中文版 | English
Title

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

Author
Corresponding AuthorPhiri,Peter
Publication Years
2022-11-02
DOI
Source Title
EISSN
2673-253X
Volume4
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 outcome(s) and measure(s): 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 (I = 82.86%; p = 0.02). After stratifying by country, we found that digital applications were more likely to be effective in some countries (e.g., United States, China) than others (e.g., Ireland, Norway). 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.
Keywords
URL[Source Record]
Language
English
SUSTech Authorship
Others
Scopus EID
2-s2.0-85142178184
Data Source
Scopus
Citation statistics
Cited Times [WOS]:1
Document TypeJournal Article
Identifierhttp://kc.sustech.edu.cn/handle/2SGJ60CL/412565
DepartmentDepartment of Statistics and Data Science
Affiliation
1.University College London Hospitals NHS Foundation Trust,London,United Kingdom
2.Nuffield Department of Primary Care Health Sciences,University of Oxford,Oxford,United Kingdom
3.Department of Statistics and Data Science,Southern University of Science and Technology,Shenzhen,China
4.Alan Turing Institute,London,United Kingdom
5.Queen Square Institute of Neurology,University College London,London,United Kingdom
6.Research & Innovation Department,Southern Health NHS Foundation Trust,Southampton,United Kingdom
7.Department of Women and Children’s Health,Liverpool Women’s NHS Foundation,Liverpool,United Kingdom
8.Praxisklinik am Rosengarten Mannheim,Saarland University Medical Centre,Homburg,Germany
9.Eötvös Loránd University,Budapest,Hungary
10.Academy of High Education,Sri Siddhartha University,Tumkur,India
11.Oxford University Hospitals NHS Foundation Trust,Gynaecology,Oxford,United Kingdom
12.Department of Psychiatry,University of Oxford,Oxford,United Kingdom
13.University of Manchester NHS Foundation Trust,Gynaecology,Manchester,United Kingdom
14.Robinson Research Institute,University of Adelaide,Adelaide,Australia
15.University of Geneva,Gynaecology,Geneva,Switzerland
16.Department of Orthopedics,M.S. Ramaiah Medical College,Bangalore,India
17.Chronic Pain Medicine,Royal National Orthopaedic Hospital,London,United Kingdom
18.Primary Care,Population Sciences and Medical Education Division,University of Southampton,Southampton,United Kingdom
Recommended Citation
GB/T 7714
Shetty,Ashish,Delanerolle,Gayathri,Zeng,Yutian,et al. A systematic review and meta-analysis of digital application use in clinical research in pain medicine[J]. Frontiers in Digital Health,2022,4.
APA
Shetty,Ashish.,Delanerolle,Gayathri.,Zeng,Yutian.,Shi,Jian Qing.,Ebrahim,Rawan.,...&Phiri,Peter.(2022).A systematic review and meta-analysis of digital application use in clinical research in pain medicine.Frontiers in Digital Health,4.
MLA
Shetty,Ashish,et al."A systematic review and meta-analysis of digital application use in clinical research in pain medicine".Frontiers in Digital Health 4(2022).
Files in This Item:
There are no files associated with this item.
Related Services
Fulltext link
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Export to Excel
Export to Csv
Altmetrics Score
Google Scholar
Similar articles in Google Scholar
[Shetty,Ashish]'s Articles
[Delanerolle,Gayathri]'s Articles
[Zeng,Yutian]'s Articles
Baidu Scholar
Similar articles in Baidu Scholar
[Shetty,Ashish]'s Articles
[Delanerolle,Gayathri]'s Articles
[Zeng,Yutian]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Shetty,Ashish]'s Articles
[Delanerolle,Gayathri]'s Articles
[Zeng,Yutian]'s Articles
Terms of Use
No data!
Social Bookmark/Share
No comment.

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.