A systematic review of artificial intelligence chatbots for promoting physical activity, healthy diet, and weight loss

Characteristics and perceived suitability of artificial intelligence-driven sports coaches: a pilot study on psychological and perceptual factors

Introduction

Access to human sports coaches is often limited by financial and logistical barriers, leading to disparities in the availability of high-quality coaches. Artificial intelligence (AI) coaches powered by Large Language Models (LLMs) might offer promising means to augment human coaches by supporting or autonomously performing specific coaching tasks within targeted domains. This study investigated AI coaches' associated attributes and perceived suitability in training contexts by addressing three primary questions: (A) Which attributes on a semantic differential scale effectively describe the dimensions of AI coaches in the context of training support? (B) Do participants with varying perceptions of AI suitability for their training practices differ in the attributes they associate with AI coaches, as measured by a semantic differential scale? (C) Do different individual achievement motives (AMS)-Sport influence the perception of AI coaches' suitability?

Methods

The study comprised two parts. The first involves the development of a semantic differential scale to quantify the perceptions of AI coaches and an analysis of how different AI coach personalities, designed using an LLM, are perceived concerning their training suitability and how achievement motives influence these perceptions. Six distinct AI coach personalities were created to reflect the diverse coaching styles.

Results

Factor analysis revealed four key dimensions of AI coach attributes: knowledge transfer, goal-oriented persistence, appreciation and recognition, and motivational support. The results indicated that coaches rated as more suitable exhibited supportive traits, such as motivation and goal orientation, compared to those rated less suitable. Participants with a lower Fear of Failure (FoF) also tended to rate AI coaches as more appropriate.

Conclusion

These findings underscore the importance of aligning AI coaches' characteristics with their motivational profiles to improve user engagement.

Perspective on the ethics of AI at the intersection of nutrition and behaviour change

Artificial intelligence (AI) has emerged as a powerful tool, that has the potential to impact society on multiple levels. Increased adoption as well as employment of AI in new product development and business processes have led to heightened interest and optimism on one hand, whilst increasing fears of potential negative societal consequences on the other. The ethics of AI has subsequently become a topical issue for academics, industry players, health practitioners and regulators, who have a goal and responsibility to protect the public and limit widening inequality. Despite the publication of numerous AI ethical frameworks, guidelines and regulations, none have specifically focused on nutrition and behaviour change. Advances in technology, including AI and machine learning, have opened up novel ways to deliver personalization to guide individuals towards healthier behaviours or to manage their conditions. This perspective synthesizes the key topics that intersect in nutrition and behaviour change where AI is leveraged to provide personalized advice. We propose a 7-pillar framework to guide the development of ethical and transparent AI solutions to build consumer and practitioner trust.

Artificial Intelligence in the Management of Heart Failure

Artificial intelligence (AI) has the potential to revolutionize the management of heart failure. AI-based tools can guide the diagnosis and treatment of known risk factors, identify asymptomatic structural heart disease, improve cardiomyopathy diagnosis and symptomatic heart failure treatment, and uncover patients transitioning to advanced disease. By integrating multimodal data, including omics, imaging, signals, and electronic health records, state-of-the-art algorithms allow for a more tailored approach to patient care, addressing the unique needs of the individual. The past decade has led to the development of numerous AI solutions targeting each aspect of the heart failure syndrome. However, significant barriers to implementation remain and have limited clinical uptake. Data-privacy concerns, real-world model performance, integration challenges, trust in AI, model governance, and concerns about fairness and bias are some of the topics requiring additional research and the development of best practices. This review highlights progress in the use of AI to guide the diagnosis and management of heart failure while underscoring the importance of overcoming key implementation challenges that are currently slowing progress.

Prediction of Metabolic Parameters of Diabetic Patients Depending on Body Weight Variation Using Machine Learning Techniques

Background/Objectives: Obesity is a major risk factor for diabetes mellitus, a metabolic disease characterized by elevated fasting blood glucose and glycosylated hemoglobin levels. Predicting the percentage and absolute variations in key medical parameters based on weight changes can help patients stay motivated to lose weight and assist doctors in making informed lifestyle and treatment recommendations. This study aims to assess the extent to which weight variation influences the absolute and percentage changes in various clinical parameters. Methods: The dataset includes medical records from patients in Bucharest hospitals, collected between 2012 and 2016. Several machine learning models, namely linear regression, polynomial regression, Gradient Boosting, and Extreme Gradient Boosting, were employed to predict changes in medical parameters as a function of body weight variation. Model performance was evaluated using Mean Squared Error, Mean Absolute Error, and R2 score. Results: Almost all models demonstrated promising predictive performance. Quantitative predictions were made for each parameter, highlighting the relationship between weight loss and improvements in clinical indicators. Conclusions: Weight loss led to significant improvements in dysglycemia, dyslipidemia, inflammation, uric acid levels, liver enzymes, thyroid hormones, and blood pressure, with reductions ranging from 5% to 30%, depending on the parameter.

Unraveling MASLD: The Role of Gut Microbiota, Dietary Modulation, and AI-Driven Lifestyle Interventions

Gut microbiota has a crucial role in the pathophysiology of metabolic-associated steatotic liver disease (MASLD), influencing various metabolic mechanisms and contributing to the development of the disease. Dietary interventions targeting gut microbiota have shown potential in modulating microbial composition and mitigating MASLD progression. In this context, the integration of multi-omics analysis and artificial intelligence (AI) in personalized nutrition offers new opportunities for tailoring dietary strategies based on individual microbiome profiles and metabolic responses. The use of chatbots and other AI-based health solutions offers a unique opportunity to democratize access to health interventions due to their low cost, accessibility, and scalability. Future research should focus on the clinical validation of AI-powered dietary strategies, integrating microbiome-based therapies and precision nutrition approaches. Establishing standardized protocols and ethical guidelines will be crucial for implementing AI in MASLD management, paving the way for a more personalized, data-driven approach to disease prevention and treatment.

Artificial intelligence-powered social robots for promoting physical activity in older adults: A systematic review

BACKGROUND

The aging global population necessitates innovative strategies to enhance older adults' health and quality of life. Physical activity (PA) is crucial for healthy aging, yet many older adults struggle to exercise regularly. Artificial intelligence (AI)-powered social robots offer an interactive, engaging, and personalized solution to promote PA among this demographic. This systematic review investigates the role of AI-powered social robots in encouraging PA in older adults.

METHODS

We conducted a systematic literature search in databases including PubMed, IEEE Xplore, Scopus, Cochrane Library, and Web of Science, focusing on studies published until February 2024. We included peer-reviewed articles reporting empirical findings on designing, implementing, and evaluating AI-enabled social robots to promote PA among older adults. Studies were conducted in nursing homes, rehabilitation centers, community centers, and home environments.

RESULTS

A total of 19 studies were included in the review. Analysis reveals that AI-powered social robots effectively motivate older adults to engage in PAs, leading to increased exercise adherence, higher engagement levels, and extended training durations. Social robots have demonstrated effectiveness across various environments, including nursing homes, rehabilitation centers, community centers, home environments, and elder care facilities. In structured environments like nursing homes and rehabilitation centers, robots help maintain regular exercise routines, improving adherence and recovery outcomes. In community and elder care centers, robots promote PA and social engagement by facilitating group exercises and enhancing participation. In home environments, robots provide personalized support for daily activities, offering reminders and engagement, which fosters long-term activity engagement. User acceptance and satisfaction are high, with participants finding the robots engaging and enjoyable. Additionally, several studies indicate potential health benefits, such as improved medication adherence, better sleep patterns, and enhanced overall well-being. Nevertheless, additional research is imperative to address unresolved issues concerning the technological maintenance costs, design constraints, and adaptability of AI-powered social robots to specific user demographics.

CONCLUSION

AI-powered social robots play a promising role in promoting PA among older adults, enhancing their health, well-being, and independence. This review provides insights for researchers, designers, and healthcare professionals developing AI-enabled social robotic systems for older adults.

Current and Emerging Parenteral and Peroral Medications for Weight Loss: A Narrative Review

Obesity is a growing global health challenge, necessitating effective treatment options beyond lifestyle interventions. This narrative review explores established and emerging pharmacotherapies for weight management, including parenteral agents like Liraglutide, Semaglutide, Setmelanotide, and Tirzepatide, as well as peroral medications such as Phentermine, Phentermine/Topiramate, Bupropion/Naltrexone, Orlistat, and Metformin. Newer treatments like Cagrilintide and Bimagrumab show promise for enhancing weight loss outcomes. Parenteral GLP-1 receptor agonists demonstrate superior efficacy compared to traditional peroral medications, with gastrointestinal side effects being the most common. Artificial intelligence presents intriguing opportunities to enhance weight loss strategies; however, its integration into clinical practice remains investigational and requires rigorous clinical validation. While current anti-obesity medications deliver significant benefits, future research must determine the efficacy, safety, and cost-effectiveness of AI-driven approaches. This includes exploring how AI can complement combination therapies and tailor personalized interventions, thereby grounding its potential benefits in robust clinical evidence. Future directions will focus on integrating AI into clinical trials to refine and personalize obesity management strategies.

A Scoping Review of Artificial Intelligence for Precision Nutrition

With the role of artificial intelligence (AI) in precision nutrition rapidly expanding, a scoping review on recent studies and potential future directions is needed. This scoping review examines: 1) the current landscape, including publication venues, targeted diseases, AI applications, methods, evaluation metrics, and considerations of minority and cultural factors; 2) common patterns in AI-driven precision nutrition studies; and 3) gaps, challenges, and future research directions. Following the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) process, we extracted 198 articles from major databases using search keywords in 3 categories: precision nutrition, AI, and natural language processing. The extracted literature reveals a surge in AI-driven precision nutrition research, with ∼75% (n = 148) published since 2020. It also showcases a diverse publication landscape, with the majority of studies focusing on diet-related diseases, such as diabetes and cardiovascular conditions, while emphasizing health optimization, disease prevention, and management. We highlight diverse datasets used in the literature and summarize methodologies and evaluation metrics to guide future studies. We also emphasize the importance of minority and cultural perspectives in promoting equity for precision nutrition using AI. Future research should further integrate these factors to fully harness AI's potential in precision nutrition.

Still a Long Way to Go, the Potential of ChatGPT in Personalized Dietary Prescription, From a Perspective of a Clinical Dietitian

OBJECTIVE

Prominent large language models, such as OpenAI's Chat Generative Pre-trained Transformer (ChatGPT), have shown promising implementation in the field of nutrition. Special care should be taken when using ChatGPT to prescribe protein-restricted diets for kidney-impaired patients. The objective of the current study is to simulate a chronic kidney disease (CKD) patient and evaluate the capabilities of ChatGPT in the context of dietary prescription, with a focus on protein contents of the diet.

METHODS

We simulated a scenario involving a CKD patient and replicated a clinical counseling session that covered general dietary principles, dietary assessment, energy and protein recommendation, dietary prescription, and diet customization based on dietary culture. To confirm the results derived from our qualitative observations, 10 colleagues were recruited and provided with identical dietary prescription prompts to run the process again. The actual energy and protein levels of the given meal plans were recorded and the difference from the targets were compared.

RESULTS

ChatGPT provides general principles overall aligning with best practices. The recommendations for energy and protein requirements of CKD patients were tailored and satisfactory. It failed to prescribe a reliable diet based on the target energy and protein requirements. For the quantitative analysis, the prescribed energy levels were generally lower than the targets, ranging from -28.9% to -17.0%, and protein contents were tremendously higher than the targets, ranging from 59.3% to 157%.

CONCLUSION

ChatGPT is competent in offering generic dietary advice, giving satisfactory nutrients recommendations and adapting cuisines to different cultures but failed to prescribe nutritionally accurate dietary plans for CKD patients. At present, patients with strict protein and other particular nutrient restrictions are not recommended to rely on the dietary plans prescribed by ChatGPT to avoid potential health risks.

Exploring Client Perceptions on Gaining Infant Feeding Information Through the Texas Women, Infants, and Children (WIC) Chatbot

The modernization of the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) program is a priority. The Texas WIC chatbot, Maya, streamlines client interactions through dialog-based responses. This qualitative study explored client capabilities, motivations, and opportunities for seeking nutrition information about breastfeeding, formula feeding, infant feeding safety, adequacy of infant feeding, and complementary feeding via a chatbot. A team conducted in-depth semi-structured interviews with Texas WIC clients (n = 19 women). All interviews were transcribed and subjected to a two-coder, four-phase process utilizing a theory-based codebook. Codes were compiled and thematically categorized. Identified themes included (1) motivations through necessity or resource availability, (2) client capabilities and Maya usability, and (3) opportunities for connection, support, and encouragement. Texas WIC clients that participated in this study expressed motivations, capabilities, and opportunities to engage with nutrition information through Maya. They described Maya as a favorable resource for behavior changes, and a trusted source of nutrition information, citing the credibility of WIC and reliability of the chatbot. The findings may inform future research and development of public health chatbots. Additional research is required to explore how different factors such as language and technology usage may impact client capabilities, motivations, and opportunities to seek nutrition information with regard to infant feeding.

The Impact of Chatbot Response Strategies and Emojis Usage on Customers' Purchase Intention: The Mediating Roles of Psychological Distance and Performance Expectancy

Artificial intelligence (AI) chatbots have been widely adopted in customer service, playing a crucial role in improving service efficiency, enhancing user experience, and elevating satisfaction levels. Current research on the impact of chatbots on consumers' purchase decisions primarily focuses on linguistic communication features, with limited exploration into the non-verbal social cues employed by chatbots. By conducting three scenario-based experiments, this study investigates the mechanisms through which chatbot response strategies (proactive vs. reactive) and the use of emojis (yes vs. no) influence users' purchase intention. The findings suggest that proactive response strategies by chatbots are more effective in strengthening users' purchase intention compared to reactive strategies. Psychological distance and performance expectancy serve as significant mediators in this relationship. Additionally, the use of emojis moderates the effect of chatbot response strategies on psychological distance, while its moderating effect on performance expectancy is not significant. This study offers new insights into non-verbal social cues in chatbots, revealing the psychological mechanisms underlying the influence of chatbot response strategies on users' purchase decisions and contributing to the limited evidence on visual symbols as moderating factors. Furthermore, the findings provide practical recommendations for businesses on optimizing chatbot interaction strategies to enhance user experience.

Diet Quality and Caloric Accuracy in AI-Generated Diet Plans: A Comparative Study Across Chatbots

Background/Objectives: With the rise of artificial intelligence (AI) in nutrition and healthcare, AI-driven chatbots are increasingly recognised as potential tools for generating personalised diet plans. This study aimed to evaluate the capabilities of three popular chatbots-Gemini, Microsoft Copilot, and ChatGPT 4.0-in designing weight-loss diet plans across varying caloric levels and genders. Methods: This comparative study assessed the diet quality of meal plans generated by the chatbots across a calorie range of 1400-1800 kcal, using identical prompts tailored to male and female profiles. The Diet Quality Index-International (DQI-I) was used to evaluate the plans across dimensions of variety, adequacy, moderation, and balance. Caloric accuracy was analysed by calculating percentage deviations from requested targets and categorising discrepancies into defined ranges. Results: All chatbots achieved high total DQI-I scores (DQI-I > 70), demonstrating satisfactory overall diet quality. However, balance sub-scores related to macronutrient and fatty acid distributions were consistently the lowest, showing a critical limitation in AI algorithms. ChatGPT 4.0 exhibited the highest precision in caloric adherence, while Gemini showed greater variability, with over 50% of its diet plans deviating from the target by more than 20%. Conclusions: AI-driven chatbots show significant promise in generating nutritionally adequate and diverse weight-loss diet plans. Nevertheless, gaps in achieving optimal macronutrient and fatty acid distributions emphasise the need for algorithmic refinement. While these tools have the potential to revolutionise personalised nutrition by offering precise and inclusive dietary solutions, they should enhance rather than replace the expertise of dietetic professionals.

Enhancing physical activity through a relational artificial intelligence chatbot: A feasibility and usability study

Objective

This study presents a pilot randomized controlled trial to assess the usability, feasibility, and initial efficacy of a mobile app-based relational artificial intelligence (AI) chatbot (Exerbot) intervention for increasing physical activity behavior.

Methods

The study was conducted over a 1-week period, during which participants were randomized to either converse with a baseline chatbot without relational capacity (control group) or a relational chatbot using social relational communication strategies. Objectively measured physical activity data were collected using smartphone pedometers.

Results

The study was feasible in enrolling a sample of 36 participants and with a 94% retention rate after 1 week. Daily engagement rate with the AI chatbot reached over 88% across the groups. Findings revealed that the control group experienced a significant decrease in steps on the final day, whereas the group interacting with the relational chatbot maintained their step counts throughout the study period. Importantly, individuals who engaged with the relational chatbot reported a stronger social bond with the chatbot compared to those in the control group.

Conclusions

Leveraging AI chatbot and the relationship-building capabilities of AI holds promise in the development of cost-effective, accessible, and sustainable behavior change interventions. This approach may benefit individuals with limited access to conventional in-person behavior interventions.

Clinical trial registrations

ClinicalTrials.gov; NCT05794308; https://clinicaltrials.gov/ct2/show/NCT05794308.

Beyond the Screen: The Impact of Generative Artificial Intelligence (AI) on Patient Learning and the Patient-Physician Relationship

The rapid advancement of generative artificial intelligence (AI), exemplified by tools like ChatGPT, has transformed the healthcare landscape, particularly in patient education and the patient-physician relationship. While AI in healthcare has traditionally focused on data analysis and predictive analytics, the rise of generative AI has introduced new opportunities and challenges in patient interactions, information dissemination, and the overall dynamics of patient care. This narrative review explores the dual impact of generative AI on healthcare, examining its role in enhancing patients' understanding of medical conditions, promoting self-care, and supporting healthcare decision-making. Additionally, the review considers the potential risks, such as the erosion of trust in the patient-physician relationship and the spread of misinformation, while addressing ethical implications and the future integration into clinical practice. A comprehensive literature search, conducted using databases like PubMed, MEDLINE, Scopus, and Google Scholar, included studies published between 2010 and 2024 that discussed the role of generative AI in patient education, engagement, and the patient-physician relationship. Findings show that generative AI tools significantly enhance patient health literacy by making complex medical information more accessible, personalized, and interactive, thus empowering patients to take a more active role in managing their healthcare. However, risks such as misinformation and the undermining of the patient-physician relationship were also identified, with case studies highlighting both positive and negative outcomes. To fully harness the potential of AI in healthcare, it is essential to integrate these tools thoughtfully, ensuring they complement rather than replace the personalized care provided by physicians. Future research should focus on addressing ethical challenges and optimizing AI's role in clinical practice to maintain trust, communication, and the quality of patient care.

Transforming healthcare with chatbots: Uses and applications-A scoping review

Purpose

The COVID-19 pandemic has intensified the demand and use of healthcare resources, prompting the search for efficient solutions under budgetary constraints. In this context, the increasing use of artificial intelligence and telemedicine has emerged as a key strategy to optimize healthcare delivery and resources. Consequently, chatbots have emerged as innovative tools in various healthcare fields, such as mental health and patient monitoring, offering therapeutic conversations and early interventions. This systematic review aims to explore the current state of chatbots in the healthcare sector, meticulously evaluating their effectiveness, practical applications, and potential benefits.

Methods

This systematic review was conducted following PRISMA guidelines, utilizing three databases, including PubMed, Web of Science, and Scopus, to identify relevant studies on the use and cost of chatbots in health over the past 5 years.

Results

Several articles were identified through the database search (n = 31). The chatbot interventions were categorized by similar types. The reviewed articles highlight the diverse applications of chatbot interventions in healthcare, including mental health support, medical information, appointment management, health education, lifestyle changes, and COVID-19 management, demonstrating significant potential across these areas.

Conclusion

Furthermore, there are challenges regarding the implementation of chatbots, compatibility with other systems, and ethical considerations that may arise in different healthcare settings. Addressing these issues will be essential to maximize the benefits of chatbots, mitigate risks, and ensure equitable access to these health innovations.

Rapid, Tailored Dietary and Health Education Through A Social Media Chatbot Microintervention: Development and Usability Study With Practical Recommendations

BACKGROUND

There is an urgent need to innovate methods of health education, which can often be resource- and time-intensive. Microinterventions have shown promise as a platform for rapid, tailored resource dissemination yet have been underexplored as a method of standardized health or dietary education; social media chatbots display unique potential as a modality for accessible, efficient, and affordable educational microinterventions.

OBJECTIVE

This study aims to provide public health professionals with practical recommendations on the use of social media chatbots for health education by (1) documenting the development of a novel social media chatbot intervention aimed at improving dietary attitudes and self-efficacy among South Asian American young adults and (2) describing the applied experiences of implementing the chatbot, along with user experience and engagement data.

METHODS

In 2023, the "Roti" chatbot was developed on Facebook and Instagram to administer a 4-lesson tailored dietary health curriculum, informed by formative research and the Theory of Planned Behavior, to 18- to 29-year-old South Asian American participants (recruited through social media from across the United States). Each lesson (10-15 minutes) consisted of 40-50 prescripted interactive texts with the chatbot (including multiple-choice and open-response questions). A preintervention survey determined which lesson(s) were suggested to participants based on their unique needs, followed by a postintervention survey informed by the Theory of Planned Behavior to assess changes in attitudes, self-efficacy, and user experiences (User Experience Questionnaire). This study uses a cross-sectional design to examine postintervention user experiences, engagement, challenges encountered, and solutions developed during the chatbot implementation.

RESULTS

Data from 168 participants of the intervention (n=92, 54.8% Facebook; n=76, 45.2% Instagram) were analyzed (mean age 24.5, SD 3.1 years; n=129, 76.8% female). Participants completed an average of 2.6 lessons (13.9 minutes per lesson) and answered an average of 75% of questions asked by the chatbot. Most reported a positive chatbot experience (User Experience Questionnaire: 1.34; 81/116, 69.8% positive), with pragmatic quality (ease of use) being higher than hedonic quality (how interesting it felt; 88/116, 75.9% vs 64/116, 55.2% positive evaluation); younger participants reported greater hedonic quality (P=.04). On a scale out of 10 (highest agreement), participants reported that the chatbot was relevant (8.53), that they learned something new (8.24), and that the chatbot was helpful (8.28). Qualitative data revealed an appreciation for the cheerful, interactive messaging of the chatbot and outlined areas of improvement for the length, timing, and scope of text content. Quick replies, checkpoints, online forums, and self-administered troubleshooting were some solutions developed to meet the challenges experienced.

CONCLUSIONS

The implementation of a standardized, tailored health education curriculum through an interactive social media chatbot displayed strong feasibility. Lessons learned from challenges encountered and user input provide a tangible roadmap for future exploration of such chatbots for accessible, engaging health interventions.

Lightening the Load: Generative AI to Mitigate the Burden of the New Era of Obesity Medical Therapy

Unlabelled

Highly effective antiobesity and diabetes medications such as glucagon-like peptide 1 (GLP-1) agonists and glucose-dependent insulinotropic polypeptide/GLP-1 (dual) receptor agonists (RAs) have ushered in a new era of treatment of these highly prevalent, morbid conditions that have increased across the globe. However, the rapidly escalating use of GLP-1/dual RA medications is poised to overwhelm an already overburdened health care provider workforce and health care delivery system, stifling its potentially dramatic benefits. Relying on existing systems and resources to address the oncoming rise in GLP-1/dual RA use will be insufficient. Generative artificial intelligence (GenAI) has the potential to offset the clinical and administrative demands associated with the management of patients on these medication types. Early adoption of GenAI to facilitate the management of these GLP-1/dual RAs has the potential to improve health outcomes while decreasing its concomitant workload. Research and development efforts are urgently needed to develop GenAI obesity medication management tools, as well as to ensure their accessibility and use by encouraging their integration into health care delivery systems.

Perceptions and expectations of an artificially intelligent physical activity digital assistant - A focus group study

Artificially intelligent physical activity digital assistants that use the full spectrum of machine learning capabilities have not yet been developed and examined. This study aimed to explore potential users' perceptions and expectations of using such a digital assistant. Six 90-min online focus group meetings (n = 45 adults) were conducted. Meetings were recorded, transcribed and thematically analysed. Participants embraced the idea of a 'digital assistant' providing physical activity support. Participants indicated they would like to receive notifications from the digital assistant, but did not agree on the number, timing, tone and content of notifications. Likewise, they indicated that the digital assistant's personality and appearance should be customisable. Participants understood the need to provide information to the digital assistant to allow for personalisation, but varied greatly in the extent of information that they were willing to provide. Privacy issues aside, participants embraced the idea of using artificial intelligence or machine learning in return for a more functional and personal digital assistant. In sum, participants were ready for an artificially intelligent physical activity digital assistant but emphasised a need to personalise or customise nearly every feature of the application. This poses challenges in terms of cost and complexity of developing the application.

Health Professionals' Views on the Use of Conversational Agents for Health Care: Qualitative Descriptive Study

BACKGROUND

In recent years, there has been an increase in the use of conversational agents for health promotion and service delivery. To date, health professionals' views on the use of this technology have received limited attention in the literature.

OBJECTIVE

The purpose of this study was to gain a better understanding of how health professionals view the use of conversational agents for health care.

METHODS

Physicians, nurses, and regulated mental health professionals were recruited using various web-based methods. Participants were interviewed individually using the Zoom (Zoom Video Communications, Inc) videoconferencing platform. Interview questions focused on the potential benefits and risks of using conversational agents for health care, as well as the best way to integrate conversational agents into the health care system. Interviews were transcribed verbatim and uploaded to NVivo (version 12; QSR International, Inc) for thematic analysis.

RESULTS

A total of 24 health professionals participated in the study (19 women, 5 men; mean age 42.75, SD 10.71 years). Participants said that the use of conversational agents for health care could have certain benefits, such as greater access to care for patients or clients and workload support for health professionals. They also discussed potential drawbacks, such as an added burden on health professionals (eg, program familiarization) and the limited capabilities of these programs. Participants said that conversational agents could be used for routine or basic tasks, such as screening and assessment, providing information and education, and supporting individuals between appointments. They also said that health professionals should have some oversight in terms of the development and implementation of these programs.

CONCLUSIONS

The results of this study provide insight into health professionals' views on the use of conversational agents for health care, particularly in terms of the benefits and drawbacks of these programs and how they should be integrated into the health care system. These collective findings offer useful information and guidance to stakeholders who have an interest in the development and implementation of this technology.

Systematic review of adherence to technology-based falls prevention programs for community-dwelling older adults: Reimagining future interventions

BACKGROUND

Prevention programs, and specifically exercise, can reduce falls among community-dwelling older adults, but low adherence limits the benefits of effective interventions. Technology may overcome some barriers to improve uptake and engagement in prevention programs, although less is known on adherence for providing them via this delivery mode. We aimed to synthesize evidence for adherence to technology-based falls prevention programs in community-dwelling older adults 60 years and older. We conducted a systematic review following standard guidelines to identify randomized controlled trials for remote delivered (i.e., no or limited in-person sessions) technology-based falls prevention programs for community-dwelling older adults. We searched nine sources using Medical Subject Headings (MeSH) terms and keywords (2007-present). The initial search was conducted in June 2023 and updated in December 2023. We also conducted a forward and backward citation search of included studies. Two reviewers independently conducted screening and study assessment; one author extracted data and a second author confirmed findings. We conducted a random effects meta-analysis for adherence, operationalized as participants' completion of program components, and aimed to conduct meta-regressions to examine factors related to program adherence and the association between adherence and functional mobility. We included 11 studies with 569 intervention participants (average mean age 74.5 years). Studies used a variety of technology, such as apps, exergames, or virtual synchronous classes. Risk of bias was low for eight studies. Five interventions automatically collected data for monitoring and completion of exercise sessions, two studies collected participants' online attendance, and four studies used self-reported diaries or attendance sheets. Studies included some behavior change techniques or strategies alongside the technology. There was substantial variability in the way adherence data were reported. The mean (range) percent of participants who did not complete planned sessions (i.e., dropped out or lost to follow-up) was 14% (0-32%). The pooled estimate of the proportion of participants who were adherent to a technology-based falls prevention program was 0.82 (95% CI 0.68, 0.93) for studies that reported the mean number of completed exercise sessions. Many studies needed to provide access to the internet, training, and/or resources (e.g., tablets) to support participants to take part in the intervention. We were unable to conduct the meta-regression for adherence and functional mobility due to an insufficient number of studies. There were no serious adverse events for studies reporting this information (n = 8). The use of technology may confer some benefits for program delivery and data collection. But better reporting of adherence data is needed, as well as routine integration and measurement of training and skill development to use technology, and behavior change strategies within interventions. There may be an opportunity to rethink or reimagine how technology can be used to support people's adoption and integration of physical activity into daily life routines.

Transforming Health Care Through Chatbots for Medical History-Taking and Future Directions: Comprehensive Systematic Review

BACKGROUND

The integration of artificial intelligence and chatbot technology in health care has attracted significant attention due to its potential to improve patient care and streamline history-taking. As artificial intelligence-driven conversational agents, chatbots offer the opportunity to revolutionize history-taking, necessitating a comprehensive examination of their impact on medical practice.

OBJECTIVE

This systematic review aims to assess the role, effectiveness, usability, and patient acceptance of chatbots in medical history-taking. It also examines potential challenges and future opportunities for integration into clinical practice.

METHODS

A systematic search included PubMed, Embase, MEDLINE (via Ovid), CENTRAL, Scopus, and Open Science and covered studies through July 2024. The inclusion and exclusion criteria for the studies reviewed were based on the PICOS (participants, interventions, comparators, outcomes, and study design) framework. The population included individuals using health care chatbots for medical history-taking. Interventions focused on chatbots designed to facilitate medical history-taking. The outcomes of interest were the feasibility, acceptance, and usability of chatbot-based medical history-taking. Studies not reporting on these outcomes were excluded. All study designs except conference papers were eligible for inclusion. Only English-language studies were considered. There were no specific restrictions on study duration. Key search terms included "chatbot*," "conversational agent*," "virtual assistant," "artificial intelligence chatbot," "medical history," and "history-taking." The quality of observational studies was classified using the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) criteria (eg, sample size, design, data collection, and follow-up). The RoB 2 (Risk of Bias) tool assessed areas and the levels of bias in randomized controlled trials (RCTs).

RESULTS

The review included 15 observational studies and 3 RCTs and synthesized evidence from different medical fields and populations. Chatbots systematically collect information through targeted queries and data retrieval, improving patient engagement and satisfaction. The results show that chatbots have great potential for history-taking and that the efficiency and accessibility of the health care system can be improved by 24/7 automated data collection. Bias assessments revealed that of the 15 observational studies, 5 (33%) studies were of high quality, 5 (33%) studies were of moderate quality, and 5 (33%) studies were of low quality. Of the RCTs, 2 had a low risk of bias, while 1 had a high risk.

CONCLUSIONS

This systematic review provides critical insights into the potential benefits and challenges of using chatbots for medical history-taking. The included studies showed that chatbots can increase patient engagement, streamline data collection, and improve health care decision-making. For effective integration into clinical practice, it is crucial to design user-friendly interfaces, ensure robust data security, and maintain empathetic patient-physician interactions. Future research should focus on refining chatbot algorithms, improving their emotional intelligence, and extending their application to different health care settings to realize their full potential in modern medicine.

TRIAL REGISTRATION

PROSPERO CRD42023410312; www.crd.york.ac.uk/prospero.

Evaluating the Quality and Comparative Validity of Manual Food Logging and Artificial Intelligence-Enabled Food Image Recognition in Apps for Nutrition Care

For artificial intelligence (AI) to support nutrition care, high quality and accuracy of its features within smartphone applications (apps) are essential. This study evaluated popular apps' features, quality, behaviour change potential, and comparative validity of dietary assessment via manual logging and AI. The top 200 free and paid nutrition-related apps from Australia's Apple App and Google Play stores were screened (n = 800). Apps were assessed using MARS (quality) and ABACUS (behaviour change potential). Nutritional outputs from manual food logging and AI-enabled food-image recognition apps were compared with food records for Western, Asian, and Recommended diets. Among 18 apps, Noom scored highest on MARS (mean = 4.44) and ABACUS (21/21). From 16 manual food-logging apps, energy was overestimated for Western (mean: 1040 kJ) but underestimated for Asian (mean: -1520 kJ) diets. MyFitnessPal and Fastic had the highest accuracy (97% and 92%, respectively) out of seven AI-enabled food image recognition apps. Apps with more AI integration demonstrated better functionality, but automatic energy estimations from AI-enabled food image recognition were inaccurate. To enhance the integration of apps into nutrition care, collaborating with dietitians is essential for improving their credibility and comparative validity by expanding food databases. Moreover, training AI models are needed to improve AI-enabled food recognition, especially for mixed dishes and culturally diverse foods.

Artificial intelligence in clinical nutrition and dietetics: A brief overview of current evidence

The rapid surge in artificial intelligence (AI) has dominated technological innovation in today's society. As experts begin to understand the potential, a spectrum of opportunities could yield a remarkable revolution. The upsurge in healthcare could transform clinical interventions and outcomes, but it risks dehumanization and increased unethical practices. The field of clinical nutrition and dietetics is no exception. This article finds a multitude of developments underway, which include the use of AI for malnutrition screening; predicting clinical outcomes, such as disease onset, and clinical risks, such as drug interactions; aiding interventions, such as estimating nutrient intake; applying precision nutrition, such as measuring postprandial glycemic response; and supporting workflow through chatbots trained on natural language models. Although the opportunity and scalability of AI is incalculably attractive, especially in the face of poor healthcare resources, the threat cannot be ignored. The risk of malpractice and lack of accountability are some of the main concerns. As such, the healthcare professional's responsibility remains paramount. The data used to train AI models could be biased, which could risk the quality of care to vulnerable or minority patient groups. Standardized AI-development protocols, benchmarked to care recommendations, with rigorous large-scale validation are required to maximize application among different settings. AI could overturn the healthcare landscape, and this article skims the surface of its potential in clinical nutrition and dietetics.

Human Factors in AI-Driven Digital Solutions for Increasing Physical Activity: Scoping Review

BACKGROUND

Artificial intelligence (AI) has the potential to enhance physical activity (PA) interventions. However, human factors (HFs) play a pivotal role in the successful integration of AI into mobile health (mHealth) solutions for promoting PA. Understanding and optimizing the interaction between individuals and AI-driven mHealth apps is essential for achieving the desired outcomes.

OBJECTIVE

This study aims to review and describe the current evidence on the HFs in AI-driven digital solutions for increasing PA.

METHODS

We conducted a scoping review by searching for publications containing terms related to PA, HFs, and AI in the titles and abstracts across 3 databases-PubMed, Embase, and IEEE Xplore-and Google Scholar. Studies were included if they were primary studies describing an AI-based solution aimed at increasing PA, and results from testing the solution were reported. Studies that did not meet these criteria were excluded. Additionally, we searched the references in the included articles for relevant research. The following data were extracted from included studies and incorporated into a qualitative synthesis: bibliographic information, study characteristics, population, intervention, comparison, outcomes, and AI-related information. The certainty of the evidence in the included studies was evaluated using GRADE (Grading of Recommendations Assessment, Development, and Evaluation).

RESULTS

A total of 15 studies published between 2015 and 2023 involving 899 participants aged approximately between 19 and 84 years, 60.7% (546/899) of whom were female participants, were included in this review. The interventions lasted between 2 and 26 weeks in the included studies. Recommender systems were the most commonly used AI technology in digital solutions for PA (10/15 studies), followed by conversational agents (4/15 studies). User acceptability and satisfaction were the HFs most frequently evaluated (5/15 studies each), followed by usability (4/15 studies). Regarding automated data collection for personalization and recommendation, most systems involved fitness trackers (5/15 studies). The certainty of the evidence analysis indicates moderate certainty of the effectiveness of AI-driven digital technologies in increasing PA (eg, number of steps, distance walked, or time spent on PA). Furthermore, AI-driven technology, particularly recommender systems, seems to positively influence changes in PA behavior, although with very low certainty evidence.

CONCLUSIONS

Current research highlights the potential of AI-driven technologies to enhance PA, though the evidence remains limited. Longer-term studies are necessary to assess the sustained impact of AI-driven technologies on behavior change and habit formation. While AI-driven digital solutions for PA hold significant promise, further exploration into optimizing AI's impact on PA and effectively integrating AI and HFs is crucial for broader benefits. Thus, the implications for innovation management involve conducting long-term studies, prioritizing diversity, ensuring research quality, focusing on user experience, and understanding the evolving role of AI in PA promotion.

The Role of Artificial Intelligence in Nutrition Research: A Scoping Review

Artificial intelligence (AI) refers to computer systems doing tasks that usually need human intelligence. AI is constantly changing and is revolutionizing the healthcare field, including nutrition. This review's purpose is four-fold: (i) to investigate AI's role in nutrition research; (ii) to identify areas in nutrition using AI; (iii) to understand AI's future potential impact; (iv) to investigate possible concerns about AI's use in nutrition research. Eight databases were searched: PubMed, Web of Science, EBSCO, Agricola, Scopus, IEEE Explore, Google Scholar and Cochrane. A total of 1737 articles were retrieved, of which 22 were included in the review. Article screening phases included duplicates elimination, title-abstract selection, full-text review, and quality assessment. The key findings indicated AI's role in nutrition is at a developmental stage, focusing mainly on dietary assessment and less on malnutrition prediction, lifestyle interventions, and diet-related diseases comprehension. Clinical research is needed to determine AI's intervention efficacy. The ethics of AI use, a main concern, remains unresolved and needs to be considered for collateral damage prevention to certain populations. The studies' heterogeneity in this review limited the focus on specific nutritional areas. Future research should prioritize specialized reviews in nutrition and dieting for a deeper understanding of AI's potential in human nutrition.

Depiction of conversational agents as health professionals: a scoping review

OBJECTIVE

The purpose of this scoping review was to examine the depiction of conversational agents as health professionals. We identified the professional characteristics that are used with these depictions and determined the prevalence of these characteristics among conversational agents that are used for health care.

INTRODUCTION

The depiction of conversational agents as health professionals has implications for both the users and the developers of these programs. For this reason, it is important to know more about these depictions and how they are implemented in practical settings.

INCLUSION CRITERIA

This review included scholarly literature on conversational agents that are used for health care. It focused on conversational agents designed for patients and health seekers, not health professionals or trainees. Conversational agents that address physical and/or mental health care were considered, as were programs that promote healthy behaviors.

METHODS

This review was conducted in accordance with JBI methodology for scoping reviews. The databases searched included MEDLINE (PubMed), Embase, CINAHL with Full Text (EBSCOhost), Scopus, Web of Science, ACM Guide to Computing Literature (Association for Computing Machinery Digital Library), and IEEE Xplore (IEEE). The main database search was conducted in June 2021, and an updated search was conducted in January 2022. Extracted data included characteristics of the report, basic characteristics of the conversational agent, and professional characteristics of the conversational agent. Extracted data were summarized using descriptive statistics. Results are presented in a narrative summary and accompanying tables.

RESULTS

A total of 38 health-related conversational agents were identified across 41 reports. Six of these conversational agents (15.8%) had professional characteristics. Four conversational agents (10.5%) had a professional appearance in which they displayed the clothing and accessories of health professionals and appeared in professional settings. One conversational agent (2.6%) had a professional title (Dr), and 4 conversational agents (10.5%) were described as having professional roles. Professional characteristics were more common among embodied vs disembodied conversational agents.

CONCLUSIONS

The results of this review show that the depiction of conversational agents as health professionals is not particularly common, although it does occur. More discussion is needed on the potential ethical and legal issues surrounding the depiction of conversational agents as health professionals. Future research should examine the impact of these depictions, as well as people's attitudes toward them, to better inform recommendations for practice.

A scoping review of methodologies for applying artificial intelligence to physical activity interventions

PURPOSE

This scoping review aimed to offer researchers and practitioners an understanding of artificial intelligence (AI) applications in physical activity (PA) interventions; introduce them to prevalent machine learning (ML), deep learning (DL), and reinforcement learning (RL) algorithms; and encourage the adoption of AI methodologies.

METHODS

A scoping review was performed in PubMed, Web of Science, Cochrane Library, and EBSCO focusing on AI applications for promoting PA or predicting related behavioral or health outcomes. AI methodologies were summarized and categorized to identify synergies, patterns, and trends informing future research. Additionally, a concise primer on predominant AI methodologies within the realm of PA was provided to bolster understanding and broader application.

RESULTS

The review included 24 studies that met the predetermined eligibility criteria. AI models were found effective in detecting significant patterns of PA behavior and associations between specific factors and intervention outcomes. Most studies comparing AI models to traditional statistical approaches reported higher prediction accuracy for AI models on test data. Comparisons of different AI models yielded mixed results, likely due to model performance being highly dependent on the dataset and task. An increasing trend of adopting state-of-the-art DL and RL models over standard ML was observed, addressing complex human-machine communication, behavior modification, and decision-making tasks. Six key areas for future AI adoption in PA interventions emerged: personalized PA interventions, real-time monitoring and adaptation, integration of multimodal data sources, evaluation of intervention effectiveness, expanding access to PA interventions, and predicting and preventing injuries.

CONCLUSION

The scoping review highlights the potential of AI methodologies for advancing PA interventions. As the field progresses, staying informed and exploring emerging AI-driven strategies is essential for achieving significant improvements in PA interventions and fostering overall well-being.

The Future of Artificial Intelligence in Sports Medicine and Return to Play

Artificial intelligence (AI) has shown tremendous growth over the last decade, with the more recent development of clinical applications in health care. The ability of AI to synthesize large amounts of complex data automatically allows health care providers to access previously unavailable metrics and thus enhance and personalize patient care. These innovations include AI-assisted diagnostic tools, prediction models for each treatment pathway, and various tools for workflow optimization. The extension of AI into sports medicine is still early, but numerous AI-driven algorithms, devices, and research initiatives have delved into predicting and preventing athlete injury, aiding in injury assessment, optimizing recovery plans, monitoring rehabilitation progress, and predicting return to play.

Ethics and artificial intelligence

The relationship between ethics and artificial intelligence in medicine is a crucial and complex topic that falls within its broader context. Ethics in medical artificial intelligence (AI) involves ensuring that technologies are safe, fair, and respect patient privacy. This includes concerns about the accuracy of diagnoses provided by artificial intelligence, fairness in patient treatment, and protection of personal health data. Advances in artificial intelligence can significantly improve healthcare, from more accurate diagnoses to personalized treatments. However, it is essential that developments in medical artificial intelligence are carried out with strong ethical consideration, involving healthcare professionals, artificial intelligence experts, patients, and ethics specialists to guide and oversee their implementation. Finally, transparency in artificial intelligence algorithms and ongoing training for medical professionals are fundamental.

Leveraging Machine Learning to Develop Digital Engagement Phenotypes of Users in a Digital Diabetes Prevention Program: Evaluation Study

BACKGROUND

Digital diabetes prevention programs (dDPPs) are effective "digital prescriptions" but have high attrition rates and program noncompletion. To address this, we developed a personalized automatic messaging system (PAMS) that leverages SMS text messaging and data integration into clinical workflows to increase dDPP engagement via enhanced patient-provider communication. Preliminary data showed positive results. However, further investigation is needed to determine how to optimize the tailoring of support technology such as PAMS based on a user's preferences to boost their dDPP engagement.

OBJECTIVE

This study evaluates leveraging machine learning (ML) to develop digital engagement phenotypes of dDPP users and assess ML's accuracy in predicting engagement with dDPP activities. This research will be used in a PAMS optimization process to improve PAMS personalization by incorporating engagement prediction and digital phenotyping. This study aims (1) to prove the feasibility of using dDPP user-collected data to build an ML model that predicts engagement and contributes to identifying digital engagement phenotypes, (2) to describe methods for developing ML models with dDPP data sets and present preliminary results, and (3) to present preliminary data on user profiling based on ML model outputs.

METHODS

Using the gradient-boosted forest model, we predicted engagement in 4 dDPP individual activities (physical activity, lessons, social activity, and weigh-ins) and general activity (engagement in any activity) based on previous short- and long-term activity in the app. The area under the receiver operating characteristic curve, the area under the precision-recall curve, and the Brier score metrics determined the performance of the model. Shapley values reflected the feature importance of the models and determined what variables informed user profiling through latent profile analysis.

RESULTS

We developed 2 models using weekly and daily DPP data sets (328,821 and 704,242 records, respectively), which yielded predictive accuracies above 90%. Although both models were highly accurate, the daily model better fitted our research plan because it predicted daily changes in individual activities, which was crucial for creating the "digital phenotypes." To better understand the variables contributing to the model predictor, we calculated the Shapley values for both models to identify the features with the highest contribution to model fit; engagement with any activity in the dDPP in the last 7 days had the most predictive power. We profiled users with latent profile analysis after 2 weeks of engagement (Bayesian information criterion=-3222.46) with the dDPP and identified 6 profiles of users, including those with high engagement, minimal engagement, and attrition.

CONCLUSIONS

Preliminary results demonstrate that applying ML methods with predicting power is an acceptable mechanism to tailor and optimize messaging interventions to support patient engagement and adherence to digital prescriptions. The results enable future optimization of our existing messaging platform and expansion of this methodology to other clinical domains.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04773834; https://www.clinicaltrials.gov/ct2/show/NCT04773834.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.2196/26750.

The Effects of Artificial Intelligence Chatbots on Women's Health: A Systematic Review and Meta-Analysis

PURPOSE

This systematic review and meta-analysis aimed to investigate the effects of artificial intelligence chatbot interventions on health outcomes in women.

METHODS

Ten relevant studies published between 2019 and 2023 were extracted from the PubMed, Cochrane Library, EMBASE, CINAHL, and RISS databases in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. This review focused on experimental studies concerning chatbot interventions in women's health. The literature was assessed using the ROB 2 quality appraisal checklist, and the results were visualized with a risk-of-bias visualization program.

RESULTS

This review encompassed seven randomized controlled trials and three single-group experimental studies. Chatbots were effective in addressing anxiety, depression, distress, healthy relationships, cancer self-care behavior, preconception intentions, risk perception in eating disorders, and gender attitudes. Chatbot users experienced benefits in terms of internalization, acceptability, feasibility, and interaction. A meta-analysis of three studies revealed significant effects in reducing anxiety (I2 = 0%, Q = 8.10, p < 0.017), with an effect size of -0.30 (95% CI, -0.42 to -0.18).

CONCLUSIONS

Artificial intelligence chatbot interventions had positive effects on physical, physiological, and cognitive health outcomes. Using chatbots may represent pivotal nursing interventions for female populations to improve health status and support women socially as a form of digital therapy.

Implementation of Chatbot Technology in Health Care: Protocol for a Bibliometric Analysis

BACKGROUND

Chatbots have the potential to increase people's access to quality health care. However, the implementation of chatbot technology in the health care system is unclear due to the scarce analysis of publications on the adoption of chatbot in health and medical settings.

OBJECTIVE

This paper presents a protocol of a bibliometric analysis aimed at offering the public insights into the current state and emerging trends in research related to the use of chatbot technology for promoting health.

METHODS

In this bibliometric analysis, we will select published papers from the databases of CINAHL, IEEE Xplore, PubMed, Scopus, and Web of Science that pertain to chatbot technology and its applications in health care. Our search strategy includes keywords such as "chatbot," "virtual agent," "virtual assistant," "conversational agent," "conversational AI," "interactive agent," "health," and "healthcare." Five researchers who are AI engineers and clinicians will independently review the titles and abstracts of selected papers to determine their eligibility for a full-text review. The corresponding author (ZN) will serve as a mediator to address any discrepancies and disputes among the 5 reviewers. Our analysis will encompass various publication patterns of chatbot research, including the number of annual publications, their geographic or institutional distribution, and the number of annual grants supporting chatbot research, and further summarize the methodologies used in the development of health-related chatbots, along with their features and applications in health care settings. Software tool VOSViewer (version 1.6.19; Leiden University) will be used to construct and visualize bibliometric networks.

RESULTS

The preparation for the bibliometric analysis began on December 3, 2021, when the research team started the process of familiarizing themselves with the software tools that may be used in this analysis, VOSViewer and CiteSpace, during which they consulted 3 librarians at the Yale University regarding search terms and tentative results. Tentative searches on the aforementioned databases yielded a total of 2340 papers. The official search phase started on July 27, 2023. Our goal is to complete the screening of papers and the analysis by February 15, 2024.

CONCLUSIONS

Artificial intelligence chatbots, such as ChatGPT (OpenAI Inc), have sparked numerous discussions within the health care industry regarding their impact on human health. Chatbot technology holds substantial promise for advancing health care systems worldwide. However, developing a sophisticated chatbot capable of precise interaction with health care consumers, delivering personalized care, and providing accurate health-related information and knowledge remain considerable challenges. This bibliometric analysis seeks to fill the knowledge gap in the existing literature on health-related chatbots, entailing their applications, the software used in their development, and their preferred functionalities among users.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

PRR1-10.2196/54349.

Special Issue "Sports Science in Children"

In recent times, research and technological advancements have opened an unprecedented window of opportunity for sports science to play a pivotal role in the holistic well-being of children [...].

Applications of digital health approaches for cardiometabolic diseases prevention and management in the Western Pacific region

Cardiometabolic diseases (CMDs) are the major types of non-communicable diseases, contributing to huge disease burdens in the Western Pacific region (WPR). The use of digital health (dHealth) technologies, such as wearable gadgets, mobile apps, and artificial intelligence (AI), facilitates interventions for CMDs prevention and treatment. Currently, most studies on dHealth and CMDs in WPR were conducted in a few high- and middle-income countries like Australia, China, Japan, the Republic of Korea, and New Zealand. Evidence indicated that dHealth services promoted early prevention by behavior interventions, and AI-based innovation brought automated diagnosis and clinical decision-support. dHealth brought facilitators for the doctor-patient interplay in the effectiveness, experience, and communication skills during healthcare services, with rapidly development during the pandemic of coronavirus disease 2019. In the future, the improvement of dHealth services in WPR needs to gain more policy support, enhance technology innovation and privacy protection, and perform cost-effectiveness research.

Investigating the Potential of a Conversational Agent (Phyllis) to Support Adolescent Health and Overcome Barriers to Physical Activity: Co-Design Study

BACKGROUND

Conversational agents (CAs) are a promising solution to support people in improving physical activity (PA) behaviors. However, there is a lack of CAs targeted at adolescents that aim to provide support to overcome barriers to PA. This study reports the results of the co-design, development, and evaluation of a prototype CA called "Phyllis" to support adolescents in overcoming barriers to PA with the aim of improving PA behaviors. The study presents one of the first theory-driven CAs that use existing research, a theoretical framework, and a behavior change model.

OBJECTIVE

The aim of the study is to use a mixed methods approach to investigate the potential of a CA to support adolescents in overcoming barriers to PA and enhance their confidence and motivation to engage in PA.

METHODS

The methodology involved co-designing with 8 adolescents to create a relational and persuasive CA with a suitable persona and dialogue. The CA was evaluated to determine its acceptability, usability, and effectiveness, with 46 adolescents participating in the study via a web-based survey.

RESULTS

The co-design participants were students aged 11 to 13 years, with a sex distribution of 56% (5/9) female and 44% (4/9) male, representing diverse ethnic backgrounds. Participants reported 37 specific barriers to PA, and the most common barriers included a "lack of confidence," "fear of failure," and a "lack of motivation." The CA's persona, named "Phyllis," was co-designed with input from the students, reflecting their preferences for a friendly, understanding, and intelligent personality. Users engaged in 61 conversations with Phyllis and reported a positive user experience, and 73% of them expressed a definite intention to use the fully functional CA in the future, with a net promoter score indicating a high likelihood of recommendation. Phyllis also performed well, being able to recognize a range of different barriers to PA. The CA's persuasive capacity was evaluated in modules focusing on confidence and motivation, with a significant increase in students' agreement in feeling confident and motivated to engage in PA after interacting with Phyllis. Adolescents also expect to have a personalized experience and be able to personalize all aspects of the CA.

CONCLUSIONS

The results showed high acceptability and a positive user experience, indicating the CA's potential. Promising outcomes were observed, with increasing confidence and motivation for PA. Further research and development are needed to create further interventions to address other barriers to PA and assess long-term behavior change. Addressing concerns regarding bias and privacy is crucial for achieving acceptability in the future. The CA's potential extends to health care systems and multimodal support, providing valuable insights for designing digital health interventions including tackling global inactivity issues among adolescents.

The use of internet-based smartphone apps consistently improved consumers' healthy eating behaviors: a systematic review of randomized controlled trials

Introduction

Digital tools, such as mobile apps and the Internet, are being increasingly used to promote healthy eating habits. However, there has been inconsistent reporting on the effectiveness of smartphones and web-based apps in influencing dietary behaviors. Moreover, previous reviews have been limited in scope, either by focusing on a specific population group or by being outdated. Therefore, the purpose of this review is to investigate the impacts of smartphone- and web-based dietary interventions on promoting healthy eating behaviors worldwide.

Methods

A systematic literature search of randomized controlled trials was conducted using databases such as Google Scholar, PubMed, Global Health, Informit, Web of Science, and CINAHL (EBSCO). The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed to prepare the entire document. EndNote (version 20) was used for reference management. The risk of bias in the articles was assessed using the "Revised Cochrane Risk of Bias tool for randomized trials (RoB 2.0)" by the Cochrane Collaboration. Narrative synthesis, using text and tables, was used to present the results. The study was registered in PROSPERO under protocol number CRD42023464315.

Results

This review analyzed a total of 39 articles, which consisted of 25 smartphone-based apps and 14 web-based apps. The studies involved a total of 14,966 participants. Out of the 25 studies, 13 (52%) showed that offline-capable smartphone apps are successful in promoting healthier eating habits. The impact of smartphone apps on healthy adults has been inconsistently reported. However, studies have shown their effectiveness in chronically ill patients. Likewise, internet-based mobile apps, such as social media or nutrition-specific apps, have been found to effectively promote healthy eating behaviors. These findings were consistent across 14 studies, which included healthy adults, overweight or obese adults, chronically ill patients, and pregnant mothers.

Conclusion

Overall, the findings suggest that smartphone apps contribute to improving healthy eating behaviors. Both nutrition-specific and social media-based mobile apps consistently prove effective in promoting long-term healthy eating habits. Therefore, policymakers in the food system should consider harnessing the potential of internet-based mobile apps and social media platforms to foster sustainable healthy eating behaviors.

Navigating healthier beverage consumption in adolescents using the "R-Ma Bot" chatbot: A usability and evaluation study

Objective

This pilot study aimed to evaluate the usability and effectiveness of a behavior change technique (BCT)-based chatbot developed to promote healthier beverage consumption among adolescents.

Methods

The Read and Manage your health roBot ("R-Ma Bot"), designed with 13 BCTs, was tested with 42 adolescents (13 men, 29 women, mean age 15.0 ± 0.7) for 2 weeks. Usability was assessed after the 2-week intervention using a chatbot usability questionnaire, recruitment, retention, participation, and engagement. Scores above 70 out of 100 were considered high usability. Qualitative data from open-ended questions were collected for evaluation. Effectiveness was measured by changes in knowledge, use and impact of nutrition labels, and weekly consumption of sugar, sodium, and caffeine from carbonated and/or energy drinks before and after the 2-week intervention.

Results

The score of R-Ma Bot's usability averaged 74.7, with participants addressing it useful, friendly, and easy to use, though they suggested improving unnatural conversation flow. All participants engaged with the chatbot for at least 13 out of 14 days, with over half using it daily for the entire period. After intervention, awareness of nutrition labels increased from 64.3% to 92.9%, and nonreaders decreased from 42.9% to 16.7%. Weekly sugar intake from beverages significantly decreased by 60%, from 13.1 ± 20.1 mg to 7.9 ± 12.8 mg.

Conclusions

R-Ma Bot's high usability contributed to high retention and behavioral changes, significantly reduced sugar consumption from beverages and improved awareness of nutrition labels. We suggest integrating strategies that enhance knowledge, motivation, and opportunities through BCTs with youth-friendly design elements in the development of interventions for adolescents.

Factors influencing patient engagement in mental health chatbots: A thematic analysis of findings from a systematic review of reviews

Introduction

Mental health disorders affect millions of people worldwide. Chatbots are a new technology that can help users with mental health issues by providing innovative features. This article aimed to conduct a systematic review of reviews on chatbots in mental health services and synthesized the evidence on the factors influencing patient engagement with chatbots.

Methods

This study reviewed the literature from 2000 to 2024 using qualitative analysis. The authors conducted a systematic search of several databases, such as PubMed, Scopus, ProQuest, and Cochrane database of systematic reviews, to identify relevant studies on the topic. The quality of the selected studies was assessed using the Critical Appraisal Skills Programme appraisal checklist and the data obtained from the systematic review were subjected to a thematic analysis utilizing the Boyatzis's code development approach.

Results

The database search resulted in 1494 papers, of which 10 were included in the study after the screening process. The quality assessment of the included studies scored the papers within a moderate level. The thematic analysis revealed four main themes: chatbot design, chatbot outcomes, user perceptions, and user characteristics.

Conclusion

The research proposed some ways to use color and music in chatbot design. It also provided a systematic and multidimensional analysis of the factors, offered some insights for chatbot developers and researchers, and highlighted the potential of chatbots to improve patient-centered and person-centered care in mental health services.

Protocol of a mixed-methods evaluation of Perfect Fit: A personalized mHealth intervention with a virtual coach to promote smoking cessation and physical activity in adults

Objective

Adopting healthy behavior is vital for preventing chronic diseases. Mobile health (mHealth) interventions utilizing virtual coaches (i.e., artificial intelligence conversational agents) can offer scalable and cost-effective solutions. Additionally, targeting multiple unhealthy behaviors, like low physical activity and smoking, simultaneously seems beneficial. We developed Perfect Fit, an mHealth intervention with a virtual coach providing personalized feedback to simultaneously promote smoking cessation and physical activity. Through innovative methods (e.g., sensor technology) and iterative development involving end-users, we strive to overcome challenges encountered by mHealth interventions, such as shortage of evidence-based interventions and insufficient personalization. This paper outlines the content of Perfect Fit and the protocol for evaluating its feasibility, acceptability, and preliminary effectiveness, the role of participant characteristics, and the study's feasibility.

Methods

A single-arm, mixed-method, real-world evaluation study will be conducted in the Netherlands. We aim to recruit 100 adult daily smokers intending to quit within 6 weeks. The personalized intervention will last approximately 16 weeks. Primary outcomes include Perfect Fit's feasibility and acceptability. Secondary outcomes are preliminary effectiveness and study feasibility, and we will measure participant characteristics. Quantitative data will be collected through questionnaires administered at baseline, post-intervention and 2, 6, and 12 months post-intervention. Qualitative data will be gathered via semi-structured interviews post-intervention. Data analysis will involve descriptive analyses, generalized linear mixed models (quantitative) and the Framework Approach (qualitative), integrating quantitative and qualitative data during interpretation.

Conclusions

This study will provide novel insight into the potential of interventions like Perfect Fit, as a multiple health behavior change strategy. Findings will inform further intervention development and help identify methods to foster feasibility and acceptability. Successful mHealth interventions with virtual coaches will prevent chronic diseases and promote public health.

Usability and Overall Perception of a Health Bot for Nutrition-Related Questions for Patients Receiving Bariatric Care: Mixed Methods Study

BACKGROUND

Currently, over 4000 bariatric procedures are performed annually in Switzerland. To improve outcomes, patients need to have good knowledge regarding postoperative nutrition. To potentially provide them with knowledge between dietetic consultations, a health bot (HB) was created. The HB can answer bariatric nutrition questions in writing based on artificial intelligence.

OBJECTIVE

This study aims to evaluate the usability and perception of the HB among patients receiving bariatric care.

METHODS

Patients before or after bariatric surgery tested the HB. A mixed methods approach was used, which consisted of a questionnaire and qualitative interviews before and after testing the HB. The dimensions usability of, usefulness of, satisfaction with, and ease of use of the HB, among others, were measured. Data were analyzed using R Studio (R Studio Inc) and Excel (Microsoft Corp). The interviews were transcribed and a summary inductive content analysis was performed.

RESULTS

A total of 12 patients (female: n=8, 67%; male: n=4, 33%) were included. The results showed excellent usability with a mean usability score of 87 (SD 12.5; range 57.5-100) out of 100. Other dimensions of acceptability included usefulness (mean 5.28, SD 2.02 out of 7), satisfaction (mean 5.75, SD 1.68 out of 7), and learnability (mean 6.26, SD 1.5 out of 7). The concept of the HB and availability of reliable nutrition information were perceived as desirable (mean 5.5, SD 1.64 out of 7). Weaknesses were identified in the response accuracy, limited knowledge, and design of the HB.

CONCLUSIONS

The HB's ease of use and usability were evaluated to be positive; response accuracy, topic selection, and design should be optimized in a next step. The perceptions of nutrition professionals and the impact on patient care and the nutrition knowledge of participants need to be examined in further studies.

Comparison of Answers between ChatGPT and Human Dieticians to Common Nutrition Questions

Background

More people than ever seek nutrition information from online sources. The chatbot ChatGPT has seen staggering popularity since its inception and may become a resource for information in nutrition. However, the adequacy of ChatGPT to answer questions in the field of nutrition has not been investigated. Thus, the aim of this research was to investigate the competency of ChatGPT in answering common nutrition questions.

Methods

Dieticians were asked to provide their most commonly asked nutrition questions and their own answers to them. We then asked the same questions to ChatGPT and sent both sets of answers to other dieticians (N = 18) or nutritionists and experts in the domain of each question (N = 9) to be graded based on scientific correctness, actionability, and comprehensibility. The grades were also averaged to give an overall score, and group means of the answers to each question were compared using permutation tests.

Results

The overall grades for ChatGPT were higher than those from the dieticians for the overall scores in five of the eight questions we received. ChatGPT also had higher grades on five occasions for scientific correctness, four for actionability, and five for comprehensibility. In contrast, none of the answers from the dieticians had a higher average score than ChatGPT for any of the questions, both overall and for each of the grading components.

Conclusions

Our results suggest that ChatGPT can be used to answer nutrition questions that are frequently asked to dieticians and provide encouraging support for the role of chatbots in offering nutrition support.

A Digital Lifestyle Program for Psychological Distress, Wellbeing and Return-to-Work: A Proof-of-Concept Study

OBJECTIVE

To demonstrate proof-of-concept for a chatbot-led digital lifestyle medicine program in aiding rehabilitation for return-to-work.

DESIGN

Retrospective cohort study with pre-post measures.

SETTING

Community setting, Australia.

PARTICIPANTS

78 adult participants (mean age 46 years, 32% female) with an active workers' compensation claim (N=78).

INTERVENTIONS

A 6-week digital lifestyle medicine program led by an artificially intelligent virtual health coach and weekly telehealth calls with a health coach.

MAIN OUTCOME MEASURES

Adherence (% program completions) and engagement (% of daily and weekly sessions completed), changes in depression, anxiety and distress (K10), psychological wellbeing (WHO-5), return-to-work confidence and anxiety and change in work status.

RESULTS

Sixty participants completed the program (72%), with improvements in psychological distress (P≤.001, r=.47), depression (P<.001, r=.55), anxiety (P<.001, r=.46) and wellbeing (P<.001, r=.62) were noted, as well as increased confidence about returning to work (P≤.001, r=.51) and improved work status (P≤.001). Anxiety about returning to work remained unchanged. Participants completed an average of 73% of daily virtual coach sessions and 95% of telehealth coaching sessions.

CONCLUSIONS

Artificial intelligence technology may be able to provide a practical, supportive, and low-cost intervention to improve psychosocial outcomes among individuals on an active workers' compensation claim. Further, controlled research is needed to confirm these findings.

Exploring the design and utility of an integrated web-based chatbot for young adults to support healthy eating: a qualitative study

BACKGROUND

There is a lack of understanding of the potential utility of a chatbot integrated into a website to support healthy eating among young adults. Therefore, the aim was to interview key informants regarding potential utility and design of a chatbot to: (1) increase young adults' return rates and engagement with a purpose-built healthy eating website and, (2) improve young adults' diet quality.

METHODS

Eighteen qualitative, semi-structured interviews were conducted across three stakeholder groups: (i) experts in dietary behaviour change in young adults (n = 6), (ii) young adult users of a healthy eating website (n = 7), and (iii) experts in chatbot design (n = 5). Interview questions were guided by a behaviour change framework and a template analysis was conducted using NVivo.

RESULTS

Interviewees identified three potential roles of a chatbot for supporting healthy eating in young adults; R1: improving healthy eating knowledge and facilitating discovery, R2: reducing time barriers related to healthy eating, R3: providing support and social engagement. To support R1, the following features were suggested: F1: chatbot generated recommendations and F2: triage to website information or externally (e.g., another website) to address current user needs. For R2, suggested features included F3: nudge or behavioural prompts at critical moments and F4: assist users to navigate healthy eating websites. Finally, to support R3 interviewees recommended the following features: F5: enhance interactivity, F6: offer useful anonymous support, F7: facilitate user connection with content in meaningful ways and F8: outreach adjuncts to website (e.g., emails). Additional 'general' chatbot features included authenticity, personalisation and effective and strategic development, while the preferred chatbot style and language included tailoring (e.g., age and gender), with a positive and professional tone. Finally, the preferred chatbot message subjects included training (e.g., would you like to see a video to make this recipe?), enablement (e.g., healthy eating doesn't need to be expensive, we've created a budget meal plan, want to see?) and education or informative approaches (e.g., "Did you know bananas are high in potassium which can aid in reducing blood pressure?").

CONCLUSION

Findings can guide chatbot designers and nutrition behaviour change researchers on potential chatbot roles, features, style and language and messaging in order to support healthy eating knowledge and behaviours in young adults.

Enhancing head and neck tumor management with artificial intelligence: Integration and perspectives

Head and neck tumors (HNTs) constitute a multifaceted ensemble of pathologies that primarily involve regions such as the oral cavity, pharynx, and nasal cavity. The intricate anatomical structure of these regions poses considerable challenges to efficacious treatment strategies. Despite the availability of myriad treatment modalities, the overall therapeutic efficacy for HNTs continues to remain subdued. In recent years, the deployment of artificial intelligence (AI) in healthcare practices has garnered noteworthy attention. AI modalities, inclusive of machine learning (ML), neural networks (NNs), and deep learning (DL), when amalgamated into the holistic management of HNTs, promise to augment the precision, safety, and efficacy of treatment regimens. The integration of AI within HNT management is intricately intertwined with domains such as medical imaging, bioinformatics, and medical robotics. This article intends to scrutinize the cutting-edge advancements and prospective applications of AI in the realm of HNTs, elucidating AI's indispensable role in prevention, diagnosis, treatment, prognostication, research, and inter-sectoral integration. The overarching objective is to stimulate scholarly discourse and invigorate insights among medical practitioners and researchers to propel further exploration, thereby facilitating superior therapeutic alternatives for patients.

Artificial intelligence & clinical nutrition: What the future might have in store

Artificial Intelligence (AI) is a rapidly emerging technology in healthcare that has the potential to revolutionise clinical nutrition. AI can assist in analysing complex data, interpreting medical images, and providing personalised nutrition interventions for patients. Clinical nutrition is a critical aspect of patient care, and AI can help clinicians make more informed decisions regarding patients' nutritional requirements, disease prevention, and management. AI algorithms can analyse large datasets to identify novel associations between diet and disease outcomes, enabling clinicians to make evidence-based nutritional recommendations. AI-powered devices and applications can also assist in tracking dietary intake, providing feedback, and motivating patients towards healthier food choices. However, the adoption of AI in clinical nutrition raises several ethical and regulatory concerns, such as data privacy and bias. Further research is needed to assess the clinical effectiveness and safety of AI-powered nutrition interventions. In conclusion, AI has the potential to transform clinical nutrition, but its integration into clinical practice should be carefully monitored to ensure patient safety and benefit. This article discusses the current and future applications of AI in clinical nutrition and highlights its potential benefits.

"But can chatbots understand sex?" Attitudes towards artificial intelligence chatbots amongst sexual and reproductive health professionals: An exploratory mixed-methods study

BACKGROUND

Artificial Intelligence (AI)-enabled chatbots can offer anonymous education about sexual and reproductive health (SRH). Understanding chatbot acceptability and feasibility allows the identification of barriers to the design and implementation.

METHODS

In 2020, we conducted an online survey and qualitative interviews with SRH professionals recruited online to explore the views on AI, automation and chatbots. Qualitative data were analysed thematically.

RESULTS

Amongst 150 respondents (48% specialist doctor/consultant), only 22% perceived chatbots as effective and 24% saw them as ineffective for SRH advice [Mean = 2.91, SD = 0.98, range: 1-5]. Overall, there were mixed attitudes towards SRH chatbots [Mean = 4.03, SD = 0.87, range: 1-7]. Chatbots were most acceptable for appointment booking, general sexual health advice and signposting, but not acceptable for safeguarding, virtual diagnosis, and emotional support. Three themes were identified: "Moving towards a 'digital' age'", "AI improving access and service efficacy", and "Hesitancy towards AI".

CONCLUSIONS

Half of SRH professionals were hesitant about the use of chatbots in SRH services, attributed to concerns about patient safety, and lack of familiarity with this technology. Future studies should explore the role of AI chatbots as supplementary tools for SRH promotion. Chatbot designers need to address the concerns of health professionals to increase acceptability and engagement with AI-enabled services.

Chatbot Artificial Intelligence for Genetic Cancer Risk Assessment and Counseling: A Systematic Review and Meta-Analysis

PURPOSE

Most individuals with a hereditary cancer syndrome are unaware of their genetic status to underutilization of hereditary cancer risk assessment. Chatbots, or programs that use artificial intelligence to simulate conversation, have emerged as a promising tool in health care and, more recently, as a potential tool for genetic cancer risk assessment and counseling. Here, we evaluated the existing literature on the use of chatbots in genetic cancer risk assessment and counseling.

METHODS

A systematic review was conducted using key electronic databases to identify studies which use chatbots for genetic cancer risk assessment and counseling. Eligible studies were further subjected to meta-analysis.

RESULTS

Seven studies met inclusion criteria, evaluating five distinct chatbots. Three studies evaluated a chatbot that could perform genetic cancer risk assessment, one study evaluated a chatbot that offered patient counseling, and three studies included both functions. The pooled estimated completion rate for the genetic cancer risk assessment was 36.7% (95% CI, 14.8 to 65.9). Two studies included comprehensive patient characteristics, and none involved a comparison group. Chatbots varied as to the involvement of a health care provider in the process of risk assessment and counseling.

CONCLUSION

Chatbots have been used to streamline genetic cancer risk assessment and counseling and hold promise for reducing barriers to genetic services. Data regarding user and nonuser characteristics are lacking, as are data regarding comparative effectiveness to usual care. Future research may consider the impact of chatbots on equitable access to genetic services.

Artificial Intelligence-Based Consumer Health Informatics Application: Scoping Review

BACKGROUND

There is no doubt that the recent surge in artificial intelligence (AI) research will change the trajectory of next-generation health care, making it more approachable and accessible to patients. Therefore, it is critical to research patient perceptions and outcomes because this trend will allow patients to be the primary consumers of health technology and decision makers for their own health.

OBJECTIVE

This study aimed to review and analyze papers on AI-based consumer health informatics (CHI) for successful future patient-centered care.

METHODS

We searched for all peer-reviewed papers in PubMed published in English before July 2022. Research on an AI-based CHI tool or system that reports patient outcomes or perceptions was identified for the scoping review.

RESULTS

We identified 20 papers that met our inclusion criteria. The eligible studies were summarized and discussed with respect to the role of the AI-based CHI system, patient outcomes, and patient perceptions. The AI-based CHI systems identified included systems in mobile health (13/20, 65%), robotics (5/20, 25%), and telemedicine (2/20, 10%). All the systems aimed to provide patients with personalized health care. Patient outcomes and perceptions across various clinical disciplines were discussed, demonstrating the potential of an AI-based CHI system to benefit patients.

CONCLUSIONS

This scoping review showed the trend in AI-based CHI systems and their impact on patient outcomes as well as patients' perceptions of these systems. Future studies should also explore how clinicians and health care professionals perceive these consumer-based systems and integrate them into the overall workflow.

Can digital health researchers make a difference during the pandemic? Results of the single-arm, chatbot-led Elena+: Care for COVID-19 interventional study

Background

The current paper details findings from Elena+: Care for COVID-19, an app developed to tackle the collateral damage of lockdowns and social distancing, by offering pandemic lifestyle coaching across seven health areas: anxiety, loneliness, mental resources, sleep, diet and nutrition, physical activity, and COVID-19 information.

Methods

The Elena+ app functions as a single-arm interventional study, with participants recruited predominantly via social media. We used paired samples T-tests and within subjects ANOVA to examine changes in health outcome assessments and user experience evaluations over time. To investigate the mediating role of behavioral activation (i.e., users setting behavioral intentions and reporting actual behaviors) we use mixed-effect regression models. Free-text entries were analyzed qualitatively.

Results

Results show strong demand for publicly available lifestyle coaching during the pandemic, with total downloads (N = 7'135) and 55.8% of downloaders opening the app (n = 3,928) with 9.8% completing at least one subtopic (n = 698). Greatest areas of health vulnerability as assessed with screening measures were physical activity with 62% (n = 1,000) and anxiety with 46.5% (n = 760). The app was effective in the treatment of mental health; with a significant decrease in depression between first (14 days), second (28 days), and third (42 days) assessments: F2,38 = 7.01, p = 0.003, with a large effect size (η2G = 0.14), and anxiety between first and second assessments: t54 = 3.7, p = <0.001 with a medium effect size (Cohen d = 0.499). Those that followed the coaching program increased in net promoter score between the first and second assessment: t36 = 2.08, p = 0.045 with a small to medium effect size (Cohen d = 0.342). Mediation analyses showed that while increasing number of subtopics completed increased behavioral activation (i.e., match between behavioral intentions and self-reported actual behaviors), behavioral activation did not mediate the relationship to improvements in health outcome assessments.

Conclusions

Findings show that: (i) there is public demand for chatbot led digital coaching, (ii) such tools can be effective in delivering treatment success, and (iii) they are highly valued by their long-term user base. As the current intervention was developed at rapid speed to meet the emergency pandemic context, the future looks bright for other public health focused chatbot-led digital health interventions.

Facilitators and barriers to physical activity among English adolescents in secondary schools: a mixed method study

Background

It is evident that physical activity (PA) programmes implemented in schools were not effective in improving PA behaviours among adolescents. This study investigated students' perceptions of barriers to PA among inactive English adolescents in secondary schools based on the Capability, Opportunity, Motivation, and Behaviour (COM-B) model, the Behaviour Change Wheel (BCW), and Theoretical Domains Framework (TDF). The study compared barriers faced by inactive and active groups participating in sports and PA in secondary schools to identify sources of behaviour contributing to inactivity.

Methods

A pre-intervention online survey was distributed to affiliated schools by 233 Teaching Schools Alliances (TSAs) as part of the monitoring and evaluation of the Secondary Teacher Training study. Data were cross-tabulated to analyse activity levels and behavioural barriers for active and inactive groups, using the COM-B domains. The research team followed a seven-step process to categorise barriers based on their relevant domain in the TDF mapped to the COM-B.

Results

The findings were derived from one of the most extensive surveys of adolescents ever undertaken involving 200,623 active and 8,231 inactive respondents. The study identified 52 barriers and 68 behaviours that prevent adolescents from participating in PA. Psychological and social barriers were found to affect all activity levels, genders, and ethnic groups, with a lack of confidence and self-consciousness being the most prevalent. Certain demographic groups, such as those from minority ethnic groups and disabled individuals, were found to be overrepresented among inactive populations. The finding of the study indicated that there were common barriers that affected both inactive and active groups, with further similarity when examining barriers between active and inactive girls. The study also found that girls were more likely to experience the main barriers compared to boys, while inactive boys were more likely to encounter these barriers compared to active boys. The findings suggest that common barriers could be addressed across the population, while recognising some differences in demographics, and the need to provide personalised support. Targeted interventions are also suggested for all girls and inactive boys.

Conclusion

This study highlights the range of barriers that impact adolescents and provides insight into potential mechanisms for behaviour change, including intervention functions, policy categories, and evidence-based behaviour change tools. The study highlights the need for further research to address the barriers to PA among adolescents, particularly those who are inactive. Utilising the findings of this study, future research should investigate the effectiveness of novel digital exercise interventions and policies in increasing PA levels among children and adolescents. Complex digital exercise interventions, including conversational AI solutions, could provide personalised tools to identify and revolutionise support around the multitude of barriers that impact adolescents globally."For the purpose of open access, the author has applied a Creative Commons Attribution (CC BY) licence to any Author Accepted Manuscript version arising from this submission."