JMIR Publications

ABSTRACT

Background:

Chronic disease management increasingly depends on monitoring symptoms and health behaviors outside clinical settings, yet most mobile health (mHealth) systems treat behavioral data capture and intervention delivery as separate functions. Platforms that collect high-resolution sensor or ecological momentary assessment (EMA) data often provide limited tools for clinician-authored intervention content, whereas intervention applications commonly embed content and decision logic directly in application code. This coupling slows iteration, limits personalization, and creates dependence on software developers.

Objective:

This paper presents CanBeWell, a disease-agnostic telehealth platform that integrates multimodal behavioral data collection with a clinician-operated, code-free intervention authoring and delivery workflow. CanBeWell consists of a patient-facing mobile application, a backend application programming interface (API) and data layer, and a clinician-facing dashboard. We describe the platform's system architecture, design rationale, implementation for cancer survivorship care, and formative stakeholder evaluation.

Methods:

CanBeWell was developed as a platform-oriented system informed by prior work in remote patient monitoring, mHealth sensing, and adaptive intervention theory. The platform was evaluated through formative semi-structured interviews with cancer patients (n=12) and their caregivers (n=9). [CX3.1]All study participants reviewed the mobile application prototype and provided feedback during in-person interviews on its usability, perceived value, and suggested improvements. Participants also rated the platform's overall usability on a 10-point scale. Additionally, clinicians (n = 6) were provided access to both the patient-facing mobile application and the clinician dashboard to evaluate the platform's usability, functionality, clinical relevance, and intervention workflow. Qualitative data were analyzed using thematic analysis by two independent coders

Results:

Twenty-one participants (patients and caregivers) completed the demographic survey (mean age 49.4 years; 61.9% female; 57.1% non-Hispanic White). Four themes emerged based on patient and caregiver interviews: ease of use, interest in the application, valued features, and overall user-friendliness[CX4.1]. The mean usability rating across patients and caregivers was 9 out of 10. Patients and caregivers valued the phased intervention structure, baseline activity tracking, and incremental goal progression. Clinicians responded positively to the no-code authoring workflow, noting that the ability to review behavioral data, draft content, and deploy approved phases without developer involvement could reduce technical barriers and accelerate intervention refinement[CX5.1]. The most common requested improvement by clinicians was stronger notification support to sustain engagement over longer deployments.

Conclusions:

CanBeWell extends remote patient monitoring infrastructure by adding a clinician-governed intervention authoring and delivery layer that closes the loop between behavioral sensing and patient-facing support. Formative validation in a cancer survivorship context suggests that the platform is usable and clinically meaningful to patients, caregivers, and clinicians. By separating intervention content from application code, the platform enables clinicians to create and update personalized interventions without extensive programming support, reducing engineering bottlenecks and facilitating scalable implementation across chronic disease settings.