Table 2.

Personalization features of conversational agents in the included studies.

Conversational agent (author, year)	CAa purpose	Automation (the basis for personalization)	Target (for whom to personalize)	What to personalize
				Content	User interface
Tess (Fulmer et al, 2018) [44]	Delivery of cognitive behavioral therapy to reduce symptoms of depression and anxiety in college students	Explicit: Expressed emotions and mental health concerns of participants to provide personalized responses. Users' feedback and reported mood used to tailor interventions	Individuated	Personalized conversations based on emotions and mental health concerns Personalized therapeutic choices based on user feedback	NRb
Wysa (Inkster et al, 2018) [45]	Wellbeing support app for users with symptoms of depression, aiming to build mental resilience and promote mental wellbeing	Explicit: User responses to built-in assessment questionnaire and emotions expressed in a written conversation	Individuated	Personalized conversational pathways based on a user’s interaction, messages, and context	NR
Reflection Companion (Kocielnik et al, 2018) [46]	Support reflection on personal physical activity data from fitness trackers	Explicit: Users enter their behavior change goals and demographic data Implicit: Observed physical activity of the user	Individuated	Dialogues to encourage reflection Incorporating user goals into adaptive mini-dialogues Follow-up questions based on users’ earlier responses Visualization of past physical activity	NR
Relational Agent (Sillice et al, 2018) [47]	Promote regular exercising and sun protection	Explicit: Users provide their demographic information, exercising habits, sun protection behaviors and lifestyle goals Implicit: CA tracks user progress to send reminders if needed	Individuated	Acknowledgement of difficulties and tailored strategies to overcome these Feedback on progress and encouragement for achieving goals A weekly tracking chart to help participants monitor their exercise and sun protection behaviors Email reminders to support retention	NR
Woebot (Fitzpatrick et al, 2017) [35]	Deliver cognitive-behavioral therapy for anxiety and depression to college students	Explicit: Users enter their mood and goals	Individuated	Empathic responses tailored to the reported mood Tailoring of support content depending on the reported mood Daily prompting messages to initiate a conversation Weekly charts depicting the reported mood and textual summary	NR
Social Skills Trainer (Tanaka et al, 2017) [36]	Social skills training for people with autism spectrum disorders	Implicit: CA analyzes the user's audio-visual features, facial expression (smile), and head position to determine its feedback and then performs feature selection	Individuated	Personalized score showing similarity to a role model with respect to 10 features Encouraging comments to reinforce motivation, based on features closest to the model Comments on the points that need improvement, based on features dissimilar to the model Homework challenges for participants to complete on their own time throughout the week	NR
mASMAAc (Rhee et al, 2014) [37]	Facilitate asthma symptom monitoring, treatment adherence, and adolescent-parent partnership	Explicit: Users enter symptoms, activity level, and use of rescue and control medications	Individuated	Automated inquiries and reminders sent according to user-defined preferences on monitoring symptoms and managing medications and activity Processing of and responses to user-initiated messages at any time Daily report summarizing symptoms, activity, and use emailed to parents	NR
Chris (Hudlicka, 2013) [38]	Embodied CA that provides mindfulness training and coaching	Explicit: Users answer questions asked by the CA and set preferences via multiple-choice questions	Individuated	CA’s facial expressions and its responses adapting to the users’ learning needs and motivational state CA's affective reaction adapting to the users' utterances Conversational expressions communicating mental state Customized advice about meditation practice, based on the expressed concerns	Using didactic, relational, or motivational conversational styles according to the user models
DI@l-log (Harper et al, 2008; Black et al, 2005) [39,40]	Voice logbook to document home monitored data by diabetes patients	Explicit: Users provide weight, blood sugar and blood pressure values	Individuated	An alert feature generating a verbal warning if readings are too high Personalized feedback to patients on their current progress	NR
Pain Monitoring Voice Diary (Levin and Levin, 2006) [41]	Real-time collection of information from patients for health, behavioral, and lifestyle studies and monitoring	Explicit: Users answer a series of questions about their pain (location, type, intensity, etc) Implicit: CA utilizes previous sessions to provide personalized content and conversational style	Individuated Categorical (novice and experienced users)	Content (what data is collected) and style (how it is collected) of the reporting session Adaptive question-asking (additional questions for follow-ups to sessions with high levels of pain) Adaptive interruptions to better support experienced users	Adaptive conversational style (eg, shorter question formats for follow-up sessions)
Intelligent dialogue system (Giorgino et al, 2004; Azzini et al, 2003) [42,43]	Home care and data acquisition from hypertension patients	Explicit: Users answer questions about heart rate, pressure, weight, compliance, and more Implicit: CA changes its behavior depending on the progress of the current call and the clinical history of the caller	Individuated	The questions to be asked were determined by user profiles Gives advice on recommended health behavior and next visits Issues alerts and prompts	NR

^aCA: conversational agent.

^bNR: not reported.

^cmASMAA: mobile phone-based asthma self-management aid.