Tailoring eHealth design to support the self-care needs of patients with cardiovascular diseases: a vignette survey experiment

Abstract

Self-care support is a key cornerstone of treatment for patients with a cardiovascular disease. The success of any supportive intervention requires adaptation to the distinct needs of individuals. This requirement also applies to eHealth interventions. This study investigates how experts from multiple fields of science assess the potential success of different eHealth design strategies when matched to key self-care needs. An online vignette survey experiment was conducted. Nine vignettes representing different combinations of self-care needs (maintenance, monitoring, management) and eHealth persuasive design strategies (primary task support, dialogue support, social support) were evaluated. In total, 118 experts from 18 different countries participated in the survey. Their evaluations show primary task support as a promising design strategy across all self-care needs. In contrast, dialogue support and social support showed more promise for specific self-care needs. Above all, according to experts, the success of design strategies could be enhanced by (i) personalising the pacing of the intervention and (ii) tailoring the information to the literacy and culture of the person. Adding to that, self-care support should distinctly (iii) support the three self-care needs, be (iv) patient-centered, (v) support the collaboration with caregivers, and (vi) be aligned to the life goals and values of individuals.

A self-management psychoeducational eHealth program to support and empower people with advanced cancer and their family caregivers: Development using the scrum methodology

“…Tailoring can ensure that the program is applicable to the dyad by increasing the perceived program relevance ( Lustria et al, 2013 ). Moreover, it increases program engagement and improves outcomes ( Penedo et al, 2020 ), but also adds complexity to the coding and can result in errors ( Ryan et al, 2019 ; Cruz-Martínez et al, 2021 ; Islam et al, 2019 ). Additionally, although eHealth interventions might be more cost-effective than face-to-face interventions, designing and developing tailored programs is a very resource-consuming endeavor ( Abdelrazek et al, 2018 ) which explains why we limited the level of tailoring in iFOCUS.…”

The shift from disease-centric to patient-centric healthcare: Assessing physicians’ intention to use AI doctors

“…The current study measures physicians' intentions scientifically to predict their behaviors in the future. And we know that physicians are already using smart technologies and disease-oriented, "just on the subject AI" for inpatients [19,51,70,78] , which shapes their attitudes, and behavioral intention to use a holistic AI doctor system for all patients. Past research has proved that people who are aware of the uses and outcomes of AI technology accept AI-based decision-making systems more easily [13,30] .…”

“…Disease prevention and chronic disease management are good starting points in managing healthcare workload and expenses because AI is capable of handling the health activities of patients [68,69] . For example, companies such as Omada, Glooko, Virta, Living, and Lark offer AI health coaching to their customers (who are patients) who have diabetes, hypertension, and obesity [14,70] . Even for follow-up patients in the treatment process, AI is in use not only for physicians' tasks but also for nurses' [46] and pharmacists' [71] tasks.…”

Assessing acceptance of AI nurses for outpatients with chronic diseases: From nurses’ perspective

“…A change from a disease-centric to an individual-centric perspective of AI's value has been underway in recent years [8,9,37,38] . AI is now being utilized for managing people's health in daily activities as well as by medical personnel [9,14,39] . These efforts are not aimed at the level of disease but rather at the level of evaluation of everyone as a single event.…”

“…Because AI may handle the health activities of outpatients [6,8,9,13] . For example, companies such as Omada, Glooko, Virta, Living, and Lark offer AI health coaching to their customers (who are patients) who have diabetes, hypertension, and obesity [7,14] .…”