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Enregistrement W3137203448 · doi:10.1093/ajh/hpaa158

Wearable Technology to Detect Stress-Induced Blood Pressure Changes: The Next Chapter in Ambulatory Blood Pressure Monitoring?

2021· letter· en· W3137203448 sur OpenAlexaff
Jennifer Ringrose, Raj Padwal

Notice bibliographique

RevueAmerican Journal of Hypertension · 2021
Typeletter
Langueen
DomaineEngineering
ThématiqueNon-Invasive Vital Sign Monitoring
Établissements canadiensWomen and Children’s Health Research InstituteUniversity of Alberta
Organismes subventionnairesnon disponible
Mots-clésMedicineBlood pressureAmbulatory blood pressureWearable computerAmbulatoryCardiologyInternal medicineIntensive care medicineEmbedded system

Résumé

récupéré en direct d'OpenAlex

Without tradition, art is a flock of sheep without a shepherd. Without innovation, it is a corpse. Sir Winston Churchill Cuff-based blood pressure (BP) measurement is a century-old technique, dating back to 1896 when Riva-Rocci first used a cuff to measure the pressure required to occlude an artery.1 Oscillometric BP measurement, first described by Marey in 1876,2 has now surpassed auscultation as the preferred clinical measurement method because it is easier to perform and requires less user training. Given that both technologies have changed relatively little over the last 120 years, one cannot help but reflect that innovation in this field has been slow to evolve. However, an important aspect of seeking innovation is that it should not be pursued at the expense of accuracy. Self-monitoring of BP is gaining in popularity and strongly endorsed by clinical practice guidelines because it promotes self-engagement in care. The self-monitoring space is highly competitive, estimated to be worth $1.03 billion USD in 2017, with projected growth to 2.07 billion by 2025.3 Many wearable BP devices (WBPM) known as “cuffless BP” products that estimate BP using pulse transit time or pulse arrival time have been introduced into the market.4 Pulse transit time is inversely related to BP and is the time taken by a pressure wave to travel between 2 arteries as measured by photoplethysmography waveforms.4 Pulse arrival time is the measured time delay between 2 R-peaks of electrocardiogram and a characteristic point on the photoplethysmography waveform.4 To “measure” BP, A “calibration” measurement(s) is performed using the cuffless device and a conventional cuffed monitor to generate a personalized mathematical relationship between pulse transit time or pulse arrival time and BP and then this relationship is used to subsequently estimate (i.e., mathematically calculate) future BP.5 Manufacturers market these devices as accurate, comfortable, and convenient.6 However, in reality, they do not measure BP, they drift (lose accuracy of the original calibration), and are vulnerable to motion artifact.6 Lack of formal accuracy assessment (clinical validation) is another major limitation of most cuffless devices. Proper validation requires performance of an independent study done using a globally accepted BP measurement standard. Further eroding accuracy is the practice or performing initial calibration using an oscillometric device. While pragmatic, this practice introduces further error because each oscillometric device has its own margin of error relative to the true reference standard, blinded, 2 observer mercury-based auscultation. Notably, no current requirement exists mandating that manufacturers validate their device released into the market. An urgent call to action from the Lancet Commission on Hypertension group has been made for mandating independent validation of all BP devices (both cuffless and cuffed) according to the International Standards Organization (ISO) Standard and for the development of validation standards for new BP technologies.7 In this issue, Tomitani et al.8 describe a post hoc analysis of a study comparing the HeartGuide wearable watch-type device (HEM-6410T: Omron Healthcare, Kyoto, Japan) to the TM-2441 ambulatory blood pressure monitoring (ABPM) device (A&D, Tokyo, Japan). Both devices were applied to 50 outpatients who wore the ABPM device for 24 hours and the HeartGuide wearable watch-type device only during daytime hours. Every 30 minutes during daytime hours, the participants had an ABPM measurement and were instructed to stop their activities during these measurements. They were also instructed to self-measure a WBPM measurement after each ABPM measurement with the WBPM device held at heart level. The participants were provided a diary to document the location, physical activity, and emotional state during each measurement Emotional state was categorized into positive (happy/calm) and negative (anxious/tense), generating 575 positive emotional state reports and 67 negative emotional state instances. The study demonstrated a statistically significant BP difference between negative and positive emotional states (9.3 ± 2.1 (systolic blood pressure)/8.4 ± 1.4 (diastolic blood pressure) mm Hg, P < 0.001) with the WBPM. This BP difference between negative and positive emotional states was similar with ABPM (10.7 ± 2.1 (systolic blood pressure)/5.6 ± 1.4 (diastolic blood pressure) mm Hg, P <0.001). Some limitation of this study should be acknowledged up front. The analysis is post hoc and the groups and emotional categorizations were not specified a priori. Of 956 paired (ABPM/WBPM) readings, 100 were eliminated because they represented the initial readings for each participant, a further 139 were eliminated due to lack of accompanying emotional state at the time of the WBPM reading, and 18 readings were excluded due to lack of certainty of the associated emotional state. Overall, 27% of the readings were excluded. Further, the study population was predominately male, with normal body mass index and recorded a narrow range of BP measurements. Whether these results are widely generalizable and repeatable will require further study. Limitations aside, the study is highly innovative because it gives a window into future uses of oscillometric technology. The WBPM is much less obtrusive and has more streamlined ergonomics than a conventional ABPM device, yet it still produces valid BP measurements unlike typical cuffless devices.9 Notably, the accuracy of the WBPM has been confirmed by performing an independent study using to the rigorous ISO validation standard.10 A particularly compelling aspect of this study is that it shows how a WBPM can be used to conveniently study associations between BP variability and emotional state. Although the relationship between emotions and BP has been described for at least 90 years,11 measurement of BP during extremes of emotion for diagnostic, prognostic, and therapeutic purposes has not been routinely recommended. Importantly, foundational studies that have generated the prognostic and therapeutic evidence underpinning the BP thresholds and targets used for the diagnosis and management of hypertension have been performed using measurement protocols that remove emotional state or physical activity as influencers of the BP measurement. However, perhaps BP response to emotional stress is an important parameter to consider and use of this new technology thereby opens up new lines of investigation. One could envision that the WBPM technology, given its convenience factor, could be also be used to study broader relationships between BP and ambulatory activities in individuals and populations, such as ambulatory BP responses within different disease states, and across differing work environments or environmental states. Although this device is limited by the fact that the user has to stop activity, remain still, and use the recommended BP measurement procedures to obtain a measurement, this is still a substantial advance and we note that no technology yet exists that accurately measures BP during motion. If coupled with secure, real-time remote transmission of BP measurements to a cloud and big data analytic and artificial intelligence capabilities, future lines of inquiry could be pursued that assess and predict BP responses in individuals, communities, and populations. In conclusion, the study by Tomitani et al. while limited in size and scope, represents an initial step toward potential broadening use of oscillometric technology, enabling us to gain new insights into ambulatory BP assessment and its relationships to human health and disease. J.R. and R.P. are cofounders of mmHg, a university-based start-up company focused on innovations in BP measurement. R.P. is a member of the Canadian Standards Association and International Standards Organization sphygmomanometer committee.

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 machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,004
score de la tête « metaresearch » (Gemma)0,016
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,020
Score d'incertitude au seuil0,022

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0040,016
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,000
Études des sciences et des technologies0,0010,002
Communication savante0,0030,005
Science ouverte0,0010,001
Intégrité de la recherche0,0200,020
Charge utile insuffisante (le modèle a refusé de juger)0,0060,005

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.

Tête enseignante Opus0,023
Tête enseignante GPT0,222
Écart entre enseignants0,199 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreCommentaire

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations7
Publié2021
Routes d'admission1
Résumé présentnon

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