Digital technologies and adherence in respiratory diseases: the road ahead

John D Blakey, Bruce G Bender, Alexandra L Dima, John Weinman, Guilherme Safioti, Richard W Costello, John D Blakey, Bruce G Bender, Alexandra L Dima, John Weinman, Guilherme Safioti, Richard W Costello

Abstract

Outcomes for patients with chronic respiratory diseases remain poor despite the development of novel therapies. In part, this reflects the fact that adherence to therapy is low and clinicians lack accurate methods to assess this issue. Digital technologies hold promise to overcome these barriers to care. For example, algorithmic analysis of large amounts of information collected on health status and treatment use, along with other disease relevant information such as environmental data, can be used to help guide personalised interventions that may have a positive health impact, such as establishing habitual and correct inhaler use. Novel approaches to data analysis also offer the possibility of statistical algorithms that are better able to predict exacerbations, thereby creating opportunities for preventive interventions that may adapt therapy as disease activity changes. To realise these possibilities, digital approaches to disease management should be supported by strong evidence, have a solid infrastructure, be designed collaboratively as clinically effective and cost-effective systems, and reflect the needs of patients and healthcare providers. Regulatory standards for digital interventions and strategies to handle the large amounts of data generated are also needed. This review highlights the opportunities provided by digital technologies for managing patients with respiratory diseases.

Conflict of interest statement

Conflict of interest: J.D. Blakey reports personal fees (for an advisory board meeting) from Teva, personal fees (for travel and lectures) and non-financial support from Napp, personal fees (for a presentation) from Novartis, personal fees and non-financial support (for travel and lectures) from AstraZeneca, and personal fees and non-financial support (for travel and lectures) from Boehringer Ingelheim, outside the submitted work. B.G. Bender has nothing to disclose. Conflict of interest: A.L. Dima reports grants and non-financial support (for travel and research) from Respiratory Effectiveness Group, outside the submitted work. Conflict of interest: J. Weinman reports personal fees from Atlantis Healthcare, Boehringer Ingelheim, Chugai/Roche, Ferring, Sanofi and Teva, and grants from Merck, outside the submitted work. Conflict of interest: G. Safioti is an employee of Teva. Conflict of interest: R.W. Costello has received funding for research from Aerogen and GSK, speaker's and consultancy fees from Aerogen, Boehringer Inghelheim, AstraZeneca, GSK, Novartis and Teva. He has licensed an acoustic device to assess adherence to Vitalograph and has a patent pending for the identification of inhaler use (P10961USPC) and two pending for methods to assess adherence and remotely predict exacerbations.

Copyright ©ERS 2018.

Figures

FIGURE 1
FIGURE 1
Four patterns of digitally monitored lung function, adherence and inhaler technique, as assessed by a digital audio recording device attached to the inhaler. a) A patient with well-controlled asthma with stable peak expiratory flow (PEF) and regular use of a twice daily preventer inhaler. Normal lung function (peak expiratory flow rate (PEFR)) is maintained by proficiently and regularly taken treatment (green dots on the lower graph in each panel indicate correct inhaler technique). b) A patient with poor lung function (PEFR recordings in red indicate lung function at 80% of baseline) due to poor inhaler technique (shown by orange squares) and missed doses (shown as red triangles). c) A patient with initial poor lung function who subsequently improves. Improved lung function is associated with regular and correct inhaler use. d) A patient with periods of intermittent inhaler use and poor lung function, followed by periods of improved adherence and improved lung function. Drops in lung function are associated with intermittant inhaler use and appear to prompt the patient to restart use. The absence of dots on the time graph indicates that no doses were taken.
FIGURE 2
FIGURE 2
Results of the Digital Maturity Self-Assessment survey in 2016, measuring how well secondary care providers in England are making use of digital technology to achieve a health and care system that is paper-free at the point of care. Readiness indicates how well providers are able to plan and deploy digital services, while capabilities indicate whether providers have staff with the digital skills needed. The infrastructure score is based on whether providers have the right technology in place. Data was from the National Health Service (NHS), England. Reproduced with permission of the rights holder, Royal College of Physicians from [93].

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