Publications by authors named "David A Selby"
Background: The ability to predict rheumatoid arthritis (RA) flares between clinic visits based on real-time, longitudinal patient-generated data could potentially allow for timely interventions to avoid disease worsening.
Objective: This exploratory study aims to investigate the feasibility of using machine learning methods to classify self-reported RA flares based on a small data set of daily symptom data collected on a smartphone app.
Methods: Daily symptoms and weekly flares reported on the Remote Monitoring of Rheumatoid Arthritis (REMORA) smartphone app from 20 patients with RA over 3 months were used.
Background: As management of chronic pain continues to be suboptimal, there is a need for tools that support frequent, longitudinal pain self-reporting to improve our understanding of pain. This study aimed to assess the feasibility and acceptability of daily pain self-reporting using a smartphone-based pain manikin.
Methods: For this prospective feasibility study, we recruited adults with lived experience of painful musculoskeletal condition.
Introduction: People living with multiple long-term conditions (MLTC-M) (multimorbidity) experience a range of inter-related symptoms. These symptoms can be tracked longitudinally using consumer technology, such as smartphones and wearable devices, and then summarised to provide useful clinical insight.
Aim: We aimed to perform an exploratory analysis to summarise the extent and trajectory of multiple symptom ratings tracked via a smartwatch, and to investigate the relationship between these symptom ratings and demographic factors in people living with MLTC-M in a feasibility study.
Objectives: To investigate associations of socioeconomic position (SEP) and obesity with incident osteoarthritis (OA), and to examine whether body mass index (BMI) mediates the association between SEP and incident OA.
Methods: Data came from the English Longitudinal Study of Ageing, a population-based cohort study of adults aged ≥50 years. The sample population included 9,281 people.
Objective: We aimed to explore the frequency of self-reported flares and their association with preceding symptoms collected through a smartphone app by people with RA.
Methods: We used data from the Remote Monitoring of RA study, in which patients tracked their daily symptoms and weekly flares on an app. We summarized the number of self-reported flare weeks.
Article Synopsis
- This study analyzes the effectiveness of immunoglobulin (Ig) treatment for chronic inflammatory demyelinating polyradiculoneuropathy (CIDP) and multifocal motor neuropathy with conduction block (MMNCB) over a period from 2009 to 2020, focusing on monitoring patient outcomes through grip strength and disability scales.
- Results indicate minimal group changes in outcome measures over time, but significant intra-individual variation is noted, with a considerable percentage of patients showing changes exceeding the minimal clinically important differences (MCID).
- The study identifies trends of improvement and deterioration among patients, particularly linking older age with deterioration in CIDP, and highlights the complexity of interpreting outcome measures to guide
Introduction: People living with multiple long-term conditions (multimorbidity) (MLTC-M) experience an accumulating combination of different symptoms. It has been suggested that these symptoms can be tracked longitudinally using consumer technology, such as smartphones and wearable devices.
Aim: The aim of this study was to investigate longitudinal user engagement with a smartwatch application, collecting survey questions and active tasks over 90 days, in people living with MLTC-M.
Background: The huge increase in smartphone use heralds an enormous opportunity for epidemiology research, but there is limited evidence regarding long-term engagement and attrition in mobile health (mHealth) studies.
Objective: The objective of this study was to examine how representative the Cloudy with a Chance of Pain study population is of wider chronic-pain populations and to explore patterns of engagement among participants during the first 6 months of the study.
Methods: Participants in the United Kingdom who had chronic pain (≥3 months) and enrolled between January 20, 2016 and January 29, 2016 were eligible if they were aged ≥17 years and used the study app to report any of 10 pain-related symptoms during the study period.