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Introduction Chronic disease is prevalent and costly in the U.S. (Tu & Cohen, 2009). Poor eating habit is one factor that account for risk of chronic disease (Arsand, Varmedal, & Hartvigsen, 2007). Smartphone technology has been promising to improve preventive health outcomes. However, its great potential has not been widely applied to people's eating behaviors and its impact is unknown.
Professional and peer supports can improve health status (Elkjaer et al., 2010; Lorig et al., 1999; Perri, Sears, & Clark, 1993). However, the former is usually delivered didactically or passively with limited use of smartphones. There is also little evidence of the effect of peer support delivered by smartphones in the domain of healthy eating. This research aims to study what smartphone technology can do to upgrade professional and peer supports, and to evaluate the impact of these mobile-app enabled supports on people's behavior of healthy eating and user engagement.
Hypotheses
According to Social Cognitive Theory, we hypothesize the following:
Experiment Design The hypotheses will be examined bya 4-month randomized field experiment. 375 subjects will be recruited and assigned to one of the five arms to receive the corresponding tool for diet management at no cost.
Arm 1: a mobile App with both professional and peer support Arm 2: a mobile App with peer support only Arm 3: a mobile App with professional support only Arm 4: a mobile App without any support Arm 5: a non-mobile web App In addition to the App usage data, five surveys are conducted at baseline and the end of each month. Respondents will be compensated by $8 and a chance to win $200 for each completed survey.
Interventions
All subjects receive the following interventions: an education package includes the importance of healthy eating, concept of MyPlate, personalized daily food plans; reminders throughout the study; goal setting capabilities;
Self-monitoring provided by an Android App: a heuristic approach inspired by MyPlate to record their food consumption which allows users to record their meals by images and doesn't require estimations in cups and ounces; daily reports and trend reports
Self-monitoring provided by the web App: a traditional approach to record their food consumption which requires estimations in cups and ounces, and no images are allowed; no daily reports and trend reports are provided
Professional support provided by a registered dietitian via the Android App: the supports include the following:
Peer support provided by other subjects via the Android App: the App provides platforms for subjects who have the same interest to communicate to each other. The actions the subjects can do in the platforms include:
Measurements
Dependent Variables
Independent Variables
Mediator Variables: self-efficacy, outcome expectation, and impediments will be measured by survey instruments
Data
Analysis Confirmatory factor analysis and statistical modeling such as structural equation modeling and mixed models will be conducted to test our hypotheses.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Mobile: Professional and peer support | Experimental | A mobile app that provides both professional support and peer support |
|
| Mobile: Peer support | Experimental | A mobile app that provides peer support, but not professional support |
|
| Mobile: Professional Support | Experimental | A mobile app that provides professional support, but no peer support |
|
| Mobile: No Support | Active Comparator | A mobile app that provides neither peer support nor professional support |
|
| Web: No Support | Active Comparator | A web app that provides neither peer support, nor professional support |
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Self-monitoring via Smartphone | Behavioral |
|
| Measure | Description | Time Frame |
|---|---|---|
| Healthy Eating Behavior | This will be measured by a score representing the healthiness of each selected meal, evaluated by dietitians. Specifically, the score is composed of three sub-scores: 1) portion of fruits and vegetables (7-point Likert scale); 2) portion of the entire meal (7-point Likert scale); 3) evaluators confidence level (4-point Likert scale). One subject will get one score per week, and hence 16 scores in 4-months. | 4 months after signed up |
| Engaging Behavior | This will be measured by the number of records submitted by the subject via the assigned tool in a week. One subject will get one score per week, and hence 16 scores in 4-months. | 4 months after signed up |
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Inclusion Criteria:
Exclusion Criteria:
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| Name | Affiliation | Role |
|---|---|---|
| Yi-Chin Lin, MS | Carnegie Mellon University | Principal Investigator |
| Rema Padman, PhD | Carnegie Mellon University | Study Chair |
| Julie Downs, PhD | Carnegie Mellon University | Study Director |
| Vibhanshu Abhishek, PhD | Carnegie Mellon University | Study Director |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Carnegie Mellon University | Pittsburgh | Pennsylvania | 15213 | United States |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 26958294 | Derived | Kato-Lin YC, Padman R, Downs J, Abhishek V. Evaluating Consumer m-Health Services for Promoting Healthy Eating: A Randomized Field Experiment. AMIA Annu Symp Proc. 2015 Nov 5;2015:1947-56. eCollection 2015. |
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| Peer support via Smartphone | Behavioral |
|
| Professional support via Smartphone | Behavioral |
|
| Self-monitoring via Web (Static) | Behavioral |
|
| ID | Term |
|---|---|
| D005247 | Feeding Behavior |
| D010349 | Patient Compliance |
| ID | Term |
|---|---|
| D001522 | Behavior, Animal |
| D001519 | Behavior |
| D010342 | Patient Acceptance of Health Care |
| D000074822 | Treatment Adherence and Compliance |
| D015438 | Health Behavior |
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