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

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 (I 2 = 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.

Tele-Rehabilitation to Combat Rehabilitation Service Disruption During COVID-19 in Hong Kong: Observational Study.

BACKGROUND: A tele-rehabilitation platform was developed to improve access to ambulatory rehabilitation services in Hong Kong. The development was completed in October 2019 and rolled out for use to occupational therapists, physiotherapists, and speech therapists. During the COVID-19 pandemic, rehabilitation services were severely interrupted. Tele-rehabilitation was used extensively to meet the demand for rehabilitation service delivery. OBJECTIVE: The aims of this study were to (1) describe the design and development process of a tele-rehabilitation service, and (2) study how the tele-rehabilitation platform was used to overcome the disruption of rehabilitation service during the COVID-19 pandemic. METHODS: Tele-rehabilitation was developed utilizing 4 core determinants of Unified Theory of Acceptance and Use of Technology as guiding principles. A generic prescription platform, called the activity-based prescription system, and a mobile app, called the Rehabilitation App, were built. Five outcomes were used to examine the utilization of tele-rehabilitation both before and during the pandemic: throughput, patient demographic, patient conditions, workforce, and satisfaction from patients and staff. RESULTS: There was a tremendous increase in the use of tele-rehabilitation during pandemic. The total number of patients (up until July 2020) was 9101, and the main age range was between 51 to 70 years old. Tele-rehabilitation was used for a much wider scope of patient conditions than originally planned. More than 1112 therapists, which constituted 50.6% of the total workforce (1112/2196), prescribed tele-rehabilitation to their patients. Moreover, there was a high satisfaction rate from patients, with a mean rating of 4.2 out of 5, and a high adherence rate to prescribed rehabilitation activities (107840/131995, 81.7%). CONCLUSIONS: The findings of our study suggested that tele-rehabilitation in the form of a generic prescription platform and mobile app can be an effective means to provide rehabilitation to patient. During the COVID-19 pandemic, tele-rehabilitation has been used extensively and effectively to mitigate service disruption. Our findings also provide support that there is a high level of satisfaction with tele-rehabilitation; however, a longer duration study is required to demonstrate the sustained use of tele-rehabilitation, especially after the pandemic.

Machine learning application for the prediction of SARS-CoV-2 infection using blood tests and chest radiograph.

Triaging and prioritising patients for RT-PCR test had been essential in the management of COVID-19 in resource-scarce countries. In this study, we applied machine learning (ML) to the task of detection of SARS-CoV-2 infection using basic laboratory markers. We performed the statistical analysis and trained an ML model on a retrospective cohort of 5148 patients from 24 hospitals in Hong Kong to classify COVID-19 and other aetiology of pneumonia. We validated the model on three temporal validation sets from different waves of infection in Hong Kong. For predicting SARS-CoV-2 infection, the ML model achieved high AUCs and specificity but low sensitivity in all three validation sets (AUC: 89.9-95.8%; Sensitivity: 55.5-77.8%; Specificity: 91.5-98.3%). When used in adjunction with radiologist interpretations of chest radiographs, the sensitivity was over 90% while keeping moderate specificity. Our study showed that machine learning model based on readily available laboratory markers could achieve high accuracy in predicting SARS-CoV-2 infection.

Neuronal intranuclear inclusions in SCA2: a genetic, morphological and immunohistochemical study of two cases.

Spinocerebellar ataxia 2 (SCA2) belongs to the family of autosomal dominant cerebellar ataxias (ADCA), a genetically heterogeneous group of neurodegenerative diseases. The SCA2 gene maps to chromosome 12q24 and the causative mutation involves the expansion of a CAG repeat within the coding region of the gene. Pathologically, SCA2 presents as olivo-ponto-cerebellar atrophy (OPCA). We present the cases of a 41-year-old man and a 54-year-old woman who died after a long illness characterized by severe cerebellar ataxia. Diagnosis of SCA2 was confirmed by genetic analysis. The brains were moderately to severely atrophic and atrophy was particularly obvious in the cerebellum and brainstem. Histological examination revealed extreme loss of pontine and olivary nuclei and Purkinje cells, with preservation of the dentate nuclei, and of the pigmented cells in the substantia nigra. The whole spinal cord was also severely affected, with shrinkage of the dorsal columns and reduction in the number of neurones in the motor pool and Clarke's nuclei. Immunohistochemistry with 1C2 antibody showed granular neuronal cytoplasmic deposits in all the areas examined and widespread intranuclear inclusions, which were particularly numerous in the residual pontine nuclei. Intranuclear inclusions were not considered a feature in SCA2. Our results support the view that intranuclear inclusions are an integral part of the pathology of this mutation.