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Dialog agents (chatbots) have a long history of application in health care, where they have been used for tasks such as supporting patient self-management and providing counseling. Their use is expected to grow with increasing demands on health systems and improving artificial intelligence (AI) capability. Approaches to the evaluation of health care chatbots, however, appear to be diverse and haphazard, resulting in a potential barrier to the advancement of the field. This study aims to identify the technical (nonclinical) metrics used by previous studies to evaluate health care chatbots. Studies were identified by searching 7 bibliographic databases (eg, MEDLINE and PsycINFO) in addition to conducting backward and forward reference list checking of the included studies and relevant reviews. The studies were independently selected by two reviewers who then extracted data from the included studies. Extracted data were synthesized narratively by grouping the identified metrics into categories based on the aspect of chatbots that the metrics evaluated. Of the 1498 citations retrieved, 65 studies were included in this review. Chatbots were evaluated using 27 technical metrics, which were related to chatbots as a whole (eg, usability, classifier performance, speed), response generation (eg, comprehensibility, realism, repetitiveness), response understanding (eg, chatbot understanding as assessed by users, word error rate, concept error rate), and esthetics (eg, appearance of the virtual agent, background color, and content). The technical metrics of health chatbot studies were diverse, with survey designs and global usability metrics dominating. The lack of standardization and paucity of objective measures make it difficult to compare the performance of health chatbots and could inhibit advancement of the field. We suggest that researchers more frequently include metrics computed from conversation logs. In addition, we recommend the development of a framework of technical metrics with recommendations for specific circumstances for their inclusion in chatbot studies.

Most cardiovascular disease risk prediction equations in use today were derived from cohorts established last century and with participants at higher risk but less socioeconomically and ethnically diverse than patients they are now applied to. We recruited a nationally representative cohort in New Zealand to develop equations relevant to patients in contemporary primary care and compared the performance of these new equations to equations that are recommended in the USA. The PREDICT study automatically recruits participants in routine primary care when general practitioners in New Zealand use PREDICT software to assess their patients' risk profiles for cardiovascular disease, which are prospectively linked to national ICD-coded hospitalisation and mortality databases. The study population included male and female patients in primary care who had no prior cardiovascular disease, renal disease, or congestive heart failure. New equations predicting total cardiovascular disease risk were developed using Cox regression models, which included clinical predictors plus an area-based deprivation index and self-identified ethnicity. Calibration and discrimination performance of the equations were assessed and compared with 2013 American College of Cardiology/American Heart Association Pooled Cohort Equations (PCEs). The additional predictors included in new PREDICT equations were also appended to the PCEs to determine whether they were independent predictors in the equations from the USA. Outcome events were derived for 401 752 people aged 30–74 years at the time of their first PREDICT risk assessment between Aug 27, 2002, and Oct 12, 2015, representing about 90% of the eligible population. The mean follow-up was 4·2 years, and a third of participants were followed for 5 years or more. 15 386 (4%) people had cardiovascular disease events (1507 [10%] were fatal, and 8549 [56%] met the PCEs definition of hard atherosclerotic cardiovascular disease) during 1 685 521 person-years follow-up. The median 5-year risk of total cardiovascular disease events predicted by the new equations was 2·3% in women and 3·2% in men. Multivariable adjusted risk increased by about 10% per quintile of socioeconomic deprivation. Māori, Pacific, and Indian patients were at 13–48% higher risk of cardiovascular disease than Europeans, and Chinese or other Asians were at 25–33% lower risk of cardiovascular disease than Europeans. The PCEs overestimated of hard atherosclerotic cardiovascular disease by about 40% in men and by 60% in women, and the additional predictors in the new equations were also independent predictors in the PCEs. The new equations were significantly better than PCEs on all performance metrics. We constructed a large prospective cohort study representing typical patients in primary care in New Zealand who were recommended for cardiovascular disease risk assessment. Most patients are now at low risk of cardiovascular disease, which explains why the PCEs based mainly on old cohorts substantially overestimate risk. Although the PCEs and many other equations will need to be recalibrated to mitigate overtreatment of the healthy majority, they also need new predictors that include measures of socioeconomic deprivation and multiple ethnicities to identify vulnerable high-risk subpopulations that might otherwise be undertreated. Health Research Council of New Zealand, Heart Foundation of New Zealand, and Healthier Lives National Science Challenge.

Background Wide-ranging concerns exist regarding the use of black-box modelling methods in sensitive contexts such as healthcare. Despite performance gains and hype, uptake of artificial intelligence (AI) is hindered by these concerns. Explainable AI is thought to help alleviate these concerns. However, existing definitions for explainable are not forming a solid foundation for this work. Methods We critique recent reviews on the literature regarding: the agency of an AI within a team; mental models, especially as they apply to healthcare, and the practical aspects of their elicitation; and existing and current definitions of explainability, especially from the perspective of AI researchers. On the basis of this literature, we create a new definition of explainable , and supporting terms, providing definitions that can be objectively evaluated. Finally, we apply the new definition of explainable to three existing models, demonstrating how it can apply to previous research, and providing guidance for future research on the basis of this definition. Results Existing definitions of explanation are premised on global applicability and don’t address the question ‘understandable by whom ?’. Eliciting mental models can be likened to creating explainable AI if one considers the AI as a member of a team. On this basis, we define explainability in terms of the context of the model, comprising the purpose , audience , and language of the model and explanation. As examples, this definition is applied to regression models, neural nets, and human mental models in operating-room teams. Conclusions Existing definitions of explanation have limitations for ensuring that the concerns for practical applications are resolved. Defining explainability in terms of the context of their application forces evaluations to be aligned with the practical goals of the model. Further, it will allow researchers to explicitly distinguish between explanations for technical and lay audiences, allowing different evaluations to be applied to each.

As wearable devices, which allow individuals to track and self-manage their health, become more ubiquitous, the opportunities are growing for researchers to use these sensors within interventions and for data collection. They offer access to data that are captured continuously, passively, and pragmatically with minimal user burden, providing huge advantages for health research. However, the growth in their use must be coupled with consideration of their potential limitations, in particular, digital inclusion, data availability, privacy, ethics of third-party involvement, data quality, and potential for adverse consequences. In this paper, we discuss these issues and strategies used to prevent or mitigate them and recommendations for researchers using wearables as part of interventions or for data collection.

The number of young people in New Zealand (Aotearoa) who experience mental health challenges is increasing. As those in Aotearoa went into the initial COVID-19 lockdown, an ongoing digital mental health project was adapted and underwent rapid content authoring to create the Aroha chatbot. This dynamic digital support was designed with and for young people to help manage pandemic-related worry. Aroha was developed to provide practical evidence-based tools for anxiety management using cognitive behavioral therapy and positive psychology. The chatbot included practical ideas to maintain social and cultural connection, and to stay active and well. Stay-at-home orders under Aotearoa’s lockdown commenced on March 20, 2020. By leveraging previously developed chatbot technology and broader existing online trial infrastructure, the Aroha chatbot was launched promptly on April 7, 2020. Dissemination of the chatbot for an open trial was via a URL, and feedback on the experience of the lockdown and the experience of Aroha was gathered via online questionnaires and a focus group, and from community members. In the 2 weeks following the launch of the chatbot, there were 393 registrations, and 238 users logged into the chatbot, of whom 127 were in the target age range (13-24 years). Feedback guided iterative and responsive content authoring to suit the dynamic situation and motivated engineering to dynamically detect and react to a range of conversational intents. The experience of the implementation of the Aroha chatbot highlights the feasibility of providing timely event-specific digital mental health support and the technology requirements for a flexible and enabling chatbot architectural framework.

Mental health chatbots are increasingly used to address the global mental health treatment gap by offering scalable, accessible, and anonymous support. While prior research suggests that users may develop relationships with these chatbots, the mechanisms and individual differences underlying such relational experiences remain underexplored. As the concept of the digital therapeutic alliance (DTA) gains traction, a deeper understanding of subjective relationship-building processes is essential to inform the design of more effective digital mental health interventions. This study aimed to investigate how people subjectively perceive and develop relationships with mental health chatbots over time. We sought to identify key experiential dimensions and interactional dynamics that facilitate or hinder the formation of such bonds, contributing to the evolving conceptualization of the DTA. We conducted a 4-week short-term longitudinal diary study with 26 adult participants who interacted with two widely available mental health chatbots (Woebot and Wysa). Data were collected through weekly surveys, conversation screenshots, and semistructured interviews. A reflexive thematic analysis was used to identify recurring themes and interpret the emotional, communicative, and contextual factors shaping participants' relational experiences with the chatbots. A total of 18 participants reported forming a bond or light bond with at least one chatbot. Interview narratives revealed three relational categories: Bond (clear emotional connection), Light Bond (tentative or partial connection), and No Bond (absence of connection). Both participants with lower and higher psychological well-being (based on the World Health Organization-Five Well-Being Index scores) reported forming such relationships, suggesting that bonding capacity is not strictly dependent on mental health status. Thematic analysis identified six key themes that explain why people did or did not form bonds: the desire to lead or be led in conversation, alignment between preferred style of self-expression and accepted inputs, expectations for caring and nurturing from the chatbot, perceived effectiveness of the chatbot's advice and proposed activities, appreciation for colloquial communication, and valuing a private and nonjudgmental conversation. Our findings provide empirical insight into how people interpret and engage in relational processes with mental health chatbots, advancing the theoretical foundation of the DTA. Rather than favoring one design style, our analysis highlights the importance of alignment between preferences and the chatbot's interaction style and conversational role. Participants' initial expectations around empathy and trust also shaped how relationships developed. Drawing on these insights, we suggest that chatbots may better support early therapeutic relationships by blending emotional support with relevant guidance, allowing flexible input methods, and maintaining continuity through context-aware responses. These features may enhance their therapeutic value and foster stronger relationships.

Multimodal smartphone sensor data provide rich insights into real-world behavioural patterns associated with anxiety and depression symptoms. We propose that variability in daily routines across life aspects may act as a personalised digital marker, linking behaviours to self-reported affective states. Using non-negative matrix factorisation (NMF), we decomposed mobile sensing data into individual-specific routines and their weekly variability. Generalised linear models (GLMs) were built per individual to associate variability from specific sensing categories with anxiety or depression states. Using a large language model (LLM; GPT-4o), we translated the modelling results into more accessible language that potentially helps individuals engage with their own routine phenotypes for self-regulation insights. Population-level grouping of GLMs revealed significant between-group differences in mental health measures. The LLM output shows a potential to uncover personalised behavioural patterns and support self-understanding of mood-related drivers. This approach may also inform group-based interventions by identifying individuals likely to benefit from shared treatment strategies based on routine similarity.

Pulmonary rehabilitation (PR) is vital in the management of chronic respiratory disorders (CRDs) although uptake, attendance and completion are poor. Differing models of delivering PR are emerging in an attempt to increase the uptake and completion of this intervention. This study aimed to evaluate participant rate of attendance and completion of PR when given a preference regarding model of delivery (centre-based and mPR). Secondary aims were to evaluate the factors affecting patient preference for model of delivery and determine whether mPR is non-inferior to centre-based PR in health outcomes. A multi-centre non-inferiority preference based clinical trial in Auckland, New Zealand. Participants with a CRD referred for PR were offered the choice of centre-based or mHealth PR (mPR). The primary outcome was completion rate of chosen intervention. A total of 105 participants were recruited to the study with 67 (64%) preferring centre-based and 38 (36%) mPR. The odds of completing the PR programme were higher in the centre-based group compared to mPR (odds ratio 1.90 95% CI [0.83-4.35]). Participants opting for mPR were significantly younger (p = 0.002) and significantly more likely to be working (p = 0.0001). Results showed that mPR was not inferior to centre-based regarding changes in symptom scores (CAT) or time spent in sedentary behaviour (SBQ). When services were forced to transition to telehealth services during COVID-19 restrictions, the attendance and completion rates were higher with telephone calls and video conferencing compared to mPR - suggesting that synchronous interpersonal interactions with clinicians may facilitate the best attendance and completion rates. When offered the choice of PR delivery method, the majority of participants preferred centre-based PR and this facilitated the best completion rates. mPR was the preferred choice for younger, working participants suggesting that mPR may offer a viable alternative to centre-based PR for some participants, especially younger, employed participants.

Although there is considerable historical literature describing the social and economic impact of drought on the prairies in the 1930s, little has been written about the challenges presented by drought in more contemporary times. The drought of 2001-02 was, for example, the most recent large-area, intense, and prolonged drought in Canada and one of Canada's most costly natural disasters in a century. Vulnerability and Adaptation to Drought describes the impacts of droughts and the adaptations made in prairie agriculture over recent decades. These adaptations have enhanced the capacity of rural communities to withstand drought. However, despite the high levels of technical adaptation that have occurred, and the existing human capital and vibrant social and information networks, agricultural producers in the prairie region remain vulnerable to severe droughts that last more than a couple of years. Research findings and projections suggest that droughts could become more frequent, more severe, and of longer duration in the region over the course of the 21st century. This book provides insights into the conditions generating these challenges and the measures required to reduce vulnerability of prairie communities to them. Developing greater understanding of the social forces and conditions that have contributed to enhanced resilience, as well as those which detract from successful adaptation, is a principal theme of the book. To that end, the book examines drought through an interdisciplinary lens encompassing climate science and the social sciences. Two of the chapters are based on the drought experiences of other countries in order to provide a comparative assessment.

Background Ward rounds are an important and ubiquitous element of hospital care with a history extending well over a century. Although originally intended as a means of educating medical trainees and junior doctors, over time they have become focused on supporting clinical practice. Surprisingly, given their ubiquity and importance, they are under-researched and inadequately understood. This study aims to contribute knowledge in human reasoning within medical teams, meeting a pressing need for research concerning the reasoning occurring in rounds. Methods The research reported here aimed to improve the understanding of ward round reasoning by conducting a critical realist case study exploring the collaborative group reasoning mechanisms in the ward rounds of two hospitals in Victoria, Australia. The data collection involved observing rounds, interviewing medical practitioners and holding focus group meetings. Results Nine group reasoning mechanisms concerning sharing, agreeing and recording information in the categories of information accumulation, sense-making and decision-making were identified, together forming a program theory of ward round reasoning. In addition, themes spanning across mechanisms were identified, further explaining ward round reasoning and suggesting avenues for future exploration. Themes included the use of various criteria, tensions involving mechanisms, time factors, medical roles and hierarchies. Conclusions This paper contributes to the literature by representing rounds in a manner that strengthens understanding of the form of the group reasoning occurring within, thus supporting theory-based evaluation strategies, redesigned practices and training enhancements.