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\"Licensed under the Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 United States license\"--Title page verso.

Artificial intelligence (AI) is advancing healthcare globally and in Saudi Arabia, enhancing patient care, diagnostics, and administrative efficiency, despite challenges such as data privacy and regulation. This study explores knowledge, attitudes, and perceptions (KAP) regarding AI in medication adherence among chronic patients in Makkah region, Saudi Arabia. A cross-sectional study was conducted among patients with chronic diseases in the Makkah region, Saudi Arabia, from 1 July to 31 December 2024. The study included adult patients with chronic diseases (≥18 years) receiving primary care in the Makkah region. KAP levels were analyzed using descriptive statistics and composite scores, with demographic associations evaluated through Pearson chi-square tests (p<0.05). A total of 385 participants were included in the study. Most participants were women (60%), and those belonging to the 50 years or older group comprised the highest percentage (51.2%). The most reported chronic conditions were diabetes (30.7%), hypertension (19.7%), and asthma (14%). Knowledge levels were at a good level among 72.7% of the study participants, and 45.5% expressed a positive attitude towards AI's role. Perception was high among 50.9% of the respondents but low among 23.4%. Demographic factors, particularly age, significantly improved KAP (p-values of 0.048, 0.046, and 0.031, respectively). A positive attitude towards AI's role in medication adherence was observed in 58.2% of the participants with good knowledge levels compared to only 11.4% of those with poor knowledge (p=0.001). Variations in perception levels regarding AI's role in medication adherence were evident across demographics, with statistically significant associations found for age and overall knowledge level (p-values of 0.031 and 0.001, respectively). The results highlight AI's potential to enhance medication adherence and healthcare efficiency while maintaining a human-centred approach. To ensure effective integration, it's crucial to address concerns related to privacy, trust, and reduced human interaction. AI should be positioned as a supportive tool that complements-not replaces-human care, with transparent governance and targeted education playing key roles.

An award-winning author and reviewer describes her tomboy childhood, challenging Catholic school education, broken family life, marriage, struggle with chronic illness which rendered her unable to have children, and literary career.

Functional impairment is one of the most decisive prognostic factors in patients with complex chronic diseases. A more significant functional impairment indicates that the disease is progressing, which requires implementing diagnostic and therapeutic actions that stop the exacerbation of the disease. This study aimed to predict alterations in the clinical condition of patients with complex chronic diseases by predicting the Barthel Index (BI), to assess their clinical and functional status using an artificial intelligence model and data collected through an internet of things mobility device. A 2-phase pilot prospective single-center observational study was designed. During both phases, patients were recruited, and a wearable activity tracker was allocated to gather physical activity data. Patients were categorized into class A (BI≤20; total dependence), class B (2060; moderate or mild dependence, or independent). Data preprocessing and machine learning techniques were used to analyze mobility data. A decision tree was used to achieve a robust and interpretable model. To assess the quality of the predictions, several metrics including the mean absolute error, median absolute error, and root mean squared error were considered. Statistical analysis was performed using SPSS and Python for the machine learning modeling. Overall, 90 patients with complex chronic diseases were included: 50 during phase 1 (class A: n=10; class B: n=20; and class C: n=20) and 40 during phase 2 (class B: n=20 and class C: n=20). Most patients (n=85, 94%) had a caregiver. The mean value of the BI was 58.31 (SD 24.5). Concerning mobility aids, 60% (n=52) of patients required no aids, whereas the others required walkers (n=18, 20%), wheelchairs (n=15, 17%), canes (n=4, 7%), and crutches (n=1, 1%). Regarding clinical complexity, 85% (n=76) met patient with polypathology criteria with a mean of 2.7 (SD 1.25) categories, 69% (n=61) met the frailty criteria, and 21% (n=19) met the patients with complex chronic diseases criteria. The most characteristic symptoms were dyspnea (n=73, 82%), chronic pain (n=63, 70%), asthenia (n=62, 68%), and anxiety (n=41, 46%). Polypharmacy was presented in 87% (n=78) of patients. The most important variables for predicting the BI were identified as the maximum step count during evening and morning periods and the absence of a mobility device. The model exhibited consistency in the median prediction error with a median absolute error close to 5 in the training, validation, and production-like test sets. The model accuracy for identifying the BI class was 91%, 88%, and 90% in the training, validation, and test sets, respectively. Using commercially available mobility recording devices makes it possible to identify different mobility patterns and relate them to functional capacity in patients with polypathology according to the BI without using clinical parameters.

\"Author Kathlyn Conway, a three-time cancer survivor, believes that the triumphalist approach to writing about illness fails to do justice to the shattering experience of disease. By wrestling with the challenge of writing about the reality of serious illness and injury, she argues, writers can offer a truer picture of the complex relationship between body and mind\"--Provided by publisher.

Background In the medical field, value co-creation involves patients’ active involvement. By collaborating with service providers, patients can contribute to the creation of more targeted and effective value. Patients’ self-efficacy and behavior are crucial in this process, as their active participation and support can enhance their service experience. This study investigated the impact of chronic disease patients’ self-efficacy and value co-creation behaviors on the outcomes of value co-creation. Methods Relevant data were acquired through a questionnaire survey using statistical methods, such as the t-test, analysis of variance, and stratified linear regression. This approach was used to examine the current conditions and factors influencing value co-creation outcomes among community-dwelling patients with chronic diseases. Additionally, a structural equation model was employed to systematically investigate and validate the impact pathways and mechanisms related to the influence of self-efficacy and value co-creation behaviors on value co-creation outcomes. We also explored the moderating effect of digital health technology application capabilities on the relationship between self-efficacy and value co-creation behaviors. Results Self-efficacy, information search, interactive collaboration, feedback provision, and shared decision-making exert significant positive influences on the value co-creation outcomes among individuals with chronic diseases. The path analysis of the structural equation model indicates that self-efficacy and value co-creation behaviors may directly impact value co-creation outcomes. Concurrently, value co-creation behaviors partially mediate the association between self-efficacy and value co-creation outcomes. Furthermore, the digital health technology application capability exhibits a negative moderating effect in the pathway from self-efficacy to value co-creation behaviors. Conclusions The implementation of health education and social support measures by healthcare institutions and communities may augment patient self-efficacy, facilitate doctor-patient interactions, and promote shared decision-making. These initiatives could enhance the value of chronic disease services and optimize patient experiences. Additionally, healthcare institution managers are encouraged to focus on optimizing internet hospital platforms, organizing digital health training for patients, and bolstering patients’ proficiency in digital health technology applications. This strategy aims to instill a sense of health responsibility among patients with chronic diseases by fostering positive behaviors in interactive collaboration, information search, feedback provision, and other dimensions.

Background Chronic patients persistently seek for health information on the internet for medication information seeking, nutrition, disease management, information regarding disease preventive actions and so on. Consumers ability to search, find, appraise and use health information from the internet is known as eHealth literacy skill. eHealth literacy is a congregate set of six basic skills (traditional literacy, health literacy, information literacy, scientific literacy, media literacy and computer literacy). The aim of this study was to assess eHealth literacy level and associated factors among internet user chronic patients in North-west Ethiopia. Methods Institutional based cross-sectional study design was conducted. Stratified sampling technique was used to select 423 study participants among chronic patients. The eHealth literacy scale (eHEALS) was used for data collection. The eHEALS is a validated eight-item Likert scaled questionnaire used to asses self-reported capability of eHealth consumers to find, appraise, and use health related information from the internet to solve health problems. Statistical Package for Social science version 20 was used for data entry and further analysis. Multivariable logistic regression was used to examine the association between the eHealth literacy skill and associated factors. Significance was obtained at 95% CI and p  < 0.05. Result In total, 423 study subjects were approached and included in the study from February to May, 2019. The response rate to the survey was 95.3%. The majority of respondents 268 (66.3%) were males and mean age was 35.58 ± 14.8 years. The multivariable logistic regression model indicated that participants with higher education (at least having the diploma) are more likely to possess high eHealth literacy skill with Adjusted Odds Ratio (AOR): 3.48, 95% CI (1.54, 7.87). similarly, being government employee AOR: 1.71, 95% CI (1.11, 2.68), being urban resident AOR: 1.37, 95% CI (0.54, 3.49), perceived good health status AOR: 3.97, 95% CI (1.38, 11.38), having higher income AOR: 4.44, 95% CI (1.32, 14.86), Daily internet use AOR: 2.96, 95% CI (1.08, 6.76), having good knowledge about the availability and importance of online resources AOR: 3.12, 95% CI (1.61, 5.3), having positive attitude toward online resources AOR: 2.94, 95% CI (1.07, 3.52) and higher level of computer literacy AOR: 3.81, 95% CI (2.19, 6.61) were the predictors positively associated with higher eHealth literacy level. Conclusion Besides the mounting indication of efficacy, the present data confirm that internet use and eHealth literacy level of chronic patients in this setting is relatively low which clearly implicate that there is a need to fill the skill gap in eHealth literacy among chronic patients which might help them in finding and evaluating relevant online sources for their health-related decisions.

Despite the implementation of different COVID-19 prevention measures, the incidence of the disease continues to rise. Hence, vaccines have been taken as the best option for controlling the transmission of the disease. Although the approved COVID-19 vaccines have proven to be safe and effective, multiple beliefs and misconceptions still exist influencing its acceptance. To assess the acceptance of the COVID-19 vaccine and determinant factors among chronic patients visiting Dessie Comprehensive Specialized Hospital, Northeastern Ethiopia. Institution-based cross-sectional study design was used among patients with chronic diseases visiting Dessie Comprehensive Specialized Hospital from May 1 to 20, 2021 using a consecutive sampling technique. Binary logistic regression analysis using crude odd ratio (COR) and adjusted odd ratio (AOR) was performed to assess the association between independent and dependent variables. Variables having p values of less than 0.05 at the 95% confidence interval (CI) were considered as factors of COVID-19 vaccine acceptance. A total of 416 respondents participated in the survey, with a response rate of 98.6%. About 59.4% of the respondents were willing to accept the COVID-19 vaccine. Participants who had health insurance (AOR=1.812; 95% CI: 1.703-3.059), knew anyone diagnosed with COVID-19 (AOR=2.482; 95% CI: 1.427-4.317), having good knowledge of the COVID-19 vaccine (AOR=6.890; 95% CI: 3.900-120.17), and having a positive attitude towards COVID-19 vaccine (AOR=7.725; 95% CI: 4.024-14.830) were factors affecting the acceptance of COVID-19 vaccine. The acceptance of the COVID-19 vaccine was low. Use of health insurance, knowing anyone who had been diagnosed with COVID-19, knowledge, and attitude towards the COVID-19 vaccine were factors of COVID-19 vaccine acceptance. Healthcare professionals should conduct continuous awareness creation campaigns on the importance of the COVID-19 vaccine, safety, and its efficacy. Further studies like longitudinal and qualitative studies should be conducted to identify additional barriers to vaccine acceptance particularly in high-risk groups.

The objective of this study was to explore the Herpes Zoster (HZ) knowledge and the willingness to receive the HZ vaccination in adults and older people in Italy. The study was conducted on a sample of patients aged ≥65 years and over 50 years with chronic conditions who went to the clinics of general practitioners (GPs) in Campania region, Italy. Data was collected with a questionnaire administered through an interview. Multivariate logistic regression analysis was performed. 427 participants (83.2 %) had heard about HZ infection and correctly knew the main symptoms of the HZ disease, and 196 of them (45.9 %) were aware of the main complications of the infection, such as post-Herpetic Neuralgia (NPE) and Herpes Zoster ophthalmicus (HZO). Only 61 participants (11.8 %) had heard of the availability of a vaccination against HZ in Italy and 39 of them (63.9 %) knew that the vaccination is recommended in at-risk patients aged at least 50 years and for adults aged ≥65 years. 137 participants (26.6 %) had a positive attitude toward the willingness to receive the HZ vaccination. Participants aged 50–64 years, those who have more than one chronic disease, those who have received at least one recommended vaccination, those who had a positive attitude on the usefulness of HZ vaccination, and those who feel the need to receive additional information about HZ vaccination were more likely to have a positive attitude toward the willingness to receive the HZ vaccination. It is needed to implement effective strategies to improve HZ vaccination coverage in order to protect especially frail patients from the most serious complications of the disease.

Background Failure to keep medical appointments results in inefficiencies and, potentially, in poor outcomes for patients. The aim of this study is to describe non-attendance rate and to investigate predictors of non-attendance among patients receiving hospital outpatient treatment for chronic diseases. Methods We conducted a historic, register-based cohort study using data from a regional hospital and included patients aged 18 years or over who were registered in ongoing outpatient treatment courses for seven selected chronic diseases on July 1, 2013. A total of 5895 patients were included and information about their appointments was extracted from the period between July 1, 2013 and June 30, 2015. The outcome measure was occurrence of non-attendance. The associations between non-attendance and covariates (age, gender, marital status, education level, occupational status, specific chronic disease and number of outpatient treatment courses) were investigated using multivariate logistic regression models, including mixed effect. Results During the two-year period, 35% of all patients (2057 of 5895 patients) had one or more occurrences of non-attendance and 5% of all appointments (4393 of 82,989 appointments) resulted in non-attendance. Significant predictors for non-attendance were younger age (OR 4.17 for 18 ≤ 29 years as opposed to 80+ years), male gender (OR 1.35), unmarried status (OR 1.39), low educational level (OR 1.18) and receipt of long-term welfare payments (OR 1.48). Neither specific diseases nor number of treatment courses were associated with a higher non-attendance rate. Conclusions Patients undergoing hospital outpatient treatments for chronic diseases had a non-attendance rate of 5%. We found several predictors for non-attendance but undergoing treatment for several chronic diseases simultaneously was not a predictor. To reduce non-attendance, initiatives could target the groups at risk. Trial registration This study was approved by the Danish Data Protection Agency (Project ID 18/35695 ).