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DOI: 10.1055/s-0041-1726277
InspirerMundi—Remote Monitoring of Inhaled Medication Adherence through Objective Verification Based on Combined Image Processing Techniques
Funding This work was funded by ERDF (European Regional Development Fund) through the operations: POCI-01-0145-36 FEDER-029130 (“mINSPIRE—mHealth to measure and improve adherence to medication in chronic obstructive respiratory diseases, generalization, and evaluation of gamification, peer support and advanced image processing technologies”) cofounded by the COMPETE2020 (Programa Operacional Competitividade e Internacionalização), Portugal 2020 and by Portuguese Funds through FCT (Fundação para a Ciência e a Tecnologia).![](https://www.thieme-connect.de/media/10.1055-s-00035037/2021S01/lookinside/thumbnails/10-1055-s-0041-1726277_20010072-1.jpg)
Abstract
Background The adherence to inhaled controller medications is of critical importance for achieving good clinical results in patients with chronic respiratory diseases. Self-management strategies can result in improved health outcomes and reduce unscheduled care and improve disease control. However, adherence assessment suffers from difficulties on attaining a high grade of trustworthiness given that patient self-reports of high-adherence rates are known to be unreliable.
Objective Aiming to increase patient adherence to medication and allow for remote monitoring by health professionals, a mobile gamified application was developed where a therapeutic plan provides insight for creating a patient-oriented self-management system. To allow a reliable adherence measurement, the application includes a novel approach for objective verification of inhaler usage based on real-time video capture of the inhaler's dosage counters.
Methods This approach uses template matching image processing techniques, an off-the-shelf machine learning framework, and was developed to be reusable within other applications. The proposed approach was validated by 24 participants with a set of 12 inhalers models.
Results Performed tests resulted in the correct value identification for the dosage counter in 79% of the registration events with all inhalers and over 90% for the three most widely used inhalers in Portugal. These results show the potential of exploring mobile-embedded capabilities for acquiring additional evidence regarding inhaler adherence.
Conclusion This system helps to bridge the gap between the patient and the health professional. By empowering the first with a tool for disease self-management and medication adherence and providing the later with additional relevant data, it paves the way to a better-informed disease management decision.
Publication History
Received: 19 May 2020
Accepted: 20 December 2020
Article published online:
27 April 2021
© 2021. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/)
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References
- 1 Hajat C, Stein E. The global burden of multiple chronic conditions: A narrative review. Prev Med Rep 2018; 12: 284-293
- 2 GBD Chronic Respiratory Disease Collaborators. Prevalence and attributable health burden of chronic respiratory diseases, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet Respir Med 2020; 8 (06) 585-596
- 3 European Respiratory Society. The economic burden of lung disease. Accessed September 18, 2020 at: https://www.erswhitebook.org/chapters/the-economic-burden-of-lung-disease/
- 4 European Respiratory Society. Respiratory Diseases in the World. Realities of Today – Opportunities for Tomorrow. European Respiratory Society 2013
- 5 Nunes C, Pereira AM, Morais-Almeida M. Asthma costs and social impact. Asthma Res Pract 2017; 3: 1
- 6 GBD 2016 Causes of Death Collaborators. Global, regional, and national age-sex specific mortality for 264 causes of death, 1980-2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet 2017; 390 (10100): 1151-1210
- 7 Bateman ED, Hurd SS, Barnes PJ. et al. Global strategy for asthma management and prevention: GINA executive summary. Eur Respir J 2008; 31 (01) 143-178
- 8 Global Initiative for Asthma. Global strategy for asthma managment and prevention. Accessed September 18, 2020 at: https://ginasthma.org/wp-content/uploads/2019/06/GINA-2019-main-report-June-2019-wms.pdf
- 9 Taylor SJC, Pinnock H, Epiphaniou E. et al. A rapid synthesis of the evidence on interventions supporting self-management for people with long-term conditions: PRISMS - Practical systematic Review of Self-Management Support for long-term conditions. Southampton, UK: NIHR Journals Library; 2014
- 10 Pinnock H, Parke HL, Panagioti M. et al; PRISMS and RECURSIVE groups. Systematic meta-review of supported self-management for asthma: a healthcare perspective. BMC Med 2017; 15 (01) 64
- 11 O'Byrne PM, Barnes PJ, Rodriguez-Roisin R. et al. Low dose inhaled budesonide and formoterol in mild persistent asthma: the OPTIMA randomized trial. Am J Respir Crit Care Med 2001; 164 (8 Pt 1): 1392-1397
- 12 Pauwels RA, Pedersen S, Busse WW. et al; START Investigators Group. Early intervention with budesonide in mild persistent asthma: a randomised, double-blind trial. Lancet 2003; 361 (9363): 1071-1076
- 13 Stanford RH, Averell C, Parker ED, Blauer-Peterson C, Reinsch TK, Buikema AR. Assessment of adherence and asthma medication ratio for a once-daily and twice-daily inhaled corticosteroid/long-acting β-agonist for asthma. J Allergy Clin Immunol Pract 2019; 7 (05) 1488-1496.e7
- 14 Engelkes M, Janssens HM, de Jongste JC, Sturkenboom MC, Verhamme KM. Medication adherence and the risk of severe asthma exacerbations: a systematic review. Eur Respir J 2015; 45 (02) 396-407
- 15 Doz M, Chouaid C, Com-Ruelle L. et al. The association between asthma control, health care costs, and quality of life in France and Spain. BMC Pulm Med 2013; 13: 15
- 16 Wang PS, Benner JS, Glynn RJ, Winkelmayer WC, Mogun H, Avorn J. How well do patients report noncompliance with antihypertensive medications?: a comparison of self-report versus filled prescriptions. Pharmacoepidemiol Drug Saf 2004; 13 (01) 11-19
- 17 Lam WY, Fresco P. Medication adherence measures: an overview. BioMed Res Int 2015; 2015: 217047
- 18 Jácome C, Pereira AM, Almeida R. et al; Inspirers group. Patient-physician discordance in assessment of adherence to inhaled controller medication: a cross-sectional analysis of two cohorts. BMJ Open 2019; 9 (11) e031732
- 19 van Boven JFM, Lavorini F, Dekhuijzen PNR, Blasi F, Price DB, Viegi G. Urging Europe to put non-adherence to inhaled respiratory medication higher on the policy agenda: a report from the First European Congress on Adherence to Therapy. Eur Respir J 2017; 49 (05) 1700076
- 20 Wilson SR, Strub P, Buist AS. et al; Better Outcomes of Asthma Treatment (BOAT) Study Group. Shared treatment decision making improves adherence and outcomes in poorly controlled asthma. Am J Respir Crit Care Med 2010; 181 (06) 566-577
- 21 Lima-Dellamora EDC, Osorio-de-Castro CGS, Madruga LGDSL, Azeredo TB. Use of pharmacy records to measure treatment adherence: a critical review of the literature. Cad Saude Publica 2017; 33 (03) e00136216
- 22 Garfield S, Clifford S, Eliasson L, Barber N, Willson A. Suitability of measures of self-reported medication adherence for routine clinical use: a systematic review. BMC Med Res Methodol 2011; 11: 149
- 23 Kalichman SC, Amaral CM, Swetzes C. et al. A simple single-item rating scale to measure medication adherence: further evidence for convergent validity. J Int Assoc Physicians AIDS Care (Chic) 2009; 8 (06) 367-374
- 24 D'Arcy S, MacHale E, Seheult J. et al. A method to assess adherence in inhaler use through analysis of acoustic recordings of inhaler events. PLoS One 2014; 9 (06) e98701
- 25 Taylor TE, Zigel Y, Egan C, Hughes F, Costello RW, Reilly RB. Objective assessment of patient inhaler user technique using an audio-based classification approach. Sci Rep 2018; 8 (01) 2164
- 26 van Heuckelum M, van den Ende CHM, Houterman AEJ, Heemskerk CPM, van Dulmen S, van den Bemt BJF. The effect of electronic monitoring feedback on medication adherence and clinical outcomes: a systematic review. PLoS One 2017; 12 (10) e0185453
- 27 Pilcher J, Holliday M, Ebmeier S. et al. Validation of a metered dose inhaler electronic monitoring device: implications for asthma clinical trial use. BMJ Open Respir Res 2016; 3 (01) e000128
- 28 Chan AH, Harrison J, Black PN, Mitchell EA, Foster JM. Using electronic monitoring devices to measure inhaler adherence: a practical guide for clinicians. J Allergy Clin Immunol Pract 2015; 3 (03) 335-49.e1 , 5
- 29 Howard S, Lang A, Sharples S, Shaw D. What are the pros and cons of electronically monitoring inhaler use in asthma? A multistakeholder perspective. BMJ Open Respir Res 2016; 3 (01) e000159
- 30 Statista.com. Number of smartphone users worldwide from 2016 to 2021. 2019 . Accessed 18/09/2020
- 31 Hamine S, Gerth-Guyette E, Faulx D, Green BB, Ginsburg AS. Impact of mHealth chronic disease management on treatment adherence and patient outcomes: a systematic review. J Med Internet Res 2015; 17 (02) e52
- 32 Whitehead L, Seaton P. The effectiveness of self-management mobile phone and tablet apps in long-term condition management: a systematic review. J Med Internet Res 2016; 18 (05) e97
- 33 Bousquet J, Chavannes NH, Guldemond N, Haahtela T, Hellings PW, Sheikh A. Realising the potential of mHealth to improve asthma and allergy care: how to shape the future. Eur Respir J 2017; 49 (05) 1700447
- 34 Liu WT, Huang CD, Wang CH, Lee KY, Lin SM, Kuo HP. A mobile telephone-based interactive self-care system improves asthma control. Eur Respir J 2011; 37 (02) 310-317
- 35 Ryan D, Pinnock H, Lee AJ. et al. The CYMPLA trial. Mobile phone-based structured intervention to achieve asthma control in patients with uncontrolled persistent asthma: a pragmatic randomised controlled trial. Prim Care Respir J 2009; 18 (04) 343-345
- 36 Morrison D, Mair FS, Yardley L, Kirby S, Thomas M. Living with asthma and chronic obstructive airways disease: Using technology to support self-management - an overview. Chron Respir Dis 2017; 14 (04) 407-419
- 37 Deterding S. The lens of intrinsic skill atoms: a method for Gameful design. Hum Comput Interact 2015; 30 (3–4): 294-335
- 38 Johnson D, Deterding S, Kuhn KA, Staneva A, Stoyanov S, Hides L. Gamification for health and wellbeing: a systematic review of the literature. Internet Interv 2016; 6: 89-106
- 39 Tinschert P, Jakob R, Barata F, Kramer JN, Kowatsch T. The potential of mobile apps for improving asthma self-management: a review of publicly available and well-adopted asthma apps. JMIR Mhealth Uhealth 2017; 5 (08) e113
- 40 Ramadan WH, Sarkis AT. Patterns of use of dry powder inhalers versus pressurized metered-dose inhalers devices in adult patients with chronic obstructive pulmonary disease or asthma: An observational comparative study. Chron Respir Dis 2017; 14 (03) 309-320
- 41 Ibrahim M, Verma R, Garcia-Contreras L. Inhalation drug delivery devices: technology update. Med Devices (Auckl) 2015; 8: 131-139
- 42 Gupta N, Jalal AS. A robust model for salient text detection in natural scene images using MSER feature detector and Grabcut. Multimedia Tools Appl 2019; 78 (08) 10821-10835
- 43 Banharnsakun A, Tanathong S. Object detection based on template matching through use of best-so-far ABC. Comput Intell Neurosci 2014; 2014: 919406
-
44
Machine learning for mobile developers.
Accessed September 18, 2020 at: https://developers.google.com/ml-kit/
-
45
Core ML: Integrate machine learning models into your app..
Accessed September 18, 2020 at: https://developer.apple.com/documentation/coreml
- 46 Çürükoğlu NMÖ. Buse. Deep Learning on Mobile Systems. Paper presented at: 2018 Innovations in Intelligent Systems and Applications Conference (ASYU) 2018; Adana, Turkey.
- 47 Vieira-Marques P, Teixeira JF, Valente J. et al. Combined Image-Based Approach for Monitoring the Adherence to Inhaled Medications. . Paper presented at: XV Mediterranean Conference on Medical and Biological Engineering and Computing – MEDICON 20192020; Coimbra.
- 48 Jácome CAR, Teixeira J, Vieira Marques P. et al. Inspirers: An app to measure and improve adherence to inhaled treatment. Paper presented at: International Conference e Health 2017 2017; Lisboa.
- 49 Fonseca JA, Nogueira-Silva L, Morais-Almeida M. et al. Validation of a questionnaire (CARAT10) to assess rhinitis and asthma in patients with asthma. Allergy 2010; 65 (08) 1042-1048
- 50 Canny J. A computational approach to edge detection. IEEE Trans Pattern Anal Mach Intell 1986; 8 (06) 679-698
- 51 Sá-Sousa A, Almeida R, Vicente R. et al. High oral corticosteroid exposure and overuse of short-acting beta-2-agonists were associated with insufficient prescribing of controller medication: a nationwide electronic prescribing and dispensing database analysis. Clin Transl Allergy 2019; 9: 47
- 52 Cook KA, Modena BD, Simon RA. Improvement in asthma control using a minimally burdensome and proactive smartphone application. J Allergy Clin Immunol Pract 2016; 4 (04) 730-737.e1
- 53 Simpson AJ, Honkoop PJ, Kennington E. et al. Perspectives of patients and healthcare professionals on mHealth for asthma self-management. Eur Respir J 2017; 49 (05) 1601966
- 54 Fonseca JA, Costa-Pereira A, Delgado L, Fernandes L, Castel-Branco MG. Asthma patients are willing to use mobile and web technologies to support self-management. Allergy 2006; 61 (03) 389-390
- 55 DeSmet A, Van Ryckeghem D, Compernolle S. et al. A meta-analysis of serious digital games for healthy lifestyle promotion. Prev Med 2014; 69: 95-107