Aparito and the University of Leicester are pleased to announce a significant update from their INCLINE study, which compares the rate of exacerbations between adherent and non-adherent groups in an observational cohort study of inhaler adherence and blood eosinophil count (BEC) in exacerbations of Chronic Obstructive Pulmonary Disease (COPD).

As of now, the INCLINE study has successfully enrolled 42 out of the targeted 104 participants. We are delighted that there is an impressive 81% engagement rate, with 34 patients actively participating in the study with recruitment set to continue for another 10 months.

What is COPD?

COPD is a debilitating condition characterised by inflammation of the airways, leading to airflow obstruction and various respiratory symptoms. Exacerbations, which are episodes of worsened inflammation and symptoms, contribute significantly to the burden of COPD. These exacerbations can occur due to disease progression or non-adherence to prescribed treatments, often resulting in increased rescue therapy usage or even hospitalisation. 

The University of Leicester is delighted to be able to provide patients with a novel way to monitor their condition and provide important information to help them manage their COPD.

Dr Neil Greening, Associate Professor at the University of Leicester

It is wonderful to be able to use Atom5TM to broaden the options for patients to participate in research studies from home and enjoy the benefits of having their condition monitored remotely. The patients are finding Atom5™ easy to use and consequently, we are finding compliance is very high.

Dr Hnin Aung, Clinical Fellow at the University of Leicester

INCLINE Objectives

The project has primary and secondary objectives:


To compare the rate of exacerbations between adherent and non-adherent groups


  1. To describe the completeness and feasibility of using a digital data collection platform for exacerbations of COPD
  2. To compare the following between adherent and non-adherent groups
    • Changes in blood eosinophil count over time
    • Lung function test
    • Health-related quality of life
    • Symptom burden

To shed light on the impact of inhaler adherence and blood eosinophil count on COPD exacerbations, the INCLINE study utilises digital platforms to capture real-time adherence data, identify unrecorded exacerbation events, and potentially predict future exacerbations. Furthermore, it seeks to explore the interplay between patients’ medication-taking habits and the perspectives of their healthcare providers.

The INCLINE study will enrol a cohort of patients diagnosed with COPD. These participants will be provided with a smarthaler monitor, which will passively record their adherence to usual therapy. By simulating real-life conditions, this study aims to obtain accurate and representative data. Recruitment will be carried out in both primary and secondary care respiratory clinics, targeting individuals who are on “triple inhaled therapy” (ICS + LAMA + LABA) and have a history of frequent exacerbations. During the study period, participants will have clinic visits at the beginning and end, along with remote visits in between.

Aparito has enjoyed working closely with the University of Leicester to bring their vision for remote monitoring of COPD patients to life. At Aparito, one of our fundamental goals in digitising data collection is to allow more people to participate in research remotely and thereby capture real-world data through Atom5™. We are overjoyed to hear the participants are finding Atom5™ easy to use and wish the team every success in this important study.

Rachel Kneafsey, Senior Project Manager at Aparito

What’s next?

This groundbreaking study holds immense potential to enhance our understanding of COPD exacerbations and the role of adherence in managing the disease. Through the utilisation of digital platforms and collaboration between patients and healthcare providers, we intend to show the feasibility of using a digital data collection platform to better control COPD exacerbations and predict events.

Ultimately, we hope to see a real-life benefit to these patients in the management of their disease.

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