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Digital, Personalized Clinical Trials Among Older Adults, Lessons Learned From the COVID-19 Pandemic, and Directions for the Future: Aggregated Feasibility Data From Three Trials Among Older Adults

Digital, Personalized Clinical Trials Among Older Adults, Lessons Learned From the COVID-19 Pandemic, and Directions for the Future: Aggregated Feasibility Data From Three Trials Among Older Adults

All trials used Fitbit activity monitors to track participant levels of physical activity. For the current analysis, we examined several metrics of adherence to the Fitbit devices used in each trial to determine the feasibility of device integration. First, we identified the mean and SD of daily Fitbit wear time for each trial and by age group in the trial ( All trials administered satisfaction surveys after trial completion to assess trial acceptability.

Lindsay Arader, Danielle Miller, Alexandra Perrin, Frank Vicari, Ciaran P Friel, Elizabeth A Vrany, Ashley M Goodwin, Mark Butler

J Med Internet Res 2025;27:e54629

Using Wear Time for the Analysis of Consumer-Grade Wearables’ Data: Case Study Using Fitbit Data

Using Wear Time for the Analysis of Consumer-Grade Wearables’ Data: Case Study Using Fitbit Data

This intervention leveraged the combination of Fitbit and survey data collected using an app (TBI-Care QOL). It is important to note that participants were reminded to sync their Fitbit data every Monday and Friday if they had not already done so. The details of this study protocol can be found in Cislo et al [19].

Loubna Baroudi, Ronald Fredrick Zernicke, Muneesh Tewari, Noelle E Carlozzi, Sung Won Choi, Stephen M Cain

JMIR Mhealth Uhealth 2025;13:e46149

Usability and Implementation Considerations of Fitbit and App Intervention for Diverse Cancer Survivors: Mixed Methods Study

Usability and Implementation Considerations of Fitbit and App Intervention for Diverse Cancer Survivors: Mixed Methods Study

No data were collected regarding the usability of the Fitbit: Health & Wellness app, as the app was primarily used to continuously synchronize Fitbit data with the My Data Helps app. Clinician semistructured interviews were conducted following the data collection period for the intervention (June-July 2023).

Zakery Dabbagh, Reem Najjar, Ariana Kamberi, Ben S Gerber, Aditi Singh, Apurv Soni, Sarah L Cutrona, David D McManus, Jamie M Faro

JMIR Cancer 2025;11:e60034

Changing User Experience of Wearable Activity Monitors Over 7 Years: Repeat Cross-Sectional Survey Study

Changing User Experience of Wearable Activity Monitors Over 7 Years: Repeat Cross-Sectional Survey Study

The 2023 survey results showed that Apple (210/475, 44%) was the most popular WAT brand, followed by Fitbit (101/475, 21%), Garmin (92/475, 19%), Samsung (47/475, 10%), and other brands (22/475, 5%).

Darcy Beckett, Rachel Curtis, Kimberley Szeto, Carol Maher

J Med Internet Res 2025;27:e56251

Using a Consumer Wearable Activity Monitoring Device to Study Physical Activity and Sleep Among Adolescents in Project Viva: Cohort Study

Using a Consumer Wearable Activity Monitoring Device to Study Physical Activity and Sleep Among Adolescents in Project Viva: Cohort Study

Consumer-based wearable devices such as Fitbit (Google) have become popular for objectively measuring physical activity and sleep due to the advancements in microtechnology, wireless communication, battery capacity, and multidimension measurements. In addition, Fitbit devices use Bluetooth for easy data transfer to Fitbit servers through a smartphone or tablet, allowing for synchronization of real-time data.

Yutong Zhang, Nicole Bornkamp, Marie-France Hivert, Emily Oken, Peter James

JMIR Pediatr Parent 2025;8:e59159

Reliability and Accuracy of the Fitbit Charge 4 Photoplethysmography Heart Rate Sensor in Ecological Conditions: Validation Study

Reliability and Accuracy of the Fitbit Charge 4 Photoplethysmography Heart Rate Sensor in Ecological Conditions: Validation Study

As this type of device is meant to be used in non–laboratory-controlled conditions or free-living conditions, this study aimed to evaluate the accuracy and validity of the PPG HR data from the Fitbit Charge 4 (FC4; Fitbit LLC) across multiple physical activity types. Therefore, the objectives of this study were to (1) assess the accuracy and reliability of FC4 HR measurement in ecological conditions and (2) quantify the potential impact of activity type on accuracy.

Maxime Ceugniez, Hervé Devanne, Eric Hermand

JMIR Mhealth Uhealth 2025;13:e54871

Enhancing Interpretable, Transparent, and Unobtrusive Detection of Acute Marijuana Intoxication in Natural Environments: Harnessing Smart Devices and Explainable AI to Empower Just-In-Time Adaptive Interventions: Longitudinal Observational Study

Enhancing Interpretable, Transparent, and Unobtrusive Detection of Acute Marijuana Intoxication in Natural Environments: Harnessing Smart Devices and Explainable AI to Empower Just-In-Time Adaptive Interventions: Longitudinal Observational Study

Participants were provided with Fitbit Charge 2 devices and asked to wear them as much as possible. Fitbit collected physiological data (eg, HR), activity data (eg, step count), and sleep. The study hypothesized that HR and behavioral data could signal episodes of acute marijuana intoxication. Fitbit data were retrieved from the Fitbit server at the end of the study using the Fitbit application programming interface.

Sang Won Bae, Tammy Chung, Tongze Zhang, Anind K Dey, Rahul Islam

JMIR AI 2025;4:e52270

Exploring the Use of Activity Trackers to Support Physical Activity and Reduce Sedentary Behavior in Adults Diagnosed With Type 2 Diabetes: Qualitative Interview Study Using the RE-AIM Framework

Exploring the Use of Activity Trackers to Support Physical Activity and Reduce Sedentary Behavior in Adults Diagnosed With Type 2 Diabetes: Qualitative Interview Study Using the RE-AIM Framework

Fitbit (Google Inc) consumer activity trackers are a valid and reliable method of measuring physical activity (steps, distance walked, energy expenditure, physical activity intensity, and sedentary behavior). When compared with laboratory-based tests of physical activity, Fitbit activity trackers have been shown to have large significant correlation coefficients of between 96.5 and 99.1 [10].

William Hodgson, Alison Kirk, Marilyn Lennon, Xanne Janssen

JMIR Diabetes 2024;9:e60066

The Effect of Inhaled Beta-2 Agonists on Heart Rate in Patients With Asthma: Sensor-Based Observational Study

The Effect of Inhaled Beta-2 Agonists on Heart Rate in Patients With Asthma: Sensor-Based Observational Study

The continuous monitoring of the heart rate was assessed with the Fitbit Charge HR (Fitbit Inc). The Fitbit Charge HR is a “wearable” commonly known as a fitness tracker. Every single minute of the HR data is stored as a separate measurement. The Fitbit Charge HR also measures the number of steps taken, stairs walked, calories burned, and minutes slept. It was advised to only wear the Fitbit at night if patients were comfortable wearing the device during sleep.

Rishi Jayant Khusial, Jacob K Sont, Omar S Usmani, Matteo Bonini, Kian Fan Chung, Stephen James Fowler, Persijn J Honkoop

JMIR Cardio 2024;8:e56848

Relationship Between Heart Rate and Perceived Stress in Intensive Care Unit Residents: Exploratory Analysis Using Fitbit Data

Relationship Between Heart Rate and Perceived Stress in Intensive Care Unit Residents: Exploratory Analysis Using Fitbit Data

With advances in sensors, the Internet of Things, sensing technology, and artificial intelligence, wearables like wristbands (eg, Fitbit), smartwatches (Apple and Samsung smartwatches), smart rings (Oura rings), and smart belts (Polar belt) are capable of tracking physiological signals like heart rate (HR), HR variability (HRV), electrodermal activity, body temperature, and body movements (via accelerometers) constantly, and based on which, computing and reporting human status in real-time.

Ruijing Wang, Olya Rezaeian, Onur Asan, Linghan Zhang, Ting Liao

JMIR Form Res 2024;8:e60759