Sedentary Lifestyle
Conditions
Keywords
activity, exercise, sedentary, artificial intelligence
Brief summary
To evaluate the impact of two personalized nudging strategies delivered as pop-up notifications via the Fitbit app on user step count. Specifically, to personalize the following parameters of the pop-up notification system: message content, and timing (hr of the day).
Detailed description
Phase 1 \[Model calibration\]: Recruit up to 1,000 Fitbit users for a 4 week pilot study. Nudge content and timing will be varied randomly in order to collect training data to prime the RL architecture prior to Phase 2. Phase 2 \[Performance evaluation\]: Recruit up to 12,000 Fitbit users for a 60 day study in which they are randomized evenly between the following 4 arms: \[Control\] No nudges Randomly selected nudges from the custom nudge library, delivered at constant time and frequency Behavior science (BS)-only nudge agent PEARL agent Phase 3 \[Micro-randomized Trial (MRT) & LLM Feasibility\]: Recruit up to 6,000 Fitbit users for a 60 day study in which they are randomized evenly between the following arms: Behavior science Micro-randomized Trial (BS-MRT): Once per day users will be randomized across the below factors, and receive a message written by a behavior scientist (the same messages from Phase 1 & 2). COM-B Theme: 6 themes, and 1 control (no nudge) Time of day: 3 timeframes, and 1 control (no nudge) Large Language Model Micro-randomized Trial (LLM-MRT): Once per day users will be randomized across the below factors, and receive a message written by a large language model. COM-B Theme: 6 themes, and 1 control (no nudge) Time of day: 3 timeframes, and 1 control (no nudge) Large Language Model + Reinforcement Learning (LLM-RL): Once per day, a reinforcement learning model will run to select the optimal COM-B theme and time of day (the same RL model from Phase 2 Arm 4). The nudges will be selected from a repository that is written by a large language model (LLM). Primary Purpose: Phase 1: Model calibration Phase 2: Evaluate performance on step count Phase 3: Micro-randomized Trial (MRT) & LLM feasibility
Interventions
BEHAVIORALMobile App Push Notification (Random Selection)
Participants use Fitbit devices, with the addition of randomly deployed nudge interventions (random timing and theme).
BEHAVIORALMobile App Push Notification (Behavior Science Heuristics)
Participants use Fitbit devices, with the addition of nudges deployed based on behavior science heuristics.
BEHAVIORALMobile App Push Notification (RL)
Participants use Fitbit devices, with the addition of nudges deployed based on a reinforcement learning algorithm.
Sponsors
Fitbit LLC
Google LLC.
Study design
Allocation
RANDOMIZED
Intervention model
PARALLEL
Primary purpose
OTHER
Masking
SINGLE (Subject)
Eligibility
Sex/Gender
ALL
Age
22 Years to 60 Years
Healthy volunteers
Yes
Inclusion criteria
* Age 22-60 years * \[For Phase 2 \& 3\] Daily average steps 1 month prior to recruitment is less than 8000 * Owns a Fitbit device \& smartphone with Fitbit app * Willing to share a month of step count data prior to enrollment data * Wear a Fitbit device regularly (user-reported)
Exclusion criteria
* Advised by a doctor to avoid exercising or limit physical activity * Current employees of Alphabet * \[For phase 2\] Have participated in Phase 1 of the study * \[For phase 3\] Have participated in Phase 1 or 2 of the study
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Change in step count from baseline. | One month prior to enrollment (Baseline) to the second month post-enrollment (60 days total enrollment). | The average daily step count during the final month minus baseline daily step count during one month before enrollment. |
Countries
United States
Contacts
PRINCIPAL_INVESTIGATORJames A Taylor, MD
Google LLC.
Outcome results
None listed