Digital technologies and adherence in respiratory diseases: the road ahead
Open accessAbstract
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.
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User adherence is crucial to the effectiveness of DHT applications in the real world, which can be affected by convenience, user experience, and true benefits brought by this technology.
“User adherence is crucial to the effectiveness of DHT applications in the real world, which can be affected by convenience, user experience, and true benefits brought by this technology (for example, helping them decide when to contact their provider, being more aware of their symptoms, and feeling more connected to their provider).138,139”
From Artificial intelligence in diabetes management: Advancements, opportunities, and challenges (2023) · cites this paper as [138]
In ‘Considering the intended users of the interventions’ (page unavailable), the paper says sustained real-world use is critical to digital technologies’ success and discusses user experience, desirable features, and reasons patients disengage. It does not specifically identify convenience as a factor or substantiate the cited sentence’s examples of deciding when to contact a provider, becoming more aware of symptoms, or feeling more connected to a provider. (LLM, conf=0.83)
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“138,139 Thus, the potential ways to promote user adherence include smart design, visible EHRs, integration of electronic patient-reported outcomes in clinics, and voice enablement.”
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Self-citationDoes adherence to inhaled corticosteroids predict asthma-related outcomes over time? A cohort study
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Self-citationConsiderations for Conducting Bring Your Own “Device” (BYOD) Clinical Studies
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Self-citationPeak Inspiratory Flow as a Predictive Therapeutic Biomarker in COPD
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Current technological advancement in asthma care
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Self-citationImproving Care for People with Chronic Respiratory Diseases: Taking a Policy Lens
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Telemedicine as a Means to an End, Not an End in Itself
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Modelling Drug Delivery to the Small Airways: Optimization Using Response Surface Methodology
Min, H. J., Payne, S. J., & Stride, E. (2024) · Pharmaceutical Research · doi:10.1007/s11095-024-03706-1 · Cited by 4 · see citing works
Cvietusa, P. J., Wagner, N., Shoup, J., Goodrich, G. K., Shetterly, S., King, D. K., Raebel, M., Riggs, C., & Bender, B. G. (2020) · The Permanente Journal · doi:10.7812/tpp/20.189 · Cited by 4 · see citing works
Self-citationChronic disease self-efficacy and factors influencing this in patients with ischemic stroke
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Poot, C. C., de Boer, J., Goto, L., van de Hei, S. J., Chavannes, N. H., Visch, V., & Meijer, E. (2023) · Patient Preference and Adherence · doi:10.2147/ppa.s423161 · Cited by 4 · see citing works
Developments in respiratory self-management interventions over the last two decades
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Technological and digital innovations in improving adherence to asthma medication therapy
Nikolov, P., & Petrova, G. I. (2025) · Pharmacia · doi:10.3897/pharmacia.72.e148243 · Cited by 4 · see citing works
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Self-citationDigital technology for monitoring adherence to inhaled therapies in people with cystic fibrosis
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Connected real-life research, a pillar of P4 medicine
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Digital technology for early identification of exacerbations in people with cystic fibrosis
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“I just want to get on with my life”: adolescent psychology for respiratory clinicians
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Stand der digitalen Medizin in der Pneumologie
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Does inhaler adherence really matter in severe asthma?
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Screening for atrial fibrillation with or without general practice involvement: a controlled study
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- Authors
- Blakey, J. D., Bender, B. G., Dima, A. L., Weinman, J., Safioti, G., & Costello, R. W.
- Affiliations
- University of Liverpool (GB)
- National Jewish Health (US)
- Lyon 1 Université (FR)
- King's College London (GB)
- Teva Pharmaceuticals (Netherlands) (NL)
- Royal College of Surgeons in Ireland (IE)
- Venue
- European Respiratory Journal
- Publisher
- European Respiratory Society (ERS)
- Published
- 2018-11
- Volume
- 52
- Issue
- 5
- Pages
- 1801147
- ISSN
- 0903-1936 (ISSN-L)
- 1399-3003
- PMC
- PMC6364097
- OpenAlex
- W2899639745
- Cited by
- 136
- References
- 97
- Citations by year
- 2026: 4 · 2025: 19 · 2024: 25 · 2023: 22 · 2022: 14 · 2021: 22 · 2020: 19 · 2019: 10 · 2018: 1
- FWCI
- 2.37
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by-nc
- Locations
- European Respiratory Journal (publishedVersion, cc-by-nc)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Royal College of Surgeons in Ireland (submittedVersion, cc-by-nc-sa)
- Figshare (submittedVersion, cc-by-nc-sa)
- Research Portal (King's College London) (submittedVersion)
- Funders
- Teva Pharmaceutical Industries
- Topics
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Chronic Obstructive Pulmonary Disease (COPD) Research
- Health Sciences › Medicine › Family Practice › Medication Adherence and Compliance
- Keywords
- digital health technologies, medication adherence, chronic respiratory disease, inhaler technique, exacerbation prediction, personalized interventions, disease management, environmental data, regulatory standards, data privacy, cost-effectiveness
- MeSH terms
- Asthma, Chronic Disease, Humans, Respiratory Tract Diseases, Self Care, Telemetry, Telemedicine, Pulmonary Disease, Chronic Obstructive, Medication Adherence
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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