AI-based chatbot micro-intervention for parents: Meaningful engagement, learning, and efficacy.

Introduction: Mental health issues have been on the rise among children and adolescents, and digital parenting programs have shown promising outcomes. However, there is limited research on the potential efficacy of utilizing chatbots to promote parental skills. This study aimed to understand whether parents learn from a parenting chatbot micro intervention, to assess the overall efficacy of the intervention, and to explore the user characteristics of the participants, including parental busyness, assumptions about parenting, and qualitative engagement with the chatbot. Methods: A sample of 170 parents with at least one child between 2-11 years old were recruited. A randomized control trial was conducted. Participants in the experimental group accessed a 15-min intervention that taught how to utilize positive attention and praise to promote positive behaviors in their children, while the control group remained on a waiting list. Results: Results showed that participants engaged with a brief AI-based chatbot intervention and were able to learn effective praising skills. Although scores moved in the expected direction, there were no significant differences by condition in the praising knowledge reported by parents, perceived changes in disruptive behaviors, or parenting self-efficacy, from pre-intervention to 24-hour follow-up. Discussion: The results provided insight to understand how parents engaged with the chatbot and suggests that, in general, brief, self-guided, digital interventions can promote learning in parents. It is possible that a higher dose of intervention may be needed to obtain a therapeutic change in parents. Further research implications on chatbots for parenting skills are discussed.

Development of a chatbot for depression: adolescent perceptions and recommendations.

BACKGROUND: Chatbots are a relatively new technology that has shown promising outcomes for mental health symptoms in adults; however, few studies have been done with adolescents or reported adolescent user experiences and recommendations for chatbot development. METHODS: Twenty three participants ages 13-18 (Mage  = 14.96) engaged in user testing of a chatbot developed to psychoeducate adolescents on depression, teach behavioral activation, and change negative thoughts. Thematic analysis was conducted of participants' responses to user experience questions, impressions, and recommendations. RESULTS: Over half (56.5%) of the sample completed the full intervention and provided user experience feedback online. The average NPS score was 6.04 (SD = 2.18), and 64.3% (n = 9) said they would use the chatbot in the future. Of all user experience responses, 54.5% were positive. The most common impressions were related to symptom improvement (61.1%) and availability (52.8%) The most frequent recommendations were related to solving technical problems (66%). CONCLUSIONS: Chatbots for mental health are acceptable to some adolescents, a population that tends to be reluctant to engage with traditional mental health services. Most participants reported positive experiences with the chatbot, believing that it could help with symptom improvement and is highly available. Adolescents highlighted some technical and stylistic problems that developers should consider. More pilot and user testing is needed to develop mental health chatbots that are appealing and relevant to adolescents.

Disseminating online parenting resources in the community during the COVID-19 pandemic: Lessons learned.

Online parenting programs are an effective way to teach behavioral management skills to parents in the absence of in-person resources. This community-engaged study aimed to examine strategies for disseminating online parenting resources in schools. Online resources were disseminated to parents in a Northern California school district. Dissemination strategies were informed by conversations with school principals, teachers, and parents and considered agent, message, and format. A total of 685 parents and teachers clicked on the online resources: 151 parents and 114 teachers attended synchronous classes. The use of dissemination strategies had a compounding influence on the number of synchronous class attendees and clicks. Emails sent by the school district yielded the greatest number of clicks, which was influenced by message content and format. A community-academic partnership (CAP) led to the dissemination of evidence-based online parenting resources to a large population and led to lessons learned that could inform future research involving CAPs.

Psychometric Properties of a Chatbot Version of the PHQ-9 With Adults and Older Adults.

Background: The Patient Health Questionnaire-9 (PHQ-9) is a brief depression measure that has been validated. A chatbot version of the PHQ-9 would allow the assessment of depressive symptoms remotely, at a large scale and low cost. Objective: The current study aims to: Assess the feasibility of administering the PHQ-9 in a sample of adults and older adults via chatbot, report the psychometric properties of and identify the relationship between demographic variables and PHQ-9 total scores. Methods: A sample of 3,902 adults and older adults in the US and Canada were recruited through Facebook from August 2019 to February 2020 to complete the PHQ-9 using a chatbot. Results: A total of 3,895 (99.82%) completed the PHQ-9 successfully. The internal consistency of the PHQ-9 was 0.896 (p < 0.05). A one factor structure was found to have good model fit [X 2 (27, N = 1,948) = 365.396, p < 0.001; RMSEA = 0.080 (90% CI: 0.073, 0.088); CFI and TLI were 0.925 and 0.900, respectively, and SRMR was 0.039]. All of the demographic characteristics in this study were found to significantly predict PHQ-9 total score, however; their effect was negligible to weak. Conclusions: There was a large sample of adults and older adults were open to completing assessments via chatbot including those over 75. The psychometric properties of the chatbot version of the PHQ-9 provide initial support to the utilization of this assessment method.

Bonding With Bot: User Feedback on a Chatbot for Social Isolation.

Social isolation has affected people globally during the COVID-19 pandemic and had a major impact on older adult's well-being. Chatbot interventions may be a way to provide support to address loneliness and social isolation in older adults. The aims of the current study were to (1) understand the distribution of a chatbot's net promoter scores, (2) conduct a thematic analysis on qualitative elaborations to the net promoter scores, (3) understand the distribution of net promoter scores per theme, and (4) conduct a single word analysis to understand the frequency of words present in the qualitative feedback. A total of 7,099 adults and older adults consented to participate in a chatbot intervention on reducing social isolation and loneliness. The average net promoter score (NPS) was 8.67 out of 10. Qualitative feedback was provided by 766 (10.79%) participants which amounted to 898 total responses. Most themes were rated as positive (517), followed by neutral (311) and a minor portion as negative (70). The following five themes were found across the qualitative responses: positive outcome (277, 30.8%), user did not address question (262, 29.2%), bonding with the chatbot (240, 26.7%), negative technical aspects (70, 7.8%), and ambiguous outcome (49, 5.5%). Themes with a positive valence were found to be associated with a higher NPS. The word "help" and it's variations were found to be the most frequently used words, which is consistent with the thematic analysis. These results show that a chatbot for social isolation and loneliness was perceived positively by most participants. More specifically, users were likely to personify the chatbot (e.g., "Cause I feel like I have a new friend!") and perceive positive personality features such as being non-judgmental, caring, and open to listen. A minor portion of the users reported dissatisfaction with chatting with a machine. Implications will be discussed.

Artificial Intelligence Chatbot for Depression: Descriptive Study of Usage.

BACKGROUND: Chatbots could be a scalable solution that provides an interactive means of engaging users in behavioral health interventions driven by artificial intelligence. Although some chatbots have shown promising early efficacy results, there is limited information about how people use these chatbots. Understanding the usage patterns of chatbots for depression represents a crucial step toward improving chatbot design and providing information about the strengths and limitations of the chatbots. OBJECTIVE: This study aims to understand how users engage and are redirected through a chatbot for depression (Tess) to provide design recommendations. METHODS: Interactions of 354 users with the Tess depression modules were analyzed to understand chatbot usage across and within modules. Descriptive statistics were used to analyze participant flow through each depression module, including characters per message, completion rate, and time spent per module. Slide plots were also used to analyze the flow across and within modules. RESULTS: Users sent a total of 6220 messages, with a total of 86,298 characters, and, on average, they engaged with Tess depression modules for 46 days. There was large heterogeneity in user engagement across different modules, which appeared to be affected by the length, complexity, content, and style of questions within the modules and the routing between modules. CONCLUSIONS: Overall, participants engaged with Tess; however, there was a heterogeneous usage pattern because of varying module designs. Major implications for future chatbot design and evaluation are discussed in the paper.