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Record W4402509289 · doi:10.2196/57108

The Potential Use and Value of a Wearable Monitoring Bracelet for Patients With Chronic Obstructive Pulmonary Disease: Qualitative Study Investigating the Patient and Health Care Professional Perspectives

2024· article· en· W4402509289 on OpenAlexvenueno aff
Suzanne M Debeij, Jiska J Aardoom, Miriam L. Haaksma, Wieteke A M Stoop, Eléonore F van Dam van Isselt, Marise J. Kasteleyn

Bibliographic record

VenueJMIR Formative Research · 2024
Typearticle
Languageen
FieldMedicine
TopicChronic Obstructive Pulmonary Disease (COPD) Research
Canadian institutionsnot available
Fundersnot available
KeywordsFocus groupMedicineQualitative researchCOPDExacerbationWearable computerHealth carePulmonary diseaseHealth professionalsQualitative propertyNursingFamily medicineMedical emergencyInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: The occurrence of exacerbations has major effects on the health of people with chronic obstructive pulmonary disease (COPD). Monitoring devices that measure (vital) parameters hold promise for timely identification and treatment of exacerbations. Stakeholders' perspectives on the use of monitoring devices are of importance for the successful development and implementation of a device. OBJECTIVE: This study aimed to explore the potential use and value of a wearable monitoring bracelet (MB) for patients with COPD at high risk for exacerbation. The perspectives of health care professionals as well as patients were examined, both immediately after hospitalization and over a longer period. Furthermore, potential facilitators and barriers to the use and implementation of an MB were explored. METHODS: Data for this qualitative study were collected from January to April 2023. A total of 11 participants (eg, n=6 health care professionals [HCPs], 2 patients, and 3 additional patients) participated. In total, 2 semistructured focus groups were conducted via video calls; 1 with HCPs of various professional backgrounds and 1 with patients. In addition, 3 semistructured individual interviews were held with patients. The interviews and focus groups addressed attitudes, wishes, needs, as well as factors that could either support or impede the potential MB use. Data from interviews and focus groups were coded and analyzed according to the principles of the framework method. RESULTS: HCPs and patients both predominantly emphasized the importance of an MB in terms of promptly identifying exacerbations by detecting deviations from normal (vital) parameters, and subsequently alerting users. According to HCPs, this is how an MB should support the self-management of patients. Most participants did not anticipate major differences in value and use of an MB between the short-term and the long-term periods after hospitalization. Facilitators of the potential use and implementation of an MB that participants highlighted were ease of use and some form of support for patients in using an MB and interpreting the data. HCPs as well as patients expressed concerns about potential costs as a barrier to use and implementation. Another barrier that HCPs mentioned, was the prerequisite of digital literacy for patients to be able to interpret and react to the data from an MB. CONCLUSIONS: HCPs and patients both recognize that an MB could be beneficial and valuable to patients with COPD at high risk for exacerbation, in the short as well as the long term. In particular, they perceived value in supporting self-management of patients with COPD. Stakeholders would be able to use the obtained insights in support of the effective implementation of MBs in COPD patient care, which can potentially improve health care and the overall well-being of patients with COPD.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.013
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.066

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.017
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0070.005
Scholarly communication0.0030.003
Open science0.0010.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.039
GPT teacher head0.415
Teacher spread0.376 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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

Citations7
Published2024
Admission routes1
Has abstractyes

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