Ignite Creation Date:
2025-12-24 @ 5:24 PM
Ignite Modification Date:
2026-01-01 @ 2:39 PM
Study NCT ID:
NCT03384550
Status:
COMPLETED
Last Update Posted:
2018-03-27
First Post:
2017-12-14
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Can a Smartphone App That Includes a Chatbot-based Coaching and Incentives Increase Physical Activity in Healthy Adults?
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D009043', 'term': 'Motor Activity'}], 'ancestors': [{'id': 'D001519', 'term': 'Behavior'}]}}, 'protocolSection': {'designModule': {'phases': ['NA'], 'studyType': 'INTERVENTIONAL', 'designInfo': {'allocation': 'RANDOMIZED', 'maskingInfo': {'masking': 'SINGLE', 'whoMasked': ['OUTCOMES_ASSESSOR']}, 'primaryPurpose': 'PREVENTION', 'interventionModel': 'FACTORIAL'}, 'enrollmentInfo': {'type': 'ACTUAL', 'count': 274}}, 'statusModule': {'overallStatus': 'COMPLETED', 'startDateStruct': {'date': '2017-10-24', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2017-12', 'completionDateStruct': {'date': '2018-01-31', 'type': 'ACTUAL'}, 'lastUpdateSubmitDate': '2018-03-24', 'studyFirstSubmitDate': '2017-12-14', 'studyFirstSubmitQcDate': '2017-12-19', 'lastUpdatePostDateStruct': {'date': '2018-03-27', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2017-12-27', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2017-12-17', 'type': 'ACTUAL'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Daily goal achievement', 'timeFrame': 'seven weeks', 'description': 'Goal achievement will be assessed daily by comparing participants daily step count to the respective individualized step goal'}], 'secondaryOutcomes': [{'measure': 'Steps per day', 'timeFrame': 'seven weeks', 'description': "Steps will be measured daily via the GoogleFit or Apple Health application using the smartphone's built-in accelerometer"}, {'measure': 'Behavioral regulation in physical activity', 'timeFrame': 'seven weeks', 'description': 'Behavioral regulation in physical activity will be measured using the second version of the Behavioral Regulation in Exercise Questionnaire (BREQ-2). The BREQ-2 consists of five subscales which represent different forms of regulation along the extrinisc-intrinsic continuum of motivation. Because the external regulation subscale in the BREQ-2 exclusively relates to external regulation by other people, it is substituted by the corresponding sub-scale of the Situational Motivation Scale (SIMS). Scores will be reported for each subscale separately. Answers are given on a five-point likert scale with the endpoints 0-not true for me and 4-very true for me.\n\nScores range from 0 to 16 for the subscales amotivation, external regulation, identified regulation and intrinsic regulation and from 0-12 for the subscale introjected regulation with higher scores representing a stronger manifestation of the respective form of regulation.'}, {'measure': 'Engagement', 'timeFrame': 'seven weeks', 'description': 'App engagement is measured using the number and length of app launch sessions per day. An app launch session is defined as any interaction of the user with the Ally app, separated by five minutes between events. If a user left the app open and did not take action for 5 minutes or more, then the next interaction with the app counts as a new session.'}, {'measure': 'Non-usage attrition', 'timeFrame': 'seven weeks', 'description': 'Non-usage attrition is measured using the proportion of users that have stopped using the app for at least 7 days (i.e. there are \\>7 days between their last session and the end of the study,'}]}, 'oversightModule': {'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'conditions': ['Physical Activity']}, 'referencesModule': {'references': [{'pmid': '32182353', 'type': 'DERIVED', 'citation': 'Kramer JN, Kunzler F, Mishra V, Smith SN, Kotz D, Scholz U, Fleisch E, Kowatsch T. Which Components of a Smartphone Walking App Help Users to Reach Personalized Step Goals? Results From an Optimization Trial. Ann Behav Med. 2020 Jun 12;54(7):518-528. doi: 10.1093/abm/kaaa002.'}, {'pmid': '30702430', 'type': 'DERIVED', 'citation': 'Kramer JN, Kunzler F, Mishra V, Presset B, Kotz D, Smith S, Scholz U, Kowatsch T. Investigating Intervention Components and Exploring States of Receptivity for a Smartphone App to Promote Physical Activity: Protocol of a Microrandomized Trial. JMIR Res Protoc. 2019 Jan 31;8(1):e11540. doi: 10.2196/11540.'}]}, 'descriptionModule': {'briefSummary': "The investigators conduct a micro-randomized trial to test main effects and moderators of three different intervention components of Ally, a mHealth intervention to promote physical activity that is offered to customers of a large Swiss health insurance. Interventions include the use of different incentive strategies, a weekly planning intervention and daily message prompts to support self-regulation. The Health Action Process Approach (HAPA) as well as principles from behavioral economics were used to guide the development of interventions. Further, sensor data is collected in order to enable prediction of latent contextual variables. These data can be used to build prediction models for the user's state of receptivity, i.e. points in time where the user is able and/or willing to receive, process and utilize the support provided. The results of this study enable the evidence-based development of a just-in-time adaptive intervention for physical activity.", 'detailedDescription': "Just-in-time adaptive interventions (JITAIs) have recently been proposed as framework for health interventions that exploit the potential of mobile health information and sensing technologies. By obtaining contextual information for example from smartphone sensors (e.g. location, time of day), a JITAI adapts the provision of interventions over time with the goal to deliver support when the person needs it most (state of vulnerability) and is most likely to be receptive (state of receptivity).\n\nTo facilitate the development of a JITAI for physical activity, the present study has the following objectives:\n\n1. To quantify main effects and interactions of three intervention components of Ally, a mHealth intervention for physical activity.\n2. To identify moderators for these intervention components to formulate evidence-based decision rules.\n3. To train machine learning models that predict the user's state of receptivity\n\nA micro-randomized trial design is used to meet the objectives of the study. Customers of a large Swiss health insurance company will use Ally over a 10-day baseline and a 6-week study period. During the baseline period, participants only have access to the dashboard of the app and no interventions are administered. During the intervention period, Ally provides daily personalized step goals and different interventions via an interactive chatbot interface based on the MobileCoach system (www.mobile-coach.eu). We investigate the following intervention components as between-subject or within-subject experimental factors during the intervention period: daily self-regulation coaching (two levels, within-subjects), a weekly planning intervention (3 levels, within-subjects) and different incentive strategies (3 levels, between-subjects).\n\nPrimary outcome will be the difference in achievement of the daily personalized step goal between intervention and control conditions for all intervention components. We expect all intervention components to increase the probability of goal achievement. Sensitivity analyses will be conducted for per protocol analysis and adjustment for covariates. Moderators of intervention components will be investigated exploratively.\n\nTo reach objective 3, we will collect a wide range of smartphone sensor data as well as usage logs of the Ally app throughout the study."}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '18 Years', 'healthyVolunteers': True, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* Possession of iPhone (5s or newer) or Android smartphone (Android 4.0 or higher)\n\nExclusion Criteria:\n\n* not enrolled in a complementary health insurance plan\n* actively using an activity tracker or comparable smartphone app\n* working night shifts\n* presence of medical condition(s) that prohibit increased levels of physical activity'}, 'identificationModule': {'nctId': 'NCT03384550', 'briefTitle': 'Can a Smartphone App That Includes a Chatbot-based Coaching and Incentives Increase Physical Activity in Healthy Adults?', 'organization': {'class': 'OTHER', 'fullName': 'University of St.Gallen'}, 'officialTitle': 'Investigating Different Intervention Components of a Smartphone App to Promote Physical Activity: The ALLY Micro-Randomized Trial', 'orgStudyIdInfo': {'id': '001-Ally'}}, 'armsInterventionsModule': {'armGroups': [{'type': 'ACTIVE_COMPARATOR', 'label': 'Control', 'description': 'Participants in this arm receive no incentives.\n\nAs all participants, participants in this arm receive self-regulation coaching on 50% of the days during the intervention period. For each participant, days during the intervention period are randomly allocated to a coaching or a no coaching condition using an allocation ratio of 1:1.\n\nAs all participants, participants in this arm also receive either an action planning, a coping planning or no planning condition each Sunday during the intervention period. Participants are randomized to one out of nine sequences of planning interventions according to a uniform and strongly balanced intervention schedule', 'interventionNames': ['Behavioral: Self-regulation coaching', 'Behavioral: Planning']}, {'type': 'EXPERIMENTAL', 'label': 'Financial Incentives', 'description': 'Participants in this arm receive financial incentives.\n\nAs all participants, participants in this arm receive self-regulation coaching on 50% of the days during the intervention period. For each participant, days during the intervention period are randomly allocated to a coaching or a no coaching condition using an allocation ratio of 1:1.\n\nAs all participants, participants in this arm also receive either an action planning, a coping planning or no planning condition each Sunday during the intervention period. Participants are randomized to one out of nine sequences of planning interventions according to a uniform and strongly balanced intervention schedule', 'interventionNames': ['Behavioral: Self-regulation coaching', 'Behavioral: Planning', 'Behavioral: Financial Incentives']}, {'type': 'EXPERIMENTAL', 'label': 'Charity Incentives', 'description': 'Participants in this receive charity incentives.\n\nAs all participants, participants in this arm receive self-regulation coaching on 50% of the days during the intervention period. For each participant, days during the intervention period are randomly allocated to a coaching or a no coaching condition using an allocation ratio of 1:1.\n\nAs all participants, participants in this arm also receive either an action planning, a coping planning or no planning condition each Sunday during the intervention period. Participants are randomized to one out of nine sequences of planning interventions according to a uniform and strongly balanced intervention schedule', 'interventionNames': ['Behavioral: Self-regulation coaching', 'Behavioral: Planning', 'Behavioral: Charity Incentives']}], 'interventions': [{'name': 'Self-regulation coaching', 'type': 'BEHAVIORAL', 'description': 'Short (2-5 min.) dialogue with the digital coach who provides information relevant for behavioral self-regulation, such as a goal reminder, the distance between the current step count and the goal and strategies to increase daily steps. Participants are randomized to self-regulation coaching or control (no coaching) on a daily basis.', 'armGroupLabels': ['Charity Incentives', 'Control', 'Financial Incentives']}, {'name': 'Planning', 'type': 'BEHAVIORAL', 'description': 'A dialogue with the digital coach who prompts the participant to either formulate action plans (when and where the participant can go for a walk) or coping plans (strategies to respond to barriers for increasing daily steps) for the upcoming week. Participants are randomized on a weekly basis to action planning, coping planning or control (no planning).', 'armGroupLabels': ['Charity Incentives', 'Control', 'Financial Incentives']}, {'name': 'Financial Incentives', 'type': 'BEHAVIORAL', 'description': 'Participants receive CHF 1 ($1) for each day they meet a personalized adaptive step goal.', 'armGroupLabels': ['Financial Incentives']}, {'name': 'Charity Incentives', 'type': 'BEHAVIORAL', 'description': 'Participants donate CHF 1 ($1) to a charity of choice for each day they meet a personalized adaptive step goal.', 'armGroupLabels': ['Charity Incentives']}]}, 'contactsLocationsModule': {'locations': [{'zip': '9000', 'city': 'Sankt Gallen', 'country': 'Switzerland', 'facility': 'Center for Digital Health Interventions', 'geoPoint': {'lat': 47.42391, 'lon': 9.37477}}], 'overallOfficials': [{'name': 'Tobias Kowatsch, PhD', 'role': 'PRINCIPAL_INVESTIGATOR', 'affiliation': 'University of St.Gallen'}]}, 'ipdSharingStatementModule': {'timeFrame': 'After completion of all analyses and publication of the main results of the study', 'ipdSharing': 'YES', 'description': 'All collected data will be irreversibly anonymized and made available in a public non-profit data repository. Data that can potentially identify participants will be deleted (e.g. e-mail addresses) or altered in a way that prevents identification (e.g. global positioning system (GPS) data will be converted to abstract features, such as "at home/not at home"). Consent to publish anonymized data is obtained from all participants.'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'University of St.Gallen', 'class': 'OTHER'}, 'collaborators': [{'name': 'CSS health insurance', 'class': 'UNKNOWN'}], 'responsibleParty': {'type': 'SPONSOR'}}}}