Careful Curation of Care Content:A Case Study of a Technology-Supported Atrial Fibrillation Outpatient Clinic

Medical care received outside the doctor’s office and the hospital is gaining traction. Lifestyle programs for transmural and remote care are increasingly facilitated by hospitals as part of rehabilitation, by general practitioners as preventative measures, and by various private (health) organizations through consumer apps. What is often overlooked is the time and energy spent on creating the content (i.e., health information and education) in these programs so that it is effective, appealing, relatable, and personalized. In this article, we discuss the elaborate content creation process of lifestyle content for an outpatient clinic for atrial fibrillation patients. We describe the close collaboration between clinicians, design researchers, data designers, and a copywriter, and reflect on how to streamline and formalize the clinical content creation process. Additionally, we highlight opportunities for further content personalization by making it dynamic, more versatile in terms of delivery and expanding the system further into the home context

Augmenting K-Means Clustering With Qualitative Data to Discover the Engagement Patterns of Older Adults With Multimorbidity When Using Digital Health Technologies: Proof-of-Concept Trial

Background:Multiple chronic conditions (multimorbidity) are becomingmore prevalent among aging populations. Digital health technologies have thepotential to assist in the self-management of multimorbidity, improving theawareness and monitoring of health and well-being, supporting a betterunderstanding of the disease, and encouraging behavior change.Objective:The aim of this study was to analyze how 60 older adults(mean age 74, SD 6.4; range 65-92 years) with multimorbidity engaged withdigital symptom and well-being monitoring when using a digital health platformover a period of approximately 12 months. Methods:Principal component analysis and clustering analysis wereused to group participants based on their levels of engagement, and the dataanalysis focused on characteristics (eg, age, sex, and chronic healthconditions), engagement outcomes, and symptom outcomes of the differentclusters that were discovered.Results:Three clusters were identified: the typical user group, theleast engaged user group, and the highly engaged user group. Our findings showthat age, sex, and the types of chronic health conditions do not influenceengagement. The 3 primary factors influencing engagement were whether the samedevice was used to submit different health and well-being parameters, thenumber of manual operations required to take a reading, and the daily routineof the participants. The findings also indicate that higher levels ofengagement may improve the participants’ outcomes (eg, reduce symptomexacerbation and increase physical activity).Conclusions:The findings indicate potential factors that influence olderadult engagement with digital health technologies for home-based multimorbidityself-management. The least engaged user groups showed decreased health andwell-being outcomes related to multimorbidity self-management. Addressing thefactors highlighted in this study in the design and implementation ofhome-based digital health technologies may improve symptom management andphysical activity outcomes for older adults self-managing multimorbidity.</p