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      Features of a mobile health intervention to manage chronic obstructive pulmonary disease: a qualitative study

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          Abstract

          Background:

          The use of mobile health (mHealth) interventions has the potential to enhance chronic obstructive pulmonary disease (COPD) treatment outcomes. Further research is needed to determine which mHealth features are required to potentially enhance COPD self-management.

          Aim:

          The aim of this study was to explore the potential features of an mHealth intervention for COPD management with healthcare providers (HCPs) and patients with COPD. It could inform the development and successful implementation of mHealth interventions for COPD management.

          Methods:

          This was a qualitative study. We conducted semi-structured individual interviews with HCPs, including nurses, pharmacists and physicians who work directly with patients with COPD. Interviews were also conducted with a diverse sample of patients with COPD. Interview topics included demographics, mHealth usage, the potential use of medical devices and recommendations for features that would enhance an mHealth intervention for COPD management.

          Results:

          A total of 40 people, including nurses, physicians and pharmacists, participated. The main recommendations for the proposed mHealth intervention were categorised into two categories: patient interface and HCP interface. The prevalent features suggested for the patient interface include educating patients, collecting baseline data, collecting subjective data, collecting objective data via compatible medical devices, providing a digital action plan, allowing patients to track their progress, enabling family members to access the mHealth intervention, tailoring the features based on the patient’s unique needs, reminding patients about critical management tasks and rewarding patients for their positive behaviours. The most common features of the HCP interface include allowing HCPs to track their patients’ progress, allowing HCPs to communicate with their patients, educating HCPs and rewarding HCPs.

          Conclusion:

          This study identifies important potential features so that the most effective, efficient and feasible mHealth intervention can be developed to improve the management of COPD.

          The reviews of this paper are available via the supplemental material section.

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          Most cited references56

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          Using Artificial Intelligence to Reduce the Risk of Nonadherence in Patients on Anticoagulation Therapy.

          This study evaluated the use of an artificial intelligence platform on mobile devices in measuring and increasing medication adherence in stroke patients on anticoagulation therapy. The introduction of direct oral anticoagulants, while reducing the need for monitoring, have also placed pressure on patients to self-manage. Suboptimal adherence goes undetected as routine laboratory tests are not reliable indicators of adherence, placing patients at increased risk of stroke and bleeding.
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            Usability of Commercially Available Mobile Applications for Diverse Patients.

            Mobile applications or 'apps' intended to help people manage their health and chronic conditions are widespread and gaining in popularity. However, little is known about their acceptability and usability for low-income, racially/ethnically diverse populations who experience a disproportionate burden of chronic disease and its complications.
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              Valuable Features in Mobile Health Apps for Patients and Consumers: Content Analysis of Apps and User Ratings

              Background The explosion of mobile phones with app capabilities coupled with increased expectations of the patient-consumers’ role in managing their care presents a unique opportunity to use mobile health (mHealth) apps. Objectives The aim of this paper is to identify the features and characteristics most-valued by patient-consumers (“users”) that contribute positively to the rating of an app. Methods A collection of 234 apps associated with reputable health organizations found in the medical, health, and fitness categories of the Apple iTunes store and Google Play marketplace was assessed manually for the presence of 12 app features and characteristics. Regression analysis was used to determine which, if any, contributed positively to a user’s rating of the app. Results Analysis of these 12 features explained 9.3% (R 2=.093 n=234, P<.001) of the variation in an app’s rating, with only 5 reaching statistical significance. Of the 5 reaching statistical significance, plan or orders, export of data, usability, and cost contributed positively to a user’s rating, while the tracker feature detracted from it. Conclusions These findings suggest that users appreciate features that save time over current methods and identify an app as valuable when it is simple and intuitive to use, provides specific instructions to better manage a condition, and shares data with designated individuals. Although tracking is a core function of most health apps, this feature may detract from a user’s experience when not executed properly. Further investigation into mHealth app features is worthwhile given the inability of the most common features to explain a large portion of an app’s rating. In the future, studies should focus on one category in the app store, specific diseases, or desired behavior change, and methods should include measuring the quality of each feature, both through manual assessment and evaluation of user reviews. Additional investigations into understanding the impact of synergistic features, incentives, social media, and gamification are also warranted to identify possible future trends.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: SoftwareRole: ValidationRole: VisualizationRole: Writing original draftRole: Writing review editing
                Role: ConceptualizationRole: Formal analysisRole: InvestigationRole: MethodologyRole: SupervisionRole: ValidationRole: Writing review editing
                Role: ConceptualizationRole: Formal analysisRole: InvestigationRole: MethodologyRole: Project administrationRole: SupervisionRole: Writing review editing
                Role: ConceptualizationRole: Formal analysisRole: InvestigationRole: MethodologyRole: Project administrationRole: Writing review editing
                Role: ConceptualizationRole: Formal analysisRole: InvestigationRole: MethodologyRole: Writing review editing
                Role: ConceptualizationRole: InvestigationRole: MethodologyRole: Project administrationRole: SupervisionRole: Writing review editing
                Journal
                Ther Adv Respir Dis
                Ther Adv Respir Dis
                TAR
                sptar
                Therapeutic Advances in Respiratory Disease
                SAGE Publications (Sage UK: London, England )
                1753-4658
                1753-4666
                7 September 2020
                Jan-Dec 2020
                : 14
                : 1753466620951044
                Affiliations
                [1-1753466620951044]Health Sciences Centre, Memorial University of Newfoundland, 300 Prince Philip Drive, St John’s, NL A1B 3V6, Canada
                [2-1753466620951044]Memorial University of Newfoundland, St John’s, NL, Canada
                [3-1753466620951044]Memorial University of Newfoundland, St John’s, NL, Canada
                [4-1753466620951044]Memorial University of Newfoundland, St John’s, NL, Canada
                [5-1753466620951044]School of Pharmacy, Faculty of Science, University of Waterloo, Waterloo, ON, Canada
                [6-1753466620951044]Memorial University of Newfoundland, St John’s, NL, Canada
                Author notes
                Author information
                https://orcid.org/0000-0001-5052-5911
                Article
                10.1177_1753466620951044
                10.1177/1753466620951044
                7479870
                32894025
                dc8174c5-ac2a-4eea-ab8a-cca256907e9b
                © The Author(s), 2020

                This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License ( https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages ( https://us.sagepub.com/en-us/nam/open-access-at-sage).

                History
                : 15 April 2020
                : 7 July 2020
                Categories
                Original Research
                Custom metadata
                January-December 2020
                ts1

                digital health,copd,lung disease,mhealth,telehealth,chronic disease management,smartphone

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