BackgroundMany patients with chronic obstructive pulmonary disease (COPD) suffer from exacerbations, a worsening of their respiratory symptoms that warrants medical treatment. Exacerbations are often poorly recognized or managed by patients, leading to increased disease burden and health care costs.ObjectiveThis study aimed to examine the effects of a smart mobile health (mHealth) tool that supports COPD patients in the self-management of exacerbations by providing predictions of early exacerbation onset and timely treatment advice without the interference of health care professionals.MethodsIn a multicenter, 2-arm randomized controlled trial with 12-months follow-up, patients with COPD used the smart mHealth tool (intervention group) or a paper action plan (control group) when they experienced worsening of respiratory symptoms. For our primary outcome exacerbation-free time, expressed as weeks without exacerbation, we used an automated telephone questionnaire system to measure weekly respiratory symptoms and treatment actions. Secondary outcomes were health status, self-efficacy, self-management behavior, health care utilization, and usability. For our analyses, we used negative binomial regression, multilevel logistic regression, and generalized estimating equation regression models.ResultsOf the 87 patients with COPD recruited from primary and secondary care centers, 43 were randomized to the intervention group. We found no statistically significant differences between the intervention group and the control group in exacerbation-free weeks (mean 30.6, SD 13.3 vs mean 28.0, SD 14.8 weeks, respectively; rate ratio 1.21; 95% CI 0.77-1.91) or in health status, self-efficacy, self-management behavior, and health care utilization. Patients using the mHealth tool valued it as a more supportive tool than patients using the paper action plan. Patients considered the usability of the mHealth tool as good.ConclusionsThis study did not show beneficial effects of a smart mHealth tool on exacerbation-free time, health status, self-efficacy, self-management behavior, and health care utilization in patients with COPD compared with the use of a paper action plan. Participants were positive about the supportive function and the usability of the mHealth tool. mHealth may be a valuable alternative for COPD patients who prefer a digital tool instead of a paper action plan.Trial RegistrationClinicalTrials.gov NCT02553096; https://clinicaltrials.gov/ct2/show/NCT02553096.

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Four distinct stages of grief are recognized in COPD. The ADIQ is a valid and reliable instrument to measure these stages: denial, resistance, sorrow, and acceptance. Measuring the stages of grieving is important for disease management: addressing patients with a specific therapeutic approach for the stage they are in could help to motivate patients to engage in self-management and change their lifestyle.

Current tools for recording chronic obstructive pulmonary disease (COPD) exacerbations are limited and often lack validity testing. We assessed the validity of an automated telephonic exacerbation assessment system (TEXAS) and compared its outcomes with existing tools.Over 12 months, 86 COPD patients (22.1% females; mean age 66.5 yrs; mean postbronchodilator forced expiratory volume in 1 s 53.4% predicted) were called once every 2 weeks by TEXAS to record changes in respiratory symptoms, unscheduled healthcare utilisation and use of respiratory medication. The responses to TEXAS were validated against exacerbation-related information collected by observations made by trained research assistants during home visits. No care assistance was provided in any way. Diagnostic test characteristics were estimated using commonly used definitions of exacerbation. Detection rates, compliance and patient preference were assessed, and compared with paper diary cards and medical record review.A total of 1,824 successful calls were recorded, of which 292 were verified by home visits (median four calls per patient, interquartile range three to five calls per patient). Independent of the exacerbation definition used, validity was high, with sensitivities and specificities between 66% and 98%. Detection rates and compliance differed extensively between the different tools, but were highest with TEXAS. Patient preference did not differ.TEXAS is a valid tool to assess COPD exacerbation rates in prospective clinical studies. Using different tools to record exacerbations strongly affects exacerbation occurrence rates.

PurposeThe purposes of the study are to identify clinical phenotypes that reflect the level of adaptation to the disease and to examine whether these clinical phenotypes respond differently to treatment as usual (TAU) and pulmonary rehabilitation (PR), the latter with its strong emphasis on improving adaptation.MethodsClusters were identified by a cluster analysis using data on many subdomains of the four domains of health status (HS) (physiological functioning, functional impairment, symptoms and quality of life) in 160 outpatients with chronic obstructive pulmonary disease (COPD) receiving TAU. By discriminant analysis in the TAU sample, all 459 PR patients could be assigned to one of the identified clusters. The effect of TAU and PR on HS was examined with paired t tests.ResultsThree distinct phenotypes were identified in the TAU sample. Two types were labelled adapted: phenotype 1 (moderate COPD–low impact on HS, n = 53) and phenotype 3 (severe COPD–moderate impact on HS, n = 73). One type was labelled non-adapted: phenotype 2 (moderate COPD–high impact on HS, n = 34). After 1-year TAU, the integral health status of all patients did not improve in any subdomain. In contrast, at the end of PR, significant improvements in HS were found in all three phenotypes especially the non-adapted.ConclusionsDifferent phenotypes exist in COPD that are based on behavioural aspects (i.e. the level of adaptation to the disease). Non-adapted patient responds better to treatments with a strong emphasis on improving adaptation by learning the patient better self-management skills. Knowing to which clinical phenotype a patient belongs helps to optimize patient-tailored treatment.