A systematic review and meta-analysis of digital application use in clinical research in pain medicine
Authors: Shetty Ashish , Delanerolle Gayathri , Zeng Yutian , Shi Jian Qing , Ebrahim Rawan , Pang Joanna , Hapangama Dharani , Sillem Martin , Shetty Suchith , Shetty Balakrishnan , Hirsch Martin , Raymont Vanessa , Majumder Kingshuk , Chong Sam , Goodison William , O’Hara Rebecca , Hull Louise , Pluchino Nicola , Shetty Naresh , Elneil Sohier , Fernandez Tacson , Brownstone Robert M. , Phiri Peter 
Published: November 2022

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 (I2 = 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.