will be measured daily directly via tracking mechanisms in the app.</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>User Engagement 2</measure>
    <time_frame>12 weeks from beginning to end of intervention</time_frame>
    <description>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.</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Food Purchasing Behavior - Fruit &amp; Vegetable Purchases</measure>
    <time_frame>12 weeks</time_frame>
    <description>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.</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Food Purchasing Behavior - Sugar (excluding Fructose &amp; Lactose)</measure>
    <time_frame>12 weeks</time_frame>
    <description>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.</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Motivational Self-Efficacy</measure>
    <time_frame>12 weeks</time_frame>
    <description>Motivational Self-Efficacy scale adjusted from Schwarzer et al. 2007; Minimum Value: 1 (totally disagree), Maximum Value: 7 (totally agree)</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Recovery Self-Efficacy</measure>
    <time_frame>12 weeks</time_frame>
    <description>Recovery Self-Efficacy scale adjusted from Schwarzer et al. 2007; Minimum Value: 1 (totally disagree), Maximum Value: 7 (totally agree)</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Perceived behavior-related control over future health</measure>
    <time_frame>12 weeks</time_frame>
    <description>Perceived behavior-related control scale adjusted from Renner and Schwarzer, 2005; Minimum Value: 1 (totally disagree), Maximum Value: 7 (totally agree)</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Autonomous Motivation</measure>
    <time_frame>12 weeks</time_frame>
    <description>Treatment Self-Regulation Questionnaire (TSRQ); Minimum Value: 1 (totally disagree), Maximum Value: 7 (totally agree)</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Controlled Motivation</measure>
    <time_frame>12 weeks</time_frame>
    <description>Treatment Self-Regulation Questionnaire (TSRQ); Minimum Value: 1 (totally disagree), Maximum Value: 7 (totally agree)</description>
  </secondary_outcome>
  <number_of_arms>2</number_of_arms>
  <enrollment type="Actual">95</enrollment>
  <condition>Healthy</condition>
  <arm_group>
    <arm_group_label>FutureMe</arm_group_label>
    <arm_group_type>Experimental</arm_group_type>
    <description>Participants use the FutureMe app for 12 weeks. The app has the following functionality:&#xD;
Opportunity to personalize one's avatar&#xD;
Tracking of PA (steps) and nutrition (purchasing behavior at food retailers)&#xD;
Feedback on health behaviors represented through a Future-self avatar (consequential and visual feedback)&#xD;
Individualized shopping tipps</description>
  </arm_group>
  <arm_group>
    <arm_group_label>Control</arm_group_label>
    <arm_group_type>Active Comparator</arm_group_type>
    <description>Participants use the a control app for 12 weeks. The control app has the following functionality:&#xD;
Tracking of PA (steps) and nutrition (purchasing behavior at food retailers)&#xD;
Feedback on health behaviors represented through conventional dashboards (numeric &amp; text feedback)&#xD;
Individualized shopping tipps</description>
  </arm_group>
  <intervention>
    <intervention_type>Behavioral</intervention_type>
    <intervention_name>FutureMe App</intervention_name>
    <description>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.</description>
    <arm_group_label>FutureMe</arm_group_label>
  </intervention>
  <intervention>
    <intervention_type>Behavioral</intervention_type>
    <intervention_name>Conventional TrackingApp</intervention_name>
    <description>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.</description>
    <arm_group_label>Control</arm_group_label>
  </intervention>
  <eligibility>
    <criteria>
      <textblock>
        Inclusion Criteria:&#xD;
&#xD;
          -  Living in Switzerland&#xD;
&#xD;
          -  German speaking&#xD;
&#xD;
          -  Participating in at least one grocery loyalty program (Migros Cumulus and/or Coop&#xD;
             SuperCard)&#xD;
&#xD;
          -  Apple or Android smartphone&#xD;
&#xD;
          -  Healthy (self-declaration)&#xD;
&#xD;
        Exclusion Criteria&#xD;
&#xD;
          -  &lt;18 years&#xD;
&#xD;
          -  Increasing PA or adjusting nutrition creates health risk (e.g. diabetic)&#xD;
&#xD;
          -  Not living in Switzerland&#xD;
&#xD;
          -  Not German speaking&#xD;
&#xD;
          -  Not using a grocery loyalty card&#xD;
      </textblock>
    </criteria>
    <gender>All</gender>
    <minimum_age>18 Years</minimum_age>
    <maximum_age>N/A</maximum_age>
    <healthy_volunteers>Accepts Healthy Volunteers</healthy_volunteers>
  </eligibility>
  <overall_official>
    <last_name>Annette Mönninghoff</last_name>
    <role>Principal Investigator</role>
    <affiliation>University of St. Gallen, Institute for Customer Insight</affiliation>
  </overall_official>
  <location>
    <facility>
      <name>University of St. Gallen</name>
      <address>
        <city>Saint Gallen</city>
        <zip>9000</zip>
        <country>Switzerland</country>
      </address>
    </facility>
  </location>
  <location_countries>
    <country>Switzerland</country>
  </location_countries>
  <link>
    <url>http://www.gesundheitsrisiko.de/docs/RACKEnglish.pdf</url>
    <description>Renner and Schwarzer, 2005</description>
  </link>
  <reference>
    <citation>Steinemann N, Grize L, Ziesemer K, Kauf P, Probst-Hensch N, Brombach C. Relative validation of a food frequency questionnaire to estimate food intake in an adult population. Food Nutr Res. 2017 Mar 29;61(1):1305193. doi: 10.1080/16546628.2017.1305193. eCollection 2017.</citation>
    <PMID>28469546</PMID>
  </reference>
  <reference>
    <citation>Schwarzer R, Schuz B, Ziegelmann JP, Lippke S, Luszczynska A, Scholz U. Adoption and maintenance of four health behaviors: theory-guided longitudinal studies on dental flossing, seat belt use, dietary behavior, and physical activity. Ann Behav Med. 2007 Apr;33(2):156-66. doi: 10.1007/BF02879897.</citation>
    <PMID>17447868</PMID>
  </reference>
  <verification_date>May 2021</verification_date>
  <study_first_submitted>June 26, 2020</study_first_submitted>
  <study_first_submitted_qc>August 6, 2020</study_first_submitted_qc>
  <study_first_posted type="Actual">August 10, 2020</study_first_posted>
  <last_update_submitted>May 5, 2021</last_update_submitted>
  <last_update_submitted_qc>May 5, 2021</last_update_submitted_qc>
  <last_update_posted type="Actual">May 6, 2021</last_update_posted>
  <responsible_party>
    <responsible_party_type>Sponsor-Investigator</responsible_party_type>
    <investigator_affiliation>University of St.Gallen</investigator_affiliation>
    <investigator_full_name>Annette Mönninghoff</investigator_full_name>
    <investigator_title>PhD Candidate and Project Leader</investigator_title>
  </responsible_party>
  <keyword>mHealth</keyword>
  <keyword>Physical Activity</keyword>
  <keyword>Nutrition</keyword>
  <keyword>Motivation</keyword>
  <keyword>Self-competence</keyword>
  <keyword>Self-efficacy</keyword>
  <keyword>Perceived behavior related control</keyword>
  <keyword>Avatar</keyword>
  <keyword>Randomized controlled trial</keyword>
  <patient_data>
    <sharing_ipd>No</sharing_ipd>
    <ipd_description>Based on the data privacy statement of the study and the ethic's commission application, data cannot be analyzed or shared on an individual participant basis.</ipd_description>
  </patient_data>
  <!-- Results have not yet been posted for this study                                          -->
</clinical_study>


download date: ClinicalTrials.gov processed this data on September 20, 2023
link text: Link to the current ClinicalTrials.gov record.
url: https://clinicaltrials.gov/ct2/show/NCT04505124
org study id: FSA2020
nct id: NCT04505124
lead sponsor: 
collaborator: 
collaborator: 
has dmc: No
is fda regulated drug: No
is fda regulated device: No
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.
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.
allocation: Randomized
intervention model: Parallel Assignment
primary purpose: Prevention
masking: Quadruple (Participant, Care Provider, Investigator, Outcomes Assessor)
masking description: Participants are blinded. Investigators are also blinded. The intervention is delivered through a mobile phone application (care provider) and automatically and objectively assessed (through sensor and purchasing data).
Some constructs are assessed via self-reported questionnaire at baseline, 12 weeks and during the 6 month voluntary follow-up (Treatment Self-Regulation Questionnaire (TSRQ); International Physical Activity Questionnaire (IPAQ short-form); Perceived competence maintaining a healthy diet (Williams, Deci & Ryan); Perceived behavior-related control (Renner & Schwarzer)).
measure: Physical Activity
time frame: 12 weeks
description: Steps will be measured daily via the GoogleFit or Apple Health application using the Smartphone's built-in accelerometer.
measure: Nutri-Score
time frame: 12 weeks
description: 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.
measure: Food Purchasing Behavior - Salt
time frame: 12 weeks
description: 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.
measure: Food Purchasing Behavior - Proteins
time frame: Continuous measurement during study (12 weeks)
description: 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.
measure: Food Purchasing Behavior - Fibers
time frame: Continuous measurement during study (12 weeks)
description: 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.
measure: Food Purchasing Behavior - Saturated Fats
time frame: Continuous measurement during study (12 weeks)
description: 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.
measure: User Engagement 1
time frame: 12 weeks from beginning to end of intervention
description: Number of app openings during 12 week intervention period. App openings will be measured daily directly via tracking mechanisms in the app.
measure: User Engagement 2
time frame: 12 weeks from beginning to end of intervention
description: 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.
measure: Food Purchasing Behavior - Fruit & Vegetable Purchases
time frame: 12 weeks
description: 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.
measure: Food Purchasing Behavior - Sugar (excluding Fructose & Lactose)
time frame: 12 weeks
description: 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.
measure: Motivational Self-Efficacy
time frame: 12 weeks
description: Motivational Self-Efficacy scale adjusted from Schwarzer et al. 2007; Minimum Value: 1 (totally disagree), Maximum Value: 7 (totally agree)
measure: Recovery Self-Efficacy
time frame: 12 weeks
description: Recovery Self-Efficacy scale adjusted from Schwarzer et al. 2007; Minimum Value: 1 (totally disagree), Maximum Value: 7 (totally agree)
measure: Perceived behavior-related control over future health
time frame: 12 weeks
description: Perceived behavior-related control scale adjusted from Renner and Schwarzer, 2005; Minimum Value: 1 (totally disagree), Maximum Value: 7 (totally agree)
measure: Autonomous Motivation
time frame: 12 weeks
description: Treatment Self-Regulation Questionnaire (TSRQ); Minimum Value: 1 (totally disagree), Maximum Value: 7 (totally agree)
measure: Controlled Motivation
time frame: 12 weeks
description: Treatment Self-Regulation Questionnaire (TSRQ); Minimum Value: 1 (totally disagree), Maximum Value: 7 (totally agree)
arm group label: FutureMe
arm group type: Experimental
description: Participants 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
arm group label: Control
arm group type: Active Comparator
description: Participants 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
intervention type: Behavioral
intervention name: FutureMe App
description: 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.
arm group label: FutureMe
intervention type: Behavioral
intervention name: Conventional TrackingApp
description: 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.
arm group label: Control
criteria: 
gender: All
minimum age: 18 Years
maximum age: N/A
healthy volunteers: Accepts Healthy Volunteers
last name: Annette Mönninghoff
role: Principal Investigator
affiliation: University of St. Gallen, Institute for Customer Insight
facility: 
country: Switzerland
url: http://www.gesundheitsrisiko.de/docs/RACKEnglish.pdf
description: Renner and Schwarzer, 2005
citation: Steinemann N, Grize L, Ziesemer K, Kauf P, Probst-Hensch N, Brombach C. Relative validation of a food frequency questionnaire to estimate food intake in an adult population. Food Nutr Res. 2017 Mar 29;61(1):1305193. doi: 10.1080/16546628.2017.1305193. eCollection 2017.
PMID: 28469546
citation: Schwarzer R, Schuz B, Ziegelmann JP, Lippke S, Luszczynska A, Scholz U. Adoption and maintenance of four health behaviors: theory-guided longitudinal studies on dental flossing, seat belt use, dietary behavior, and physical activity. Ann Behav Med. 2007 Apr;33(2):156-66. doi: 10.1007/BF02879897.
PMID: 17447868
responsible party type: Sponsor-Investigator
investigator affiliation: University of St.Gallen
investigator full name: Annette Mönninghoff
investigator title: PhD Candidate and Project Leader
sharing ipd: No
ipd description: Based on the data privacy statement of the study and the ethic's commission application, data cannot be analyzed or shared on an individual participant basis.