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).
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
Study Type
INTERVENTIONAL
Allocation
RANDOMIZED
Purpose
OTHER
Masking
SINGLE
Enrollment
13,463
Participants use Fitbit devices, with the addition of randomly deployed nudge interventions (random timing and theme).
Participants use Fitbit devices, with the addition of nudges deployed based on behavior science heuristics.
Participants use Fitbit devices, with the addition of nudges deployed based on a reinforcement learning algorithm.
Google Office (Digital Study)
Mountain View, California, United States
Change in step count from baseline.
The average daily step count during the final month minus baseline daily step count during one month before enrollment.
Time frame: One month prior to enrollment (Baseline) to the second month post-enrollment (60 days total enrollment).
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