Personalized Interventions Research Articles

Racial Disparities in Hospital Outcomes of Autologous Stem Cell Transplantation for Multiple Myeloma: A Comparative NIS 2016 to 2020 Database Study

Objective: Due to the paucity of data and the difference in-hospital outcomes in the two most common races in America remains controversial. This study aims to compare hospital outcomes in multiple myeloma patients undergoing autologous stem cell transplantation (ASCT) between White and Black patients using data from the National Inpatient Sample (NIS) from 2016 to 2020. Methods: A retrospective analysis of the NIS database was conducted using International Classification of Diseases (ICD-10) codes to identify patients who underwent ASCT for multiple myeloma. STATA version MP14.2 was used for analysis. We used Fischer's exact test to compare proportions, the student's t-test to compare continuous variables, and multivariate regression analysis to calculate the adjusted odds ratio after controlling age, gender, primary payer, and CCI. Following are included in transplant complications (transplant rejection, failure, transplant infection). Results: Among 31,389 hospitalizations, 19,065 (60.74%) were whites, and 6,110 (19.47%) were blacks. The mean age was 62 in whites and 59 in blacks (P < 0.001). Most patients were male, constituting 59.46% among Whites and 71.11% among Blacks. A significantly higher proportion of blacks had comorbidities compared to whites, including CKD (18.9% vs. 14.98%), diabetes mellitus (25.94% vs. 14.29%), obesity (16.35% vs. 11.6%), and mean Charlson Comorbidity Index (2.51 vs. 2.42, P < 0.001). On multivariate analysis compared to whites, blacks had a non-significant difference in the risk of acute kidney injury (adjusted odds ratio [AOR]: 0.94, 95% confidence interval [CI]: 0.76-1.17, P =0.58), cardiac arrest (AOR: 0.71, 95% CI: 0.16-3.20, P = 0.71), and blood transfusion (AOR: 1.11, 95% CI: 0.89-1.37, P = 0.36). Additionally, there was no significant difference in the average length (16.53 vs. 16.52, P = 0.85) and total cost ($195,971 vs. $196,300, P = 0.76) of hospital stay between blacks and whites. However, blacks had significantly lower transplant complications (AOR: 0.56, 95% CI: 0.37-0.87, P = 0.01), ICU admission (AOR: 0.58, 95% CI: 0.35-0.97, P = 0.04) and in-hospital mortality (AOR: 0.28, 95% CI: 0.09-0.91, P = 0.03) compared to whites. Conclusion: According to our study, blacks who underwent ASCT for multiple myeloma had better outcomes than Whites. Despite having a higher mean age and more comorbidities, this finding emphasizes the importance of conducting additional research and providing personalized interventions to enhance patient outcomes. It also suggests the potential for more black Americans to undergo ASTCT and benefit from it while avoiding delays and improving overall survival rates.

Long-Term Efficacy of an AI-Based Health Coaching Mobile App in Slowing the Progression of Non-Dialysis Dependent CKD: A Retrospective Cohort Study.

Chronic kidney disease (CKD) is a significant public health concern, with an escalating global prevalence ranging from 11% to 13%. Therefore, practical strategies for slowing CKD progression and improving patient outcomes are imperative. There is limited evidence to substantiate the efficacy of mobile app-based nursing systems for decelerating CKD progression. This study aimed to evaluate the long-term efficacy of the KidneyOnline intelligent care system in slowing the progression of non-dialysis-dependent CKD. In this retrospective study, the KidneyOnline app was utilized for CKD patients in China who were registered between January 2017 and April 2023. Patients were divided into two groups: an intervention group using the app's nurse-led, patient-oriented management system and a conventional care group that didn't use the app. Patients' uploaded health data were processed via deep learning optical character recognition, and the AI system provided personalized healthcare plans and interventions. Conversely, the conventional care group received suggestions from nephrologists during regular visits without AI assistance. Monitoring extended for an average duration of 2.1 years post-recruitment, with the study's objective being to assess the app's effectiveness in preserving kidney function. The primary outcome was the eGFR slope over the follow-up period, and secondary outcomes included changes in albumin-to-creatinine ratio (ACR) and mean arterial pressure (MAP). A total of 12297 eligible patients who registered on the KidneyOnline app from January 2017 to April 2023 were enrolled for the analysis. Among them, 808 patients were successfully matched using 1:1 propensity score matching, resulting in 404(50%) patients in the KidneyOnline care system group and another 404(50%) patients in the conventional care group. The eGFR slope in the KidneyOnline care group was significantly lower than that in the conventional care group (-1.3, 95% CI: -2.4, -0.1 mL/min/1.73 m2 per year vs. -2.8, 95% CI: -3.8, -1.9 mL/min/1.73 m2 per year，P=.009). Subgroup analysis revealed that the effect of the KidneyOnline care group was more significant in males, patients over 45 years old, and patients with worse baseline kidney function, higher blood pressure, and heavier proteinuria. After 3 and 6 months, the MAP in the KidneyOnline care group decreased to 85.6 (SD 9.2) and 83.6 (SD 10.5) mmHg, respectively, compared to 94.9 (SD 10.6) and 95.2 (SD 11.6) mmHg in the conventional care group (P<.001). The ACR in the KidneyOnline care group showed a more significant reduction after 3 and 6 months (736 mg/g vs. 980 mg/g and 572 mg/g vs. 840 mg/g, P=.074, .027)); however, there was no significant difference in ACR between the two groups at the end of the follow-up period (618 mg/g vs. 639 mg/g, P=.904). The utilization of KidneyOnline, an AI-based, nurse-led, patient-centered care system, may be beneficial in slowing the progression of non-dialysis-dependent CKD.

Multimodal personalised executive function intervention (E-Fit) for school-aged children with complex congenital heart disease: protocol for a randomised controlled feasibility study

IntroductionChildren with congenital heart disease (CHD) are at risk for executive functions (EF) impairments. To date, interventions have limited effects on EF in children and adolescents with complex CHD. Therefore,...

Efficacy of personalised text message intervention in reducing smoking frequency and amount for non-abstinent smokers: A double-blind, randomised controlled trial.

Emerging evidence supports the efficacy of mobile phone interventions for smoking cessation. However, behaviour changes of smokers who fail to reach abstinence and the related psychological mechanism are still understudied. We aimed to evaluate the efficacy of a behaviour change theory-based smoking cessation intervention delivered through personalised text messages from the perspective of smokers who fail to reach abstinence. We conducted a two-arm, double-blind, randomised controlled trial, with the intervention group receiving personalised text messages developed specifically for this study, and the control group receiving non-personalised ones related to smoking cessation. These messages were sent over a period of three months. We looked at three outcomes: changes in smoking frequency, in smoking amount, and in protection motivation theory (PMT) construct scores. We obtained smoking cessation results for 722 participants who went through the randomisation process (intervention: 360, control: 362; block randomisation design). Overall, 32.3% of baseline daily smokers in the intervention group and 20.0% in the control group changed to weekly smokers during the follow-up period (P < 0.001), while 43.4% of consistent daily smokers in the intervention group and 32.8% in the control group continuously reduced their smoking amount (P < 0.001). We observed associations between the intrinsic rewards of smoking and changes from daily to weekly smoking, the perceived severity of smoking and reductions in smoking amount, as well as the self-efficacy of quitting and changes from daily to weekly smoking/reductions in smoking amount. We found that a personalised text message-based intervention was more likely to decrease the frequency and amount of smoking in smokers who failed to reach abstinence compared to a non-personalised one. We also explored the possible psychological mechanism of such positive effects. Here we provide evidence for countries to consider the promotion of smoking cessations using behaviour theory-driven personalised text messages, which may be more cost-effective than traditional approaches. Peking University: ChiCTR2100041942.

Developing Population Health Management in Ireland-Challenges and Opportunities

Abstract Population Health Management (PHM) is the concept of aggregating population data with data from multiple care and service settings, the analysis of that data into a single, actionable patient record, and using the insights gained to identify that population's specific healthcare needs and develop a tailored response to them. This is achieved through the use of data analytics, artificial intelligence, and digital technologies, by stratifying populations according to the risk of deterioration in health. This stratification allows the development of early personalised interventions with an emphasis on prevention, which are key to better outcomes. The overall aims of PHM are to improve the health and well-being of the population, and the patient experience of care, to reduce healthcare costs, to improve workforce well-being and engagement, and to address inequalities in health and care. This discussion paper will cover the core building blocks for PHN, the tools required to carry out population health needs assessment; population segmentation, and risk stratification. The development of PHM and its use internationally will be discussed together with developments and opportunities provided in the context of the Irish healthcare system. It will discuss the current status of PHM development in Ireland using a population health maturity matrix. Evidence and examples of impact will be provided and recommendations to advance its progression within the Irish healthcare system. Challenges for the future advancement of PHM include a better definition of the content of the segmentation analysis and the intervention steps, demonstration of its added value, in particular its cost-effectiveness, and aligning the rationale for action that underpins current customization efforts. Regarding programme implementation, those programmes that are well-supported, funded, and given sufficient time to develop are most likely to demonstrate impact. Key messages • Future advancement of PHM requires a better definition of the content of the segmentation analysis and the intervention steps, and demonstration of its effectiveness and cost-effectiveness. • Programmes that are well-supported, funded, and given sufficient time to develop are most likely to demonstrate impact.

Identifying treatment non-responders based on pre-treatment gait characteristics - A machine learning approach

BackgroundPaediatric movement disorders such as cerebral palsy often negatively impact walking behaviour. Although clinical gait analysis is usually performed to guide therapy decisions, not all respond positively to their assigned treatment. Identifying these individuals based on their pre-treatment characteristics could guide clinicians towards more appropriate and personalized interventions. Using routinely collected pre-treatment gait and anthropometric features, we aimed to assess whether standard machine learning approaches can be effective in identifying patients at risk of negative treatment outcomes. MethodsObservational data of 119 patients with movement disorders were retrospectively extracted from a local clinical database, comprising sagittal joint angles and spatiotemporal parameters, derived from motion capture data pre- and post-treatment (physiotherapy, orthosis, botulin toxin injections, or surgery). Participants were labelled based on their change in gait profile score (GPS, non-responders with a decline in GPS of <1.6° vs. responders). Their pre-treatment features (sagittal joint angles, spatiotemporal parameters, anthropometrics) were used to train a support vector machine classifier with 5-fold cross-validation and Bayesian optimization within a MATLAB-based Classification Learner App. ResultsAn average accuracy of 88.2 ± 0.5 % was achieved for identifying participants whose gait will not respond to treatment, with 64 % true negative rate and an area under the curve of 88 %. ConclusionOverall, a classical machine learning model was able to identify patients at risk of not responding to treatment, based on gait features and anthropometrics collected prior to treatment. The output of such a model could function as a warning signal, notifying clinicians that a certain individual might not respond well to the standard of care and that a more personalized intervention might be needed.

Improving patient activation with a tailored nursing discharge teaching intervention for multimorbid inpatients: A quasi-experimental study

ObjectivePreliminary effectiveness test of a novel structured personalized discharge teaching intervention for multimorbid inpatients. MethodsUsing a 2-group sequential pre/post-intervention design, the sample comprised 68 pre-intervention control group and 70 post- intervention group participants. The discharge teaching intervention by trained clinical nurses used structured tools to engage patients and individualize discharge teaching. Outcomes measures included Patient Activation Measure, Readiness for Hospital Discharge Scale, Discharge Care Experiences Survey, and readmission with 10 days post-discharge. ResultsThe intervention had a statistically significant positive effect on improving patient activation (M=4.8; p = 0.05) from admission to post-discharge. The participation subscale of the Discharge Care Experiences Survey was higher in the intervention (M=4.1, SD=0.7) than the control group (M=3.8, SD=0.7; t (127)= −2.79, p = .01, effect size= .34). There were no significant between-group differences in Readiness for Hospital Discharge Scale and readmission. ConclusionsOur results suggest that a structured personalized discharge teaching intervention can improve patient activation and participation in discharge care. Further refinement of the intervention is needed to evaluate and improve specific components of the intervention. Practice ImplicationsStructured personalized discharge teaching should include patient engagement strategies in the teaching-learning process.

Group-level analysis of engagement poorly reflects individual students’ processes: Why we need idiographic learning analytics

A central assumption of the scientific method is that inferences derived from group-level analysis align with and generalize to the individual level. This study was conducted to put this assumption to the test to examine if and to what extent our analysis, inferences, and assumptions hold, and which variables generalize from the group to the individual level. We use engagement as the underpinning of this study. However, the same methods and questions apply elsewhere. The study included 238 students over six courses and applied the latest advances in psychological networks. Two networks were estimated using the same data: a between-person model that captures the group-level engagement and a within-person model that captures the within-person processes. The results showed that there were significant differences between the two networks and a lack of generalizability regarding regularity, academic achievement, and online disengagement. Such findings cast doubts on inferences drawn from group-level data about our understanding of learners’ performance or engagement, or to design personalized interventions. More attention and efforts are needed to further model within-person processes to understand, and possibly deliver precise personalized support and interventions that are more generalizable and truly personalized.

An mHealth Text Messaging Program Providing Symptom Detection Training and Psychoeducation to Improve Hypoglycemia Self-Management: Intervention Development Study.

Hypoglycemia remains a challenge for roughly 25% of people with type 1 diabetes (T1D) despite using advanced technologies such as continuous glucose monitors (CGMs) or automated insulin delivery systems. Factors impacting hypoglycemia self-management behaviors (including reduced ability to detect hypoglycemia symptoms and unhelpful hypoglycemia beliefs) can lead to hypoglycemia development in people with T1D who use advanced diabetes technology. This study aims to develop a scalable, personalized mobile health (mHealth) behavioral intervention program to improve hypoglycemia self-management and ultimately reduce hypoglycemia in people with T1D who use advanced diabetes technology. We (a multidisciplinary team, including clinical and health psychologists, diabetes care and education specialists, endocrinologists, mHealth interventionists and computer engineers, qualitative researchers, and patient partners) jointly developed an mHealth text messaging hypoglycemia behavioral intervention program based on user-centered design principles. The following five iterative steps were taken: (1) conceptualization of hypoglycemia self-management processes and relevant interventions; (2) identification of text message themes and message content development; (3) message revision; (4) patient partner assessments for message readability, language acceptability, and trustworthiness; and (5) message finalization and integration with a CGM data-connected mHealth SMS text message delivery platform. An mHealth web-based SMS text message delivery platform that communicates with a CGM glucose information-sharing platform was also developed. The mHealth SMS text messaging hypoglycemia behavioral intervention program HypoPals, directed by patients' own CGM data, delivers personalized intervention messages to (1) improve hypoglycemia symptom detection and (2) elicit self-reflection, provide fact-based education, and suggest practical health behaviors to address unhelpful hypoglycemia beliefs and promote hypoglycemia self-management. The program is designed to message patients up to 4 times per day over a 10-week period. A rigorous conceptual framework, a multidisciplinary team (including patient partners), and behavior change techniques were incorporated to create a scalable, personalized mHealth SMS text messaging behavioral intervention. This program was systematically developed to improve hypoglycemia self-management in advanced diabetes technology users with T1D. A clinical trial is needed to evaluate the program's efficacy for future clinical implementation.

CURATE.AI COR-Tx platform as a digital therapy and digital diagnostic for cognitive function in patients with brain tumour postradiotherapy treatment: protocol for a prospective mixed-methods feasibility clinical trial

IntroductionConventional interventional modalities for preserving or improving cognitive function in patients with brain tumour undergoing radiotherapy usually involve pharmacological and/or cognitive rehabilitation therapy administered at fixed doses or intensities, often...

A risk assessment tool for predicting fragility fractures in people with HIV: derivation and internal validation of the FRESIA model.

People with HIV have a higher risk of fracture than the general population. Because of the low performance of the existing prediction tools, there is controversy surrounding fracture risk estimation in this population. The aim of the study was to develop a model for predicting the long-term risk of fragility fractures in people with HIV. We included 11,899 individuals aged ≥ 30 years from the Spanish HIV/AIDS research network cohort. We identified incident fragility fractures from medical records, defined as those non-traumatic or occurring after a casual fall, at major osteoporotic sites (hip, clinical spine, forearm, proximal humerus). Our model accounted for the competing risk of death and included 12 candidate predictors to estimate the time to first fragility fracture. We assessed the discrimination and calibration of the model and compared it with the FRAX tool. The incidence rate of fragility fractures was 4.34 (95% CI 3.61-5.22) per 1,000 person-years. The final prediction model included age, chronic kidney disease, and chronic obstructive pulmonary disease as significant predictors. The model accurately predicted the five-year and ten-year risk of fragility fractures, with an area under the receiving operator characteristic curve of 0.768 (95% CI 0.722-0.814), and agreement between the observed and expected probabilities. Furthermore, it demonstrated better discrimination and calibration than the FRAX tool, improving the classification of over 35% of individuals with fragility fractures compared to FRAX. Our prediction model demonstrated accuracy in predicting the long-term risk of fragility fractures. It can assist in making personalized intervention decisions for individuals with HIV and may potentially replace the current tools recommended for fracture risk assessment in this population. This article is protected by copyright. All rights reserved.

Translation and validation of the Chinese version of the maternal postpartum stress scale

ObjectiveTo translate the Maternal Postpartum Stress Scale (MPSS) into Chinese and validate its psychometric properties in postpartum women.MethodsA total of 406 postpartum women were recruited from six hospitals in Nantong, Jiangsu Province, China. Cronbach’s α co-efficient, split-half reliability, and test-retest reliability were used to evaluate the reliability of the translated scale. Exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were used to evaluate the structural validity of the scale. The Edinburgh Postnatal Depression Scale, Depression Anxiety Stress Scale-21 anxiety dimension, and Perceived Stress Scale were used as calibration scales to measure the correlation of MPSS. All data were analyzed using SPSS 25.0 and Amos 24.0.ResultsThe Cronbach’s α co-efficient of the Chinese version of MPSS and its three dimensions were 0.940 and 0.882–0.911, respectively. The split-half reliability was 0.825, and the test-retest reliability was 0.912. The scale’s content validity index was 0.926. Three common factors were extracted from the EFA. The CFA validated the explored 3-factor structure, and the indicators were fitted well (χ2/Df = 2.167, comparative fit index = 0.918, Tucker–Lewis index = 0.907, incremental fit index = 0.919, and root mean square error of approximation = 0.075).ConclusionThe translated Chinese version of MPSS had suitable reliability and validity in assessing postpartum stress in Chinese women. The translated scale can also help with the early identification of postpartum stress and provide a scientific basis for the formulation of early personalized intervention measures. Overall, the scale has certain clinical value and practical significance for enhancing the physical and mental health of postpartum women. However, future studies including large, diverse populations are warranted.

Identifying obesity subtypes: A review of studies utilising clinical biomarkers and genetic data.

Obesity is a complex and multifactorial condition that poses significant health risks. Recent advancements in our understanding of obesity have highlighted the heterogeneity within this disorder. Identifying distinct subtypes of obesity is crucial for personalised treatment and intervention strategies. This review paper aims to examine studies that have utilised clinical biomarkers and genetic data to identify clusters or subtypes of obesity. The findings of these studies may provide valuable insights into the underlying mechanisms and potential targeted approaches for managing obesity-related health issues such as type 2 diabetes.

Gender Differences in Psychosocial Outcomes and Coping Strategies of Patients with Colorectal Cancer: A Systematic Review.

(1) Background: Gender is an important factor impacting cancer experience. This review mainly aimed to summarize colorectal cancer (CRC) patients' gender differences in psychosocial outcomes and coping strategies. (2) Methods: Relevant studies were searched for in four electronic databases from 2007 to July 2023. And manual searching was performed on the included studies' reference lists to identify additional eligible studies. (3) Results: A total of 37 eligible articles were included in this review. These studies were conducted in 19 countries, and they targeted CRC patients at various treatment stages. Significant results showed that female patients tended to have more psychological distress, complex social functioning, and less sexual distress and to choose more positive coping strategies than male patients. But there was no gender difference in psychosocial outcomes and/or coping strategies in some studies, which implied that gender similarity also existed. (4) Conclusions: The findings support the fact that there are both gender differences and similarities in CRC patients' psychosocial outcomes and coping strategies. A perspective beyond the simple masculine-feminine binary improved our in-depth understanding of gender tendency. Importantly, taking gender tendency into account is critical for medical staff to provide more personalized support and communication interventions.

Well-being, character strengths, and depression in emerging adults.

Emerging adulthood is a period of transition between adolescence and adulthood, during which individuals explore different roles and possibilities. This period is often characterized by high levels of stress and uncertainty, which can increase the risk of depression. The aim of this study was to investigate the associations between emerging adulthood dimensions, well-being, character strengths and depression, as well as to verify the differences in the levels of these elements among different groups of depressive symptomatology. Participants were 1,060 individuals (741 females, 305 males) between the ages of 18 and 30 years (M = 22.3, SD = 3.72). Spearman's correlation, linear regression, and Kruskal- Wallis with pairwise post-hoc analyses were used to analyze the data. There were significant positive correlations between emerging adulthood dimensions, well-being, and character strengths, and negative correlations between these variables and depression. There were also significant differences in the means of the evaluated constructs among the symptomm groups. The findings suggest that emerging adulthood dimensions, well-being, and character strengths are all associated with depression in emerging adults and have important implications for the provision of interventions in health and education for emerging adults. The study provides a deeper understanding of the factors influencing depression during emerging adulthood and emphasize the importance of promoting positive psychology elements and developing personalized interventions for emerging adults. This also suggest that public policies related to mental health and education should take into account the unique needs of emerging adults.

Usability and Acceptability of a Corneal-Plane α-Opic Light Logger in a 24-h Field Trial

Introduction: Exposure to light fundamentally influences human physiology and behaviour by synchronising our biological clock to the external light-dark cycle and controlling melatonin production. In addition to well-controlled laboratory studies, more naturalistic approaches to examining these “non-visual” effects of light have been developed in recent years. As naturalistic light exposure is quite unlike well-controlled stimulus conditions in the laboratory, it is critical to measure light exposure in a person-referenced way, the “spectral diet.” To this end, light loggers have been developed to capture personalised light exposure. As an alternative to light sensors integrated into wrist-worn actimeters, pendants, or brooch-based light loggers, a recently developed wearable light logger laterally attached to spectacle frames enables the measurement of biologically relevant quantities in the corneal plane. Methods: Here, we examine the usability and acceptability of using the light logger in an undergraduate student sample (n = 18, mean±1SD: 20.1 ± 1.7 years; 9 female; Oxford, UK) in real-world conditions during a 24-h measurement period. We probed the acceptability of the light logger using rating questionnaires and open-ended questions. Results: Our quantitative results show a modest acceptability of the light logger. A thematic analysis of the open-ended questions reveals that the form factor of the device, in particular, size, weight, and stability, and reactions from other people to the wearer of the light logger, were commonly mentioned aspects. Conclusion: In sum, the results indicate the miniaturisation of light loggers and “invisible” integration into extant everyday objects as key areas for future technological development, facilitating the availability of light exposure data for developing personalised intervention strategies in both research, clinical and consumer contexts.

SUPPORT AND TREATMENT AFTER JOINT REPLACEMENT (STAR): LONG-TERM FOLLOW-UP OF A CARE PATHWAY FOR PATIENTS WITH CHRONIC PAIN AFTER KNEE ARTHROPLASTY

AbstractIntroductionApproximately 15–20% of patients report chronic pain three months after total knee replacement (TKR). The STAR care pathway is a clinically important and cost-effective personalised intervention for patients with pain 3 months after TKR. The pathway comprises screening, assesment, onward referral for treatment and follow-up over one year. In a multicentre randomised controlled trial comparing the pathway with usual care, the pathway improved pain at 6 and 12 months. This study examined the longer-term clinical and cost-effectiveness of the STAR care pathway.MethodologySTAR trial participants were followed-up at a median of 4 years post-randomisation. Co-primary outcomes were self-reported pain severity and interference in the replaced knee, assessed with the Brief Pain Inventory (BPI). Resource use from electronic hospital records was valued with UK reference costs.ResultsOf the 337 participants active at trial completion, 326 had confirmed vital status, and 226 (69%) provided outcome data at a median of 4 years. The between-group difference in mean BPI severity score was −0.42 (95% CI −1.07, 0.23; p=0.20) and for BPI interference was −0.64 (95% CI −1.41, 0.12; p=0.10), favouring the intervention. Multiple imputation analyses led to attenuations of about 0.2 points.Mean hospital admission costs over four years were £2461.49 (95% CI £1354.42, £3568.57) in the intervention arm and £3791.68 (95% CI £2095.12, to £5488.24) in usual care.ConclusionThese data are consistent with sustained benefits from the STAR care pathway at 4 years, albeit attenuated and with wider confidence intervals, in part attributable to attrition after 1 year.

A Heuristic-Concatenated Feature Classification Algorithm (H-CFCA) for autism and epileptic seizure detection

ObjectiveThe objective is to implement a comprehensive Heuristic-CFCA (H-CFCA) a feature classification system using Machine Learning and Deep Learning principles for detecting Autism and Epileptic Seizures with EEG data. This involves a sequential integration of functional blocks that includes EEG Pre-Processing, Dimensionality Reduction, Feature Extraction, and a Concatenated Feature Classification Scheme (CFCA). MethodsThe methodology involves db4 Wavelet-based decomposition (WD) for pre-processing the EEG into five frequency bands, followed by Singular Value Decomposition (SVD) for dimensionality reduction. Subsequently, Heuristic Independent Component Analysis (H-ICA) is applied to extract Independent Components (ICs). These ICs are fed to H-CFCA comprises of Support Vector Machine (SVM) as first stage classifier, followed by a Bi-Stack Long Short-Term Memory (BiS-LSTM) as second stage classifier to detect the presence of autism and epileptic seizure. ResultsThe evaluation of the proposed algorithm achieves an accuracy of 99.51 % and a Sensitivity of 98.86 % and F1 score of 99.32 % ConclusionThe article realizes an effective and feasible H-CFCA, with an ability to handle large size EEG datasets and achieves reduced computational complexity, and improved feature classification. Comparative analysis shows that H-CFCA achieves the highest accuracy and sensitivity compared to existing ML/DL-based algorithms. SignificanceThe EEG feature classification system significantly advances diagnostic accuracy, improves social well-being by enabling personalized interventions, and drives technological innovation in healthcare by leveraging advanced classification methods and data analysis techniques.

Illness Perceptions, Cognitions, and Beliefs on COPD Patients' Adherence to Treatment - A Systematic Review.

Chronic Obstructive Pulmonary Disease (COPD) is a chronic inflammatory lung disease characterized by persistent respiratory symptoms and airflow limitation. Besides its irreversibility, COPD is a treatable condition, and patients would strongly benefit from being adherent to their treatments. However, almost half of them are non-adherent, and, according to several recent studies, the way the patient perceives the disease might influence this variable. This systematic review provided a synthesis of studies about the relationships between illness perceptions (IP), cognitions, beliefs, and adherence in COPD. English language publications were searched in PubMed, Medline, Scopus, ResearchGate, PsycINFO, and Cochrane Library databases from November 2022 to February 2023, following PRISMA guidelines. The reference lists of eligible studies were also searched. Data extraction and critical appraisal were undertaken by two reviewers working independently. A total of 14 studies were included. Adherence to treatment in COPD is confirmed to be low, using both self-report questionnaires and objective assessment systems. Most studies concluded that COPD is perceived as a moderate threat destined to last forever, even if many participants referred to little disease knowledge. This perception did not change between adherent and non-adherent groups. Those who considered more necessary to take their medicines and had a caregiver were more adherent and less concerned about their future. On the other side, forgetfulness, lack of trust in medications, and difficulties in understanding how to take them were perceived as the main causes of non-adherence. Other predictors of non-adherence, like depression, low self-efficacy, and severity of disease were confirmed. The systematic review highlights the variability of the relationship between IP, cognitions and beliefs, and COPD treatment adherence. A new level of awareness of the relationship between patients' subjective point of view and treatment adherence may inform future treatment options and promote a more personalized intervention.

Exploring patient, informal carer and care professional perspectives to understand the key clinical and non-clinical care needs of those living with multiple long-term conditions (MLTC).

Multiple long-term conditions (MLTC) are the co-occurrence of two or more chronic long-term conditions. While numerous studies have examined health and social care needs associated with MLTC, there has been limited research exploring both clinical and non-clinical care needs of this patient cohort from a range of perspectives. To explore patient, informal carer, and care professional perspectives to identify what are the key clinical and non-clinical needs for optimal care in people with MLTC. Semi-structured interviews were conducted with a representative sample of 29 patients, informal carers, and professionals, using video-link or telephone. Inductive reflexive thematic analysis was used to analyse and interpret the data. A range of clinical and non-clinical care needs were identified, including supporting patient self-management of conditions, informal carer involvement in care decisions, limited knowledge of MLTC among care professionals, and mental health and socioeconomic needs of individuals were emphasised as important to optimal care in MLTC.Improvements to optimise care included: more personalised care interventions, improved care professional understanding of MLTC care needs, involvement of informal carers in care decisions and promoting patient self-management of conditions and increased consideration given to non-clinical care needs. MLTC patients often have a complex range of clinical and non-clinical care needs. This requires a holistic and coordinated care approach, enabling care to be tailored to personal care needs of individuals with MLTC.