AIMS:This article presents an adapted framework that integrates the Patient Health Engagement (PHE) model with Orem's Nursing Systems theory. The framework highlights the nursing role in encouraging Health Information Technology (HIT) tools, such as secure messaging and patient portals, to enhance patient activation and support their self-care capabilities, particularly in chronic disease management aided by nursing actions. BACKGROUND:Despite HIT's role in improving patient care and the increased government incentives for its adoption, utilisation remains low due to various sociodemographic factors and psychosocial factors. The nursing discipline addresses these factors in its practice and thus could facilitate the use of HIT-related tools for patients. We propose an adapted framework embedded in nursing priorities for building self-care agencies in patients centred around HIT use. METHODS:We aligned Orem's nursing system model and PHE model to propose an adapted Nurse PATHIT framework that provides actions and considerations for nursing discipline to target the HIT tools for enhancement of patient activation in chronic care engagement based on patient's readiness. RESULTS:The integration of the PHE model with Nursing System theory offers a framework for promoting HIT use as one of the tools for managing chronic diseases by building self-care agency in patients. Three of the four stages of the PHE model-blackout, arousal and adhesion-correspond to the wholly compensatory, partially compensatory and supportive approaches within Nursing Systems theory. CONCLUSION:Adapting the PHE model with Orem's Nursing System theory in the form of Nurse PATHIT creates a comprehensive framework for nurses to encourage the use of HIT tools in chronic disease management. Future research on patient-centred outcomes using HIT can test this framework by incorporating support and motivation for HIT use for patients as one of the tools to actively engage patients in nursing care planning to increase patients' self-care agency.

ObjectiveResearchers strive to develop more precise prediction models to understand smoking behaviors, facilitate tailored tobacco treatment and improve early detection of lung cancer, including the use of polygenic risk scores (PRS). This study aimed to better understand participants' knowledge, interest and recommendations for receipt of PRS information. Its specific aims were to (1) describe participants' knowledge and interest in obtaining PRS in the context of smoking behaviors, tobacco treatment and/or early detection of lung cancer and (2) identify patient-reported recommendations for incorporating genetic risk information into clinical care.MethodsA descriptive qualitative approach was used to gather data. A one-time semi-structured interview was conducted at the conclusion of a lung health intervention among individuals who smoked long-term and were eligible for lung cancer screening. Sociodemographic, tobacco, alcohol, and comorbidity data were gathered through an electronic survey. Interviews were audio-recorded, transcribed and analyzed using Braun and Clarke's methods for thematic analysis.ResultsForty-six participants were interviewed. The themes for aim 1 included: (1) knowing about PRS and (2) wanting PRS to prevent and treat tobacco addiction. The themes for aim 2 included: (1) receiving information from health professionals and (2) considering the risks of learning PRS.ConclusionsResults indicate high interest in PRS in clinical settings to help people who smoke to understand their health habits and change behaviors. The need for appropriate framing of risk messages and shared decision making emerged in the interviews.Trial RegistrationNCT0469129T

CONTEXT:Dyspnea (breathlessness) is a distressing and disabling symptom affecting over 70% of patients with advanced lung cancer. Although dyspnea treatments are limited, recent research on a brief, nurse-led behavioral intervention for dyspnea in patients with advanced lung cancer demonstrated improvements in dyspnea-related functioning compared to usual care. OBJECTIVES:We examined whether depression and anxiety moderate the efficacy of a brief behavioral intervention for dyspnea in advanced lung cancer. METHODS:This secondary analysis of a randomized controlled trial examined a two-session, nurse-led behavioral intervention for dyspnea in 247 patients with advanced lung cancer. Patients self-reported dyspnea-related functioning (Modified Medical Research Council Dyspnea Scale), multidimensional dyspnea (Cancer Dyspnea Scale), and depression and anxiety (Hospital Anxiety and Depression Scale [HADS]) at baseline and post-treatment (8 weeks later). The PROCESS macro tested depression and anxiety as treatment moderators for dyspnea and probed interactions when P's < 0.15 using the Johnson-Neyman procedure due to reduced power in testing moderators. RESULTS:Baseline depressive symptoms moderated the intervention's impact on dyspnea functioning (b = -0.074, P = 0.075), with significant benefits observed in those reporting >6 on baseline scores of the HADS-Depression subscale. Any post-treatment improvement on the HADS-Anxiety subscale (b = 0.069, P = 0.135) and improvements of at least 3 on the HADS-Depression subscale (b = 0.671, P = 0.009) significantly enhanced outcomes for total dyspnea and dyspnea functioning, respectively. CONCLUSIONS:Patients with elevated baseline depression and improved distress may benefit more from this intervention for dyspnea. Considering treatment moderators helps optimize resources, but additional research on treatment adaptations is needed to enhance care for all.

Objet : La première réunion d’infirmières expertes en science des symptômes du cancer s’est tenue les 11 et 12 octobre 2023, à Lausanne, en Suisse. Quarante infirmières chercheuses représentant sept pays ont assisté à cette réunion qui visait à renforcer la collaboration au sein de la communauté internationale des chercheurs en science des symptômes, préciser les domaines d’intérêt commun, faire ressortir les lacunes dans les connaissances et les possibilités de recherche, et élaborer des stratégies pour surmonter les difficultés et accélérer la recherche en science des symptômes à l’échelle internationale. Le présent livre blanc sert à résumer les discussions et les recommandations formulées lors de cette réunion et à présenter la Global Research Alliance in Symptom Science (alliance GRASS). Déroulement : Au cours de la réunion de deux jours, on a présenté des exposés sur des problèmes cruciaux et des questions qui restent sans réponses en science des symptômes du cancer et d’autres maladies chroniques. Quatre groupes de travail ont repéré des lacunes dans les connaissances et des occasions à saisir, et défini les orientations stratégiques et les mesures indispensables à prendre pour faire progresser la science des symptômes. Résultats :Voici les recommandations formulées par les groupes de travail : GT1) utiliser les meilleures méthodes possibles pour recueillir, analyser et utiliser des données sur les symptômes destinées à servir en clinique et en recherche; GT2) créer un ensemble minimum de données, ou un modèle de données commun, pour la recherche en science des symptômes; GT3) raffiner les pratiques exemplaires de mise en œuvre de stratégies scientifiques de manière à améliorer la gestion des symptômes fondée sur les données probantes dans les soins de routine; et GT4) renforcer les capacités et l’infrastructure pour créer une alliance internationale en science des symptômes du cancer (Alliance GRASS). Conclusions :La communauté internationale se mobilise pour faire progresser la science des symptômes. Le mini-symposium a permis de jeter les bases de la création de l’alliance GRASS, qui se consacre à la science des symptômes du cancer et d’autres maladies chroniques. Organiser des réunions scientifiques à intervalles réguliers, promouvoir la collaboration interdisciplinaire et mobiliser des chercheurs de la science des symptômes, voilà les orientations que l’alliance s’est choisies pour l’avenir. Mots-clés : cancer; maladie chronique; états comorbides; santé globale; science des symptômes

PURPOSE:Dyspnea impacts most patients with advanced lung cancer. However, research on dyspnea has been limited by using unidimensional self-report measures despite its multidimensional nature (sensory-perceptual experience, affective distress, and functional impact), which requires a comprehensive evaluation. To identify distinct patient profiles of dyspnea presentation and evaluate differences in demographic, clinical characteristics, and patient-reported outcomes (i.e., functional impairment, quality of life, co-occurring symptoms, and self-efficacy). METHODS:A cross-sectional, secondary analysis of 247 patients with advanced lung cancer reporting moderate-to-severe dyspnea was conducted using baseline data from a randomized controlled trial testing a behavioral intervention for dyspnea. The patient profiles of dyspnea were identified using latent profile analysis of the Cancer Dyspnea Scale. Differences among the profiles were assessed through parametric and non-parametric methods. RESULTS:Four-class solutions were identified: All Mild (A-Mild: 53%), Moderate Effort and Discomfort & Mild Anxiety (Moderate ED & Mild A: 25.9%), All Moderate (A-Moderate: 16.6%), and All Severe (A-Severe: 4.5%) dyspnea profiles. No significant differences were found among demographic and clinical variables across the profiles. Compared to the A-Mild profile, the other three profiles reported more significant functional impairment due to dyspnea, increased levels of depression, anxiety, and fatigue, and reduced quality of life. The A-Severe profile exhibited lower self-efficacy than the Moderate ED & Mild A and the A-Moderate profiles. CONCLUSION:Our findings highlight the multidimensional nature of dyspnea, which results in distinct patient presentations. Clinicians can create targeted interventions tailored to individual needs by classifying dyspnea symptom profiles.

PURPOSE: To explore the experiences and unmet clinical needs of patients with cancer during the COVID-19 pandemic. PARTICIPANTS & SETTING: The authors recruited patients with cancer who received cancer-directed therapy in March 2020 at a National Cancer Institute-designated comprehensive cancer center. Interviews with patients were conducted between June 2021 and January 2023. METHODOLOGICAPPROACH: In this deductive-inductive descriptive qualitative investigation, participants completed a one-time 45-minute semistructured telephone interview via Zoom. FINDINGS: The qualitative analysis revealed (a) a myriad of psychological stressors, (b) bolstered human connectedness, (c) disruptions to daily life, (d) clinical support and education from healthcare teams, and (e) looking ahead to postpandemic life. Participants experienced changes in health behaviors and material hardships but highlighted supportfrom family, friends, and healthcare teams. IMPLICATIONS FOR NURSING: The cohort of patients experienced significant distress and disruptions to their lives duringthe COVID-19 pandemic. Interventions implemented duringan unanticipated event such as a pandemic need to be developed and tested to support patients with cancer.

BACKGROUND:Even though lung cancer screening decreases mortality, uptake remain low. Research has focused on integrating smoking cessation into lung cancer screening programs rather than providing lung cancer screening education to eligible adults. Engaging individuals who are at high-risk for lung cancer and educating them about lung cancer screening is a critical next step. This study tested efficacy of a digital intervention to improve smoking cessation and lung cancer screening adoption. METHODS:Participants (n = 152) were enrolled into a randomized controlled trial of a digital intervention (counseling, nicotine replacement treatment, storytelling videos, and lung cancer screening decision-aide) versus a brief intervention (brief advice for smoking cessation, referral to the Quitline and mailing lung cancer screening decision-aide). Demographic, tobacco, and lung health data were collected electronically. Salivary cotinine test verified 7-day point-prevalence abstinence (PPA) rates were obtained at 3 and 6-months after study entry. Descriptive statistics, Fisher's exact test, Wilcoxon rank-sum test and logistic regression were used for analyses. RESULTS:As compared to the brief arm, the digital intervention had higher rates of biochemically verified 7-day PPA at 3 (35.1 % vs. 15.4 %, p = 0.006) and 6-months (24.3 % vs.10.3 %, p = 0.03). Participants in the digital arm had higher rates of lung cancer screening adoption at 6-months after study entry (58.1 % vs. 29.5 %, p < 0.001). CONCLUSIONS:The digital intervention increased smoking cessation and uptake of lung cancer screening adoption compared to a brief intervention. Testing of the digital intervention among a diverse population is warranted to promote earlier detection and decreased mortality from lung cancer.

PROBLEM STATEMENT: The aims of this study were to characterize patients' distress, psychological symptoms, and resilience during the COVID-19 pandemic, and to evaluate differences in the experiences, resilience, and psychological symptoms of patients with and without distress. DESIGN: Convergent parallel mixed-methods. DATA SOURCES: Semi structured interviews and structured questionnaires. ANALYSIS: Interview transcripts were analyzed using content analysis. Differences in demographic and clinical characteristics, depression, anxiety, and resilience were identified using chi-square, Fisher's exact, and independent sample t tests. Joint displays facilitated data integration and meta-inferences. FINDINGS: Of54 patients, 25 patients who were distressed were more likely to have low resilience, exhibit symptoms of anxiety and depression, report difficulty paying their bills, and identify as Hispanic. IMPLICATIONS FOR PRACTICE: A patient-centered approach to cancer care in which clinicians assess psychological, social, and economic resources and make referrals to supportive care services is warranted.

OBJECTIVES:The inaugural "Cancer Symptom Science Expert Meeting," held in Lausanne, Switzerland, on October 11 to 12, 2023, brought together 40 nurse scientists from seven countries. The event aimed to enhance collaboration across the global symptom science community; identify common research interests, gaps in knowledge, and opportunities for research; and develop strategies to address challenges and accelerate symptom science research internationally. This White Paper summarizes the discussions and recommendations deliberated during the meeting and introduces the Global Research Alliance in Symptom Science (GRASS). METHODS:This 2-day meeting featured presentations that highlighted critical issues and unanswered questions in cancer symptom science. Four core topic areas based on knowledge gaps were reflected throughout presentations. The co-occurrence of cancer with other chronic conditions (eg, cardiovascular disease, diabetes) that may share similar contributors and underlying mechanisms were included. Four working groups (WGs) were formed to identify gaps and opportunities associated with each topic and to outline strategic directions and essential actions to advance symptom science. RESULTS:WGs developed four recommendations. WG1 explored optimal approaches to collect, analyze, and use symptom data for research and clinical purposes. WG2 addressed the development of a minimum dataset or common data model for symptom science. WG3 focused on enhancement of best practices in implementation science strategies to improve uptake of evidence-based symptom management in routine care. WG4 addressed capacity building and infrastructure for the creation of a GRASS. CONCLUSIONS:WGs' recommendations underscore the commitment of an international coalition of scientists to advance symptom science. The symposium established the groundwork for the development of GRASS, dedicated to symptom science in cancer and other chronic conditions. Future directions include establishing regular scientific meetings, fostering interdisciplinary collaboration, and engaging with symptom scientists. IMPLICATIONS FOR NURSING PRACTICE:GRASS is an alliance for symptom science and its implementation into clinical practice. Nurses are at the forefront of this work.

PURPOSE:Health-related social needs (HRSNs) are associated with adverse cancer health outcomes. We assessed the processes for screening and responding to both HRSNs and financial distress and described the methods used across National Cancer Institute Community Oncology Research Program (NCORP) practices. METHODS:The NCORP 2022 Landscape Assessment survey focused on services to screen for and respond to HRSNs and financial distress within a national network of community oncology practices. We calculated the proportions of oncology practices that screened for and responded to HRSNs and financial distress, separately, and described the staff, tools, and methods used for each process. Multivariable logistic regression models estimated the associations between oncology practice characteristics and screening for HRSNs and financial distress. RESULTS:The majority of community oncology practices reported screening for HRSNs (79%), and of those, most inquired about transportation (96%), family and social support (93%), housing (80%), and food security (80%). Most oncology practices reported screening for financial distress (78%). Social worker evaluation was the most common method used to screen for both HRSNs (77%) and financial distress (65%). Most oncology practices reported social work referral as the method for responding to HRSNs (89%) and financial distress (96%). Oncology practice characteristics such as having a survivorship clinic and geographic region were associated with screening for HRSNs and financial distress. CONCLUSION:Research is needed to understand the impact of different HRSN screening and referral approaches on care delivery, clinic costs, care quality, and health outcomes of patients with cancer. These efforts are critical to generate evidence to inform best practices, clinical guidelines, and novel interventions aimed to improve cancer health equity.

Rationale: Dyspnea affects former and current smokers, significantly impairing physical functioning, quality of life (QOL), and survival. This multifactorial symptom stems from various conditions, including cardiopulmonary disease. Developing accurate predictive models for dyspnea is essential for identifying key risk factors that predict its onset and progression based on individual characteristics. Aims: This study aims to (1) identify associations between clinical, spirometric, and quantitative chest CT (qCT) emphysema, bronchial, and vascular imaging features linked to dyspnea and (2) build a robust multimodal dyspnea prediction model in smokers. Methods: This predictive modeling study utilized data from the COPDGene Study, a multi-center, prospective observational study of non-Hispanic White and African American individuals with a smoking history. Dyspnea occurrence was defined as a self-reported modified Medical Research Council (mMRC) dyspnea scale score ≥ 2. Among smokers (N =7,285), 2,962 (∼41%) reported dyspnea. Then, the dataset was split into training and testing samples (80%/20%) to develop and validate a dyspnea predictive model. Elastic net (EN) regression was used to build the model, with alpha and lambda values optimized on the training set. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC) on the test set. AUROC assesses the model's ability to distinguish between individuals meeting the mMRC score threshold of ≥ 2 and those below it. Calibration was assessed using the Brier score, which measures how closely predicted probabilities match actual outcomes, and Spiegelhalter's z-tests, which verifies if predicted probabilities statistically align with observed results. Predictors in the multivariable models were ranked by importance scores, calculated as the absolute values of the coefficients. Results: The final prediction model exhibited robust predictive ability in the test set, achieving an AUROC of 0.85 (Figure 1). Calibration metrics included a Brier score of 0.15 and a Spiegelhalter Z-statistic of -8.6e-8, indicating no significant difference between observed and expected dyspnea occurrences. Among continuous variables, pre-bronchodilator FEV1 (mL) emerged as the most important predictor of dyspnea, followed by the regional distribution of qCT emphysema and older age. For categorical variables, the most important predictor was having respiratory exacerbations ≥ 2 in the past 12 months, followed by self-reported diagnoses of HF and chronic bronchitis. Conclusions: Our findings suggest that dyspnea in smokers can be accurately predicted through a robust multimodal model integrating clinical, spirometric, and imaging features. Such a model is valuable for dyspnea risk stratification, enabling tailored dyspnea management in this high-risk population.

BackgroundThe inaugural "Cancer Symptom Science Expert Meeting," held in Lausanne, Switzerland, on October 11-12, 2023, brought together 40 nurse scientists from seven countries to enhance collaboration across the global symptom science community; identify common research interests, gaps in knowledge, and opportunities for research; and develop strategies to address challenges and accelerate symptom science research internationally.ObjectivesThe aim of this white paper were to summarize the discussions and recommendations deliberated during the meeting and introduce the Global Research Alliance in Symptom Science (GRASS).MethodsThis 2-day meeting featured presentations that highlighted critical issues and unanswered questions in cancer symptom science and other chronic conditions. Attendees identified four core topic areas based on the knowledge gaps reflected throughout the presentations. Four working groups (WGs) were formed to identify gaps and opportunities associated with each topic and to outline strategic directions and essential actions to advance symptom science.ResultsThe WGs developed recommendations on four core topic areas. WG1 explored optimal approaches to collect, analyze, and use symptom data for research and clinical purposes. WG2 addressed the development of a minimum dataset or common data model for symptom science research. WG3 focused on enhancement of best practices in implementation science strategies to improve uptake of evidence-based symptom management strategies in routine clinical care. WG4 addressed the questions of capacity building and infrastructure for the creation of a global alliance in symptom science (GRASS).DiscussionWGs' recommendations underscore the commitment of an international coalition of scientists to advance symptom science. The symposium established the groundwork for the group to constitute GRASS, a global research alliance dedicated to symptom science in cancer and other chronic conditions. Future directions include establishing regular scientific meetings, fostering interdisciplinary collaboration, and engaging with symptom scientists.

Asian Americans are less likely to receive smoking cessation advice and more likely to be diagnosed with lung cancer at a distant stage of the disease than the general U.S. population. The primary outcomes of this study are the uptake of a lung cancer screening (LCS) test and biochemically verified smoking cessation abstinence 6 months after baseline assessment among 128 Asian Americans. The secondary outcomes include self-reported 7-day point prevalence abstinence at 1, 3, and 6 months. Participants will be randomized to either an experimental or a control group. The experimental group will receive a deep-level Asian culturally tailored lung health (ACT) intervention consisting of eight smoking cessation counseling sessions, education about the LCS test, and family coaching. Participants in the control group will receive a surface-level ACT intervention that includes six smoking cessation counseling sessions. Both groups will have the option of having counseling sessions via either video or telephone calls, depending on their preference. Counselors in both groups will be matched with participants based on ethnicity and language. Additionally, all participants irrespective of group allocation will receive combined nicotine replacement therapy (NRT) products and educational materials about LCS decision-aids and NRT products. The deep-level ACT intervention is a departure from previous interventions conducted with the Asian American population since it integrates smoking cessation with lung cancer screening education and actively addresses negative attitudes and beliefs toward NRT assessed at baseline during counseling sessions.

OBJECTIVES:Electronic patient-reported outcome measures (ePROs) are being implemented in clinical care to monitor symptoms in patients with cancer. Although the use of these measures is associated with improved outcomes, challenges remain with integrating ePROs into the workflow due to burdensome training and unclear roles for who will manage uncontrolled symptoms. The use of clinical decision support (CDS) can mitigate these challenges. This article discusses the use of CDS, the integration of CDS into the electronic health record (EHR), lessons learned, and future directions. METHODS:The Sapphire Cancer Symptom Management CDS system is used to illustrate the development, integration, and testing of CDS in the EHR. A team of experts designed this system based on previous experience and research literature. The discussion synthesizes peer-reviewed literature, expert opinion, systematic reviews, and meta-analysis as sources of data. RESULTS:Symptom management algorithms were created for nine cancer symptoms and then programmed into the CDS platform and integrated into the EHR. Integration involved using innovative technologies, which included application programming interfaces and interoperable data standards, to integrate the system into the EHR. Relevant EHR and patient-reported data were used to generate individually tailored symptom management recommendations. Testing of the system was accomplished using test patients that reflected real-world patient experiences. We found that the data needed for the algorithms were complex and some elements were not readily available in the EHR. Having clinicians verify a few critical elements assured accuracy and safety of the recommendations. CONCLUSION:Innovative technologies enabled the integration of CDS into the EHR and the generation of individually tailored cancer symptom management recommendations. Future testing is needed to evaluate whether the use of CDS improves patient outcomes. IMPLICATIONS FOR NURSING PRACTICE:CDS has the potential to improve guideline-concordant symptom management and facilitate supportive care referrals at the point-of-care to improve clinical care.