A smartphone-based health care chatbot to promote self-management of chronic pain (SELMA) : pilot randomized controlled trial

Background: Ongoing pain is one of the most common diseases and has major physical, psychological, social, and economic impacts. A mobile health intervention utilizing a fully automated text-based health care chatbot (TBHC) may offer an innovative way not only to deliver coping strategies and psychoeducation for pain management but also to build a working alliance between a participant and the TBHC. Objective: The objectives of this study are twofold: (1) to describe the design and implementation to promote the chatbot painSELfMAnagement (SELMA), a 2-month smartphone-based cognitive behavior therapy (CBT) TBHC intervention for pain self-management in patients with ongoing or cyclic pain, and (2) to present findings from a pilot randomized controlled trial, in which effectiveness, influence of intention to change behavior, pain duration, working alliance, acceptance, and adherence were evaluated. Methods: Participants were recruited online and in collaboration with pain experts, and were randomized to interact with SELMA for 8 weeks either every day or every other day concerning CBT-based pain management (n=59), or weekly concerning content not related to pain management (n=43). Pain-related impairment (primary outcome), general well-being, pain intensity, and the bond scale of working alliance were measured at baseline and postintervention. Intention to change behavior and pain duration were measured at baseline only, and acceptance postintervention was assessed via self-reporting instruments. Adherence was assessed via usage data. Results: From May 2018 to August 2018, 311 adults downloaded the SELMA app, 102 of whom consented to participate and met the inclusion criteria. The average age of the women (88/102, 86.4%) and men (14/102, 13.6%) participating was 43.7 (SD 12.7) years. Baseline group comparison did not differ with respect to any demographic or clinical variable. The intervention group reported no significant change in pain-related impairment (P=.68) compared to the control group postintervention. The intention to change behavior was positively related to pain-related impairment (P=.01) and pain intensity (P=.01). Working alliance with the TBHC SELMA was comparable to that obtained in guided internet therapies with human coaches. Participants enjoyed using the app, perceiving it as useful and easy to use. Participants of the intervention group replied with an average answer ratio of 0.71 (SD 0.20) to 200 (SD 58.45) conversations initiated by SELMA. Participants’ comments revealed an appreciation of the empathic and responsible interaction with the TBHC SELMA. A main criticism was that there was no option to enter free text for the patients’ own comments. Conclusions: SELMA is feasible, as revealed mainly by positive feedback and valuable suggestions for future revisions. For example, the participants’ intention to change behavior or a more homogenous sample (eg, with a specific type of chronic pain) should be considered in further tailoring of SELMA

Game-inspired Pedagogical Conversational Agents: A Systematic Literature Review

Pedagogical conversational agents (PCAs) are an innovative way to help learners improve their academic performance via intelligent dialog systems. However, PCAs have not yet reached their full potential. They often fail because users perceive conversations with them as not engaging. Enriching them with game-based approaches could contribute to mitigating this issue. One could enrich a PCA with game-based approaches by gamifying it to foster positive effects, such as fun and motivation, or by integrating it into a game-based learning (GBL) environment to promote effects such as social presence and enable individual learning support. We summarize PCAs that are combined with game-based approaches under the novel term “game-inspired PCAs”. We conducted a systematic literature review on this topic, as previous literature reviews on PCAs either have not combined the topics of PCAs and GBL or have done so to a limited extent only. We analyzed the literature regarding the existing design knowledge base, the game elements used, the thematic areas and target groups, the PCA roles and types, the extent of artificial intelligence (AI) usage, and opportunities for adaptation. We reduced the initial 3,034 records to 50 fully coded papers, from which we derived a morphological box and revealed current research streams and future research recommendations. Overall, our results show that the topic offers promising application potential but that scholars and practitioners have not yet considered it holistically. For instance, we found that researchers have rarely provided prescriptive design knowledge, have not sufficiently combined game elements, and have seldom used AI algorithms as well as intelligent possibilities of user adaptation in PCA development. Furthermore, researchers have scarcely considered certain target groups, thematic areas, and PCA roles. Consequently, our paper contributes to research and practice by addressing research gaps and structuring the existing knowledge base

Exploring Automated Leadership and Agent Interaction Modalities

Advances in computer technology and research in the field of artificial intelligence have enabled computers to take on roles traditionally held by humans. Insights from leadership research have identified behaviors that, when applied strategically and systematically, can improve individual and team performance. We propose that some aspects of leadership are candidates for automation. This paper briefly reviews relevant leadership literature and describes three leadership behaviors that may be possibly automated: goal setting, performance monitoring, and performance consequences. The paper also explores the relationship of different embodiments of the artificial leaders, the impact of these embodiments in conveying social presence and the impact of this presence on performance and satisfaction outcomes. We conducted an experiment to investigate the effect of automated leadership on follower attitudes and behavior. Initial results suggest that automated leadership may positively influence performance and accuracy for individuals engaged in a clerical task

Digital Human Representations for Health Behavior Change: A Structured Literature Review

Organizations have increasingly begun using digital human representations (DHRs), such as avatars and embodied agents, to deliver health behavior change interventions (BCIs) that target modifiable risk factors in the smoking, nutrition, alcohol overconsumption, and physical inactivity (SNAP) domain. We conducted a structured literature review of 60 papers from the computing, health, and psychology literatures to investigate how DHRs’ social design affects whether BCIs succeed. Specifically, we analyzed how differences in social cues that DHRs use affect user psychology and how this can support or hinder different intervention functions. Building on established frameworks from the human-computer interaction and BCI literatures, we structure extant knowledge that can guide efforts to design future DHR-delivered BCIs. We conclude that we need more field studies to better understand the temporal dynamics and the mid-term and long-term effects of DHR social design on user perception and intervention outcomes