BACKGROUND: The availability of a wide range of innovative wearable sensor technologies today allows for the ability to capture and collect potentially important health-related data in ways not previously possible. These sensors can be adopted in digitalized clinical trials, i.e., clinical trials conducted outside the clinic to capture data about study participants in their day-to-day life. However, having participants activate, charge, and wear the digital sensors for long hours may prove to be a significant obstacle to the success of these trials. OBJECTIVE: This study explores a broad question of wrist-wearable sensor effectiveness in terms of data collection as well as data that are analyzable per individual. The individuals who had already consented to be part of an asymptomatic atrial fibrillation screening trial were directly sent a wrist-wearable activity and heart rate tracker device to be activated and used in a home-based setting. METHODS: A total of 230 participants with a median age of 71 years were asked to wear the wristband as frequently as possible, night and day, for at least a 4-month monitoring period, especially to track heart rhythm during sleep. RESULTS: Of the individuals who received the device, 43% never transmitted any data. Those who used the device wore it a median of ∼15 weeks (IQR 2-24) and for 5.3 days (IQR 3.2-6.5) per week. For rhythm detection purposes, only 5.6% of all recorded data from individuals were analyzable (with beat-to-beat intervals reported). CONCLUSIONS: This study provides some important learnings. It showed that in an older population, despite initial enthusiasm to receive a consumer-quality wrist-based fitness device, a large proportion of individuals never activated the device. However, it also found that for a majority of participants it was possible to successfully collect wearable sensor data without clinical oversight inside a home environment, and that once used, ongoing wear time was high. This suggests that a critical barrier to overcome when incorporating a wearable device into clinical research is making its initiation of use as easy as possible for the participant.
BACKGROUND: The availability of a wide range of innovative wearable sensor technologies today allows for the ability to capture and collect potentially important health-related data in ways not previously possible. These sensors can be adopted in digitalized clinical trials, i.e., clinical trials conducted outside the clinic to capture data about study participants in their day-to-day life. However, having participants activate, charge, and wear the digital sensors for long hours may prove to be a significant obstacle to the success of these trials. OBJECTIVE: This study explores a broad question of wrist-wearable sensor effectiveness in terms of data collection as well as data that are analyzable per individual. The individuals who had already consented to be part of an asymptomatic atrial fibrillation screening trial were directly sent a wrist-wearable activity and heart rate tracker device to be activated and used in a home-based setting. METHODS: A total of 230 participants with a median age of 71 years were asked to wear the wristband as frequently as possible, night and day, for at least a 4-month monitoring period, especially to track heart rhythm during sleep. RESULTS: Of the individuals who received the device, 43% never transmitted any data. Those who used the device wore it a median of ∼15 weeks (IQR 2-24) and for 5.3 days (IQR 3.2-6.5) per week. For rhythm detection purposes, only 5.6% of all recorded data from individuals were analyzable (with beat-to-beat intervals reported). CONCLUSIONS: This study provides some important learnings. It showed that in an older population, despite initial enthusiasm to receive a consumer-quality wrist-based fitness device, a large proportion of individuals never activated the device. However, it also found that for a majority of participants it was possible to successfully collect wearable sensor data without clinical oversight inside a home environment, and that once used, ongoing wear time was high. This suggests that a critical barrier to overcome when incorporating a wearable device into clinical research is making its initiation of use as easy as possible for the participant.
Authors: Ryan J Shaw; Dori M Steinberg; Jonathan Bonnet; Farhad Modarai; Aaron George; Traven Cunningham; Markedia Mason; Mohammad Shahsahebi; Steven C Grambow; Gary G Bennett; Hayden B Bosworth Journal: J Am Med Inform Assoc Date: 2016-01-17 Impact factor: 4.497
Authors: E Ray Dorsey; Yu-Feng Yvonne Chan; Michael V McConnell; Stanley Y Shaw; Andrew D Trister; Stephen H Friend Journal: Acad Med Date: 2017-02 Impact factor: 6.893
Authors: Steven R Steinhubl; Rajesh R Mehta; Gail S Ebner; Marissa M Ballesteros; Jill Waalen; Gregory Steinberg; Percy Van Crocker; Elise Felicione; Chureen T Carter; Shawn Edmonds; Joseph P Honcz; Gines Diego Miralles; Dimitri Talantov; Troy C Sarich; Eric J Topol Journal: Am Heart J Date: 2016-02-23 Impact factor: 4.749
Authors: Geoffrey H Tison; José M Sanchez; Brandon Ballinger; Avesh Singh; Jeffrey E Olgin; Mark J Pletcher; Eric Vittinghoff; Emily S Lee; Shannon M Fan; Rachel A Gladstone; Carlos Mikell; Nimit Sohoni; Johnson Hsieh; Gregory M Marcus Journal: JAMA Cardiol Date: 2018-05-01 Impact factor: 14.676
Authors: Mintu P Turakhia; Manisha Desai; Haley Hedlin; Amol Rajmane; Nisha Talati; Todd Ferris; Sumbul Desai; Divya Nag; Mithun Patel; Peter Kowey; John S Rumsfeld; Andrea M Russo; Mellanie True Hills; Christopher B Granger; Kenneth W Mahaffey; Marco V Perez Journal: Am Heart J Date: 2018-09-08 Impact factor: 4.749
Authors: Craig J Goergen; MacKenzie J Tweardy; Steven R Steinhubl; Stephan W Wegerich; Karnika Singh; Rebecca J Mieloszyk; Jessilyn Dunn Journal: Annu Rev Biomed Eng Date: 2021-12-21 Impact factor: 11.324
Authors: Alex Hunter; Todd Leckie; Oliver Coe; Benjamin Hardy; Daniel Fitzpatrick; Ana-Carolina Gonçalves; Mary-Kate Standing; Christina Koulouglioti; Alan Richardson; Luke Hodgson Journal: JMIR Rehabil Assist Technol Date: 2022-05-02