Search Results (192)

Coordinating multiple unmanned aerial vehicles (UAVs) for cooperative missions requires agents that perceive their environment, reason about objectives, and generate joint actions. Vision–language–action (VLA) models unify these capabilities but lack a principled source of multi-agent training data and suffer from a training–inference discrepancy in closed-loop control. We propose MiniUAV-VLA, a compact centralized VLA controller for simulated multi-UAV search-and-elimination based on multi-agent reinforcement learning (MARL) expert distillation. A QMIX expert policy achieving 100% mission success generates multimodal demonstrations pairing rendered tactical map images with structured textual state prompts. A 158 M-parameter VLA model with approximately 65 M trainable parameters in the MiniMind-3V backbone and vision projection is fine-tuned with a multi-agent discrete action head that jointly predicts actions for all UAVs in a single forward pass. We identify a training–inference feature mismatch in behavior cloning and address it via prompt-end action pooling, which extracts action-relevant hidden states at the user–prompt boundary rather than after the generated response. In closed-loop evaluation with four drones and six mobile targets averaged over five evaluation seeds, MiniUAV-VLA reaches 74.4 ± 4.6% mission success against 9.4 ± 2.1% for a random policy and 16.2 ± 3.2% for an observation-limited greedy baseline. Across five independent training runs, prompt-end action pooling improves mean closed-loop success from 40.6% to 76.2% over the last-token alternative. These results support MARL expert distillation as a data-efficient route to compact multi-agent VLA control in this simulated setting. Full article

Background: Biological aging varies substantially among individuals of the same chronological age, yet biological aging biomarkers require biospecimen collection and laboratory processing, limiting scalability for repeated monitoring. Objective: This study evaluated whether consumer smartwatch-derived digital biomarkers are associated with the pace of biological aging, quantified using DunedinPACE, a biomarker that measures biological aging rates. Methods: Baseline data were analyzed from the Healthy Minds for Life study within the Precision Aging Network. Among 1305 enrolled participants, 621 community-dwelling adults aged 50–79 years had both DunedinPACE and smartwatch data available. Smartwatch-derived biomarkers were grouped into mobility, cardiorespiratory physiology, sleep, and physiological stress domains. Associations with DunedinPACE were examined using Spearman correlations and adjusted linear regression models. Results: Significant associations were observed across all four domains. Daily step count showed the strongest mobility-domain association with DunedinPACE (r = −0.25, p < 0.01). Mean sleep peripheral oxygen saturation showed the strongest sleep-domain association (r = −0.22, p < 0.01). Resting heart rate showed the strongest physiological stress-domain association (r = 0.24, p < 0.01). Adjusted regression models supported these findings, with lower daily mobility, poorer sleep oxygenation and efficiency, greater wake after sleep onset, and higher resting heart rate showing the most consistent associations with higher DunedinPACE values. Conclusions: Smartwatch-derived biomarkers captured a multidomain profile of accelerated biological aging characterized by lower mobility, poorer sleep health, and elevated physiological stress. Consumer wearables can provide low-burden, real-world indicators of biological vulnerability, although longitudinal studies are needed before clinical use. Full article

Background/Objectives: First responders frequently encounter high-stress environments that challenge physiological resilience and autonomic regulation. Heart rate variability (HRV) complexity is a critical marker of adaptive capacity and stress regulation. This study assessed the impact of a wearable-based mindfulness intervention on HRV complexity among first responders using a smartwatch. Methods: A total of 87 first responders participated in a one-month wearable-based intervention. Participants wore Garmin Vivosmart 5 devices to continuously collect PPG data (photoplethysmogram), focusing on beat-to-beat intervals (BBIs). The intervention involved daily Ecological Momentary Assessments (EMAs) and individual randomization to either a mindfulness message, a prompt to access an audio exercise, or no-treatment/control; interventions were delivered via the MYAPT.MIND mobile application. HRV metrics, including Sample Entropy, Multiscale Entropy (MSE), Recurrence Rate (RR), and Determinism (Det), were analyzed pre- and post-intervention/control using paired-samples t-tests. Results: Significant improvements were observed in HRV complexity metrics post-intervention. Sample Entropy increased (M = 1.42, SD = 0.11) compared to pre-intervention (M = 1.39, SD = 0.10; p = 0.007). MSE also showed significant gains (p = 0.038), particularly at lower scales, indicating enhanced short-term autonomic flexibility. Reductions were noted in RR (p = 0.025) and Det (p = 0.018), suggesting improved cardiovascular adaptability and reduced physiological rigidity. Other traditional time-domain metrics, such as Mean HR, SDNN, and RMSSD, did not exhibit significant changes. Conclusions: The wearable-based intervention significantly enhanced HRV complexity, reflecting improved autonomic regulation and adaptive capacity in first responders. These findings support the integration of digital mindfulness strategies for stress management in high-risk occupations. Future research should explore the longitudinal effects and mechanisms mediating these autonomic adaptations. Full article

Background/Objectives: Mindfulness has emerged as a widely studied approach for promoting psychological well-being, evolving from its contemplative origins into a secular, evidence-based intervention. In recent years, the proliferation of mobile applications has enabled the delivery of mindfulness-based interventions (MBIs) in accessible and scalable formats. This systematic review examined the efficacy of MBIs delivered via mobile applications in non-clinical adult populations, with a focus on well-being, compassion, and aggression. Methods: A comprehensive search was conducted across six databases (Web of Science, PubMed, MEDLINE, Scopus, SciELO, and Dialnet) for studies published from 2014 onward, following PRISMA 2020 guidelines. A total of 23 randomized controlled trials met the inclusion criteria. Additionally, a risk-of-bias assessment was performed using the Cochrane RoB-2 tool. Results: Results indicated small to moderate improvements in well-being outcomes, including positive affect, life satisfaction, and psychological well-being. Evidence regarding compassion was limited and mixed, while no studies addressing aggression met the inclusion criteria. Additionally, substantial variability in adherence rates and a high overall risk of bias were observed. Conclusions: These findings suggest that mindfulness applications may represent accessible tools for enhancing individual well-being; however, their effect on broader socio-emotional functioning remains unclear. Further research should prioritize more rigorous study designs, including active control conditions and behavioral outcome measures, to better establish their effectiveness and underlying mechanisms. Full article

Background: The global burden of mental disorders continues to escalate, necessitating scalable, evidence-based interventions. Artificial intelligence (AI)-delivered health promotion programs represent a promising approach to addressing treatment gaps by targeting the neuropsychological mechanisms that underlie mental health outcomes. This meta-analysis synthesizes evidence on the effectiveness of AI-delivered interventions in improving executive function, emotion regulation, and clinical outcomes across diverse populations. Methods: A systematic search identified 186 studies (n = 22,755 participants) published between 2020 and 2025. Random-effects meta-analyses estimated pooled effect sizes (Hedges’ g, calculated as between-group standardized mean differences with small-sample correction [J = 1 − 3/(4df − 1)]) for primary outcomes. Between-study heterogeneity was quantified using I² and τ² statistics. To address dependency among effect sizes from studies reporting multiple outcomes, robust variance estimation (RVE) was employed. Subgroup analyses examined intervention modalities, delivery formats, and clinical populations. Moderator analyses explored sources of heterogeneity, including publication year, sample size, intervention duration, control condition type, risk-of-bias rating, geographic region, and AI sophistication tier, and mediational models tested putative therapeutic mechanisms. Results: AI-delivered interventions demonstrated a significant overall effect on health outcomes (g = 0.68, 95% CI [0.58, 0.78]; τ² = 0.12; I² = 73.4%). Executive function outcomes showed moderate effects (g = 0.61, τ² = 0.08), with working memory improvements being strongest (g = 0.72). Emotion regulation outcomes demonstrated moderate-to-large effects (g = 0.61, 95% CI [0.51, 0.70], τ² = 0.006); formal subgroup pooled estimates by emotion regulation strategy were not calculated due to insufficient studies per strategy (k < 3 per category); individual study effect sizes ranged from g = 0.27 to g = 1.11. Among 41 studies examining neuropsychological mechanisms, convergent patterns suggested involvement of prefrontal neural circuits (DLPFC), enhanced alpha-band activity, and improved heart rate variability; however, formal mediation was tested in only 18 studies (9.7%). Among clinical populations, interventions for cognitive impairment yielded the largest effects (g = 1.02; this finding should be interpreted cautiously given modest cumulative sample size [n = 482], potential small-study effects [Egger’s p = 0.08], and trim-and-fill adjusted estimate of g = 0.85), followed by mental health conditions (g = 0.72), while other clinical populations showed smaller but significant improvements (g = 0.19). Mobile applications (g = 0.78) and chatbot-based interventions (g = 0.74) demonstrated the strongest effects among delivery formats. Among studies testing formal mediation, analyses suggested mindfulness (β = 0.42), decentering (β = 0.38), and cognitive reappraisal (β = 0.45) as processes associated with therapeutic outcomes. Conclusions: AI-delivered health promotion programs demonstrate significant effectiveness across executive function, emotion regulation, and clinical outcomes, though substantial heterogeneity (I² = 45–82%) indicates meaningful variability warranting attention to subgroup-specific effects. Given the diversity of intervention types included (chatbots, mobile apps, VR systems, neuromodulation), pooled estimates should be interpreted as characterizing the average effect across this heterogeneous landscape; subgroup-specific estimates provide more precise guidance for clinical decision-making regarding specific modalities. Effects are associated with convergent patterns of neuropsychological mechanisms, though mechanistic conclusions remain preliminary given that only 22% of studies (41/186) examined neuropsychological mechanisms, and formal mediation analyses were conducted in only 18 studies (9.7%); most of the mechanistic evidence is correlational rather than causal. Future research should establish standardized AI taxonomies, optimize adaptive algorithms, conduct adequately powered replication studies in populations with cognitive impairment, prioritize experimental mediation designs to establish causal pathways, and evaluate long-term maintenance effects with a minimum of 6–12-month follow-up periods. Full article

Background/Objectives: Nomophobia, the irrational fear of being without a mobile phone, is increasingly prevalent among university students and has emerged as a concerning form of digital dependence. Among nursing students, this condition is particularly relevant due to the emotional demands and cognitive challenges of healthcare education. Nomophobia has been linked with adverse psychological outcomes, sleep disturbances, and impaired academic and clinical performance. However, existing evidence remains fragmented and lacks an integrated conceptual synthesis. This review aimed to synthesize current evidence on the prevalence, correlates, and outcomes of nomophobia among nursing students. Methods: An integrative review was conducted following Whittemore and Knafl’s methodology and PRISMA guidelines. A systematic search was performed in PubMed, CINAHL, PsycINFO, PsycArticles, and Medline (between 2015 and 2025), supplemented by Google Scholar. Cross-sectional studies and literature focusing on nomophobia in nursing students were included. The primary studies and selected review articles were considered when no overlap with the included primary evidence was identified. Methodological quality appraisal was assessed using validated tools (QuADS and JBI). Results: Twenty-two studies were included (19 cross-sectional and 3 reviews). Four thematic areas emerged: prevalence and severity (50–90% moderate to severe); psychological correlates (anxiety, depression, stress, insomnia, alexithymia, fear of missing out); academic and cognitive outcomes (impaired performance, procrastination, reduced decision-making); and behavioural predictors (excessive smartphone use and emotional dysregulation). The Nomophobia Questionnaire (NMP-Q) was the most frequently used instrument. Conclusions: Nomophobia represents a relevant dimension of the mind–technology relationship in nursing education, with implications for students’ mental health, academic engagement, and clinical readiness. Addressing nomophobia may support healthier learning environments and contribute to the development of emotionally competent and safe future healthcare professionals. However, significant gaps remain, particularly regarding longitudinal evidence and intervention-based approaches. Full article

Background/Objectives: People with mobility limitations face disproportionately high rates of chronic health conditions and demonstrate lower adherence to wellness interventions. Digital programs such as MENTOR offer accessible alternatives but often face high rates of attrition. This study applied machine learning (ML) methods to predict adherence to the eight-week MENTOR telewellness program and identify key predictors of participant attendance. Methods: Data were drawn from 1218 adults enrolled in MENTOR (2023–2024). Adherence was defined as the percentage of 40 sessions attended. Baseline demographic, socioeconomic, psychosocial, mindfulness, resilience, health status, and physical activity variables were included as predictors. Following preprocessing and imputation, 13 ML regression models were trained using an 80/20 train–test split. The best-performing model was identified using mean absolute error (MAE), followed by feature selection and SHAP interpretability analyses. Pairwise synergy analysis quantified interactions between top predictors. Results: Model performance was modest overall. Bayesian ridge regression achieved the best performance (MAE 20.98; RMSE 25.26; R² = 0.12). SHAP analyses revealed that education, race, emotional support, Area Deprivation Index, household size, mindfulness, life satisfaction, and disability onset were the strongest predictors of adherence. Higher emotional support, mindfulness, and life satisfaction were associated with greater adherence, while socioeconomic disadvantage predicted lower adherence. Synergy analyses showed the strongest predictive interactions between low education and psychosocial resources (emotional support and life satisfaction). Conclusions: Baseline characteristics alone modestly predicted adherence to a digital wellness program. However, psychosocial and socioeconomic factors emerged as meaningful predictors, underscoring the need for personalized support strategies to reduce dropout among participants with mobility limitations. Full article

Background: Motor decline associated with ageing compromises mobility, postural control and the ability, thereby increasing risk among older adults. Biomechanical characterization of movement, particularly using non-linear methods, offers a process-oriented approach capable of detecting subtle changes in motor coordination. The MIND&GAIT programme has previously demonstrated benefits in physical function in frail older individuals; however, its potential to improve motor coordination parameters that underpin fall risk remains insufficiently explored. Objectives: To analyse the impact of the MIND&GAIT program on motor coordination during sit-to-stand (STS) and walking tasks, two daily activities strongly associated with fall risk, using advanced non-linear and biomechanical metrics in institutionalized older adults. Methods: Fourteen institutionalized older adults (82.21 ± 7.14 years) participated. Three-dimensional acceleration and angular velocity were recorded using inertial sensors. Motor variability and predictability were quantified using the multivariate Lyapunov exponent (LyEM) and multivariate incremental entropy (MIE). STS (30 s) and walking-in-place (2 min) tasks were assessed pre- and post-intervention following a three-month, thrice-weekly programme. Results: Although no statistically significant differences emerged (ps > 0.05), trends were observed suggesting increases in LyEM during STS and in both MIE and LyEM during walking were found post-intervention. These exploratory findings may indicate enhanced motor complexity, stability and adaptability, features associated with reduced fall vulnerability. Conclusions: Despite the absence of statistical significance, the biomechanical trends observed suggest improvements in motor coordination patterns relevant to fall risk reduction in institutionalized older adults following the MIND&GAIT programme. These findings highlight the potential of structured exercise-based interventions for promoting safer movement behaviors in frail populations. Full article

Background: This pilot study examined the feasibility, acceptability, usability, and preliminary outcomes of apt.mind, a mobile app-based mindfulness intervention with an exploratory smartwatch component, among healthcare worker families during the COVID-19 pandemic. Methods: Using a micro-randomized trial (MRT) design, 102 healthcare workers and co-parents of children aged 4–13 years were randomized once per day over 30 days to one of three conditions: (1) an audio-guided mindfulness exercise delivered via the apt.mind mobile app, (2) an in-app push notification prompting a brief mindfulness activity, or (3) no intervention. Feasibility was assessed through participant enrollment, retention, and daily engagement rates, while acceptability and usability were evaluated through qualitative and quantitative feedback. Exploratory multilevel analyses examined proximal effects of intervention conditions on momentary stress. Results: Retention was high, with all participants completing the 30-day protocol, and 80% of participants completed at least one daily survey. Participants reported moderate-to-high acceptability and usability. However, smartwatch battery life and sensor reliability limited the collection of usable physiological data. Multilevel analyses did not identify any significant main effects of intervention condition on momentary stress, but age moderated the association between the audio exercise condition and stress, benefiting older participants. Conclusions: Mobile-based mindfulness interventions appear feasible and acceptable for healthcare worker families in high-stress contexts. Although proximal stress effects were limited and exploratory, the findings inform future optimization of just-in-time adaptive interventions. Improvements in wearable technology and MRT implementation strategies are needed to enhance physiological data quality and reduce assessment-related anxiety. Full article

This study examines the systemic impact of mobile device usage on sleep health and overall wellbeing through a systems thinking (ST) approach. By employing a systems approach, this study offers a novel contribution to this area by exploring the intricate interdependencies among mobile device usage, sleep hygiene practices (a set of everyday behaviors that promote good sleep health), and related health outcomes. To understand the dynamics and the interdependent nature of sleep, health, and holistic wellbeing, the study examines three interrelated cases: (Case 1) mapping factors that negatively influence sleep and their causal relationships; (Case 2) modeling causal loop diagrams (CLDs) to illustrate the interdependencies among sleep duration, sleep quality, and health outcomes; and (Case 3) modeling CLDs and analyzing the impact of nighttime mobile and electronic device usage on sleep patterns and long-term wellbeing. Rather than presenting empirical data, this paper employs an ST methodology and structurally validated qualitative modeling through CLDs to provide a conceptual framework that complements existing correlational studies and informs future public health research and policy interventions. A significant gap exists in this domain regarding the use of ST tools—such as CLDs—in sleep research to capture the complex, interdependent relationships between sleep health factors and overall health. By focusing on CLDs as one of the ST methods, this paper highlights the dynamic feedback loops through which behavioral, psychological, and environmental factors interact—often negatively reinforced over time by nighttime mobile device usage—to shape sleep quality and overall wellbeing. It underscores the need for novel interdisciplinary strategies and the application of ST to design interventions to mitigate sleep-related health risks and promote digital and holistic wellbeing. Full article

Background: Advancing age is frequently associated with balance impairment, increased fall risk, and psychological distress, which together contribute to loss of independence and reduced quality of life. Yoga, as a mind–body practice, has the potential to enhance physical stability as well as mental well-being in older adults. Therefore, the objective of this study was to evaluate the effects of a structured yoga program on balance, fear of falling, mobility, and mental health outcomes among older adults. Methods: A quasi-experimental pretest–post-test study was conducted at Nagpur, India. A total of 64 eligible participants (65–85 years) were purposively assigned to a yoga intervention group (n = 32) or a waitlist control group (n = 32). The 12-week intervention comprised preparatory exercises, yoga postures, breathing practices, and meditation. Outcomes assessed at baseline and post-intervention included balance, fear of falling, mobility, depression, and anxiety. Results: Data from 50 participants (yoga: n = 26; control: n = 24) were analyzed. The yoga group showed significant improvements in balance (p < 0.001) and functional mobility (p < 0.001), with significant reductions in fear of falling (p = 0.009), anxiety (p = 0.0003), and depression (p = 0.004). In contrast, the control group exhibited deterioration in functional mobility (p = 0.001) and anxiety (p = 0.009), with no significant gains in other measures. Between-group comparisons confirmed significantly greater improvements in the yoga group across all outcomes. Conclusions: A 12-week yoga program was feasible and effective in improving balance, functional mobility, and mental health, while reducing fear of falling among older adults. Yoga may serve as a safe, non-pharmacological intervention to promote healthy aging in institutionalized populations. Trial registration: This study was prospectively registered with the Clinical Trial Registry of India (Registration No: CTRI/2023/10/058682; Registered on: 16 October 2023). Full article

Ageing is a dynamic biological process involving interconnected physiological, psychological, and social changes, making the promotion of healthy ageing a global public health priority. The World Health Organization (WHO) defines healthy ageing as the process of developing and maintaining functional ability that enables well-being in older age. The WHO’s Decade of Healthy Aging (2021–2030) outlines four key action areas: changing attitudes toward ageing, creating age-friendly environments, delivering integrated and person-centred care, and ensuring access to long-term care. This Perspective examines yoga, a holistic mind–body practice integrating physical postures, breath regulation, and mindfulness, as a potentially safe, adaptable, and scalable intervention for older adults. Evidence suggests that yoga may improve flexibility, balance, mobility, and cardiovascular function, reduce pain, and support the management of chronic conditions commonly associated with ageing. Psychological and cognitive research further indicates reductions in stress, anxiety, and depressive symptoms, alongside potential benefits for attention, memory, and executive function. Improvements in health-related quality of life (HRQoL) have been reported across physical, psychological, and social domains, with benefits sustained through regular practice. Adaptations such as chair-based practices, restorative postures, and the use of props enhance accessibility and safety, allowing participation across diverse functional levels. Mindfulness and breath-focused components of yoga may additionally support emotional regulation, resilience, and psychological well-being, particularly among older adults experiencing stress or limited mobility. Yoga interventions are generally well tolerated, demonstrate high adherence, and can be delivered through in-person and digital formats, addressing common access barriers. Despite this growing evidence base, yoga remains underintegrated within health policy and care systems in the US, UK, and India. Strengthening its role may require coordinated efforts across research, policy, and implementation to support healthy ageing outcomes. Full article

Background and Objectives: Bladder cancer (BC) represents a significant global health burden, ranking as the tenth most commonly diagnosed malignancy worldwide, with an incidence rate of 5.6 per 100,000 person-years annually. The research team aimed to summarize evidence on specialized nursing-led interventions for bladder cancer management across the disease continuum. Materials and Methods: This scoping review used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) methodology to search four databases from January 2018 to November 2025. Results: This concise but informative scoping review of 20 studies revealed substantial clinical and patient-reported benefits from specialized nursing interventions. Enhanced recovery after surgery (ERAS) protocols incorporating structured nursing care demonstrated a 35% reduction in postoperative complications. Integrated nursing interventions during postoperative intravesical therapy significantly improved patient satisfaction, treatment compliance, and self-efficacy while reducing anxiety and depression. Digital health platforms, including internet-based and mobile applications, proved effective in reducing caregiver burden, enhancing disease knowledge, and improving coping strategies. Preoperative stoma education and postoperative ostomy care management significantly improved self-efficacy, stoma care knowledge, and overall health-related quality of life. Psychosocial interventions, including cognitive behavioral therapy and mindfulness-based approaches, demonstrated significant improvements in quality of life and reductions in fear of recurrence, depression, and anxiety. However, a critical evidence gap exists regarding bladder cancer-specific mental health interventions. Conclusions: Specialized nursing-led care plays a critical role in strengthening clinical and assistive practice in bladder cancer. Evidence from this scoping review shows that nursing-led interventions significantly improve clinical outcomes, patient satisfaction, symptom management, and quality of life across all phases of bladder cancer care while reducing caregiver burden and enhancing psychological well-being for both patients and families, reinforcing the value of integrating specialized nursing roles into routine bladder cancer management. Full article

Background/Objectives: Gestalt therapy traditionally opposes categorical diagnostic labelling due to its fundamental inconsistency with phenomenological and process-oriented ontology. However, this epistemological rigour can limit integration with structured evidence-based interventions for complex personality organizations such as Borderline Personality Disorder (BPD). Despite the evidence base for DBT and Schema Therapy in treating BPD, these approaches may inadvertently minimize the lived phenomenological experience and organismic wisdom central to recovery. Meanwhile, Gestalt therapy’s anti-diagnostic stance limits its integration with structured evidence-based protocols. This paper proposes a hybrid theoretical model that addresses this gap by integrating the clinical epistemology of Gestalt therapy with Linehan’s biosocial theory of Dialectical Behaviour Therapy (DBT) and schema-focused interventions, while preserving the core principles of Gestalt. Methods: we present a model of theoretical integration that draws on Gestalt contact theory, the four modules of DBT (mindfulness, distress tolerance, emotional regulation, interpersonal effectiveness) and the experiential techniques of Schema Therapy. The integration focuses on the dialectic of acceptance and change, which mirrors Gestalt’s paradoxical theory of change. The proposed framework preserves the non-protocol dimension of Gestalt therapy while incorporating the pragmatic utility of DBT and Schema Therapy. Results: key conceptual contributions we propose include: (1) theorizing the “Draft Self” as the object and subject of therapeutic work, (2) integrating mindfulness and grounding as embodied processes within live Gestalt experiments, (3) activation techniques to explore the identity fragmentation endemic to BPD. Conclusions:his integration offers a coherent, embodied, and process-oriented framework for understanding and treating BPD that validates patients’ lived experience, mobilizes evidence-based interventions, and opens up meaningful intertheoretical dialogue. Full article

Background/Objectives: Burnout syndrome among nurses can significantly contribute to the nursing shortage, leading to high turnover and negative impacts on both nurses and patient care. The primary objective of this project was to evaluate the feasibility, acceptability, and preliminary effect of a mindfulness-based mobile application (MBMA) on burnout and well-being in emergency department (ED) nurses over four weeks. Methods: An EBPQI with a descriptive approach was taken to evaluate ED nurses’ burnout and well-being, which was measured with the Mini-Z Single Item (MZSI) and Nurses’ Well-Being Index (NWBI). We also asked three open-ended questions about their experience using the once-daily MBMA over the four-week period. Twelve participants from a mid-western hospital were recruited, and six completed both the pre-test and post-test surveys. Results: Results found no statistically significant improvement in burnout (p = 1.00) or well-being (p = 0.783). However, upon a secondary analysis using imputed data, a statistically significant improvement in burnout was found (p = 0.012). Among the six participants who completed the post-intervention, a significant and positive correlation between burnout and well-being was identified (r = 0.81, p = 0.048). Themes from qualitative responses included perceived helpfulness of MBMA tools, perceived usefulness, and lack of time for daily participation. Although statistical improvements were not observed, individual comments indicated that the tool was helpful; however, setting aside time to engage with it remained difficult. Conclusions: Findings from this project support the need for further research exploring the impact of individualized interventions specifically targeting ED nurses as well as organizational strategies aimed at those already experiencing burnout or impaired well-being. Full article