Conversational Agents as Mediating Social Actors in Chronic Disease Management Involving Health Care Professionals, Patients, and Family Members: Multisite Single-Arm Feasibility Study

Abstract

Background Successful management of chronic diseases requires a trustful collaboration between health care professionals, patients, and family members. Scalable conversational agents, designed to assist health care professionals, may play a significant role in supporting this collaboration in a scalable way by reaching out to the everyday lives of patients and their family members. However, to date, it remains unclear whether conversational agents, in such a role, would be accepted and whether they can support this multistakeholder collaboration. Objective With asthma in children representing a relevant target of chronic disease management, this study had the following objectives: (1) to describe the design of MAX, a conversational agent–delivered asthma intervention that supports health care professionals targeting child-parent teams in their everyday lives; and (2) to assess the (a) reach of MAX, (b) conversational agent–patient working alliance, (c) acceptance of MAX, (d) intervention completion rate, (e) cognitive and behavioral outcomes, and (f) human effort and responsiveness of health care professionals in primary and secondary care settings. Methods MAX was designed to increase cognitive skills (ie, knowledge about asthma) and behavioral skills (ie, inhalation technique) in 10-15-year-olds with asthma, and enables support by a health professional and a family member. To this end, three design goals guided the development: (1) to build a conversational agent–patient working alliance; (2) to offer hybrid (human- and conversational agent–supported) ubiquitous coaching; and (3) to provide an intervention with high experiential value. An interdisciplinary team of computer scientists, asthma experts, and young patients with their parents developed the intervention collaboratively. The conversational agent communicates with health care professionals via email, with patients via a mobile chat app, and with a family member via SMS text messaging. A single-arm feasibility study in primary and secondary care settings was performed to assess MAX. Results Results indicated an overall positive evaluation of MAX with respect to its reach (49.5%, 49/99 of recruited and eligible patient-family member teams participated), a strong patient-conversational agent working alliance, and high acceptance by all relevant stakeholders. Moreover, MAX led to improved cognitive and behavioral skills and an intervention completion rate of 75.5%. Family members supported the patients in 269 out of 275 (97.8%) coaching sessions. Most of the conversational turns (99.5%) were conducted between patients and the conversational agent as opposed to between patients and health care professionals, thus indicating the scalability of MAX. In addition, it took health care professionals less than 4 minutes to assess the inhalation technique and 3 days to deliver related feedback to the patients. Several suggestions for improvement were made. Conclusions This study provides the first evidence that conversational agents, designed as mediating social actors involving health care professionals, patients, and family members, are not only accepted in such a “team player” role but also show potential to improve health-relevant outcomes in chronic disease management.

Human Cues in eHealth to Promote Lifestyle Change: an Experimental Field Study to Examine Adherence to Self-help Interventions (Preprint)

“…Finally, we found that people who reported a better working alliance with the CA were more adherent to the intervention. This result is in line with studies about regular face-to-face interventions [24,25], digital therapy or treatment [26,27], and automated digital interventions [30,31,32,33,34]. Nonetheless, we did not find an effect of human cues on the reported working alliance with the CA.…”

“…Moreover, in studies that did find an improved working alliance with a CA either the interactions with the agent or the intervention itself were longer compared to those in our study [30,32]. In other studies, although a high working alliance was reported within shorter periods of time, the interactions with the CA followed after introduction by a human healthcare professional [33,34]. It is therefore unclear whether a TCA is less able to build a relationship with the user, or that it requires a longer time or introduction in a face-to-face introduction to do so.…”

“…People interact with computers as they would do with human beings, and apply similar social rules and heuristics [28,29]. Studies show that people can also develop a working alliance within fully automated digital interventions, and that this leads to more positive treatment outcomes [30][31][32][33][34].…”

A scoping review of mHealth monitoring of pediatric bronchial asthma before and during COVID-19 pandemic

“…Out of 19 publications that utilized some mHealth app for patient monitoring, 13 (68.4%) publications [39] , [40] , [41] , [44] , [45] , [46] , [47] , [48] , [49] , [50] , [51] , [52] , [53] tested the apps which had been developed by publication authors. An interesting app developing model has been presented in one publication [51] .…”

“…Some of these apps even address disease aspects beyond the conventional patient education or self-monitoring. For instance, three included publications [41] , [45] , [47] targeted family management of pediatric asthma (that is, by actively involving parents or caregivers in the management process) or aimed to foster efficient communication between the family and a healthcare practitioner. Such complex mHealth apps for pediatric asthma were developed by the evidence-informed multidisciplinary teams and benefited from participatory patient contributions (Supplemental Tables 5 and 6).…”