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Record W4388917254 · doi:10.1177/20543581231212125

Use of Wearable and Wireless Technology in Real-World Clinical Settings to Improve Patient Outcomes in Chronic Kidney Disease: A Mixed Methods Pilot Prospective Trial

2023· article· en· W4388917254 on OpenAlexaffabout
Domenic Pieroni, Silvia J. Leon, Amanda L Krueger, Lauren Burton, Olivier Tremblay-Savard, Navdeep Tangri, Paul Komenda, Clara Bohm, Claudio Rigatto

Bibliographic record

VenueCanadian Journal of Kidney Health and Disease · 2023
Typearticle
Languageen
FieldHealth Professions
TopicMobile Health and mHealth Applications
Canadian institutionsUniversity of ManitobaSeven Oaks General Hospital
Fundersnot available
KeywordsMedicineKidney diseasePopulationPhysical therapyClinical trialDialysisRandomized controlled trialInternal medicine

Abstract

fetched live from OpenAlex

Background: During the 30-day period prior to initiating dialysis, there is a 10-fold rise in emergency department visits and hospitalizations related to kidney failure. Objective: The Virtual Ward Incorporating Electronic Wearables (VIEWER) trial implemented a home telemonitoring system to track changes in patients’ vitals and assess their adherence and the acceptability of telemonitoring in a chronic kidney disease (CKD) population. Design: A pilot prospective clinical trial using a mixed methods approach was performed. Setting: The research was conducted in Winnipeg, Manitoba. Participants: There were 2 phases: Phase 1 was a 2-week-long pilot trial consisting of 10 participants. Phase 2 was a 3-month-long trial with a total of 26 participants. Patients with an estimated glomerular filtration rate <15 and a >40% risk of beginning dialysis in the next 2 years according to the kidney failure risk equation were eligible to participate in the study. Methods: The primary quantitative outcome was adherence, defined as the proportion of daily self-assessments completed using VIEWER over the follow-up period. The usability and acceptability of VIEWER was assessed qualitatively at the end of the trial through structured questionnaires and focus groups. Results: Phase 1 participants (n = 10) had a median adherence of 77.17% for the 2-week observation period. Phase 2 participants (n = 26) showed a lower median adherence of 36% for the 3-month period. Focus group participants (n = 11) identified many positive aspects of VIEWER, including increased awareness and empowerment over health, simplicity of the data platform, and the ability to show clinical staff their health trends. Some challenges identified with VIEWER were connectivity issues with the Bluetooth, perceived inconvenience, and negative thoughts toward their health Limitations: Limitations of the study include a small sample size, which limited our ability to measure quantitative outcomes. In addition, patients agreeing to participate in any trial are generally more highly motivated and engaged in their care than those declining participation. Therefore, our results may not be generalizable to individuals who are not interested in self-management of their health. Conclusion: Our results suggest that home telemonitoring in patients with advanced CKD is feasible using a CKD-specific platform like VIEWER. We anticipate that improved functionality with incorporation of feedback from this study will result in greater long-term adherence. A future randomized clinical trial is planned.

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)0.022
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.115

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0220.015
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.004
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.068
GPT teacher head0.462
Teacher spread0.394 · 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 designNon-randomized trial
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

Citations2
Published2023
Admission routes2
Has abstractyes

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