Evidence map›Paper›PMID 38020485›Full record

ArticleCanadian journal of kidney health and disease2023

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.

Domenic Pieroni, Silvia J Leon, Amanda L Krueger, Lauren Burton, Olivier Tremblay-Savard, Navdeep Tangri, Paul Komenda, Clara Bohm, Claudio Rigatto

Abstract read
In one paragraph

Article in Canadian journal of kidney health and disease, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Domenic PieroniChronic Disease Innovation Centre, Seven Oaks General Hospital, Winnipeg, Manitoba, Canada.
Silvia J LeonChronic Disease Innovation Centre, Seven Oaks General Hospital, Winnipeg, Manitoba, Canada.ORCID https://orcid.org/0000-0003-0006-9596
Amanda L KruegerChronic Disease Innovation Centre, Seven Oaks General Hospital, Winnipeg, Manitoba, Canada.
Lauren BurtonChronic Disease Innovation Centre, Seven Oaks General Hospital, Winnipeg, Manitoba, Canada.
Olivier Tremblay-SavardDepartment of Computer Science, University of Manitoba, Winnipeg, Canada.
Navdeep TangriChronic Disease Innovation Centre, Seven Oaks General Hospital, Winnipeg, Manitoba, Canada.ORCID https://orcid.org/0000-0002-5075-6370
Paul KomendaChronic Disease Innovation Centre, Seven Oaks General Hospital, Winnipeg, Manitoba, Canada.
Clara BohmChronic Disease Innovation Centre, Seven Oaks General Hospital, Winnipeg, Manitoba, Canada.
Claudio RigattoChronic Disease Innovation Centre, Seven Oaks General Hospital, Winnipeg, Manitoba, Canada.ORCID https://orcid.org/0000-0002-8306-8072

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

chronic kidney diseaseehealthmobile healthtelemonitoring

Identifiers

PMID38020485
PMCPMC10666721

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.