Evidence map›Paper›PMID 31657854›Full record

Observational studyJAMA surgery2020

Using Smartphones to Capture Novel Recovery Metrics After Cancer Surgery.

Nikhil Panda, Ian Solsky, Emily J Huang, Stuart Lipsitz, Jason C Pradarelli, Megan Delisle, James C Cusack, Michele A Gadd, Carrie C Lubitz, John T Mullen and 8 more

Abstract readObservational Study
In one paragraph

Observational study in JAMA surgery, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 58 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
58citing papers in PubMed, 2 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

58 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

18 authors.

Nikhil PandaAriadne Labs, Brigham and Women's Hospital, Harvard T. H. Chan School of Public Health, Boston, Massachusetts.
Ian SolskyAriadne Labs, Brigham and Women's Hospital, Harvard T. H. Chan School of Public Health, Boston, Massachusetts.
Emily J HuangDepartment of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, Massachusetts.
Stuart LipsitzAriadne Labs, Brigham and Women's Hospital, Harvard T. H. Chan School of Public Health, Boston, Massachusetts.
Jason C PradarelliAriadne Labs, Brigham and Women's Hospital, Harvard T. H. Chan School of Public Health, Boston, Massachusetts.
Megan DelisleAriadne Labs, Brigham and Women's Hospital, Harvard T. H. Chan School of Public Health, Boston, Massachusetts.
James C CusackDepartment of Surgery, Massachusetts General Hospital, Boston.
Michele A GaddDepartment of Surgery, Massachusetts General Hospital, Boston.
Carrie C LubitzDepartment of Surgery, Massachusetts General Hospital, Boston.
John T MullenDepartment of Surgery, Massachusetts General Hospital, Boston.
Motaz QadanDepartment of Surgery, Massachusetts General Hospital, Boston.
Barbara L SmithDepartment of Surgery, Massachusetts General Hospital, Boston.
Michelle SpechtDepartment of Surgery, Massachusetts General Hospital, Boston.
Antonia E StephenDepartment of Surgery, Massachusetts General Hospital, Boston.
Kenneth K TanabeDepartment of Surgery, Massachusetts General Hospital, Boston.
Atul A GawandeAriadne Labs, Brigham and Women's Hospital, Harvard T. H. Chan School of Public Health, Boston, Massachusetts.
Jukka-Pekka OnnelaAriadne Labs, Brigham and Women's Hospital, Harvard T. H. Chan School of Public Health, Boston, Massachusetts.
Alex B HaynesAriadne Labs, Brigham and Women's Hospital, Harvard T. H. Chan School of Public Health, Boston, Massachusetts.

Funding

RESEARCH TRAINING IN ALIMENTARY TRACT SURGERYT32DK007754 · NIDDK · MASSACHUSETTS GENERAL HOSPITAL · PI HODIN, RICHARD A. · 1997 to 2025
$6.5M
NIDDK NIH HHS T32 DK007754
6 · The paper itself

Abstract

Importance: Patient-generated health data captured from smartphone sensors have the potential to better quantify the physical outcomes of surgery. The ability of these data to discriminate between postoperative trends in physical activity remains unknown. Objective: To assess whether physical activity captured from smartphone accelerometer data can be used to describe postoperative recovery among patients undergoing cancer operations. Design, Setting, and Participants: This prospective observational cohort study was conducted from July 2017 to April 2019 in a single academic tertiary care hospital in the United States. Preoperatively, adults (age ≥18 years) who spoke English and were undergoing elective operations for skin, soft tissue, head, neck, and abdominal cancers were approached. Patients were excluded if they did not own a smartphone. Exposures: Study participants downloaded an application that collected smartphone accelerometer data continuously for 1 week preoperatively and 6 months postoperatively. Main Outcomes and Measures: The primary end points were trends in daily exertional activity and the ability to achieve at least 60 minutes of daily exertional activity after surgery among patients with vs without a clinically significant postoperative event. Postoperative events were defined as complications, emergency department presentations, readmissions, reoperations, and mortality. Results: A total of 139 individuals were approached. In the 62 enrolled patients, who were followed up for a median (interquartile range [IQR]) of 147 (77-179) days, there were no preprocedural differences between patients with vs without a postoperative event. Seventeen patients (27%) experienced a postoperative event. These patients had longer operations than those without a postoperative event (median [IQR], 225 [152-402] minutes vs 107 [68-174] minutes; P < .001), as well as greater blood loss (median [IQR], 200 [35-515] mL vs 25 [5-100] mL; P = .006) and more follow-up visits (median [IQR], 2 [2-4] visits vs 1 [1-2] visits; P = .002). Compared with mean baseline daily exertional activity, patients with a postoperative event had lower activity at week 1 (difference, -41.6 [95% CI, -75.1 to -8.0] minutes; P = .02), week 3 (difference, -40.0 [95% CI, -72.3 to -3.6] minutes; P = .03), week 5 (difference, -39.6 [95% CI, -69.1 to -10.1] minutes; P = .01), and week 6 (difference, -36.2 [95% CI, -64.5 to -7.8] minutes; P = .01) postoperatively. Fewer of these patients were able to achieve 60 minutes of daily exertional activity in the 6 weeks postoperatively (proportions: week 1, 0.40 [95% CI, 0.31-0.49]; P < .001; week 2, 0.49 [95% CI, 0.40-0.58]; P = .003; week 3, 0.39 [95% CI, 0.30-0.48]; P < .001; week 4, 0.47 [95% CI, 0.38-0.57]; P < .001; week 5, 0.51 [95% CI, 0.42-0.60]; P < .001; week 6, 0.73 [95% CI, 0.68-0.79] vs 0.43 [95% CI, 0.33-0.52]; P < .001). Conclusions and Relevance: Smartphone accelerometer data can describe differences in postoperative physical activity among patients with vs without a postoperative event. These data help objectively quantify patient-centered surgical recovery, which have the potential to improve and promote shared decision-making, recovery monitoring, and patient engagement.

Indexed as

ConvalescencePhysical ExertionSmartphoneAccelerometryAgedExerciseFemaleHumansMaleMiddle AgedMobile ApplicationsNeoplasmsPostoperative ComplicationsPostoperative PeriodProspective StudiesRecovery of Function

Identifiers

PMID31657854
PMCPMC6820047

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.