Background: Cancer pain remains highly prevalent and persistent throughout survivorship, and it is crucial to investigate the potential of leveraging the advanced features of mobile health (mHealth) apps to empower individuals to self-manage their pain. Objective: This review aims to comprehensively understand the acceptability, users’ experiences, and effectiveness of mHealth apps in supporting cancer pain self-management. Methods: We conducted an integrative review following Souza and Whittemore and Knafl’s 6 review processes. Literature was searched in PubMed, Scopus, CINAHL Plus with Full Text, PsycINFO, and Embase, from 2013 to 2023. Keywords including “cancer patients,” “pain,” “self-management,” “mHealth applications,” and relevant synonyms were used in the search. The Johns Hopkins research evidence appraisal tool was used to evaluate the quality of eligible studies. A narrative synthesis was conducted to analyze the extracted data. Results: A total of 20 studies were included, with the overall quality rated as high (n=15) to good (n=5). Using mHealth apps to monitor and manage pain was acceptable for most patients with cancer. The internal consistency of the mHealth in measuring pain was 0.96. The reported daily assessment or engagement rate ranged from 61.9% to 76.8%. All mHealth apps were designed for multimodal interventions. Participants generally had positive experiences using pain apps, rating them as enjoyable and user-friendly. In addition, 6 studies reported significant improvements in health outcomes, including enhancement in pain remission (severity and intensity), medication adherence, and a reduced frequency of breakthrough pain. The most frequently highlighted roles of mHealth apps included pain monitoring, tracking, reminders, education facilitation, and support coordination. Conclusions: mHealth apps are effective and acceptable in supporting pain self-management. They offer a promising multi-model approach for patients to monitor, track, and manage their pain. These findings provide evidence-based insights for leveraging mHealth apps to support cancer pain self-management. More high-quality studies are needed to examine the effectiveness of digital technology–based interventions for cancer pain self-management and to identify the facilitators and barriers to their implementation in real-world practice.

Background: Self-directed lifestyle modifications are essential for managing symptoms in individuals diagnosed with irritable bowel syndrome (IBS). This study incorporated longitudinal multi-omics profiling to estimate the mechanisms underlying responses to a nurse-led person-centered self-management intervention in young adults with IBS. Methods: This pre-post study was nested within a 12-week parent randomized controlled trial (NCT03332537). Biospecimens (stool and blood) and clinical outcomes were collected at baseline and post-intervention. Symptoms were assessed using the Brief Pain Inventory and PROMIS^® short forms. Host transcriptomic profiling was performed using RNA sequencing, and gut microbial composition was analyzed via 16S rRNA sequencing. Host transcriptomic co-expression and microbial co-abundance modules were identified via weighted gene co-expression network analysis. Associations between multi-omics modules and symptoms were evaluated using linear mixed-effect models. Results: Among the 20 participants, most were non-Hispanic (75%), White (75%), and female (65%). The intervention significantly reduced self-reported pain severity (p = 0.019) and pain interference (p = 0.013). Decreased associations were observed between pain phenotypes and a microbial module enriched in core metabolic pathways (interference: β = −4.7, p < 0.001; severity: β = −2.4, p = 0.02). Anxiety strengthened associations with host transcriptomic cellular energy metabolism pathways post-intervention (p < 0.05). The intervention attenuated associations between fatigue, sleep disturbance, and immune–inflammatory transcriptomic and microbial adaptation modules (p < 0.05). Conclusions: Findings suggest that the IBS self-management intervention induces symptom-specific biological responses, implicating distinct host–microbe pathways. Larger longitudinal studies are warranted to validate these omics-based symptom signatures.

Background: Women from diverse socioeconomic status (SES) face a higher risk of preterm birth, increasing their infants’ vulnerability to neurodevelopmental and other health disorders; however, the predictive role of maternal ZIP code level SES in these outcomes remains underexplored. Objectives: To investigate the associations between maternal racial disparity, ZIP code-level SES, and infant breastfeeding, growth, and neurodevelopmental trajectories. Methods: In this cohort study, preterm infants were recruited from two Connecticut neonatal intensive care units (NICUs). Infant demographic data, feeding regimens, and growth during the NICU stay were documented. Neurodevelopmental outcomes were assessed using the NICU Neonatal Neurobehavioral Scale, the Bayley scale of infant and toddler development (3rd ed.), and the Brief Infant Toddler Social Emotional Assessment. To compare SES differences between infants born to Black and White mothers, both t -tests and Wilcoxon tests were conducted. We used XGBoost to analyze infant health outcomes and SHapley Additive exPlanations (SHAP) values to identify SES-related risk factors associated with feeding, growth during NICU stay, and neurodevelopmental outcomes up to 2 years of age. Results: In total, 181 preterm infants from eight ZIP code areas were included in the study. The majority of infants were born to mothers who were White and non-Hispanic. Compared with infants born to White mothers, those born to Black mothers had younger birth gestational age (GA), lower birth weights, shorter birth lengths, smaller head circumferences, and higher Score of Neonatal Acute Physiology with Perinatal Extension–II (SNAPPE–II), with all differences being statistically significant. Compared with White mothers, Black mothers were younger, single, and less educated. Black mothers also had lower median household incomes, larger average family sizes, and higher levels of poverty compared with White mothers. Based on SHAP values, the risk factors predicting infants’ feeding, growth, and neurodevelopment are ranked as follows: birth weight, birth GA, SNAPPE–II score, average family size, maternal age, median household income, poverty level, and school enrollment. Discussion: Maternal racial disparity and SES serve as predictors of feeding, growth, and neurodevelopmental outcomes in preterm infants. Understanding these associations can inform health care strategies for vulnerable preterm populations to improve long-term health outcomes.

Context: Patients with colorectal cancer undergoing chemotherapy often experience significant pain and fatigue. Limitations in understanding the complex phenotypes and biological mechanisms of these symptoms hinder effective interventions. Objectives: This study aimed to identify the pain and fatigue patterns during one chemotherapy cycle and associated gene expression profiles. Method: In a prospective longitudinal study, 34 patients with colorectal cancer from a major cancer center in the Northeastern US were recruited. Self-reported outcome measures of pain and fatigue and blood samples were collected at baseline, post-chemotherapy, and at the end of the chemotherapy cycle. RNA sequencing followed by differential expression analysis identified changes in gene expression. Linear mixed models examined associations between symptoms and possible biomarkers over time. Results: The sample had a mean age of 58.4 years old, with 97% being white and non-Hispanic. Among participants, 44.1% had stage III cancer, and 26.5% were undergoing initial chemotherapy. Abdominal pain was the most frequently reported symptom. Fatigue levels significantly worsened post-chemotherapy (P = 0.011) and after recovery (P = 0.018). Critical pathways involved inflammatory response and myeloid cell development (FDR < 5%). Mixed-effect linear regression analysis revealed statistically significant associations between the upregulation of LILRA6 and higher pain interference (β = −6.621, p = 0.010) and fatigue (β = −6.621, p = 0.010), as well as between the downregulation of CACNG6 (β = −1.043, p = 0.047) and PRSS33 upregulation (β = 1.384, p = 0.038) and increased pain interference. Given the small sample size, these findings should be interpreted with caution. Conclusion: These findings suggest inflammation and specific biomarkers may drive pain and fatigue during chemotherapy. Further preclinical models or clinical cohorts are needed to validate these results and explore potential implications for targeted interventions to reduce symptom burden in patients with colorectal cancer.

Introduction: Irritable bowel syndrome (IBS) is a prevalent disorder whose most debilitating symptom is pain. The complex, multifactorial nature of IBS pain leads to highly variable and often inadequate responses to self-management, underscoring the urgent need for personalized prediction models. Methods: This ancillary analysis of a randomized controlled trial ( NCT03332537 ) utilized data from 80 young adults with IBS. We applied the Bayesian Additive Regression Trees machine learning algorithm to develop 27 distinct predictive models for pain severity, pain interference, and quality of life (QOL) at baseline and post-intervention. Predictors included a comprehensive, multi-domain set of variables spanning genetics, quantitative sensory testing, gut microbiota, psychosocial factors, and food intake. Results: Model performance was strong, with area under the curve (AUC) values ranging from 0.753 to 0.981. A consistent hierarchy of predictors emerged. The COMT rs4680 polymorphism was the most significant predictor, featuring in 26 models, followed by ADRA1D rs1556832 in 24 models. The mechanical pain threshold was a key predictor of pain severity, while psychosocial factors, particularly pain catastrophizing, were crucial for pain interference and QOL. Gut microbiota features and food intake were also consistently important. Discussion: This study establishes a comprehensive, multi-omics framework that explains individual differences in IBS pain and treatment response. The identified predictors provide a practical tool for advancing precision medicine. By classifying patients based on their distinct profiles, clinicians can proactively customize self-management strategies, potentially transforming care for this complex condition. WHAT IS KNOWN: IBS pain involves complex, dysregulated gut-brain interactions. Treatment response is highly variable and difficult to predict. Machine learning can help phenotype complex pain conditions. WHAT IS NEW HERE: Machine learning based hierarchical models identify a core neurogenetic predisposition for IBS pain. Machine learning based prediction of individual response to self-management before treatment begins. This provides a direct pathway to precision pain management in IBS.