MyBehavior: Persuasion by Adapting to User Behavior and User Preference
Przegląd badań
Status
Warunki
Interwencja / Leczenie
Szczegółowy opis
A dramatic rise in self-tracking applications for smartphones has occurred recently. Rich user interfaces make manual logging of users' behavior easier and more pleasant; sensors make tracking effortless. To date, however, feedback technologies have been limited to providing counts or attractive visualization of tracked data. Human experts (health coaches) have needed to interpret the data and tailor make customized recommendations. No automated recommendation systems like Pandora, Netflix or personalized search for the web have been available to translate self-tracked data into actionable suggestions that promote healthier lifestyle without needing to involve a human interventionist.
MyBehavior aims to fill this gap. It takes a deeper look into physical activity and dietary intake data and reveal patterns of both healthy and unhealthy behavior that could be leveraged for personalized feedback. Based on common patterns from a user's life, suggestions are created that ask users to continue, change or avoid existing behaviors to achieve certain fitness goals. Such an approach is different from existing literature in two important aspects: (1) suggestions are contextualized to a user's life and are built on existing user behaviors. As a result, users can act on these suggestions easily, with minimal effort and interruption to daily routines; (2) unique suggestions are created for each individual. This personalized approach differs from traditional one-size-fits-all or targeted intervention models where identical suggestions are applied for groups of similar people or the entire population.
Typ studiów
Zapisy (Rzeczywisty)
Faza
- Nie dotyczy
Kontakty i lokalizacje
Lokalizacje studiów
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New York
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Ithaca, New York, Stany Zjednoczone, 14850
- Cornell University
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Kryteria uczestnictwa
Kryteria kwalifikacji
Wiek uprawniający do nauki
Akceptuje zdrowych ochotników
Płeć kwalifikująca się do nauki
Opis
Inclusion Criteria:
- In relatively healthy condition. Also, users must be interested in health and fitness.
Exclusion Criteria:
- Individuals with physical disability and dietary problems are excluded.
Plan studiów
Jak projektuje się badanie?
Szczegóły projektu
- Główny cel: Zapobieganie
- Przydział: Randomizowane
- Model interwencyjny: Przydział równoległy
- Maskowanie: Pojedynczy
Broń i interwencje
Grupa uczestników / Arm |
Interwencja / Leczenie |
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Aktywny komparator: Generic suggestions
Control group participants received suggestions generated by the a nutritionist and exercise trainer.
These suggestions didn't relate to user's life or their past behavior.
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A nutritionist and an exercise trainer jointly created 45 food and exercise suggestions based on guidelines posted by the NIH.
These suggestions ask users to walk for 30 minutes or eat healthier foods.
These suggestions however doesn't personalize to users daily behavior into account.
An Android Smartphone with operating system version higher than 2.2
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Eksperymentalny: MyBehavior
Experiment group participants received personalized suggestions from MyBehavior that relates their life and past behavior.
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An Android Smartphone with operating system version higher than 2.2
The intervention automatically provides personalized suggestions based on users behavior and user context.
Suggestions relates to users life and how often they have done them in the past.
Since the suggestions relate to users' lives, they are easy to follow.
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Co mierzy badanie?
Podstawowe miary wyniku
Miara wyniku |
Opis środka |
Ramy czasowe |
|---|---|---|
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User intentions to follow automated suggestions and behavior change
Ramy czasowe: 3 weeks
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The primary outcome is to measure efficacy of MyBehavior suggestions. Efficacy will be measured in two dimensions (1) whether users intend to follow the automated suggestions from MyBehavior (2) effectiveness of automated suggestions in actual behavior change. User intentions towards following MyBehavior suggestions are measured using a 5 point likert scale. The investigators will ask users to rate whether they can follow the suggestions on an average day within a scale of 1-5 (1- I can't follow the suggestion, 5 - I can easily follow the suggestion). On the other hand, behavior change is measured from food (calories in per meal consumed) and activity (walking, running or exercise durations per day etc.) log collected using their smartphone. Regarding physical activity, how much physical activity users are performing will be compared across experiment conditions. Similarly, calorie consumption change in food will be used to compare dietary behavior change. |
3 weeks
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Miary wyników drugorzędnych
Miara wyniku |
Opis środka |
Ramy czasowe |
|---|---|---|
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Usability improvements of automated suggestions
Ramy czasowe: 3 weeks
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MyBehavior is the first system to provide health suggestions for food and activity automatically.
Thus there are scopes of usability improvement on how to effectively present the automatically generated information to the user.
Qualitative interviews at the end of study will be conducted to gather user experience of using MyBehavior.
This interviews will help to build a better and more usable version of MyBehavior for future larger scale deployments.
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3 weeks
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Współpracownicy i badacze
Sponsor
Śledczy
- Główny śledczy: Mashfiqui Rabbi, BS, Cornell University
Publikacje i pomocne linki
Daty zapisu na studia
Główne daty studiów
Rozpoczęcie studiów
Zakończenie podstawowe (Rzeczywisty)
Ukończenie studiów (Rzeczywisty)
Daty rejestracji na studia
Pierwszy przesłany
Pierwszy przesłany, który spełnia kryteria kontroli jakości
Pierwszy wysłany (Oszacować)
Aktualizacje rekordów badań
Ostatnia wysłana aktualizacja (Oszacować)
Ostatnia przesłana aktualizacja, która spełniała kryteria kontroli jakości
Ostatnia weryfikacja
Więcej informacji
Terminy związane z tym badaniem
Słowa kluczowe
Dodatkowe istotne warunki MeSH
Inne numery identyfikacyjne badania
- 1302003617
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