Observational studyJAMA surgery2020
Using Smartphones to Capture Novel Recovery Metrics After Cancer Surgery.
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.
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.
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.
Who cites it
58 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Smartphone-Based Passive Sensing in Monitoring Patients With Cancer: A Systematic Review.JCO clinical cancer informatics · 2023Pooled it
- Readiness for implementation of novel digital health interventions for postoperative monitoring: a systematic review and clinical innovation network analysis.The Lancet. Digital health · 2023Pooled it
- Smartphone-Based Proactive Self-Screening for Ocular Surface Malignancies: A Nonrandomized Clinical Trial.JAMA ophthalmology · 2026Trial
- Digital Phenotyping in Health Research: Scoping Review of Methods, Gaps, and Opportunities.Journal of medical Internet research · 2026Article
- Use of Smartphone Activity Data to Monitor and Identify Trends in Recovery After Breast Reduction Surgery.Plastic and reconstructive surgery. Global open · 2026Article
- Feasibility of smartphone-based digital phenotyping to measure visual function and mental health outcomes in patients with inherited retinal diseases.NPJ digital medicine · 2026Article
- Patients' and healthcare providers' perceptions in outpatient palliative cancer care services at a tertiary care hospital in Sri Lanka: prospects for adoption of a digital health intervention.BMC health services research · 2026Article
- Exploring the predictive capacity of smartphone-based digital phenotyping to monitor pain and physical quality of life in advanced cancer patients, family caregivers, and dyads.Frontiers in pain research (Lausanne, Switzerland) · 2026Article
- Primary and secondary metastatic dissemination: multiple routes to cancer-related death.Molecular cancer · 2025Review
- Biorhythms derived from consumer wearables predict postoperative complications in children.Science advances · 2025Article
- Article
- Convalescence after radical orchiectomy: Detailing surgical recovery with a novel electronic patient reported outcome (ePRO) platform.Urologic oncology · 2025Article
- Article
- Application of Patient-Generated Health Data Among Older Adults With Cancer: Scoping Review.Journal of medical Internet research · 2025Article
- Observational
- Article
- Smartphone-based activity research: methodology and key insights.Frontiers in surgery · 2025Article
- Feasibility and Acceptability of Collecting Passive Smartphone Data for Potential Use in Digital Phenotyping Among Family Caregivers and Patients With Advanced Cancer.JCO clinical cancer informatics · 2025Observational
- User-Centred Design and Development of a Smartphone Application (Healthcare (Basel, Switzerland) · 2024Article
- Rapid response to selpercatinib in RET fusion positive pancreatic neuroendocrine carcinoma confirmed by smartwatch.NPJ precision oncology · 2024Article
Corrections and comments
- Commented on by
Authors and funding
18 authors.
Funding
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
Identifiers
What OpenQuestion holds
Registered trials
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.