Perceptions of Patients Regarding Mobile Health Interventions for the Management of Chronic Obstructive Pulmonary Disease: Mixed Methods Study

Alwashmi, Meshari F; FitzPatrick, Beverly; Farrell, Jamie; Gamble, John‐Michael; Davis, Erin; Nguyen, Hai; Farrell, Gerard; Hawboldt, John

doi:10.2196/17409

Cited by 28 publications

(46 citation statements)

References 54 publications

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“…In total, 37.4% (65/174) of all patients in our study had to be excluded because of lack of access to a mobile device or internet or skills to use it. This is in line with other findings of mHealth use in patients with COPD, in which only a minority owned a smartphone (23%) [ 53 ]. Technical issues and low compliance are recognized issues for digital interventions [ 54 ], and digital literacy among patients with COPD remains a challenge [ 52 ].…”

Section: Discussionsupporting

confidence: 92%

Effectiveness of a Mobile Health and Self-Management App for High-Risk Patients With Chronic Obstructive Pulmonary Disease in Daily Clinical Practice: Mixed Methods Evaluation Study

Kooij¹,

Vos²,

Dijkstra³

et al. 2021

JMIR Mhealth Uhealth

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Background Mobile health and self-management interventions may positively affect behavioral change and reduce hospital admissions for patients with chronic obstructive pulmonary disease (COPD). However, not all patients qualify for these interventions, and systematic, comprehensive information on implementation- and compliance-related aspects of mobile self-management apps is lacking. Due to the tendency to target digital services to patients in stable phases of disease, it is especially relevant to focus on the use of these services in broad clinical practice for patients recently discharged from hospital. Objective This study aims to evaluate the effects of a mobile health and self-management app in clinical practice for recently discharged patients with COPD on use of the app, self-management, expectations, and experiences (technology acceptance); patients’ and nurses’ satisfaction; and hospital readmissions. Methods A prototype of the app was pilot tested with 6 patients with COPD. The COPD app consisted of an 8-week program including the Lung Attack Action Plan, education, medication overview, video consultation, and questionnaires (monitored by nurses). In the feasibility study, adult patients with physician-diagnosed COPD, access to a mobile device, and proficiency of the Dutch language were included from a large teaching hospital during hospital admission. Self-management (Partners in Health Scale), technology acceptance (Unified Theory Acceptance and Use of Technology model), and satisfaction were assessed using questionnaires at baseline, after 8 weeks, and 20 weeks. Use was assessed with log data, and readmission rates were extracted from the electronic medical record. Results A total of 39 patients were included; 76.4% (133/174) of patients had to be excluded from participation, and 48.9% of those patients (65/133) were excluded because of lack of digital skills, access to a mobile device, or access to the internet. The COPD app was opened most often in the first week (median 6.0; IQR 3.5-10.0), but its use decreased over time. The self-management element knowledge and coping increased significantly over time (P=.04). The COPD app was rated on a scale of 1-10, with an average score by patients of 7.7 (SD 1.7) and by nurses of 6.3 (SD 1.2). Preliminary evidence about the readmission rate showed that 13% (5/39) of patients were readmitted within 30 days; 31% (12/39) of patients were readmitted within 20 weeks, compared with 14.1% (48/340) and 21.8% (74/340) in a preresearch cohort, respectively. Conclusions The use of a mobile self-management app after hospital discharge seems to be feasible only for a small number of patients with COPD. Patients were satisfied with the service; however, use decreased over time, and only knowledge and coping changed significantly over time. Therefore, future research on digital self-management interventions in clinical practice should focus on including more difficult subgroups of target populations, a multidisciplinary approach, technology-related aspects (such as acceptability), and fine-tuning its adoption in clinical pathways. Trial Registration Clinicaltrials.gov NCT04540562; https://clinicaltrials.gov/ct2/show/NCT04540562.

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Section: Discussionsupporting

confidence: 92%

Effectiveness of a Mobile Health and Self-Management App for High-Risk Patients With Chronic Obstructive Pulmonary Disease in Daily Clinical Practice: Mixed Methods Evaluation Study

Kooij¹,

Vos²,

Dijkstra³

et al. 2021

JMIR Mhealth Uhealth

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“…Refusal to complete TH interventions is primarily attributed to the interventions themselves [ 66 , 67 ]. It was noted that TH interventions with multiple components were fraught with complexities and technical difficulties that have resulted in decreased treatment sessions or even termination [ 18 , 32 ].…”

Section: Discussionmentioning

confidence: 99%

Chronic Obstructive Pulmonary Disease Patients’ Acceptance in E-Health Clinical Trials

Alghamdi

Rajah

Aldabayan

et al. 2021

IJERPH

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Introduction: Telehealth (TH) interventions with Chronic Obstructive Pulmonary Disease (COPD) management were introduced in the literature more than 20 years ago with different labeling, but there was no summary for the overall acceptance and dropout rates as well as associated variables. Objective: This review aims to summarize the acceptance and dropout rates used in TH interventions and identify to what extent clinical settings, sociodemographic factors, and intervention factors might impact the overall acceptance and completion rates of TH interventions. Methods: We conducted a systematic search up to April 2021 on CINAHL, PubMed, MEDLINE (Ovid), Cochrane, Web of Sciences, and Embase to retrieve randomized and non-randomized control trials that provide TH interventions alone or accompanied with other interventions to individuals with COPD. Results: Twenty-seven studies met the inclusion criteria. Overall, the unweighted average of acceptance and dropout rates for all included studies were 80% and 19%, respectively. A meta-analysis on the pooled difference between the acceptance rates and dropout rates (weighted by the sample size) revealed a significant difference in acceptance and dropout rates among all TH interventions 51% (95% CI 49% to 52; p < 0.001) and 63% (95% CI 60% to 67; p < 0.001), respectively. Analysis revealed that acceptance and dropout rates can be impacted by trial-related, sociodemographic, and intervention-related variables. The most common reasons for dropouts were technical difficulties (33%), followed by complicated system (31%). Conclusions: Current TH COPD interventions have a pooled acceptance rate of 51%, but this is accompanied by a high dropout rate of 63%. Acceptance and dropout levels in TH clinical trials can be affected by sociodemographic and intervention-related factors. This knowledge enlightens designs for well-accepted future TH clinical trials. PROSPERO registration number CRD4201707854.

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“…Alternatively, the barriers to adoption are technical issues, privacy and confidentiality issues, lack of awareness, lack of interest, potentially limited uptake from the elderly, potential limited connection between patients and HCPs and finances. 52 , 53 …”

Section: Discussionmentioning

confidence: 99%

“…To optimize the successful implementation of mHealth interventions, it is important to consider the perceived facilitators and barriers to mHealth adoption for COPD management that were published by our research group. 52,53 The main facilitators to mHealth adoption are possible health benefits for patients, ease of use, educating patients and their HCPs, credibility and reducing the cost to the healthcare system. Alternatively, the barriers to adoption are technical issues, privacy and confidentiality issues, lack of awareness, lack of interest, potentially limited uptake from the elderly, potential limited connection between patients and HCPs and finances.…”

Section: Discussionmentioning

confidence: 99%

“…Owing to a large amount of data, preferences regarding the barriers and facilitators of mHealth adoption for COPD management were presented in different articles. 52,53 HCP data consisted of more than 13 h of interview time with approximately 300 pages of transcription. Patient data consisted of about 4 h of interview time with approximately 100 pages of transcription.…”

Section: Methodsmentioning

confidence: 99%

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

Alwashmi

FitzPatrick

Davis

et al. 2020

Ther Adv Respir Dis

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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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Perceptions of Patients Regarding Mobile Health Interventions for the Management of Chronic Obstructive Pulmonary Disease: Mixed Methods Study

Cited by 28 publications

References 54 publications

Effectiveness of a Mobile Health and Self-Management App for High-Risk Patients With Chronic Obstructive Pulmonary Disease in Daily Clinical Practice: Mixed Methods Evaluation Study

Effectiveness of a Mobile Health and Self-Management App for High-Risk Patients With Chronic Obstructive Pulmonary Disease in Daily Clinical Practice: Mixed Methods Evaluation Study

Chronic Obstructive Pulmonary Disease Patients’ Acceptance in E-Health Clinical Trials

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

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