Characterising user engagement with mHealth for chronic disease self-management and impact on machine learning performance.

Mobile Health (mHealth) has the potential to be transformative in the management of chronic conditions. Machine learning can leverage self-reported data collected with apps to predict periods of increased health risk, alert users, and signpost interventions. Despite this, mHealth must balance the treatment burden of frequent self-reporting and predictive performance and safety. Here we report how user engagement with a widely used and clinically validated mHealth app, myCOPD (designed for the self-management of Chronic Obstructive Pulmonary Disease), directly impacts the performance of a machine learning model predicting an acute worsening of condition (i.e., exacerbations). We classify how users typically engage with myCOPD, finding that 60.3% of users engage frequently, however, less frequent users can show transitional engagement (18.4%), becoming more engaged immediately ( < 21 days) before exacerbating. Machine learning performed better for users who engaged the most, however, this performance decrease can be mostly offset for less frequent users who engage more near exacerbation. We conduct interviews and focus groups with myCOPD users, highlighting digital diaries and disease acuity as key factors for engagement. Users of mHealth can feel overburdened when self-reporting data necessary for predictive modelling and confidence of recognising exacerbations is a significant barrier to accurate self-reported data. We demonstrate that users of mHealth should be encouraged to engage when they notice changes to their condition (rather than clinically defined symptoms) to achieve data that is still predictive for machine learning, while reducing the likelihood of disengagement through desensitisation.

Exploring the Validity of GOLD 2023 Guidelines: Should GOLD C and D Be Combined?

Introduction: The GOLD (Global Initiative for Chronic Obstructive Lung Disease) 2023 guidelines proposed important changes to the stratification of disease severity using the "ABCD" assessment tool. The highest risk groups "C" and "D" were combined into a single category "E" based on exacerbation history, no longer considering symptomology. Purpose: We quantify the differential disease progression of individuals initially stratified by the GOLD 2022 "ABCD" scheme to evaluate these proposed changes. Patients and Methods: We utilise data collected from 1529 users of the myCOPD mobile app, a widely used and clinically validated app supporting people living with COPD in the UK. For patients in each GOLD group, we quantify symptoms using COPD Assessment Tests (CAT) and rate of exacerbation over a 12-month period post classification. Results: CAT scores for users initially classified into GOLD C and GOLD D remain significantly different after 12 months (Kolmogorov-Smirnov statistic = 0.59, P = 8.2 × 10-23). Users initially classified into GOLD C demonstrate a significantly lower exacerbation rate over the 12 months post classification than those initially in GOLD D (Kolmogorov-Smirnov statistic = 0.26; P = 3.1 × 10-2; all exacerbations). Further, those initially classified as GOLD B have higher CAT scores and exacerbation rates than GOLD C in the following 12 months. Conclusion: CAT scores remain important for stratifying disease progression both in-terms of symptomology and future exacerbation risk. Based on this evidence, the merger of GOLD C and GOLD D should be reconsidered.

Plasma homocysteine is elevated in COPD patients and is related to COPD severity.

BACKGROUND: Although recent studies have found that total plasma homocysteine (tHCY) and chronic obstructive pulmonary disease (COPD) are both risk factors for cardiac disease, there have been few studies of plasma homocysteine levels in COPD patients. We tested the hypothesis that total plasma homocysteine (tHCY) would be elevated in patients diagnosed with COPD compared with controls. METHODS: We studied 29 COPD outpatients and 25 asymptomatic subjects (controls) over age 55 years with measurement of forced expiratory volume in one second (FEV1), forced vital capacity (FVC), St. Georges Respiratory Questionnaire (SGRQ) score, tHCY and serum C-reactive protein (sCRP). RESULTS: There was no difference between controls vs. COPD patients in mean age or gender but mean (SD) FEV1 was 2.25 (0.77) vs. 1.43 (0.60) L; FEV1% predicted 76.1 (17.2) vs. 49.1 (16.3) p < 0.001 in both cases. Median (IQR) tHCY was 8.22 (6.63, 9.55) in controls vs. 10.96 (7.56, 13.60) micromol/l for COPD, p = 0.006 and sCRP 0.89 (0.47, 2.55) vs. 2.05 (0.86, 6.19) mg/l, p = 0.023. tHCY(log) was also higher in (r, p) smokers (0.448, 0.001), patients with low FEV1% (-0.397, 0.003), males (0.475, < 0.001), but high SGRQ Total score (0.289, 0.034), and high sCRP (0.316, 0.038). tHCY(log) was independently related to (regression coefficient, p) sCRP(log) (0.087, 0.024), male gender (0.345, < 0.001) and presence of COPD (0.194, 0.031). Median (IQR) tHCY GOLD Stage I and II 8.05 (7.28, 11.04), GOLD Stage III and IV: 11.83 (9.30, 18.30); p = 0.023. CONCLUSIONS: Plasma homocysteine is significantly elevated in COPD patients relative to age and sex-matched controls and is related to serum CRP and COPD severity.