Human Coaching Research Articles

Satisfaction, user experiences, and initial efficacy of a technology-supported self-management intervention (iSMART) to improve post-stroke functioning: a remoted randomized controlled trial

ABSTRACT Background Self-management interventions empower individuals to manage their chronic conditions and daily life after stroke. However, traditional in-person self-management interventions often face transportation and geographical barriers. Digital interventions may offer a solution to address this gap. Objectives This pilot randomized controlled trial (RCT) aimed to examine treatment satisfaction, user experiences, and the initial effect of the interactive Self-Management Augmented by Rehabilitation Technologies (iSMART) intervention in stroke survivors to improve post-stroke functioning. Methods Participants (N = 24) with mild-to-moderate chronic stroke completed a parallel, 2-arm, nonblinded, remote RCT. They were randomly assigned to either a 12-week iSMART or a control intervention (post-stroke information). iSMART was a technology-based self-management intervention involving skill-building education, human coaching, and text messaging. Participants completed a battery measuring treatment satisfaction, use experiences, and function and participation outcomes at baseline and post-intervention. Results iSMART participants expressed greater satisfaction with their treatment (r = 0.387), healthcare environment (r = 0.454), relationships with providers (r = 0.374), and higher expectations for positive treatment outcomes (r = 0.328) than control participants, with medium effect sizes. The iSMART group rated the overall program and its coaching, skill-building, and text messaging components as helpful. iSMART participants showed a medium effect in improving overall post-stroke functioning, but control participants showed a small effect. Moreover, iSMART participants showed moderate-to-large effects in improving hand function (r = 0.699), mobility (r = 0.499), memory and thinking (r = 0.436), communication (r = 0.416), social participation (r = 0.307), community reintegration (r = 0.652), and perceived recovery (r = 0.545). Conclusions Our results provide initial evidence that iSMART supports stroke survivors in managing chronic conditions and enhancing post-stroke functioning.

Conversate: Supporting Reflective Learning in Interview Practice Through Interactive Simulation and Dialogic Feedback

Job interviews play a critical role in shaping one's career, yet practicing interview skills can be challenging, especially without access to human coaches or peers for feedback. Recent advancements in large language models (LLMs) present an opportunity to enhance the interview practice experience. Yet, little research has explored the effectiveness and user perceptions of such systems or the benefits and challenges of using LLMs for interview practice. Furthermore, while prior work and recent commercial tools have demonstrated the potential of AI to assist with interview practice, they often deliver one-way feedback, where users only receive information about their performance. By contrast, dialogic feedback , a concept developed in learning sciences, is a two-way interaction feedback process that allows users to further engage with and learn from the provided feedback through interactive dialogue. This paper introduces Conversate, a web-based application that supports reflective learning in job interview practice by leveraging large language models (LLMs) for interactive interview simulations and dialogic feedback. To start the interview session, the user provides the title of a job position (e.g., entry-level software engineer) in the system. Then, our system will initialize the LLM agent to start the interview simulation by asking the user an opening interview question and following up with questions carefully adapted to subsequent user responses. After the interview session, our back-end LLM framework will then analyze the user's responses and highlight areas for improvement. Users can then annotate the transcript by selecting specific sections and writing self-reflections. Finally, the user can interact with the system for dialogic feedback, conversing with the LLM agent to learn from and iteratively refine their answers based on the agent's guidance. To evaluate Conversate, we conducted a user study with 19 participants to understand their perceptions of using LLM-supported interview simulation and dialogic feedback. Our findings show that participants valued the adaptive follow-up questions from LLMs, as they enhanced the realism of interview simulations and encouraged deeper thinking. Participants also appreciated the AI-assisted annotation, as it reduced their cognitive burden and mitigated excessive self-criticism in their own evaluation of their interview performance. Moreover, participants found the LLM-supported dialogic feedback to be beneficial, as it promoted personalized and continuous learning, reduced feelings of judgment, and allowed them to express disagreement.

Usability, acceptance, and the role of realism in virtual humans for breathing exercise training

Breathing exercises are used to address a multitude of issues ranging from mental health to physical well-being. While they are recommended for various health conditions, access to professional support is often limited due to time and cost constraints. Virtual Reality technology offers a promising solution for enhancing mindfulness and relaxation practices, including breathing exercises. In our study, we explored the effectiveness of a computer-generated “virtual breathing coach”. Our virtual coach has a human appearance, speaks in a human voice, and displays breathing physiology while guiding the participants through a set of breathing exercises of different cycle lengths. The agent exhibits breathing behaviour with the aim of augmenting the influence the agent exerts through the mechanisms of physiological entrainment known from human-to-human interaction. Our empirical, exploratory study with 20 participants, was conducted using non-immersive VR where the system ran on a web browser. On the one hand, we investigated the perception of the virtual human coach, and on the other hand, we evaluated the usability, acceptability, and adherence of the system. We also evaluated the role of realism in acceptance of and adherence to the virtual coach. Participants’ responses were recorded using questionnaires. Participants were influenced by the virtual coach and adhered to it if they perceived the virtual coach as more realistic. The participants reported that the system ran well, and they would be willing to continue using the system in the future. Although the virtual human-to-human interaction experience provided by our system is not on par with the naturalistic human-to-human interaction, the results indicate that people are willing to replace a real human coach with a virtual coach. Systems like this are a step towards improving the affordability and on-demand access to another human, such as a coach in our system, especially where access and affordability are limited such as in healthcare. The qualities such as trust and likeability in an embodied interaction can make this interaction more efficient and effective.

Preventing type 2 diabetes in Portugal: a novel person-centered digital intervention

Abstract Introduction Diabetes, particularly type 2 diabetes (T2D), stands as one of the leading noncommunicable diseases worldwide. In Portugal, the prevalence of diabetes has surged, with T2D affecting 14% of adults in 2021. Its repercussions extend beyond individual health, deeply impacting familial, social, and community dynamics, presenting a significant public health challenge globally. Objectives The VA|PREVENTION project (https://doi.org/10.54499/2022.02969.PTDC) aims to assess the (cost-) effectiveness and implementation of an innovative person-centered digital intervention to prevent T2D among high-risk adults. Methodology A comprehensive approach encompasses the Behavior Change Wheel for intervention design, scalability principles, the WHO guidelines for digital health interventions, co-design, and the ISO framework for usability assessment. Evaluation will be pursued through a type 1 hybrid effectiveness-implementation study (randomized controlled trial), supplemented by qualitative and economic studies. Results The web application prototype features Ema, an automated virtual human coach (VHC), educational menus and a dashboard (version 1.0). Co-design included 2 workshops with 11 representatives of end-users and health professionals. Ema uses standardized techniques from an ontology to support behavior change, based on an initial assessment of low-level target behaviors (e.g. walking, fruit consumption, and reducing red meat intake), and barriers to performing these behaviors. Ema communicates in a helpful-cooperative style using a synthetic voice in European Portuguese, with its speech also appearing as subtitles on the interface. Users interact with Ema by pressing buttons or entering data, such as the daily number of steps. Ethics approval was granted for usability assessment with end-users. Conclusions The project is expected to yield health, scientific, technological, and policy impacts, powered by an exploitation and sustainability roadmap. Key messages • Innovative type 2 diabetes prevention through a scalable web app with a virtual human coach. • Comprehensive and robust approach for developing and evaluating the digital behavior change intervention.

Advancing health coaching: A comparative study of large language model and health coaches

ObjectiveRecent advances in large language models (LLM) offer opportunities to automate health coaching. With zero-shot learning ability, LLMs could revolutionize health coaching by providing better accessibility, scalability, and customization. The aim of this study is to compare the quality of responses to clients' sleep-related questions provided by health coaches and an LLM. Design, setting, and participantsFrom a de-identified dataset of coaching conversations from a pilot randomized controlled trial, we extracted 100 question-answer pairs comprising client questions and corresponding health coach responses. These questions were entered into a retrieval-augmented generation (RAG)-enabled open-source LLM (LLaMa-2-7b-chat) to generate LLM responses. Out of 100 question-answer pairs, 90 were taken out and assigned to three groups of evaluators: experts, lay-users, and GPT-4. Each group conducted two evaluation tasks: (Task 1) a single-response quality assessment spanning five criteria—accuracy, readability, helpfulness, empathy, and likelihood of harm—rated on a five-point Likert scale, and (Task 2) a pairwise comparison to choose the superior response between pairs. A suite of inferential statistical methods, including the paired and independent sample t-tests, Pearson correlation, and chi-square tests, were utilized to answer the study objective. Recognizing potential biases in human judgment, the remaining 10 question-answer pairs were used to assess inter-evaluator reliability among the human evaluators, quantified using the interclass correlation coefficient and percentage agreement metrics. ResultsUpon exclusion of incomplete data, the analysis included 178 single-response evaluations (Task 1) and 83 pairwise comparisons (Task 2). Expert and GPT-4 assessments revealed no discernible disparities in health coach and LLM responses across the five metrics. Contrarily, lay-users deemed LLM responses significantly more helpful than that of human coaches (p < 0.05). LLM responses were preferred in the majority (62.25 %, n = 155) of the aggregate 249 assessments, with all three evaluator groups favoring LLM over health coach inputs. While GPT-4 rated both health coach and LLM responses significantly higher than experts in terms of readability, helpfulness, and empathy, its ratings on accuracy and likelihood of harm aligned with those of experts. Response length positively correlated with accuracy and empathy scores, but negatively affected readability across all evaluator groups. Expert and lay-user evaluators demonstrated moderate to high inter-evaluator reliability. ConclusionOur study showed encouraging findings by demonstrating that RAG-enabled LLM has comparable performance to health coaches in the domain tested. Serving as an initial step towards the creation of more sophisticated, adaptive, round-the-clock automated health coaching systems, our findings call for more extensive evaluation which could assist in the development of the model that could in the future lead to potential clinical implementation.

Unearthing AI coaching chatbots capabilities for professional coaching: a systematic literature review

PurposeRecent advances in coaching technology enhanced its accessibility and affordability for a broader population. In the imposing growth of economy and the demand for extensive coaching intervention for executives, artificial intelligence (AI)-based coaching is one of the possible solutions. While the evidence of AI coaching effectiveness is expanding, a comprehensive understanding of the field remains elusive. In particular, the true potential of AI coaching tools, ethical considerations and their current functionality are subjects of ongoing investigation.Design/methodology/approachThe systematic literature review was conducted to extract experimental results and concepts about utilizing AI in coaching practice. The paper presents the primary capabilities of state-of-the-art coaching tools and compares them with human coaching.FindingsThe review shows that AI coaching chatbots and tools are effective for narrow tasks such as goal attainment, support for various psychological conditions and induction of reflection processes. Whereas, deep long-term coaching, working alliance and individualized approach are out of current AI coaching competence. In the current state, AI coaching tools serve as complementary helping tools that cannot replace human coaching. However, they have the potential to enhance the coach’s performance and serve as valuable assistants in intricate coaching interventions.Originality/valueThe review offered insights into the current capabilities of AI coaching chatbots, aligned with International Coaching Federation set of competencies. The review outlined the drawbacks and benefits of chatbots and their areas of application in coaching.

Assessing the Short-Term Efficacy of Digital Cognitive Behavioral Therapy for Insomnia With Different Types of Coaching: Randomized Controlled Comparative Trial.

Digital cognitive behavioral therapy for insomnia (dCBTi) is an effective intervention for treating insomnia. The findings regarding its efficacy compared to face-to-face cognitive behavioral therapy for insomnia are inconclusive but suggest that dCBTi might be inferior. The lack of human support and low treatment adherence are believed to be barriers to dCBTi achieving its optimal efficacy. However, there has yet to be a direct comparative trial of dCBTi with different types of coaching support. This study examines whether adding chatbot-based and human coaching would improve the treatment efficacy of, and adherence to, dCBTi. Overall, 129 participants (n=98, 76% women; age: mean 34.09, SD 12.05 y) whose scores on the Insomnia Severity Index [ISI] were greater than 9 were recruited. A randomized controlled comparative trial with 5 arms was conducted: dCBTi with chatbot-based coaching and therapist support (dCBTi-therapist), dCBTi with chatbot-based coaching and research assistant support, dCBTi with chatbot-based coaching only, dCBTi without any coaching, and digital sleep hygiene and self-monitoring control. Participants were blinded to the condition assignment and study hypotheses, and the outcomes were self-assessed using questionnaires administered on the web. The outcomes included measures of insomnia (the ISI and the Sleep Condition Indicator), mood disturbances, fatigue, daytime sleepiness, quality of life, dysfunctional beliefs about sleep, and sleep-related safety behaviors administered at baseline, after treatment, and at 4-week follow-up. Treatment adherence was measured by the completion of video sessions and sleep diaries. An intention-to-treat analysis was conducted. Significant condition-by-time interaction effects showed that dCBTi recipients, regardless of having any coaching, had greater improvements in insomnia measured by the Sleep Condition Indicator (P=.003; d=0.45) but not the ISI (P=.86; d=-0.28), depressive symptoms (P<.001; d=-0.62), anxiety (P=.01; d=-0.40), fatigue (P=.02; d=-0.35), dysfunctional beliefs about sleep (P<.001; d=-0.53), and safety behaviors related to sleep (P=.001; d=-0.50) than those who received digital sleep hygiene and self-monitoring control. The addition of chatbot-based coaching and human support did not improve treatment efficacy. However, adding human support promoted greater reductions in fatigue (P=.03; d=-0.33) and sleep-related safety behaviors (P=.05; d=-0.30) than dCBTi with chatbot-based coaching only at 4-week follow-up. dCBTi-therapist had the highest video and diary completion rates compared to other conditions (video: 16/25, 60% in dCBTi-therapist vs <3/21, <25% in dCBTi without any coaching), indicating greater treatment adherence. Our findings support the efficacy of dCBTi in treating insomnia, reducing thoughts and behaviors that perpetuate insomnia, reducing mood disturbances and fatigue, and improving quality of life. Adding chatbot-based coaching and human support did not significantly improve the efficacy of dCBTi after treatment. However, adding human support had incremental benefits on reducing fatigue and behaviors that could perpetuate insomnia, and hence may improve long-term efficacy. ClinicalTrials.gov NCT05136638; https://www.clinicaltrials.gov/study/NCT05136638.

The influence of an artificial intelligence chatbot coach assistant on the human coach-client working alliance

ABSTRACT Using stand-alone AI chatbot coaches in organisational coaching is becoming a reality promising to democratise, scale and enhance human coaching. However there is limited research on the hybrid use of human and AI coaching. Using the working alliance theory this paper reports on the perceptions of coaches and clients on the use of chatbot-assisted human coaching. In two qualitative studies coaches (study 1) and clients (study 2) were exposed to an AI chatbot coach assistant after which interviews were conducted and data analysed using thematic analysis. Themes were classified using working alliance theory. Coaches were concerned about the chatbot’s potential negative interference in the coach-client relationship. Clients on the other hand described a more synergetic, psychologically safe experience provided the chatbot is endorsed by the human coach. Coaches and clients thought the chatbot was useful for goal tracking, accountability and convenience and stressed as a prerequisite the importance of the chatbot being flexible, reliable and humane. This study provides important initial insights into the under-researched, new frontier of human-AI collaboration in organisational coaching, highlighting the pivotal role of the coach’s attitude towards this technology. This knowledge can help to operationalise the immense potential benefits of AI into organisational coaching practice.

Effects of interacting with a large language model compared with a human coach on the clinical diagnostic process and outcomes among fourth-year medical students: study protocol for a prospective, randomised experiment using patient vignettes

IntroductionVersatile large language models (LLMs) have the potential to augment diagnostic decision-making by assisting diagnosticians, thanks to their ability to engage in open-ended, natural conversations and their comprehensive knowledge access....

‘AI coaching’: democratising coaching service or offering an ersatz?

ABSTRACT Organisational coaching is not the only field that has recently been challenged by the extraordinary developments in artificial intelligence (AI) with a threat of replacement of human practitioners by machines. The challenges of this field, however, are unique because the nature of coaching remains fairly elusive, making comparison between human coaching and AI coaching a complicated exercise. In this paper we conceptualise the essential characteristics of organisational coaching and propose a number of criteria according to which interventions can be identified as coaching. Our conclusion is that AI ‘coaching’ does not meet these criteria. However, in considering various genres of organisational coaching along simple model-based types, we identify a number of elements in human coaching that could be augmented to various degrees by the use of AI.

Abstract PS02-07: Effectiveness of 24-week mobile application based human coaching program for controlling weight, BMI and body composition in overweight/obese breast cancer survivors: Single-arm prospective cohort study

Abstract Background: Overweight/obesity has been known as a prognostic factor for breast cancer recurrence and breast cancer related death. However, weight control is hard in breast cancer survivors because of menopause, chemotherapy, anti-hormonal therapy, and psychologic issues. This study aimed to develop 24-week mobile application based human coaching program with Noom and evaluate its efficacy in overweight/obese breast cancer survivors. Methods In this single-arm prospective cohort study, 130 breast cancer survivors with BMI ≥25 were enrolled and received 24-week program including diet-,exercise-, and psychology-based contents with trained human coach in Noom during 2019-2021. For hyperactive group who joined more than 16 weeks, we evaluated weight, BMI, lipid level, bioimpedance, and Quality of Life (QoL) at baseline, 6 month and 12month follow-up. Results Among 130 breast cancer survivor, 101 participants (77.7%) and 93 participants (71.5%) completed 6 month and 12 month follow-up, respectively. In hyperactive group (68/101, 67%), body weight and BMI reduced significantly (mean difference: -1.97 kg, 95% CI (confidence interval):-2.65- -1.26, P&amp;lt; 0.001 and -0.86, 95% CI:-1.15- -0.56, P&amp;lt; 0.001, respectively) at 6 month and maintained at 12 month without the yo-yo effect. Among the lipid panel, triglycerides level decreased significantly (-34.13, 95% CI:-58.09- -10.17, P=0.006) and maintained at 12 month. In the aspect of bioimpedance components, skeletal muscle mass (SMM, kg), body fat mass (BFM, kg), percent body fat (PBF, %), waist-hip ratio (WHR), and visceral fat area (VFA, cm2) were improved for first 6 months. However, WHR and VFA increased during next 6months. Based on the EORTC QLQ C30 and BR 23, nausea/vomiting, constipation, body image, arm and breast symptoms showed significant improvement during the first 6monts. Conclusions This study demonstrated that 24-week mobile application based human coaching program is beneficial for controlling body weight, BMI, TG and body composition in bioimpedance for overweigh/obsess breast cancer survivors. ClinicalTrials.gov NCT05506189; https://clinicaltrials.gov/ct2/show/ NCT05506189. IRB: NCC2019-0098 Citation Format: So-Youn Jung, Eun-Gyeong Lee, Dong-Eun Lee, Jeongseon Kim, Jai Hong Han, Seeyoun Lee, Han-Sung Kang, Eun Sook Lee, Heejung Chae, Sung Hoon Sim, Keun Seok Lee, Jungeun Lee. Effectiveness of 24-week mobile application based human coaching program for controlling weight, BMI and body composition in overweight/obese breast cancer survivors: Single-arm prospective cohort study [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PS02-07.

Increasing Engagement of College Students at Risk for Suicide in Online Intervention: A Qualitative Analysis

This study aimed to identify barriers and facilitators to engaging with online interventions among 24 ethnoracially diverse college students self-identifying as being at risk for suicide. Thematic analysis was used to identify codes from focus groups. Participants shared mixed perceptions about online intervention use and effectiveness. Regarding barriers, they mentioned lack of motivation/self-discipline, artificiality, and lack of perceived need. Participants reported promotional information, incentives, and normalizing peer usage to facilitate uptake. Lastly, they mentioned program design, human coaching, and reminders to increase retention. These insights inform future research aimed at developing online intervention engagement models to increase uptake and retention.

Digital interventions for healthy ageing and cognitive health in older adults: a systematic review of mixed method studies and meta-analysis

BackgroundCurrently, there is no systematic review to investigate the effectiveness of digital interventions for healthy ageing and cognitive health of older adults. This study aimed to conduct a systematic review to evaluate the effectiveness of digital intervention studies for facilitating healthy ageing and cognitive health and further identify the considerations of its application to older adults.MethodsA systematic review and meta-analysis of literature were conducted across CINAHL, Medline, ProQuest, Cochrane, Scopus, and PubMed databases following the PRISMA guideline. All included studies were appraised using the Mixed Methods Appraisal Tool Checklist by independent reviewers. Meta-analyses were performed using JBI SUMARI software to compare quantitative studies. Thematic analyses were used for qualitative studies and synthesised into the emerging themes.ResultsThirteen studies were included. Quantitative results showed no statistically significant pooled effect between health knowledge and healthy behaviour (I2 =76, p=0.436, 95% CI [-0.32,0.74]), and between cardiovascular-related health risks and care dependency I2=0, p=0.426, 95% CI [0.90,1.29]). However, a statistically significant cognitive function preservation was found in older adults who had long-term use of laptop/cellphone devices and had engaged in the computer-based physical activity program (I2=0, p<0.001, 95% CI [0.01, 0.21]). Qualitative themes for the considerations of digital application to older adults were digital engagement, communication, independence, human connection, privacy, and cost.ConclusionsDigital interventions used in older adults to facilitate healthy ageing were not always effective. Health knowledge improvement does not necessarily result in health risk reduction in that knowledge translation is key. Factors influencing knowledge translation (i.e., digital engagement, human coaching etc) were identified to determine the intervention effects. However, using digital devices appeared beneficial to maintain older adults’ cognitive functions in the longer term. Therefore, the review findings suggest that the expanded meaning of a person-centred concept (i.e., from social, environmental, and healthcare system aspects) should be pursued in future practice. Privacy and cost concerns of technologies need ongoing scrutiny from policy bodies. Future research looking into the respective health benefits can provide more understanding of the current digital intervention applied to older adults.Study registrationPROSPERO record ID: CRD42023400707 https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=400707.

Robots as Mental Well-being Coaches: Design and Ethical Recommendations

The last decade has shown a growing interest in robots as well-being coaches. However, insightful guidelines for the design of robots as coaches to promote mental well-being have not yet been proposed. This paper details design and ethical recommendations based on a qualitative analysis drawing on a grounded theory approach, which was conducted with a three-step iterative design process which included user-centered design studies involving robotic well-being coaches, namely: (1) a user-centred design study conducted with 11 participants consisting of both prospective users who had participated in a Brief Solution-Focused Practice study with a human coach, as well as coaches of different disciplines, (2) semi-structured individual interview data gathered from 20 participants attending a Positive Psychology intervention study with the robotic well-being coach Pepper, and (3) a user-centred design study conducted with 3 participants of the Positive Psychology study as well as 2 relevant well-being coaches. After conducting a thematic analysis and a qualitative analysis, we collated the data gathered into convergent and divergent themes, and we distilled from those results a set of design guidelines and ethical considerations. Our findings can inform researchers and roboticists on the key aspects to take into account when designing robotic mental well-being coaches.

PoseCoach: A Customizable Analysis and Visualization System for Video-Based Running Coaching.

Videos are an accessible form of media for analyzing sports postures and providing feedback to athletes. Existing sport-specific systems embed bespoke human pose attributes and thus can be hard to scale for new attributes, especially for users without programming experiences. Some systems retain scalability by directly showing the differences between two poses, but they might not clearly visualize the key differences that viewers would like to pursue. Besides, video-based coaching systems often present feedback on the correctness of poses by augmenting videos with visual markers or reference poses. However, previewing and augmenting videos limit the analysis and visualization of human poses due to the fixed viewpoints in videos, which confine the observation of captured human movements and cause ambiguity in the augmented feedback. To address these issues, we study customizable human pose data analysis and visualization in the context of running pose attributes, such as joint angles and step distances. Based on existing literature and a formative study, we have designed and implemented a system, PoseCoach, to provide feedback on running poses for amateurs by comparing the running poses between a novice and an expert. PoseCoach adopts a customizable data analysis model to allow users' controllability in defining pose attributes of their interests through our interface. To avoid the influence of viewpoint differences and provide intuitive feedback, PoseCoach visualizes the pose differences as part-based 3D animations on a human model to imitate the demonstration of a human coach. We conduct a user study to verify our design components and conduct expert interviews to evaluate the usefulness of the system.

Development of an Automated and Scalable Virtual Assistant to Aid in PPE Adherence: A Study with Implications for Applications within Anesthesiology.

Virtual assistants (VAs) are conversational agents that are able to provide cognitive aid. We developed a VA device for donning and doffing personal protective equipment (PPE) procedures and compared it to live human coaching to explore the feasibility of using VAs in the anesthesiology setting. An automated, scalable, voice-enabled VA was built using the Amazon Alexa device and Alexa Skills application. The device utilized voice-recognition technology to allow a touch-free interactive user experience. Audio and video step-by-step instructions for proper donning and doffing of PPE were programmed and displayed on an Echo Show device. The effectiveness of VA in aiding adherence to PPE protocols was compared to traditional human coaching in a randomized, controlled, single-blinded crossover design. 70 anesthesiologists, anesthesia assistants, respiratory therapists, and operating room nurses performed both donning and doffing procedures, once under step-by-step VA instructional guidance and once with human coaching. Performance was assessed using objective performance evaluation donning and doffing checklists. More participants in the VA group correctly performed the step of "Wash hands for 20 seconds" during both donning and doffing tests. Fewer participants in the VA group correctly performed the steps of "Put cap on and ensure covers hair and ears" and "Tie gown on back and around neck". The mean doffing total score was higher in the VA group; however, the donning score was similar in both groups. Our study demonstrates that it is feasible to use commercially available technology to create a voice-enabled VA that provides effective step-by-step instructions to healthcare professionals.

Youth perspectives on mobile health adherence interventions: A qualitative study guided by the supportive accountability model

ObjectiveWe applied a Supportive Accountability Model lens to understand how youth view remote human coaching versus automated reminders targeting medication adherence. MethodsWe used thematic analysis to interpret (N = 22) youths’ responses to semi-structured interviews after 12 weeks of mobile health intervention. ResultsParticipants reported that both coaching and automated reminders prompted them to take medication, improving their adherence. Participants found coaching helpful because they developed routines and strategies, were motivated to avoid disappointing their coach, and felt their coach cared for them. Automated support could be motivational for some but demanded less engagement. Participants described phone calls as disruptive to their daily lives, but conducive to developing a personal connection with their coach, whereas texts were easier and more flexible. Youth emphasized that individual preferences often differ. ConclusionHuman coaching was viewed as a more potent, engaging adherence intervention than automated reminders, although individual needs and preferences differed. Phone calls may enhance the experience of supportive accountability for adherence, but also pose greater acceptability and usability barriers than texting. Practice ImplicationsIntervention developers should provide opportunities for youth to make personal connections with human adherence supporters and attend to youth preferences for communication modality.

Long-Term Efficacy of primary care combining a mobile healthcare application with human coaching

Abstract Background Chronic diseases have become a global burden, and digital self-healthcare using mobile devices has gained importance in managing these conditions. However, barriers to implementing mobile self-management healthcare applications in current primary care system, and previous research has focused on short-term effects. This study aims to evaluate the long-lasting effect of combining human coaching with mobile self-management healthcare applications for chronic disease patients in primary healthcare services. Methods The study recruited 178 patients with chronic diseases from participating primary care clinics. The intervention group (N = 128) received a 12-week mobile self-management healthcare application service combined with traditional primary care, while the control group (N = 50) received only traditional healthcare. The Noom application was used, with two weekly human coaching sessions by a nutritionist. A telephone survey on weight changes was conducted one year after the intervention. Results The intervention group showed a greater weight loss compared to the control group during the intervention period (Intervention: -1.29 ± 2.94 vs Control: -0.38 ± 1.72, P-value 0.002). Although there was no statistically significant difference in additional weight change between the two groups during the follow-up period, the difference in weight loss during the intervention period was maintained (Intervention: -2.16 ± 5.54 vs Control: -1.06 ± 3.24, P-value: 0.008). Additionally, among the intervention group continuously for 1 year using the health management application, the average weight loss was higher than the other groups. Conclusions Combining a mobile self-management healthcare application with human coaching has long-lasting effects and can be effective in primary healthcare services. This suggests that such an approach has the potential to be a treatment option in current primary healthcare systems, but further large-scale long-term studies are needed. Key messages • This study suggests that using mobile self-management healthcare apps with human coaching can be an effective long-term treatment option for chronic disease patients in primary healthcare services. • The combination of the two methods may provide better outcomes than traditional healthcare services alone.

Defining digital coaching: a qualitative inductive approach.

The term 'digital coaching' is widely used but ill-defined. The present study therefore investigates how digital coaching is defined and how it differentiates from face-to-face coaching and other digital-technology-enabled (DT-enabled) formats, such as digital training, digital mentoring, or digital consulting. A qualitative inductive approach was chosen for more in-depth and open-minded content. Based on previous studies on the importance of asking coaches working in the field, 260 coaches working in the field of digital coaching were surveyed. The given answers depict the importance of differing between forms of DT-enabled coaching. Thus, digital coaching is a DT-enabled, synchronous conversation between a human coach and a human coachee, which is different to artificial intelligence (AI) coaching and coaching that is supported by asynchronous digital and learning communication technologies. Due to this definition and differentiation, future studies can explore the digital coaching process and its effectiveness - particularly in comparison to other formats. Furthermore, this clear definition enables practitioners to maintain professional standards and manage client's expectations of digital coaching while helping clients understand what to expect from digital coaching.

Effective Behavior Change Techniques in Digital Health Interventions for the Prevention or Management of Noncommunicable Diseases: An Umbrella Review.

Despite an abundance of digital health interventions (DHIs) targeting the prevention and management of noncommunicable diseases (NCDs), it is unclear what specific components make a DHI effective. This narrative umbrella review aimed to identify the most effective behavior change techniques (BCTs) in DHIs that address the prevention or management of NCDs. Five electronic databases were searched for articles published in English between January 2007 and December 2022. Studies were included if they were systematic reviews or meta-analyses of DHIs targeting the modification of one or more NCD-related risk factors in adults. BCTs were coded using the Behavior Change Technique Taxonomy v1. Study quality was assessed using AMSTAR 2. Eighty-five articles, spanning 12 health domains and comprising over 865,000 individual participants, were included in the review. We found evidence that DHIs are effective in improving health outcomes for patients with cardiovascular disease, cancer, type 2 diabetes, and asthma, and health-related behaviors including physical activity, sedentary behavior, diet, weight management, medication adherence, and abstinence from substance use. There was strong evidence to suggest that credible source, social support, prompts and cues, graded tasks, goals and planning, feedback and monitoring, human coaching and personalization components increase the effectiveness of DHIs targeting the prevention and management of NCDs. This review identifies the most common and effective BCTs used in DHIs, which warrant prioritization for integration into future interventions. These findings are critical for the future development and upscaling of DHIs and should inform best practice guidelines.