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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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.
“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]
In Results, Stage 2 (Concept Development), the paper identifies self-monitoring, tailored feedback and goal setting as strategies relevant to initiating and maintaining physical activity. Results, Stage 3 (Tool Re-Design), describes daily activity planning, adjustable goals and ongoing feedback in its proposed tool. But the claim generalizes from a tool designed for people with COPD or type 2 diabetes to wearable devices broadly, and the Introduction says whether activity changes are sustainable remains unknown. The indexed passages have no page numbers. (LLM, conf=0.84)
Experimental — accuracy not yet measuredGoal setting seems central, as devices can translate broad recommendations into specific and feasible targets, such as daily step goals or activity thresholds.
“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]
In Results, Stage 3: Tool (Re-)Design (page not supplied), the paper describes using monitored activity to set realistic, personalized daily goals in minutes of moderate-to-vigorous activity, and discusses individualized activity-intensity thresholds. It does not describe setting daily step goals: the Introduction discusses step counts as pedometer feedback, not as targets. (LLM, conf=0.84)
Experimental — accuracy not yet measuredFamily involvement, peer comparison, clinician interaction, and remote feedback may broaden the social context of exercise behavior.
“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]
Results, Stage 3: Tool (Re-)Design (page unavailable), says relatives’ involvement would be encouraged and describes feedback messages and practice-nurse interaction. But it says activity data would be shared with the nurse, not peers; it does not support peer comparison as part of the tool. The Discussion, Principal Results (page unavailable), likewise specifies sharing with a care professional rather than peers. (LLM, conf=0.88)
Experimental — accuracy not yet measuredOver 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.
“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]
The Discussion (Principal Results; page not provided) describes a tool designed to increase self-efficacy and support self-management, with feedback based on goal attainment. The Results (Stage 3: Tool (Re-)Design; page not provided) describe visible progress and rewards. The paper does not say that repeated success builds mastery over time or shifts patients from external prompts to proactive self-management. (LLM, conf=0.84)
Experimental — accuracy not yet measuredCompared 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.
“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]
Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measured
Features such as individualized goals, progress visualization, reward feedback, family involvement, peer comparison, and clinician interaction may help address these psychological needs.
“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]
The Results (Stage 3: Tool (Re-)Design; page unavailable) describe personalized goals, progress displays, reward feedback, encouragement of relatives’ involvement, and feedback from a practice nurse. They do not support peer comparison: the Data Sharing subsection says activity data would be shared with the nurse, not peers, and few respondents wanted to share results with peers. The paper discusses motivation but does not establish that the full set of features addresses the particular psychological needs invoked by the citing sentence. (LLM, conf=0.90)
Experimental — accuracy not yet measuredSelf-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.
“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]
Split into 2 atoms — 1 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredSuch data may strengthen patients' awareness of their behavioral status and provide a basis for behavioral adjustment.
“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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- 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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