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The Development of a Mobile Monitoring and Feedback Tool to Stimulate Physical Activity of People With a Chronic Disease in Primary Care: A User-Centered Design
Open accessAbstract
BACKGROUND: Physical activity is an important aspect in the treatment of patients with chronic obstructive pulmonary disease or type-2 diabetes. A monitoring and feedback tool combined with guidance by a primary care provider might be a successful method to enhance the level of physical activity in these patients. As a prerequisite for useful technology, it is important to involve the end-users in the design process from an early stage. OBJECTIVE: The aim of this study was to investigate the user requirements for a tool to stimulate physical activity, embedded in primary care practice. The leading principle of this tool is to change behavior by self-monitoring, goal-setting, and feedback. METHODS: The research team collected qualitative data among 15 patients, 16 care professionals, and several experts. A prototype was developed in three stages. In stage 1, the literature was searched to identify end-users and context. In stage 2, the literature, experts and patient representatives were consulted to set up a use case with the general idea of the innovation. In stage 3, individual interviews and focus groups were held to identify the end-user requirements. Based on these requirements a prototype was built by the engineering team. RESULTS: The development process has led to a tool that generally meets the requirements of the end-users. A tri-axial activity sensor, worn on the hip, is connected by Bluetooth to a smartphone. In an app, quantitative feedback is given about the amount of activity and goals reached by means of graphical visualization, and an image shows a sun when the goal is reached. Overviews about activity per half an hour, per day, week, and month are provided. In the menu of the app and on a secured website, patients can enter information in individual sessions or read feedback messages generated by the system. The practice nurse can see the results of all patients on a secure webpage and can then discuss the results and set personalized goals in consultation with the patient. CONCLUSIONS: This study demonstrates that a user-centered approach brings in valuable details (such as the requirements for feedback in activity minutes per day) to improve the fit between the user, technology, and the organization of care, which is important for the usability and acceptability of the tool. The tool embedded in primary care will be evaluated in a randomized controlled trial.
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“In this sense, digital wearable devices contribute not only by recording or prompting exercise, but also by embedding behavior regulation into everyday life and supporting the initiation, adjustment, and maintenance of exercise behaviors (14, 15).”
From Behavior change pathways by which digital wearable devices support exercise self-management in type 2 diabetes: a scoping review with machine learning–assisted text mining (2026) · cites this paper as [14]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Goal setting also seems central, as devices can translate broad recommendations into specific and feasible targets, such as daily step goals or activity thresholds (14, 17, 21).”
From Behavior change pathways by which digital wearable devices support exercise self-management in type 2 diabetes: a scoping review with machine learning–assisted text mining (2026) · cites this paper as [14]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“while family involvement, peer comparison, clinician interaction, and remote feedback may broaden the social context of exercise behavior (14, 15, 19).”
From Behavior change pathways by which digital wearable devices support exercise self-management in type 2 diabetes: a scoping review with machine learning–assisted text mining (2026) · cites this paper as [14]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Over time, repeated goal attainment and visible progress may strengthen patients' sense of mastery and self-efficacy, thereby facilitating a shift from externally prompted behavior to proactive self-management (14, 17, 20, 25, 26).”
From Behavior change pathways by which digital wearable devices support exercise self-management in type 2 diabetes: a scoping review with machine learning–assisted text mining (2026) · cites this paper as [14]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Compared with traditional approaches that rely on recall or paper-based records, digital wearable devices provide continuous and objective tracking of step counts, activity duration, heart rate, and energy expenditure, thereby converting exercise behavior into visible and quantifiable information (14, 16–18).”
From Behavior change pathways by which digital wearable devices support exercise self-management in type 2 diabetes: a scoping review with machine learning–assisted text mining (2026) · cites this paper as [14]
Experimental — accuracy not yet measured
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Features such as individualized goals, progress visualization, reward feedback, family involvement, peer comparison, and clinician interaction may help address these psychological needs (14, 19, 20).”
From Behavior change pathways by which digital wearable devices support exercise self-management in type 2 diabetes: a scoping review with machine learning–assisted text mining (2026) · cites this paper as [14]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Self-monitoring, real-time feedback, and goal setting align closely with Social Cognitive Theory and self-regulation perspectives, as digital wearable devices support a closed-loop process of self-observation, comparison with goals, and behavioral adjustment (14, 17, 20, 27).”
From Behavior change pathways by which digital wearable devices support exercise self-management in type 2 diabetes: a scoping review with machine learning–assisted text mining (2026) · cites this paper as [14]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“Such data may strengthen patients' awareness of their behavioral status and provide a basis for behavioral adjustment (14, 17, 19, 20).”
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Metadata
- Authors
- van der Weegen, S., Verwey, R., Spreeuwenberg, M., Tange, H., van der Weijden, T., & de Witte, L.
- Affiliations
- Zuyd University of Applied Sciences (NL)
- Maastricht University (NL)
- Venue
- JMIR mhealth and uhealth
- Publisher
- JMIR Publications Inc.
- Published
- 2013-07-02
- Volume
- 1
- Issue
- 2
- Pages
- e8
- ISSN
- 2291-5222 (ISSN-L)
- PMC
- PMC4114510
- OpenAlex
- W2143825555
- Cited by
- 128
- References
- 45
- Citations by year
- 2026: 3 · 2025: 4 · 2024: 5 · 2023: 4 · 2022: 8 · 2021: 14 · 2020: 12 · 2019: 19 · 2018: 14 · 2017: 10 · 2016: 13 · 2015: 10 · 2014: 10 · 2013: 2
- FWCI
- 21.13
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- JMIR mhealth and uhealth (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- ZonMw
- Nederlandse Organisatie voor Wetenschappelijk Onderzoek
- Maastricht Universitair Medisch Centrum
- Topics
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Physical Sciences › Computer Science › Human-Computer Interaction › Innovative Human-Technology Interaction
- Keywords
- physical activity promotion, chronic disease, chronic obstructive pulmonary disease, type 2 diabetes, mobile health apps, self-monitoring, goal setting, feedback, user-centered design, user requirements, primary care, practice nurses, behavior change, wearable sensors, mobile application
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