Evidence map›Paper›PMID 34734827›Full record

ArticleJMIR formative research2021

Comparing Email, SMS, and Concurrent Mixed Modes Approaches to Capture Quality of Recovery in the Perioperative Period: Retrospective Longitudinal Cohort Study.

Jamie L Romeiser, James Cavalcante, Deborah C Richman, Sunitha M Singh, Xiaohui Liang, Allison Pei, Samanvaya Sharma, Zoe Lazarus, Tong J Gan, Elliott Bennett-Guerrero

Abstract read
In one paragraph

Article in JMIR formative research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

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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

10 authors.

Jamie L RomeiserDepartment of Anesthesiology, Stony Brook University Medical Center, Stony Brook, NY, United States.ORCID https://orcid.org/0000-0003-0281-8933
James CavalcanteDepartment of Anesthesiology, Stony Brook University Medical Center, Stony Brook, NY, United States.ORCID https://orcid.org/0000-0002-2155-143X
Deborah C RichmanDepartment of Anesthesiology, Stony Brook University Medical Center, Stony Brook, NY, United States.ORCID https://orcid.org/0000-0002-7127-6966
Sunitha M SinghDepartment of Anesthesiology, Stony Brook University Medical Center, Stony Brook, NY, United States.ORCID https://orcid.org/0000-0002-9418-9849
Xiaohui LiangDepartment of Anesthesiology, Stony Brook University Medical Center, Stony Brook, NY, United States.ORCID https://orcid.org/0000-0002-8356-2652
Allison PeiDepartment of Anesthesiology, Stony Brook University Medical Center, Stony Brook, NY, United States.ORCID https://orcid.org/0000-0003-2561-5602
Samanvaya SharmaDepartment of Anesthesiology, Stony Brook University Medical Center, Stony Brook, NY, United States.ORCID https://orcid.org/0000-0001-8892-3456
Zoe LazarusDepartment of Anesthesiology, Stony Brook University Medical Center, Stony Brook, NY, United States.ORCID https://orcid.org/0000-0001-8279-9888
Tong J GanDepartment of Anesthesiology, Stony Brook University Medical Center, Stony Brook, NY, United States.ORCID https://orcid.org/0000-0001-6991-735X
Elliott Bennett-GuerreroDepartment of Anesthesiology, Stony Brook University Medical Center, Stony Brook, NY, United States.ORCID https://orcid.org/0000-0001-8659-8098

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAs patients are discharged from the hospital more quickly, the ability to monitor patient recovery between hospital discharge and the first follow-up clinic visit is becoming increasingly important. Despite substantial increase in both internet use and smartphone ownership over the past 5 years, clinicians have been slow to embrace the use of these devices to capture patient recovery information in the period between hospital discharge and the first clinical follow-up appointment.

objectiveThis study aims to investigate the generalizability of using a web-based platform to capture patient recovery in a broad surgical patient population and compare response rates for 3 different web-based strategies for delivering recovery surveys over the perioperative period: email, SMS text messaging, and a concurrent mixed approach of using both email and SMS text messaging.

methodsPatients undergoing surgeries managed with an enhanced recovery after surgery pathway were asked to participate in a web-based quality assurance monitoring program at the time of their preoperative surgery appointment. Different follow-up methods were implemented over 3 sequential phases. Patients received Health Insurance Portability and Accountability Act-compliant web-based survey links via email (phase 1), SMS text messaging (phase 2), or concurrently using both email and SMS text messaging (phase 3) using REDCap and Twilio software. Recovery assessments using the established Quality of Recovery-9 instrument were performed 4 days before surgery and at 7 and 30 days postoperatively. Generalizability of the web-based system was examined by comparing characteristics of those who participated versus those who did not. Differences in response rates by the web-based collection method were analyzed using adjusted models.

resultsA total of 615 patients were asked to participate, with 526 (85.5%) opting for the follow-up program. Those who opted in were younger, slightly healthier, and more likely to be in a partnership. The concurrent mixed modes method was the most successful for obtaining responses at each time point compared with text or email alone (pre: 119/160, 74.4% vs 116/173, 67.1% vs 56/130, 43.1%, P<.001; 7 days: 115/172, 66.9% vs 82/164, 50.0% vs 59/126, 46.8%, P=.001; 30 days: 152/234, 65.0% vs 52/105, 49.5% vs 53/123, 43.1%, P=.001, respectively). In the adjusted model, the concurrent mixed modes method significantly predicted response compared with using email alone (odds ratio 3.4; P<.001) and SMS text messaging alone (odds ratio 1.9; P<.001). Additional significant predictors of response were race, partnership, and time.

conclusionsFor internet users and smartphone owners, electronic capture of recovery surveys appear to be possible through this mechanism. Discrepancies in both inclusion and response rates still exist among certain subgroups of patients, but the concurrent approach of using both email and text messages was the most effective approach to reach the largest number of patients across all subgroups.

Indexed as

concurrent mixed modesemailmobile phoneperioperative recoveryrecovery after surgerySMStext messages

Identifiers

PMID34734827
PMCPMC8603163

What OpenQuestion holds

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Registered trials

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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.