Ritu Trivedi

Introduction: Atrial fibrillation (AF), a common arrhythmia, is associated with impaired quality of life and increased stroke risk and mortality. Clinical guidelines recommend leveraging digital technologies to support patient education and AF self-management. Conversational artificial intelligence (AI) technologies may support patient engagement with self-management by enabling human-like conversations. This study aims to evaluate the effectiveness of a conversational AI intervention (Conversational artificial intelligence HeAlth supporT in Atrial Fibrillation Self-Management (CHAT-AF-S)) in improving quality of life in patients with AF. Methods and analysis: CHAT-AF-S is a 3-month randomised controlled trial with 1:1 allocation and embedded process evaluation. We will randomise 480 adults (aged 18 years and older) with documented AF to the CHAT-AF-S intervention or usual care. Primary outcome is the Atrial Fibrillation Effect on QualiTy-of-life overall score. We will follow the intention-to-treat principles and data analysts will be blinded. Intervention participants will be invited to complete a user experience survey and take part in an interview to explore the feasibility, acceptability, perceived use and barriers and enablers to implementing the intervention. Qualitative data will be analysed thematically. Ethics and dissemination: Ethics approval was obtained from the Western Sydney Local Health District Human Ethics Research Committee (2023/ETH00765). Written and informed consent will be obtained from all study participants before commencing any study procedures. Results will be disseminated via peer-reviewed publications and presentations at international conferences. Trial registration number: Australian New Zealand Clinical Trials Registry (registration number: ACTRN12623000850673).

Background: Continuous adhesive patch electrocardiographic (ECG) wearables are increasingly prescribed. Patient experience with these devices can influence adherence, but research in this area is limited. This study aimed to explore the perceptions and experiences of patients receiving wearable cardiac monitoring technology as part of their routine care through the lens of treatment burden. Methods: This was a qualitative study with semi-structured phone interviews conducted between February and May 2024. We recruited participants from primary care and outpatient clinics using maximum variation sampling to ensure diversity in sex, ethnicity, and education levels. Interviews were audio-recorded, transcribed, and analysed using reflexive thematic analysis. Results: Sixteen participants (mean age 51 years, 63% female) were interviewed (average duration: 33 minutes). Three themes were developed: 1) ?Experience using the device: Burden vs Ease of Use?, which captured participants? perceptions of how easily they could integrate the device in their daily lives; 2) ?Individual variability in responses to ECG self-monitoring? covered participants? emotional and cognitive response to knowing their heart rhythm was monitored; and 3) ?The care process shapes patient experiences? reflected support preferences during the set-up and monitoring period and the uncertainty regarding timely clinical and device feedback. Conclusions: Patients valued cardiac wearables for facilitating diagnosis and felt reassured knowing they were clinically monitored. However, gaps in information provided to patients seemed to cause anxiety for some participants. These concerns could be mitigated through clearer clinician communication and patient education at the time of prescription.

Background: The HeartHealth program is a 6-month SMS text messaging–based support program offered to patients with a recent cardiovascular hospitalization or recent cardiovascular clinic visit in Western Sydney, Australia. Its customized content focuses on cardiovascular risk factors, lifestyle, treatments, and general heart health information. Objective: This study aimed to evaluate the implementation of the HeartHealth program. Methods: A mixed methods study was conducted assessing program reach, effectiveness, implementation, and maintenance using program data, participant feedback surveys, and staff focus group discussions. Consecutive adult patients who had attended cardiology clinics or had been discharged from cardiology hospitalization at Westmead Hospital, between April 2020 and April 2024, were included in the analysis. Content analysis was used to interpret the qualitative data. Results: A total of 23,095 patients were invited, 8804 (38.1%) enrolled into the program, and 7964 out of 8804 (90.5%) completed the 6-month duration. Participants enrolled in the HeartHealth program had a mean age of 58.6 years, 60.3% (5302/8788) were male, and 62.4% (5382/8624) were recruited from an outpatient clinic setting. A total of 851,058 SMS text messages were sent, with 99.41% (846,009/851,058) delivered successfully. A total of 3533 out of 7964 (44.4% of program completers) participants completed the postintervention survey, and 4 HeartHealth staff members participated in a focus group discussion. Among the participants who completed the survey, 60.5% (2137/3533) reported that the program improved the healthiness of their diet, 53.6% (1894/3533) reported improved physical activity levels, and 56.1% (1982/3533) reported that it helped remind them to take their medications. Content analysis of participant feedback identified that the program was effective in prompting participants to change their diet, providing emotional support, reminding them of the importance of behavior change, improving their confidence in managing their health, and keeping participants focused. Key barriers included limited personalization, language options, and SMS text messaging scheduling flexibility. Recommended adaptations focused on enhancing personalization, greater engagement by local clinical teams, and expanding program dissemination. Conclusions: The program had a broad reach, translated to improved patient-reported health behaviors, and provided participants with needed support at low cost and low resource requirements. This analysis highlights the successful implementation and scalability of the HeartHealth program and provides key learnings for health systems that are looking to implement similar programs in the future.

Introduction: Atrial fibrillation (AF), a common arrhythmia, is associated with impaired quality of life (QoL) and increased stroke risk and mortality. Clinical guidelines recommend leveraging digital technologies to support patient education and AF self-management. Conversational artificial intelligence (AI) technologies may support patient engagement with self-management by enabling human-like conversations. This study aims to evaluate the effectiveness of a conversational AI intervention (Conversational HeAlth supporT in Atrial Fibrillation Self-Management - CHAT-AF-S) in improving QoL in patients with AF. Methods and analysis: CHAT-AF-S is a 3-month randomised controlled trial with 1:1 allocation and embedded process evaluation. We will randomise 480 adults (18 years of age and older) with documented AF to the CHAT-AF-S intervention or usual care. Primary outcome is the Atrial Fibrillation Effect on QualiTy-of-life (AFEQT) overall score. We will follow intention-to-treat principles and data analysts will be blinded. Intervention participants will be invited to complete a user experience survey and take part in an interview to explore the feasibility, acceptability, perceived utility, and barriers and enablers to implementing the intervention. Qualitative data will be analysed thematically. Ethics and dissemination: Ethics approval was obtained from the Western Sydney Local Health District Human Ethics Research Committee (2023/ETH00765). Written and informed consent will be obtained from all study participants before commencing any study procedures. Results will be disseminated via peer-reviewed publications and presentations at international conferences. Declaration of Interests: All investigators report nil conflicts of interest. Data Availability: The data that supports this project are available from the corresponding author upon reasonable request. Trial registration: Australian New Zealand Clinical Trials Registry ACTRN12623000850673 https://anzctr.org.au/Trial/Registration/TrialReview.aspx?id=386249

Background: Young adult hypertension increases risk of future cardiovascular disease (CVD) but is often overlooked. We aimed to identify opportunities to reduce blood pressure (BP) among younger adults by determining the drivers of hypertension detection, treatment and control in young adults in Australia. Methods: Cross-sectional analysis of adults aged 18–39 years using nationally representative 2017-18 Australian National Health Survey data. Descriptive analyses were performed to determine hypertension prevalence, detection, treatment and control by sex, health services interaction, geographical location and socioeconomic status, with subgroup analyses among those with co-existing cardiometabolic risk factors. Hypertension was defined as self-reported diagnosis, treatment, and/or measured BP≥140/90mmHg. Detection was defined as self-reported hypertension, treatment as taking ≥1 antihypertensive class, and control as BP<140/90mmHg. Results: The survey sample (n=5,217) was representative of N=7,533,625 adults aged 18–39 years (49.9% women). Hypertension prevalence was 11.2% (95% CI: 11.1% to 11.2%). Among those with hypertension, detection was 13.5%, treatment was 6.5% and control was 4.3%. These rates were influenced by factors related to healthcare access, for example, young adults who had a recent BP check and resided in major cities had higher rates than their counterparts. Conclusions: One in ten young Australians adult have hypertension, yet only 13.5% are detected. Differences in detection, treatment and control rates were partly explained by factors related to healthcare access. There is a need to better detect and manage high BP in young adults, potentially by developing tailored approaches that are specific to different contexts and ultimately supporting prevention of CVD in later life.

Background: Effective education improves hypertension self-management but is not routinely delivered, and there is limited guidance to overcome barriers to support delivery in primary care. This study aimed to use co-design and an implementation science framework to develop and prioritize implementation strategies for the delivery of patient education to support blood pressure (BP) management across the hypertension journey in primary care. Methods: A two-stage, co-design study using implementation science frameworks. Stage 1 involved mapping interviews with patients to the Consolidated Framework for Implementation Research and Expert Recommendations for Implementing Change to generate strategies to improve patient education for BP management along the care journey (diagnosis, treatment initiation, long-term management). Stage 2 involved a three-hour multi-disciplinary workshop to refine, rank and prioritize these strategies using patient personas, clinical scenarios, and participatory voting. Finally, the strategies were reviewed by consumer advisors with lived experience of BP management to ensure suitability and acceptability. Results: Six context-specific strategy prompts aligned to stages of the hypertension journey were paired with patient personas and clinical scenarios to guide workshop discussions. Twenty-one participants (n=2 general practitioners, n=4 nurses, n=6 pharmacists, n=10 industry professionals) attended the workshop. Ultimately, six refined strategies included: (1) in-pharmacy education for suitable device purchase using a structured protocol; (2) general practitioner consultations about home BP measurement with nurse follow-up; (3) nurse-led drop-in group education sessions; (4) nurse-led group education on interpreting readings with general practitioner follow-up; (5) nurse-led lifestyle planning with allied health referrals; and (6) pharmacist-delivered BP measurement and medication review. Pharmacy-based strategies (1) and (6) ranked highest for feasibility and impact, and cost-effectiveness, respectively. Conclusion: We identified evidence-based multidisciplinary strategies to improve the delivery of patient education for hypertension, co-designed with clinicians. Future work should focus on evaluating their implementation in real-world primary care settings. KEY MESSAGES: What is already known on this topic: Patient education improves hypertension self-management, yet it is rarely delivered consistently in primary care and there is limited guidance on how to overcome practical barriers to implementing education strategies across the care journey. What this study adds: This study co-designed and prioritized six multidisciplinary, context-specific strategies to deliver patient education for blood pressure management at diagnosis, treatment initiation, and long-term care, using implementation science frameworks. How this study might affect research, practice or policy: These strategies offer a practical roadmap for embedding structured education into routine primary care by leveraging existing workforce and infrastructure, aligning with funded models, and supported by clinician incentives to enhance uptake.

Background.: Only one in five people with hypertension have their blood pressure (BP) controlled (<140/90 mmHg). Patient education improves BP control, yet there is limited guidance to adapt to individuals’ evolving needs across the hypertension management journey. This study aimed to identify key behavioural barriers and enablers to patient education throughout the hypertension management journey, which were synthesised into patient personas to support translation and practical application. Methods.: Qualitative interviews with adults (≥18) who self-monitor BP, and primary care providers involved in hypertension care. Interviews explored the experience of accessing and delivering patient education for BP measurement and management. Using framework analysis, patient personas were developed in three steps: 1) thematic analysis of patient interviews to identify key barriers and enablers to hypertension patient education and mapping the identified barriers and enablers to key time-points in the hypertension journey (diagnosis, treatment initiation, long-term management), 2) clustering behavioural factors via the capability, opportunity, motivation-behaviour model, which were then synthesised as preliminary patient personas and refined using practitioner interviews and, 3) validation by consumer consultation feedback sessions. Results.: Patients (n=27) and practitioners (n=12; n=4 general practitioners, n=4 nurses, and n=4 pharmacists) were aged 18-60 years (52% of patients and 100% of practitioners fell within this age range). Several behavioural barriers to patient education for hypertension management included patient overwhelm, inconsistent guidance provided by health professionals, and perceived patient disengagement, while a strong desire to self-manage was a consistent enabler. Six clusters of behavioural barriers and enablers were synthesised as patient personas, capturing the factors that shape education needs and experiences across the hypertension management journey. Conclusions.: The patient personas highlight opportunities for tailored patient education strategies through the development of patient personas. Practice implications.: The patient personas provide a practical tool for designing person-centred interventions in primary care.

Background: Patient education and self-management support are critical for atrial fibrillation (AF) management. Conversational artificial intelligence (AI) has the potential to provide interactive and personalized support, but has not been evaluated in patients with AF. Objective: This study aimed to evaluate the feasibility of a conversational AI intervention to support patients with AF postdischarge. Methods: This was a single-blinded, 4:1-parallel–randomized controlled trial with process evaluation of feasibility and engagement. The primary outcome was the change in Atrial Fibrillation Effect on Quality-of-Life (AFEQT) questionnaire total score between groups. Patients with AF (18 y and older) were recruited postdischarge from Westmead Hospital cardiology services and randomized to receive either the intervention or usual care. The 6-month intervention consisted of fully automated conversational AI phone calls (with speech recognition and natural language processing) that regularly assessed patient health and symptoms and provided self-management support and education. These phone calls were supplemented with an online survey (sent via text message or email) containing replicated call content when participants could not be reached after 3 call attempts. If participant responses were concerning (eg, poor overall health, low medication confidence, and high symptom burden), they would be followed up with an ad hoc phone call and directed to clinical care if required. A semipersonalized education website was also available as part of the intervention, and participants were encouraged weekly (nudges delivered via text messages or emails) to visit it. Results: A total of 103 patients (mean age, 63.7 y, SD 11.2 y; n=72, 70% male) were randomized (82 to the intervention); the target sample size was 385. The difference in the AFEQT total score was nonsignificant (adjusted mean difference 2.08, 95% CI −7.79 to 11.96; P=.46). An exploratory prepost comparison revealed an improvement in total AFEQT score in the intervention group only (baseline: 69.9, 95% CI 64.4 to 75.5; 6 months: 79.9, 95% CI 74.9 to 84.8; P=.01). Participants completed 4 of 7 outreaches on average, and 88.4% (304/344) of completed outreaches were reported as useful. Conclusions: This proof-of-concept study demonstrates the feasibility of conversational AI in supporting patients with chronic conditions postdischarge. Intervention participants had improvement in their atrial fibrillation quality of life, though the forced shortening of the evaluation was unable to demonstrate a significant difference between groups.

Background: Conversational artificial intelligence (AI) allows for engaging interactions, however, its acceptability, barriers, and enablers to support patients with atrial fibrillation (AF) are unknown. Objective: This work stems from the Coordinating Health care with AI–supported Technology for patients with AF (CHAT-AF) trial and aims to explore patient perspectives on receiving support from a conversational AI support program. Methods: Patients with AF recruited for a randomized controlled trial who received the intervention were approached for semistructured interviews using purposive sampling. The 6-month intervention consisted of fully automated conversational AI phone calls (with speech recognition and natural language processing) that assessed patient health and provided self-management support and education. Interviews were recorded, transcribed, and thematically analyzed. Results: We conducted 30 interviews (mean age 65.4, SD 11.9 years; 21/30, 70% male). Four themes were identified: (1) interaction with a voice-based conversational AI program (human-like interactions, restriction to prespecified responses, trustworthiness of hospital-delivered conversational AI); (2) engagement is influenced by the personalization of content, delivery mode, and frequency (tailoring to own health context, interest in novel information regarding health, overwhelmed with large volumes of information, flexibility provided by multichannel delivery); (3) improving access to AF care and information (continuity in support, enhancing access to health-related information); (4) empowering patients to better self-manage their AF (encouraging healthy habits through frequent reminders, reassurance from rhythm-monitoring devices). Conclusions: Although conversational AI was described as an engaging way to receive education and self-management support, improvements such as enhanced dialogue flexibility to allow for more naturally flowing conversations and tailoring to patient health context were also mentioned. Trial Registration: Australian New Zealand Clinical Trials Registry ACTRN12621000174886; https://tinyurl.com/3nn7tk72 International Registered Report Identifier (IRRID): RR2-10.2196/34470

BACKGROUND Conversational artificial intelligence (AI) allows for engaging interactions, however, its acceptability, barriers, and enablers to support patients with atrial fibrillation (AF) are unknown. OBJECTIVE This work stems from the Coordinating Health care with AI–supported Technology for patients with AF (CHAT-AF) trial and aims to explore patient perspectives on receiving support from a conversational AI support program. METHODS Patients with AF recruited for a randomized controlled trial who received the intervention were approached for semistructured interviews using purposive sampling. The 6-month intervention consisted of fully automated conversational AI phone calls (with speech recognition and natural language processing) that assessed patient health and provided self-management support and education. Interviews were recorded, transcribed, and thematically analyzed. RESULTS We conducted 30 interviews (mean age 65.4, SD 11.9 years; 21/30, 70% male). Four themes were identified: (1) interaction with a voice-based conversational AI program (human-like interactions, restriction to prespecified responses, trustworthiness of hospital-delivered conversational AI); (2) engagement is influenced by the personalization of content, delivery mode, and frequency (tailoring to own health context, interest in novel information regarding health, overwhelmed with large volumes of information, flexibility provided by multichannel delivery); (3) improving access to AF care and information (continuity in support, enhancing access to health-related information); (4) empowering patients to better self-manage their AF (encouraging healthy habits through frequent reminders, reassurance from rhythm-monitoring devices). CONCLUSIONS Although conversational AI was described as an engaging way to receive education and self-management support, improvements such as enhanced dialogue flexibility to allow for more naturally flowing conversations and tailoring to patient health context were also mentioned. CLINICALTRIAL Australian New Zealand Clinical Trials Registry ACTRN12621000174886; https://tinyurl.com/3nn7tk72 INTERNATIONAL REGISTERED REPORT RR2-10.2196/34470