Google Office (Digital Study)
Mountain View, California, 94043, United States
NCT Number: NCT07783724
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).
Looking for future studies?
Notify Me22 year–60 year
All sexes
Interventional
Not applicable
Mountain View, California, 94043, United States
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
Healthy volunteers accepted: Yes
Only the study team can determine whether someone qualifies for participation.
Inclusion criteria
Exclusion criteria
Participants use Fitbit devices, with the addition of randomly deployed nudge interventions (random timing and theme).
Other names: Nudges
Participants use Fitbit devices, with the addition of nudges deployed based on behavior science heuristics.
Other names: Nudges
Participants use Fitbit devices, with the addition of nudges deployed based on a reinforcement learning algorithm.
Other names: Nudges
Time frame: 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.
Fitbit LLC
Industry
OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.
View the official ClinicalTrials.gov record (opens in a new tab)This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.
Published trials that share one or more normalized conditions with this study.
NCT07770997
Behavior, Body Weight
Poznan, Greater Poland Voivodeship, Poland
View Trial DetailsNCT03898206
Behavior, Cardiovascular Diseases
Bedford, Bedfordshire, United Kingdom
View Trial DetailsNCT07457047
Behavior, Cardiometabolic Risk
Ankara, Besevler, Turkey (Türkiye)
View Trial DetailsNCT07425093
Behavior, Body Fat Percentage
Amasya, Turkey (Türkiye)
View Trial Details