Editorial: Continued opportunities in wearable technologies and physiological assessment
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».