SynthesisJournal of medical Internet research2020
Electronic Data Capture Versus Conventional Data Collection Methods in Clinical Pain Studies: Systematic Review and Meta-Analysis.
Synthesis in Journal of medical Internet research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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
20 citing papers in PubMed.
- App-based assessment of neurocognitive changes in patients undergoing transcatheter aortic valve implantation - a prospective cohort study.Journal of geriatric cardiology : JGC · 2026Article
- High-Frequency Global Postoperative Status PROMs Track Pain Peaks and Analgesic Use After Degenerative Lumbar Spine Surgery.Global spine journal · 2026Article
- Cross-cultural adaptation and preliminary validation of the Korean version of the Psoriatic Arthritis Impact of Disease-12 questionnaire.The Korean journal of internal medicine · 2026Article
- The Electronic Faces Thermometer Scale (eFTS)-Construct Validity for Pain Assessment in Pediatric Postoperative Care in Sweden.Paediatric anaesthesia · 2025Article
- Rett Syndrome Behaviour Questionnaire: Variability of Scores and Related Factors.Journal of child and adolescent psychopharmacology · 2025Article
- Identification of technology-based models and efficacy of digital-based pain facial expression assessment tools among children: a systematic review.BMC nursing · 2025Article
- Assessing Postoperative Pain in Patients Who Underwent Total Knee Arthroplasty Using an Automated Self-Logging Patient-Reported Outcome Measure Collection Device: Retrospective Cohort Study.JMIR human factors · 2025Article
- Research Electronic Data Capture (REDCap) for Population-Based Data Collection in Low- and Middle-Income Countries: Opportunities, Challenges, and Solutions.Journal of medical Internet research · 2025Article
- Association Between Trust in Health Care Professionals and Health Care Access: Insights From an Online Survey Across 21 Countries.International journal of public health · 2025Article
- Does electronic data collection perform better than paper-based data collection in health research fieldwork? A participatory action research in Zanzibar.BMJ public health · 2024Article
- Evaluation of activities of daily living using an electronic version of the Longshi Scale in patients with stroke: reliability, consistency, and preference.BMC medical informatics and decision making · 2024Observational
- [Application possibilities of digital tools in postoperative pain therapy].Schmerz (Berlin, Germany) · 2023Review
- Development of the Emoji Faces Pain Scale and Its Validation on Mobile Devices in Adult Surgery Patients: Longitudinal Observational Study.Journal of medical Internet research · 2023Observational
- Impact of Electronic Chronic Pain Questions on patient-reported outcomes and healthcare utilization, and attitudes toward eCPQ use among patients and physicians: prospective pragmatic study in a US general practice setting.Frontiers in medicine · 2023Article
- Validation of an Electronic Visual Analog Scale App for Pain Evaluation in Children and Adolescents With Symptomatic Hypermobility: Cross-sectional Study.JMIR pediatrics and parenting · 2022Article
- Staff preferences towards electronic data collection from a national take-home naloxone program: a cross-sectional study.Substance abuse treatment, prevention, and policy · 2022Article
- Toward a digital citizen lab for capturing data about alternative ways of self-managing chronic pain: An attitudinal user study.Frontiers in rehabilitation sciences · 2022Article
- Use of research electronic data capture (REDCap) in a COVID-19 randomized controlled trial: a practical example.BMC medical research methodology · 2021Article
- Play the Pain: A Digital Strategy for Play-Oriented Research and Action.Frontiers in psychiatry · 2021Article
- Patient Experience and Attitudes Toward Electronic Intake and Patient-Reported Outcomes Within an Outpatient Whole Health Center.Global advances in integrative medicine and healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe most commonly used means to assess pain is by patient self-reported questionnaires. These questionnaires have traditionally been completed using paper-and-pencil, telephone, or in-person methods, which may limit the validity of the collected data. Electronic data capture methods represent a potential way to validly, reliably, and feasibly collect pain-related data from patients in both clinical and research settings.
objectiveThe aim of this study was to conduct a systematic review and meta-analysis to compare electronic and conventional pain-related data collection methods with respect to pain score equivalence, data completeness, ease of use, efficiency, and acceptability between methods.
methodsWe searched the Medical Literature Analysis and Retrieval System Online (MEDLINE), Excerpta Medica Database (EMBASE), and Cochrane Central Register of Controlled Trials (CENTRAL) from database inception until November 2019. We included all peer-reviewed studies that compared electronic (any modality) and conventional (paper-, telephone-, or in-person-based) data capture methods for patient-reported pain data on one of the following outcomes: pain score equivalence, data completeness, ease of use, efficiency, and acceptability. We used random effects models to combine score equivalence data across studies that reported correlations or measures of agreement between electronic and conventional pain assessment methods.
resultsA total of 53 unique studies were included in this systematic review, of which 21 were included in the meta-analysis. Overall, the pain scores reported electronically were congruent with those reported using conventional modalities, with the majority of studies (36/44, 82%) that reported on pain scores demonstrating this relationship. The weighted summary correlation coefficient of pain score equivalence from our meta-analysis was 0.92 (95% CI 0.88-0.95). Studies on data completeness, patient- or provider-reported ease of use, and efficiency generally indicated that electronic data capture methods were equivalent or superior to conventional methods. Most (19/23, 83%) studies that directly surveyed patients reported that the electronic format was the preferred data collection method.
conclusionsElectronic pain-related data capture methods are comparable with conventional methods in terms of score equivalence, data completeness, ease, efficiency, and acceptability and, if the appropriate psychometric evaluations are in place, are a feasible means to collect pain data in clinical and research settings.
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