Evaluation of an Activity Tracker to Detect Seizures Using Machine Learning

Mittlesteadt, Jackson; Bambach, Sven; Dawes, Alex; Wentzel, Evelynne; Debs, Andrea; Sezgın, Emre; Digby, Dan; Huang, Yungui; Ganger, Andrea; Bhatnagar, Shivani; Ehrenberg, Lori; Nunley, Sunjay; Glynn, Peter; Lin, Simon; Rust, Steve; Patel, Anup D.

doi:10.1177/0883073820937515

Cited by 4 publications

(7 citation statements)

References 22 publications

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“…Acceptability and feasibility were assessed by screening articles for (I) claims made by authors that Fitbit devices were feasible and/or acceptable and (II) author reports of compliance and/or adherence to wearing them. Nine of 25 studies indicated that Fitbit devices were advantageous, due to being cost-effective ( 29 - 33 ), non-obtrusive/discrete as a measurement device ( 29 , 30 ), accessible/popular ( 30 , 33 - 36 ), a source of continuous and long-term measurement ( 29 , 36 , 37 ), and user friendly ( 33 , 35 ). One study further claimed there are no adverse effects of Fitbit devices ( 32 ).…”

Section: Resultsmentioning

confidence: 99%

“…One study further claimed there are no adverse effects of Fitbit devices ( 32 ). Nine of 25 studies contrarily discussed disadvantages of Fitbit devices, including not being designed for children ( 38 ), being difficult to use ( 39 , 40 ), causing rash and eczema ( 40 ), falling off during exercise ( 41 ), having limited data collection abilities ( 30 , 37 , 42 ), and having a likelihood of non-compliance in adolescents ( 30 , 33 ) ( Table 6 ).…”

Section: Resultsmentioning

confidence: 99%

“…In terms of satisfaction, 85% of participants of one study enjoyed wearing a Fitbit tracker ( 44 ). Families of participants were additionally interested in Fitbit devices for personal use ( 30 , 45 ), and some satisfied participants further shared their Fitbit data with their healthcare providers ( 18 ). Finally, one study suggested development of better attachment for Fitbit devices to prevent them from falling off ( Table 6 ).…”

Section: Resultsmentioning

confidence: 99%

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Utility of Fitbit devices among children and adolescents with chronic health conditions: a scoping review

Kasparian¹,

Badawy²

2022

mHealth

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Background: While Fitbit ® devices were initially intended for leisurely, consumer use, there has been recent interest among scientific and medical communities in the prospective use of Fitbit devices for clinical and research purposes. Those who have chronic health conditions are often required to spend considerable amounts of money and time undergoing physiological tests and activity monitoring to support, stabilize, and manage their health. This disease burden is only amplified in pediatric populations. Devices that are used to collect these data can be invasive, uncomfortable, and disconcerting. Using the Fitbit tracker to acquire such biometric data could ease this burden. Our scoping review seeks to summarize the research that has been conducted on the utilization of Fitbit devices in studies of children and adolescents with chronic health conditions and the feasibility, accuracy, and potential benefits of doing so.Methods: Searches were conducted on PubMed for articles relating pediatric health to Fitbit device usage (using a Boolean search strategy). The eligibility criteria included trials being clinical and/or randomized controlled and articles being in English. Once articles were obtained, they underwent screening and exclusion processes and were charted for their titles, authors, objectives, results, and respective chronic illnesses. In the subsequent full-text review, further charting was conducted, collecting study designs, Fitbit parameters, feasibility, accuracy, and related health and clinical outcomes.Results: Fitbit trackers were unanimously demonstrated to be feasible devices in this population for physical activity monitoring and were determined to be potentially beneficial in measuring and improving overall wellbeing and physical health in children with chronic illness. Nevertheless, sufficient evidence was not found in support of Fitbit accuracy. Additional biases were identified against the population of children with chronic health conditions that may further enable inaccurate data.Conclusions: While Fitbit devices may be beneficial for those interested in improving physical health, discretion is advised for those seeking to collect accurate and/or medically necessitated data. Given the existing literature evaluated, medical-grade technologies are preferred in instances of the latter, as Fitbit devices have not been found to provide reliably accurate data.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Utility of Fitbit devices among children and adolescents with chronic health conditions: a scoping review

Kasparian¹,

Badawy²

2022

mHealth

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“…The Apple Watch and Fitbit data showed agreement with the ECG data up to 95% and 91%, respectively [ 132 ]. However, the Fitbit data did not outperform the ECG data in the detection of epileptic seizures [ 133 ]. The accuracy of wearable devices must at least be at a comparative level with the conventional diagnostic methods.…”

Section: Future Perspectives and Challengesmentioning

confidence: 99%

Smart Consumer Wearables as Digital Diagnostic Tools: A Review

Chakrabarti¹,

Biswas²,

Jones³

et al. 2022

Diagnostics

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The increasing usage of smart wearable devices has made an impact not only on the lifestyle of the users, but also on biological research and personalized healthcare services. These devices, which carry different types of sensors, have emerged as personalized digital diagnostic tools. Data from such devices have enabled the prediction and detection of various physiological as well as psychological conditions and diseases. In this review, we have focused on the diagnostic applications of wrist-worn wearables to detect multiple diseases such as cardiovascular diseases, neurological disorders, fatty liver diseases, and metabolic disorders, including diabetes, sleep quality, and psychological illnesses. The fruitful usage of wearables requires fast and insightful data analysis, which is feasible through machine learning. In this review, we have also discussed various machine-learning applications and outcomes for wearable data analyses. Finally, we have discussed the current challenges with wearable usage and data, and the future perspectives of wearable devices as diagnostic tools for research and personalized healthcare domains.

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“…The digital health technologies with the greatest potential to be amenable to BYOD studies are fitness trackers and smartwatches [ 2 ]. With intuitive and easy-to-use interfaces, embedded multi-modal sensors can derive various physiological measures, including physical activity, sleep, and vital sign data (e.g., heart rate, heart rate variability, pulse oximetry) [ 3 , 4 , 5 , 6 ]. Smartphones have increasing utility as digital health technologies with inbuilt sensors and technology such as accelerometers, global positioning system sensors, microphones, cameras, gyroscopes, and magnetometers.…”

Section: Introductionmentioning

confidence: 99%

Considerations for Conducting Bring Your Own “Device” (BYOD) Clinical Studies

Demanuele¹,

Lokker²,

Jhaveri³

et al. 2022

Digit Biomark

Self Cite

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Background: Digital health technologies are attracting attention as novel tools for data collection in clinical research. They present alternative methods compared to in-clinic data collection, which often yields snapshots of the participants’ physiology, behavior, and function that may be prone to biases and artifacts, e.g., white coat hypertension, and not representative of the data in free-living conditions. Modern digital health technologies equipped with multi-modal sensors combine different data streams to derive comprehensive endpoints that are important to study participants and are clinically meaningful. Used for data collection in clinical trials, they can be deployed as provisioned products where technology is given at study start or in a bring your own “device” (BYOD) manner where participants use their technologies to generate study data. Summary: The BYOD option has the potential to be more user-friendly, allowing participants to use technologies that they are familiar with, ensuring better participant compliance, and potentially reducing the bias that comes with introducing new technologies. However, this approach presents different technical, operational, regulatory, and ethical challenges to study teams. For example, BYOD data can be more heterogeneous, and recruiting historically underrepresented populations with limited access to technology and the internet can be challenging. Despite the rapid increase in digital health technologies for clinical and healthcare research, BYOD use in clinical trials is limited, and regulatory guidance is still evolving. Key Messages: We offer considerations for academic researchers, drug developers, and patient advocacy organizations on the design and deployment of BYOD models in clinical research. These considerations address: (1) early identification and engagement with internal and external stakeholders; (2) study design including informed consent and recruitment strategies; (3) outcome, endpoint, and technology selection; (4) data management including compliance and data monitoring; (5) statistical considerations to meet regulatory requirements. We believe that this article acts as a primer, providing insights into study design and operational requirements to ensure the successful implementation of BYOD clinical studies.

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Evaluation of an Activity Tracker to Detect Seizures Using Machine Learning

Cited by 4 publications

References 22 publications

Utility of Fitbit devices among children and adolescents with chronic health conditions: a scoping review

Utility of Fitbit devices among children and adolescents with chronic health conditions: a scoping review

Smart Consumer Wearables as Digital Diagnostic Tools: A Review

Considerations for Conducting Bring Your Own “Device” (BYOD) Clinical Studies

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