Evidence map›Paper›PMID 33629966›Full record

ReviewJMIR perioperative medicine2021

Relationship Between Mobile Digital Sensor Monitoring and Perioperative Outcomes: Systematic Review.

Ali Memon, Patrick Lec, Andrew Lenis, Vidit Sharma, Erika Wood, George Schade, Wayne Brisbane

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in JMIR perioperative medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–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. Article
  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

7 authors.

Ali MemonDepartment of Urology, University of Washington, Seattle, WA, United States.ORCID https://orcid.org/0000-0001-8838-2901
Patrick LecDepartment of Urology, University of California, Los Angeles, Los Angeles, CA, United States.ORCID https://orcid.org/0000-0002-1318-1520
Andrew LenisDepartment of Urology, University of California, Los Angeles, Los Angeles, CA, United States.ORCID https://orcid.org/0000-0001-9413-7793
Vidit SharmaDepartment of Urology, University of California, Los Angeles, Los Angeles, CA, United States.ORCID https://orcid.org/0000-0002-4715-5451
Erika WoodDepartment of Urology, University of California, Los Angeles, Los Angeles, CA, United States.ORCID https://orcid.org/0000-0002-6907-6463
George SchadeDepartment of Urology, University of Washington, Seattle, WA, United States.ORCID https://orcid.org/0000-0002-9405-3853
Wayne BrisbaneDepartment of Urology, University of California, Los Angeles, Los Angeles, CA, United States.ORCID https://orcid.org/0000-0003-0470-5262

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMonitoring surgical recovery has traditionally been confined to metrics measurable within the hospital and clinic setting. However, commercially available mobile sensors are now capable of extending measurements into a patient's home. As these sensors were developed for nonmedical applications, their clinical role has yet to be established. The aim of this systematic review is to evaluate the relationship between data generated by mobile sensors and postoperative outcomes.

objectiveThe objective of this study is to describe the current use of mobile sensors in the perioperative setting and the correlation between their data and clinical outcomes.

methodsA systematic search of EMBASE, MEDLINE, and Cochrane Library from inception until April 2019 was performed to identify studies of surgical patients monitored with mobile sensors. Sensors were considered if they collected patient metrics such as step count, temperature, or heart rate. Studies were included if patients underwent major surgery (≥1 inpatient postoperative day), patients were monitored using mobile sensors in the perioperative period, and the study reported postoperative outcomes (ie, complications and hospital readmission). For studies including step count, a pooled analysis of the step count per postoperative day was calculated for the complication and noncomplication cohorts using mean and a random-effects linear model. The Grading of Recommendations, Assessment, Development, and Evaluation tool was used to assess study quality.

resultsFrom 2209 abstracts, we identified 11 studies for review. Reviewed studies consisted of either prospective observational cohorts (n=10) or randomized controlled trials (n=1). Activity monitors were the most widely used sensors (n=10), with an additional study measuring temperature, respiratory rate, and heart rate (n=1). Low step count was associated with worse postoperative outcomes. A median step count of around 1000 steps per postoperative day was associated with adverse surgical outcomes. Within the studies, there was heterogeneity between the type of surgery and type of reported postoperative outcome.

conclusionsDespite significant heterogeneity in the type of surgery and sensors, low step count was associated with worse postoperative outcomes across surgical specialties. Further studies and standardization are needed to assess the role of mobile sensors in postoperative care, but a threshold of approximately 1000 steps per postoperative day warrants further investigation.

Indexed as

mobile sensorsperioperativeperioperative outcomessensor monitoring

Identifiers

PMID33629966
PMCPMC7952235

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.