ArticleJournal of medical Internet research2020
User-Centered Design of a Mobile Health Intervention to Enhance Exacerbation-Related Self-Management in Patients With Chronic Obstructive Pulmonary Disease (Copilot): Mixed Methods Study.
Article in Journal of medical Internet research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers, 3 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
43 citing papers in PubMed, 3 syntheses or guidelines pooled it, 87 citations in OpenAlex.
- Examining Challenges to Co-Design Digital Health Interventions With End Users: Systematic Review.Journal of medical Internet research · 2025Pooled it
- Co-designing care for multimorbidity: a systematic review.BMC medicine · 2024Pooled it
- Meaningful patient and public involvement in digital health innovation, implementation and evaluation: A systematic review.Health expectations : an international journal of public participation in health care and health policy · 2022Pooled it
- Development of a behavior change intervention to improve physical activity in patients with COPD using the behavior change wheel: a non-randomized trial.Scientific reports · 2023Trial
- Logic Models on Health Information Technology-Related Interventions: Scoping Review Across Disciplines.JMIR medical informatics · 2026Article
- Amplifying the Voices of Parents From Underserved Communities in Digital Health for Children With Medical Complexity: Interview Study Among Parents.JMIR pediatrics and parenting · 2026Article
- Identifying the Challenges in Self-Management and Preferences for Digital Health Technologies Use Among COPD Patients Based on the CeHRes Roadmap: A Formative Qualitative Study.International journal of chronic obstructive pulmonary disease · 2026Article
- Informing the Design of Individualized Self-Management Regimens from the Human, Data, and Machine Learning Perspectives.ACM transactions on computer-human interaction : a publication of the Association for Computing Machinery · 2025Article
- Co-development of an evidence-informed, theoretically driven exercise programme for people with chronic non-specific neck pain (the EPIC-Neck programme - "Exercise Prescription Improved through Co-design").BMC musculoskeletal disorders · 2025Article
- Precision management in chronic disease: An AI empowered perspective on medicine-engineering crossover.iScience · 2025Review
- The State of the Art of eHealth Self-Management Interventions for People With Chronic Obstructive Pulmonary Disease: Scoping Review.Journal of medical Internet research · 2025Article
- LoVE4MUM Mobile App to Prevent Postpartum Depression: Protocol for a Pilot Randomized Controlled Trial.JMIR research protocols · 2025Article
- Human-centered design approach to building a transition readiness mHealth intervention for early adolescents.Journal of pediatric psychology · 2025Article
- Digital health technologies for improving the management of people with chronic obstructive pulmonary disease.Frontiers in digital health · 2025Review
- Determinants of chronic disease patients' intention to use Internet diagnosis and treatment services: based on the UTAUT2 model.Frontiers in digital health · 2025Article
- Insights From Art Therapists on Using AI-Generated Art in Art Therapy: Mixed Methods Study.JMIR formative research · 2024Article
- Design and development of an eHealth intervention to support self-management in people with musculoskeletal disorders: 'eHealth: it's TIME'.Clinical rehabilitation · 2024Article
- Exploring the Needs of People With Chronic Low Back Pain and Health Care Professionals for mHealth Devices to Support Self-Managed Physical Activity and Pain: User-Centered Design Approach.JMIR human factors · 2024Article
- A Framework for the Human-Centered Design of Service Processes Enabled by Medical Devices: A Case Study of Wearable Devices for Parkinson's Disease.International journal of environmental research and public health · 2024Article
- Developing a Tailored eHealth Self-Management Intervention for Patients With Chronic Kidney Disease in China: Intervention Mapping Approach.JMIR formative research · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAdequate self-management skills are of great importance for patients with chronic obstructive pulmonary disease (COPD) to reduce the impact of COPD exacerbations. Using mobile health (mHealth) to support exacerbation-related self-management could be promising in engaging patients in their own health and changing health behaviors. However, there is limited knowledge on how to design mHealth interventions that are effective, meet the needs of end users, and are perceived as useful. By following an iterative user-centered design (UCD) process, an evidence-driven and usable mHealth intervention was developed to enhance exacerbation-related self-management in patients with COPD.
objectiveThis study aimed to describe in detail the full UCD and development process of an evidence-driven and usable mHealth intervention to enhance exacerbation-related self-management in patients with COPD.
methodsThe UCD process consisted of four iterative phases: (1) background analysis and design conceptualization, (2) alpha usability testing, (3) iterative software development, and (4) field usability testing. Patients with COPD, health care providers, COPD experts, designers, software developers, and a behavioral scientist were involved throughout the design and development process. The intervention was developed using the behavior change wheel (BCW), a theoretically based approach for designing behavior change interventions, and logic modeling was used to map out the potential working mechanism of the intervention. Furthermore, the principles of design thinking were used for the creative design of the intervention. Qualitative and quantitative research methods were used throughout the design and development process.
resultsThe background analysis and design conceptualization phase resulted in final guiding principles for the intervention, a logic model to underpin the working mechanism of the intervention, and design requirements. Usability requirements were obtained from the usability testing phases. The iterative software development resulted in an evidence-driven and usable mHealth intervention-Copilot, a mobile app consisting of a symptom-monitoring module, and a personalized COPD action plan.
conclusionsBy following a UCD process, an mHealth intervention was developed that meets the needs and preferences of patients with COPD, is likely to be used by patients with COPD, and has a high potential to be effective in reducing exacerbation impact. This extensive report of the intervention development process contributes to more transparency in the development of complex interventions in health care and can be used by researchers and designers as guidance for the development of future mHealth interventions.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.