MyBehavior: Persuasion by Adapting to User Behavior and User Preference
調査の概要
詳細な説明
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.
研究の種類
入学 (実際)
段階
- 適用できない
連絡先と場所
研究場所
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New York
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Ithaca、New York、アメリカ、14850
- Cornell University
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参加基準
適格基準
就学可能な年齢
健康ボランティアの受け入れ
受講資格のある性別
説明
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.
研究計画
研究はどのように設計されていますか？
デザインの詳細
- 主な目的：防止
- 割り当て：ランダム化
- 介入モデル：並列代入
- マスキング：独身
武器と介入
参加者グループ / アーム |
介入・治療 |
|---|---|
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アクティブコンパレータ：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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実験的：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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この研究は何を測定していますか？
主要な結果の測定
結果測定 |
メジャーの説明 |
時間枠 |
|---|---|---|
|
User intentions to follow automated suggestions and behavior change
時間枠：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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二次結果の測定
結果測定 |
メジャーの説明 |
時間枠 |
|---|---|---|
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Usability improvements of automated suggestions
時間枠：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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協力者と研究者
スポンサー
捜査官
- 主任研究者：Mashfiqui Rabbi, BS、Cornell University
出版物と役立つリンク
研究記録日
主要日程の研究
研究開始
一次修了 (実際)
研究の完了 (実際)
試験登録日
最初に提出
QC基準を満たした最初の提出物
最初の投稿 (見積もり)
学習記録の更新
投稿された最後の更新 (見積もり)
QC基準を満たした最後の更新が送信されました
最終確認日
詳しくは
本研究に関する用語
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