Wearability Testing of Ambulatory Vital Sign Monitoring Devices: Prospective Observational Cohort Study

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Abstract

Background

Timely recognition of patient deterioration remains challenging. Ambulatory monitoring systems (AMSs) may provide support to current monitoring practices; however, they need to be thoroughly tested before implementation in the clinical environment for early detection of deterioration.

Objective

The objective of this study was to assess the wearability of a selection of commercially available AMSs to inform a future prospective study of ambulatory vital sign monitors in an acute hospital ward.

Methods

Five pulse oximeters (4 with finger probes and 1 wrist-worn only, collecting pulse rates and oxygen saturation) and 2 chest patches (collecting heart rates and respiratory rates) were selected to be part of this study: The 2 chest-worn patches were VitalPatch (VitalConnect) and Peerbridge Cor (Peerbridge); the 4 wrist-worn devices with finger probe were Nonin WristOx2 3150 (Nonin), Checkme O2+ (Viatom Technology), PC-68B, and AP-20 (both from Creative Medical); and the 1 solely wrist-worn device was Wavelet (Wavelet Health). Adult participants wore each device for up to 72 hours while performing usual “activities of daily living” and were asked to score the perceived exertion and perception of pain or discomfort by using the Borg CR-10 scale; thoughts and feelings caused by the AMS using the Comfort Rating Scale (CRS); and to provide general free text feedback. Median and IQRs were reported and nonparametric tests were used to assess differences between the devices’ CRS scores.

Results

Quantitative scores and feedback were collected in 70 completed questionnaires from 20 healthy volunteers, with each device tested approximately 10 times. The Wavelet seemed to be the most wearable device ( P<.001) with an overall median (IQR) CRS score of 1.00 (0.88). There were no statistically significant differences in wearability between the chest patches in the CRS total score; however, the VitalPatch was superior in the Attachment section ( P=.04) with a median (IQR) score of 3.00 (1.00). General pain and discomfort scores and total percentage of time worn are also reflective of this.

Conclusions

Our results suggest that adult participants prefer to wear wrist-worn pulse oximeters without a probe compressing the fingertip and they prefer to wear a smaller chest patch. A compromise between wearability, reliability, and accuracy should be made for successful and practical integration of AMSs within the hospital environment.

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Most cited references 33

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Welcome to the Tidyverse

Hadley Wickham, Mara Averick, Jennifer Bryan … (2019)

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Beyond Adoption: A New Framework for Theorizing and Evaluating Nonadoption, Abandonment, and Challenges to the Scale-Up, Spread, and Sustainability of Health and Care Technologies

Trisha Greenhalgh, Joseph Wherton, Chrysanthi Papoutsi … (2018)

Background Many promising technological innovations in health and social care are characterized by nonadoption or abandonment by individuals or by failed attempts to scale up locally, spread distantly, or sustain the innovation long term at the organization or system level. Objective Our objective was to produce an evidence-based, theory-informed, and pragmatic framework to help predict and evaluate the success of a technology-supported health or social care program. Methods The study had 2 parallel components: (1) secondary research (hermeneutic systematic review) to identify key domains, and (2) empirical case studies of technology implementation to explore, test, and refine these domains. We studied 6 technology-supported programs—video outpatient consultations, global positioning system tracking for cognitive impairment, pendant alarm services, remote biomarker monitoring for heart failure, care organizing software, and integrated case management via data sharing—using longitudinal ethnography and action research for up to 3 years across more than 20 organizations. Data were collected at micro level (individual technology users), meso level (organizational processes and systems), and macro level (national policy and wider context). Analysis and synthesis was aided by sociotechnically informed theories of individual, organizational, and system change. The draft framework was shared with colleagues who were introducing or evaluating other technology-supported health or care programs and refined in response to feedback. Results The literature review identified 28 previous technology implementation frameworks, of which 14 had taken a dynamic systems approach (including 2 integrative reviews of previous work). Our empirical dataset consisted of over 400 hours of ethnographic observation, 165 semistructured interviews, and 200 documents. The final nonadoption, abandonment, scale-up, spread, and sustainability (NASSS) framework included questions in 7 domains: the condition or illness, the technology, the value proposition, the adopter system (comprising professional staff, patient, and lay caregivers), the organization(s), the wider (institutional and societal) context, and the interaction and mutual adaptation between all these domains over time. Our empirical case studies raised a variety of challenges across all 7 domains, each classified as simple (straightforward, predictable, few components), complicated (multiple interacting components or issues), or complex (dynamic, unpredictable, not easily disaggregated into constituent components). Programs characterized by complicatedness proved difficult but not impossible to implement. Those characterized by complexity in multiple NASSS domains rarely, if ever, became mainstreamed. The framework showed promise when applied (both prospectively and retrospectively) to other programs. Conclusions Subject to further empirical testing, NASSS could be applied across a range of technological innovations in health and social care. It has several potential uses: (1) to inform the design of a new technology; (2) to identify technological solutions that (perhaps despite policy or industry enthusiasm) have a limited chance of achieving large-scale, sustained adoption; (3) to plan the implementation, scale-up, or rollout of a technology program; and (4) to explain and learn from program failures.

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Confidential inquiry into quality of care before admission to intensive care.

Janette P. McQuillan, Debra Morgan, George D W Smith … (1998)

To examine the prevalence, nature, causes, and consequences of suboptimal care before admission to intensive care units, and to suggest possible solutions. Prospective confidential inquiry on the basis of structured interviews and questionnaires. A large district general hospital and a teaching hospital. A cohort of 100 consecutive adult emergency admissions, 50 in each centre. Opinions of two external assessors on quality of care especially recognition, investigation, monitoring, and management of abnormalities of airway, breathing, and circulation, and oxygen therapy and monitoring. Assessors agreed that 20 patients were well managed (group 1) and 54 patients received suboptimal care (group 2). Assessors disagreed on quality of management of 26 patients (group 3). The casemix and severity of illness, defined by the acute physiology and chronic health evaluation (APACHE II) score, were similar between centres and the three groups. In groups 1, 2, and 3 intensive care mortalities were 5 (25%), 26 (48%), and 6 (23%) respectively (P=0.04) (group 1 versus group 2, P=0.07). Hospital mortalities were 7 (35%), 30 (56%), and 8 (31%) (P=0.07) and standardised hospital mortality ratios (95% confidence intervals) were 1.23 (0.49 to 2.54), 1.4 (0.94 to 2.0), and 1.26 (0.54 to 2.48) respectively. Admission to intensive care was considered late in 37 (69%) patients in group 2. Overall, a minimum of 4.5% and a maximum of 41% of admissions were considered potentially avoidable. Suboptimal care contributed to morbidity or mortality in most instances. The main causes of suboptimal care were failure of organisation, lack of knowledge, failure to appreciate clinical urgency, lack of supervision, and failure to seek advice. The management of airway, breathing, and circulation, and oxygen therapy and monitoring in severely ill patients before admission to intensive care units may frequently be suboptimal. Major consequences may include increased morbidity and mortality and requirement for intensive care. Possible solutions include improved teaching, establishment of medical emergency teams, and widespread debate on the structure and process of acute care.

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Author and article information

Contributors

Carlos Areia:

ORCID: https://orcid.org/0000-0002-4668-7069

Critical Care Research GroupNuffield Department of Clinical NeurosciencesUniversity of OxfordKadoorie Research Centre, John Radcliffe HospitalOxfordUnited Kingdom44 1865 231440carlos.morgadoareia@ndcn.ox.ac.uk

Journal

Journal ID (nlm-ta): JMIR Mhealth Uhealth

Journal ID (iso-abbrev): JMIR Mhealth Uhealth

Journal ID (publisher-id): JMU

Title: JMIR mHealth and uHealth

Publisher: JMIR Publications (Toronto, Canada )

ISSN (Electronic): 2291-5222

Publication date Collection: December 2020

Publication date (Electronic): 16 December 2020

Volume: 8

Issue: 12

Electronic Location Identifier: e20214

Affiliations

[1 ] Critical Care Research Group Nuffield Department of Clinical Neurosciences University of Oxford Oxford United Kingdom

[2 ] National Institute for Health Research Biomedical Research Centre Oxford United Kingdom

[3 ] Department of Engineering Science Institute of Biomedical Engineering University of Oxford Oxford United Kingdom

Author notes

Corresponding Author: Carlos Areia carlos.morgadoareia@ 123456ndcn.ox.ac.uk

Author information

Carlos Areia https://orcid.org/0000-0002-4668-7069

Louise Young https://orcid.org/0000-0001-9094-1733

Sarah Vollam https://orcid.org/0000-0003-2835-6271

Jody Ede https://orcid.org/0000-0001-7289-6991

Mauro Santos https://orcid.org/0000-0002-1470-6966

Lionel Tarassenko https://orcid.org/0000-0002-0118-1646

Peter Watkinson https://orcid.org/0000-0003-1023-3927

Article

Publisher ID: v8i12e20214

DOI: 10.2196/20214

PMC ID: 7773507

PubMed ID: 33325827

SO-VID: 540750df-8f18-415a-947f-edf714189412

Copyright © ©Carlos Areia, Louise Young, Sarah Vollam, Jody Ede, Mauro Santos, Lionel Tarassenko, Peter Watkinson. Originally published in JMIR mHealth and uHealth (http://mhealth.jmir.org), 16.12.2020.

License:

This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR mHealth and uHealth, is properly cited. The complete bibliographic information, a link to the original publication on http://mhealth.jmir.org/, as well as this copyright and license information must be included.

History

Date received : 13 May 2020

Date revision requested : 16 July 2020

Date revision received : 28 August 2020

Date accepted : 21 October 2020

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