Background Psychological distress increases across adolescence and has been associated with several important health outcomes with consequences that can extend into adulthood. One type of technological innovation that may serve as a unique intervention for youth experiencing psychological distress is the conversational agent, otherwise known as a chatbot. Further research is needed on the factors that may make mental health chatbots destined for adolescents more appealing and increase the likelihood that adolescents will use them. Objective The aim of this study was to assess adolescents’ emotional reactions and likelihood of responding to questions that could be posed by a mental health chatbot. Understanding adolescent preferences and factors that could increase adolescents’ likelihood of responding to chatbot questions could assist in future mental health chatbot design destined for youth. Methods We recruited 19 adolescents aged 14 to 17 years to participate in a study with a 2×2×3 within-subjects factorial design. Each participant was sequentially presented with 96 chatbot questions for a duration of 8 seconds per question. Following each presentation, participants were asked to indicate how likely they were to respond to the question, as well as their perceived affective reaction to the question. Demographic data were collected, and an informal debriefing was conducted with each participant. Results Participants were an average of 15.3 years old (SD 1.00) and mostly female (11/19, 58%). Logistic regressions showed that the presence of GIFs predicted perceived emotional valence (β=–.40, P<.001), such that questions without GIFs were associated with a negative perceived emotional valence. Question type predicted emotional valence, such that yes/no questions (β=–.23, P=.03) and open-ended questions (β=–.26, P=.01) were associated with a negative perceived emotional valence compared to multiple response choice questions. Question type also predicted the likelihood of response, such that yes/no questions were associated with a lower likelihood of response compared to multiple response choice questions (β=–.24, P=.03) and a higher likelihood of response compared to open-ended questions (β=.54, P<.001). Conclusions The findings of this study add to the rapidly growing field of teen-computer interaction and contribute to our understanding of adolescent user experience in their interactions with a mental health chatbot. The insights gained from this study may be of assistance to developers and designers of mental health chatbots.

COVID-19 underscores the importance of understanding variation in adherence to rules concerning health behaviors. Children with conduct problems have difficulty with rule adherence, and linking early conduct problems with later adherence to COVID-19 guidelines can provide new insight into public health. The current study employed a sample (N = 744) designed to examine the longitudinal consequences of childhood conduct problems (Mean age at study entry = 8.39). The first objective was to link early conduct problems with later adherence to both general and specific COVID-19 guidelines during emerging adulthood (M age = 19.07). The second objective was to prospectively examine how interactional (i.e., callous unemotional traits, impulsivity) and cumulative (i.e., educational attainment, work status, substance use) continuity factors mediated this association. The third objective was to examine differences in sex assigned at birth in these models. Direct associations were observed between childhood conduct problems and lower general, but not specific COVID-19 guideline adherence. Conduct problems were indirectly associated with both general and specific adherence via higher levels of callous unemotional traits, and with specific adherence via higher problematic substance use. No differences in the models were observed across sex assigned at birth. Findings provide insight into both how developmental psychopathology constructs are useful for understanding COVID-19 guideline adherence, and the ways in which conduct problems may shape health outcomes.

BACKGROUND Psychological distress increases across adolescence and has been associated with a number of important health outcomes, with consequences that can extend into adulthood. One type of technological innovation that may serve as a unique intervention for youth experiencing psychological distress is the conversational agent (CA), otherwise known as a chatbot. Further research is needed on mental health chatbots - notably those designed for adolescents. The experimental mental health chatbot used in this study was designed to support adolescents experiencing psychological distress. OBJECTIVE The current study aimed to assess adolescents’ perceived emotional reactions to questions posed by a mental health chatbot and to evaluate adolescents’ preferences concerning the formulation of the chatbot’s questions. METHODS We recruited 21 adolescents aged 14 to 17 to participate in a pilot study with a 2x2x3 within-subjects factorial design. Each participant was sequentially presented with 96 chatbot questions for a duration of eight seconds per question. Following each presentation, participants were asked to indicate how likely they were to respond to the question, as well as their perceived affective reaction to the question. Demographic data and participant feedback were also collected. RESULTS Participants were an average of 15.4 years old (SD 1.05) and mostly female (60%; 12 females; 8 males). Logistic regressions showed that presence of GIFs (Graphics Interchange Format) predicted perceived emotional valence (β = -.40; P < .001), such that questions without GIFs were associated with a lower perceived emotional valence. Question type predicted emotional valence, such that yes/no questions (β = -.23; P = .03) and open-ended questions (β = -.26; P = .01) were associated with a lower perceived emotional valence compared to multiple response choice questions. Question type also predicted likelihood of response, such that yes/no questions were associated with a lower likelihood of response compared to multiple response choice questions (β = -.24; P = .03) and a higher likelihood of response compared to open-ended questions (β = .54; P < .001). CONCLUSIONS The findings of this study add to the rapidly growing field of teen-computer interaction and contribute to our understanding of adolescent user experience in their interactions with a mental health chatbot. The insights gained from this study may be of assistance to developers and designers of mental health chatbots.