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Record W4361212721 · doi:10.3389/fdgth.2023.1166088

Editorial: Continued opportunities in wearable technologies and physiological assessment

2023· editorial· en· W4361212721 on OpenAlexaboutno aff
James W. Navalta, Jennifer A. Bunn

Bibliographic record

VenueFrontiers in Digital Health · 2023
Typeeditorial
Languageen
FieldMedicine
TopicPhysical Activity and Health
Canadian institutionsnot available
Fundersnot available
KeywordsWearable computerFront (military)Wearable technologyVolume (thermodynamics)MedicineAeronauticsEngineeringMechanical engineeringPhysicsEmbedded system

Abstract

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To our gratification, this Research Topic was trusted with the work from outstanding research groups from around the globe (from Austria to Canada, and from Australia to Italy). Boyer et al. reported on the ability of an axillary thermometer to provide temperature measurements, Tindale et al. detailed motivation and barriers for use of sensors in the workplace, Trost et al. evaluated the ability of wrist-worn devices to accurately determine movement intensity in children and adolescents, and Moscato et al. characterized sources of variability on photoplethysmographic (PPG) signals.On the surface, the articles seem as disparate and as varied as our initial call. In hindsight, this should not be surprising. It did present us with a brief moment of panic when we were invited to write an editorial under a single unifying theme. We read and re-read the articles. We walked away and reflected. We distracted ourselves with other work, and in a moment of clarity were provided with an epiphany.The manuscripts presented in this Research Topic are united in difficulties that every one of us who perform work in the wearable technology and physiological assessment space are intimately familiar with -namely, limitations. The framing of limitations in the scientific literature is generally buried deep in discussion sections where the hope is that potential readers lose interest before they arrive at our list of publicly acknowledged flaws and shortcomings. For this editorial we would like to present limitations as something elseopportunities.Boyer et al. found the SteadyTemp â axillary thermometer to be susceptible to environmental disturbances and concluded the device did not return accurate temperature measurements in their clinical population. Despite this outcome, the authors noted the opportunity to use the device in appropriate use case scenarios. Another opportunity was exercised to reflect meaningfully on the arbitrary thresholds defining fever, and open a discussion into potential updates. Tindale et al. reported that 95% (body sensors) and 99% (brain sensors) of individuals did not use wearable devices to monitor employees in the workplace. To those of us who conduct research in the wearable space, these were shockingly low percentages. While wearables may provide information regarding employee wellness and safety, there is an opportunity to address concerns surrounding fears around data privacy and how information will be used prior to implementation.Trost et al. discovered that while the ActiGraph GT3X+ could accurately determine when an adolescent participant was engaged in a sedentary pursuit, the device fell short when activity was performed (light, moderate, as well as vigorous physical activity). These findings highlight an opportunity for the manufacturers of wearable technology devices to invest time and effort into the accuracy of their products in a wide array of use cases. We understand the temptation, based on financial incentives, of companies to distribute products into circulation as quickly as possible. Because wearable devices are used by individuals most often outside of a sterile laboratory condition, particular time and emphasis should be paid to ensuring that devices are accurate in free-living conditions. Moscato et al. provided evidence that a number of factors affect the PPG signaling of the Empatica E4, most importantly physical activity and health status. The findings have widereaching consequences, as many heart rate-based devices rely on PPG technology to return measurements. Because of this, there is an opportunity for industry to collaborate with scientists to refine and extend the capabilities of current technologies, and to develop devices with new technology.As this is an editorial, we will take the liberty to propose a number of other opportunities. There is an opportunity for journals and reviewers of manuscripts presenting findings on physiological variables obtained from wearable devices to reframe their mindset about what constitutes acceptable results. Not all devices will meet a predetermined threshold for accuracy. This may not mean the study design was flawed, or conducted in an inappropriate manner. Despite no "significant results" to report, the information should be disseminated, if for no other reason than to spare future researchers the time investment of needlessly replicating the study. Additionally, authors should not feel the need to resort to, or be asked by reviewers, to present their wearable data as an adjunct to what is perceived as more meaningful findings.We state in no uncertain terms that research with wearables providing physiological data is important. There is an opportunity for institutional administrators to understand the importance of applied research, whether they perceive the direction to be fundable or not. There is an opportunity for researchers to continue to communicate the importance of the work being conducted. Toward this end, there is an associated opportunity for investigators and researchers to train the next generation of students and future collaborators in the skills necessary to continue conducting this type of work.Finally, there is an opportunity for an open discussion on the limitations that are inherent in studies incorporating physiological measurements through wearable devices, and the best way to acknowledge them. Being fully transparent will prevent others from repeating avoidable pitfalls and allow the field to conduct the high-quality research that is needed.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.023
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.057
GPT teacher head0.353
Teacher spread0.296 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEditorial

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2023
Admission routes1
Has abstractyes

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