Evidence map›Paper›PMID 36405414›Full record

ReviewFrontiers in digital health2022

A systematic review and meta-analysis of digital application use in clinical research in pain medicine.

Ashish Shetty, Gayathri Delanerolle, Yutian Zeng, Jian Qing Shi, Rawan Ebrahim, Joanna Pang, Dharani Hapangama, Martin Sillem, Suchith Shetty, Balakrishnan Shetty and 13 more

Open access · goldAbstract readReview
In one paragraph

Review in Frontiers in digital health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed, 1 pooled it
3.8field-weighted citation impact, top 6% of its field
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

10 citing papers in PubMed, 1 synthesis or guideline pooled it, 26 citations in OpenAlex.

  1. Pooled it
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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

23 authors at 15 institutions in 7 countries.

Ashish ShettyUniversity College London Hospitals NHS Foundation Trust, London, United Kingdom.
Gayathri DelanerolleNuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom.
Yutian ZengDepartment of Statistics and Data Science, Southern University of Science and Technology, Shenzhen, China.
Jian Qing ShiDepartment of Statistics and Data Science, Southern University of Science and Technology, Shenzhen, China.
Rawan EbrahimQueen Square Institute of Neurology, University College London, London, United Kingdom.
Joanna PangResearch & Innovation Department, Southern Health NHS Foundation Trust, Southampton, United Kingdom.
Dharani HapangamaDepartment of Women and Children's Health, Liverpool Women's NHS Foundation, Liverpool, United Kingdom.
Martin SillemPraxisklinik am Rosengarten Mannheim, Saarland University Medical Centre, Homburg, Germany.
Suchith ShettyEötvös Loránd University, Budapest, Hungary.
Balakrishnan ShettyAcademy of High Education, Sri Siddhartha University, Tumkur, India.
Martin HirschQueen Square Institute of Neurology, University College London, London, United Kingdom.
Vanessa RaymontDepartment of Psychiatry, University of Oxford, Oxford, United Kingdom.
Kingshuk MajumderUniversity of Manchester NHS Foundation Trust, Gynaecology, Manchester, United Kingdom.
Sam ChongUniversity College London Hospitals NHS Foundation Trust, London, United Kingdom.
William GoodisonUniversity College London Hospitals NHS Foundation Trust, London, United Kingdom.
Rebecca O'HaraRobinson Research Institute, University of Adelaide, Adelaide, Australia.
Louise HullRobinson Research Institute, University of Adelaide, Adelaide, Australia.
Nicola PluchinoUniversity of Geneva, Gynaecology, Geneva, Switzerland.
Naresh ShettyDepartment of Orthopedics, M.S. Ramaiah Medical College, Bangalore, India.
Sohier ElneilUniversity College London Hospitals NHS Foundation Trust, London, United Kingdom.
Tacson FernandezQueen Square Institute of Neurology, University College London, London, United Kingdom.
Robert M BrownstoneQueen Square Institute of Neurology, University College London, London, United Kingdom.
Peter PhiriResearch & Innovation Department, Southern Health NHS Foundation Trust, Southampton, United Kingdom.
University College London Hospitals NHS Foundation Trust · GBNational Hospital for Neurology and Neurosurgery · GBSouthern Health NHS Foundation Trust · GBSouthern University of Science and Technology · CNThe University of Adelaide · AUUniversity of Oxford · GBEötvös Loránd University · HUM.S. Ramaiah Medical College · INOxford University Hospitals NHS Trust · GBRoyal National Orthopaedic Hospital · GBSaarland University · DESri Siddhartha Medical College · INUniversity of Geneva · CHUniversity of Liverpool · GBUniversity of Manchester · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Importance: Pain is a silent global epidemic impacting approximately a third of the population. Pharmacological and surgical interventions are primary modes of treatment. Cognitive/behavioural management approaches and interventional pain management strategies are approaches that have been used to assist with the management of chronic pain. Accurate data collection and reporting treatment outcomes are vital to addressing the challenges faced. In light of this, we conducted a systematic evaluation of the current digital application landscape within chronic pain medicine. Objective: The primary objective was to consider the prevalence of digital application usage for chronic pain management. These digital applications included mobile apps, web apps, and chatbots. Data sources: We conducted searches on PubMed and ScienceDirect for studies that were published between 1st January 1990 and 1st January 2021. Study selection: Our review included studies that involved the use of digital applications for chronic pain conditions. There were no restrictions on the country in which the study was conducted. Only studies that were peer-reviewed and published in English were included. Four reviewers had assessed the eligibility of each study against the inclusion/exclusion criteria. Out of the 84 studies that were initially identified, 38 were included in the systematic review. Data extraction and synthesis: The AMSTAR guidelines were used to assess data quality. This assessment was carried out by 3 reviewers. The data were pooled using a random-effects model. Main outcomes and measures: Before data collection began, the primary outcome was to report on the standard mean difference of digital application usage for chronic pain conditions. We also recorded the type of digital application studied (e.g., mobile application, web application) and, where the data was available, the standard mean difference of pain intensity, pain inferences, depression, anxiety, and fatigue. Results: 38 studies were included in the systematic review and 22 studies were included in the meta-analysis. The digital interventions were categorised to web and mobile applications and chatbots, with pooled standard mean difference of 0.22 (95% CI: -0.16, 0.60), 0.30 (95% CI: 0.00, 0.60) and -0.02 (95% CI: -0.47, 0.42) respectively. Pooled standard mean differences for symptomatologies of pain intensity, depression, and anxiety symptoms were 0.25 (95% CI: 0.03, 0.46), 0.30 (95% CI: 0.17, 0.43) and 0.37 (95% CI: 0.05, 0.69), respectively. A sub-group analysis was conducted on pain intensity due to the heterogeneity of the results ( Conclusions and relevance: The use of digital applications in improving pain-related symptoms shows promise, but further clinical studies would be needed to develop more robust applications. Systematic Review Registration: https://www.crd.york.ac.uk/prospero/, identifier: CRD42021228343.

Indexed as

chronic paindigital appdigital medicinemHealthpain management

Identifiers

PMID36405414
PMCPMC9668017
OpenAlexW4308123907

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.