Use of predictive algorithms in-home monitoring of chronic obstructive pulmonary disease and asthma: A systematic review
Open accessAbstract
Major reported factors associated with the limited effectiveness of home telemonitoring interventions in chronic respiratory conditions include the lack of useful early predictors, poor patient compliance and the poor performance of conventional algorithms for detecting deteriorations. This article provides a systematic review of existing algorithms and the factors associated with their performance in detecting exacerbations and supporting clinical decisions in patients with chronic obstructive pulmonary disease (COPD) or asthma. An electronic literature search in Medline, Scopus, Web of Science and Cochrane library was conducted to identify relevant articles published between 2005 and July 2015. A total of 20 studies (16 COPD, 4 asthma) that included research about the use of algorithms in telemonitoring interventions in asthma and COPD were selected. Differences on the applied definition of exacerbation, telemonitoring duration, acquired physiological signals and symptoms, type of technology deployed and algorithms used were found. Predictive models with good clinically reliability have yet to be defined, and are an important goal for the future development of telehealth in chronic respiratory conditions. New predictive models incorporating both symptoms and physiological signals are being tested in telemonitoring interventions with positive outcomes. However, the underpinning algorithms behind these models need be validated in larger samples of patients, for longer periods of time and with well-established protocols. In addition, further research is needed to identify novel predictors that enable the early detection of deteriorations, especially in COPD. Only then will telemonitoring achieve the aim of preventing hospital admissions, contributing to the reduction of health resource utilization and improving the quality of life of patients.
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Due to absent or insufficient motivation strategies such as interactivity, proper incentives and rewards, and experiential value, the effects of digital interventions in asthma are prone to be negatively affected by the temporal decline in patients' engagement and motivation.
“Additionally, due to absent or insufficient motivation strategies such as interactivity, proper incentives and rewards [48], and experiential value [49-51], the effects of the previously reported digital interventions in asthma, such as the health condition of the young patients, are prone to be negatively affected by the temporal decline in the patients’ engagement and motivation [10,52-54].”
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- Johnston NW, Lambert K, Hussack P, et al. Detection of COPD exacerbations and compliance with patient-reported daily symptom diaries. Chest 2013; 144: 507-514.
- Bichindaritz I, Vaidya S, Jain A, et al. (eds) Computa- tional Intelligence in Heathcare 4: Advanced Methodol- ogies, Vol. 309. Berlin Heidelberg: Springer, 2010.
Cited by
Sorted by citations
Kowatsch, T., Schachner, T., Harperink, S., Barata, F., Dittler, U., Xiao, G., Stanger, C., v Wangenheim, F., Fleisch, E., Oswald, H., et al. (2021) · Journal of Medical Internet Research · doi:10.2196/25060 · Cited by 103 · see citing works
Home monitoring in interstitial lung diseases
Wijsenbeek, M. S., Moor, C. C., Johannson, K. A., Jackson, P. D., Khor, Y. H., Kondoh, Y., Rajan, S. K., Tabaj, G. C., Varela, B. E., van der Wal, P., et al. (2023) · The Lancet Respiratory Medicine · doi:10.1016/s2213-2600(22)00228-4 · Cited by 98 · see citing works
Tarumi, S., Takeuchi, W., Chalkidis, G., Rodriguez-Loya, S., Kuwata, J., Flynn, M., Turner, K. M., Sakaguchi, F. H., Weir, C., Kramer, H., et al. (2021) · Methods of Information in Medicine · doi:10.1055/s-0041-1728757 · Cited by 84 · see citing works
Hong, Y., & Lee, S. H. (2019) · International Journal of Nursing Studies · doi:10.1016/j.ijnurstu.2018.12.006 · Cited by 73 · see citing works
Choudhury, A. (2022) · JMIR Human Factors · doi:10.2196/35421 · Cited by 56 · see citing works
- Authors
- Sanchez-Morillo, D., Fernandez-Granero, M. A., & Leon-Jimenez, A.
- Affiliations
- Biomedical Engineering and Telemedicine Research Group, University of Cádiz, Puerto Real, Cádiz, Spain
- Universidad de Cádiz (ES)
- Pulmonology, Allergy and Thoracic Surgery Unit, Puerta del Mar University Hospital, Cádiz, Spain
- Venue
- Chronic Respiratory Disease
- Publisher
- SAGE Publications
- Published
- 2016-06-23
- Volume
- 13
- Issue
- 3
- Pages
- 264-283
- ISSN
- 1479-9723 (ISSN-L)
- 1479-9731
- PMC
- PMC5720188
- OpenAlex
- W2336043415
- Cited by
- 110
- References
- 97
- Citations by year
- 2026: 7 · 2025: 15 · 2024: 8 · 2023: 7 · 2022: 17 · 2021: 12 · 2020: 13 · 2019: 9 · 2018: 12 · 2017: 8 · 2016: 2
- FWCI
- 9.24
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Chronic Respiratory Disease (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- RISalud Institutional Health Repository of Andalucía (submittedVersion, other-oa)
- RODIN (Universidad de Cádiz) (submittedVersion, cc-by-nc-nd)
- Topics
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Chronic Obstructive Pulmonary Disease (COPD) Research
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Telemedicine and Telehealth Implementation
- Health Sciences › Medicine › Physiology › Asthma and respiratory diseases
- Keywords
- chronic obstructive pulmonary disease, asthma, home health monitoring, predictive algorithms, exacerbation detection, telemedicine, physiological signals, medication adherence, hospital admission prevention, chronic respiratory disease
- MeSH terms
- Algorithms, Asthma, Humans, Telemedicine, Monitoring, Ambulatory, Pulmonary Disease, Chronic Obstructive
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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