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Background Medical adherence is important in the treatment of depression. Primary medical adherence, i.e. patients collecting their newly prescribed medications from pharmacies, is very different depending on the drug prescribed Objective To assess the rate of primary medical adherence in patients prescribed antidepressants and to identify characteristics that make patients less likely to pick up prescriptions. Methods An observational study was performed using primary health care data from Sweden on patients who were prescribed antidepressants. Univariate and multivariate logistic regression was used to determine differences in pick-up rate according to patient characteristics. Main outcome Pick-up rate, defined as collection of a prescription within 30 days. Results A total of 11 624 patients received an antidepressant prescription during the study period, and the overall pick-up rate was 85.1%. The pick-up rate differed according to country of birth: individuals born in the Middle East and other countries outside Europe had lower primary medical adherence than Swedes, with adjusted odds ratios (ORs) of 0.58 and 0.67, respectively. Patients at ages 64-79 years had a higher pick-up rate compared with those aged 25-44 years (OR 1.71). Divorced patients had a lower rate compared with married patients (OR 0.80). Conclusion Immigrants from the Middle East and other countries outside Europe and younger and divorced patients had lower primary medical adherence, which calls for clinical attention and preventive measures. Key points Primary medical adherence is important in the treatment of depression. Are patient characteristics associated with primary medical adherence? The overall primary medical adherence rate was 85%. The rate differed by country of birth, age at diagnosis of depression, and marital status. Clinical attention is needed in patients who do not pick up their antidepressants.

Medical adherence and remote patient monitoring have gained huge attention from researchers recently, especially with the need to observe the patients' health outside hospitals due to the ongoing pandemic. The main goal of this research work is to propose a health status classification model that provides a numerical indicator of the overall health condition of a patient via four major vital signs, which are body temperature, blood pressure, blood oxygen saturation level, and heart rate. A dataset has been prepared based on the data obtained from hospital records, with these four vital signs extracted for each patient. This dataset provides a label associating each patient to the number of medical diagnoses. Generally, the number of diagnoses correlates with the patient's medical condition, with no diagnoses indicating normal condition, one to two diagnoses suggest low risk, and more than that implies high risk. Thus, we propose a method to classify a patient's health status into three classes, which are normal, low risk and high risk. This would provide guidance for healthcare workers on the patient's medical condition. By training the classification model using the prepared dataset, the seriousness of a patient's health condition can be predicted. This prediction is performed by classifying the patients based on their four vital signs. Our tests have yielded encouraging results using precision and recall as the evaluation metrics. The key outcome of this work is a trained classification model that quantifies a patient's health condition based on four vital signs. Nevertheless, the model can be further improved by considering more input features such as medical history. The results obtained from this research can assist medical personnel by providing a secondary advice regarding the health status for the patients who are located remotely from the medical facilities.

Long-term daily health monitoring and management play a more significant role in telehealth management systems nowadays, which require evaluation indicators to present patients' general health conditions and become applicable to multiple chronic diseases. This study aims to evaluate the effectiveness of subjective indicators of telehealth chronic disease management system (TCDMS). We selected Web of Science, ScienceDirect, Scopus, Cochrane library, IEEE, and Chinese National Knowledge Infrastructure and Wanfang, a Chinese medical database, and searched papers published from January 1, 2015, to July 1, 2022, regarding randomized controlled trials on the effectiveness of the telehealth system on patients with chronic diseases. The narrative review summarized the questionnaire indicators presented in the selected studies. In the meta-analysis, Mean Difference (MD) and Standardized Mean Difference (SMD) with a 95% CI were pooled depending on whether the measurements were the same. Subgroup analysis was conducted if the heterogeneity was significant, and the number of studies was sufficient. Twenty RCTs with 4153 patients were included in the qualitative review. Seventeen different questionnaire-based outcomes were found, within which quality of life, psychological well-being (including depression, anxiety, and fatigue), self-management, self-efficacy, and medical adherence were most frequently used. Ten RCTs with 2095 patients remained in meta-analysis. Compared to usual care, telehealth system can significantly improve the quality of life (SMD 0.44; 95% CI 0.16-0.73; P=.002), whereas no significant effects were found on depression (SMD -0.25; 95% CI -0.72 to 0.23; P=.30), anxiety (SMD -0.10; 95% CI -0.27 to 0.07; P=.71), fatigue (SMD -0.36; 95% CI -1.06 to 0.34; P<.001), and self-care (SMD 0.77; 95% CI -0.28-1.81; P<.001). In the subdomains of quality of life, telehealth statistically significantly improved physical functioning (SMD 0.15; 95% CI 0.02 to 0.29; P=.03), mental functioning (SMD 0.37; 95% CI 0.13-0.60; P=.002), and social functioning (SMD 0.64; 95% CI 0.00-1.29; P=.05), while there was no difference on cognitive functioning (MD 8.31; 95% CI -7.33 to 23.95; P=.30) and role functioning (MD 5.30; 95% CI -7.80 to 18.39; P=.43). TCDMS positively affected patients' physical, mental, and social quality of life across multiple chronic diseases. However, no significant difference was found in depression, anxiety, fatigue, and self-care. Subjective questionnaires had the potential ability to evaluate the effectiveness of long-term telehealth monitoring and management. However, further well-designed experiments are warranted to validate TCDMS's effects on subjective outcomes, especially when tested among different chronically ill groups.

Poor adherence to medical recommendations is a well-recognized catalyst for public health consequences worldwide. The literature highlights health consciousness as a likely antecedent to patient–physician trust, which in turn promotes medical adherence. Nevertheless, principles of patient-centered care suggest that patient perceptions of their doctor’s appraisal of their emotions may influence the path between trust and medical adherence. Accordingly, this study tested the mediating role of patient–physician trust in the relation between health consciousness and medical adherence and assessed whether patient ratings of their doctor’s appraisal of their own and their patients’ emotions moderated the mediated relation. Data were collected via self-report questionnaires from two culturally and economically diverse countries: Bosnia-Herzegovina (N = 262) and the United States (N = 314). Participants were young, healthy adults who visited their primary care physician in the past year. The study employed confirmatory factor analysis, mediation, and moderated mediation analyses. The results indicate that health consciousness positively related to patient–physician trust, which was in turn related to higher medical adherence and which mediated 28% of the total effect of health consciousness. Nevertheless, among patients who rated their physicians to have low appraisal for their patients’ emotions but high appraisal for their own emotions, the path from trust to adherence was not significant. These results highlight the importance of promoting health consciousness among young individuals, all while training practitioners to be attuned to their patients’ emotions and circumstances above their own. However, additional findings indicate that the interrelation between doctors’ emotional attributes and adherence is not necessarily one directional and warrants further investigation.

Background Up to 50 % of patients do not take medications as prescribed. Interventions to improve adherence are needed, with an understanding of which patients benefit most. Objective To test the effect of two low-literacy interventions on medication adherence. Design Randomized controlled trial, 2 × 2 factorial design. Participants Adults with coronary heart disease in an inner-city primary care clinic. Interventions For 1 year, patients received usual care, refill reminder postcards, illustrated daily medication schedules, or both interventions. Main Measures The primary outcome was cardiovascular medication refill adherence, assessed by the cumulative medication gap (CMG). Patients with CMG < 0.20 were considered adherent. We assessed the effect of the interventions overall and, post-hoc, in subgroups of interest. Key Results Most of the 435 participants were elderly (mean age = 63.7 years), African-American (91 %), and read below the 9th-grade level (78 %). Among the 420 subjects (97 %) for whom CMG could be calculated, 138 (32.9 %) had CMG < 0.20 during follow-up and were considered adherent. Overall, adherence did not differ significantly across treatments: 31.2 % in usual care, 28.3 % with mailed refill reminders, 34.2 % with illustrated medication schedules, and 36.9 % with both interventions. In post-hoc analyses, illustrated medication schedules led to significantly greater odds of adherence among patients who at baseline had more than eight medications (OR = 2.2; 95 % CI, 1.21 to 4.04) or low self-efficacy for managing medications (OR = 2.15; 95 % CI, 1.11 to 4.16); a trend was present among patients who reported non-adherence at baseline (OR = 1.89; 95 % CI, 0.99 to 3.60). Conclusions The interventions did not improve adherence overall. Illustrated medication schedules may improve adherence among patients with low self-efficacy, polypharmacy, or baseline non-adherence, though this requires confirmation.

Although it is today commonly accepted that cognitive and emotional factors interact in guiding decisions and behaviour in various domains, including medical and health related decision making, the interaction between these factors remains unclear. Within this study we explored the associations between individuals’ health behaviours with different cognitive and emotional factors. In doing so, we focused on three domains important for health outcomes that include leading a healthy everyday lifestyle, engagement in healthy behaviours and medical adherence. As predictors, we used cognitive reflection and heuristics tasks for assessing individuals’ ability to engage in rational thinking, cognitive styles that included need for cognition, faith in intuition and maximization to assess their motivation to engage in such thinking, optimism as an emotional indicator and trust and satisfaction with health provider as a final factor hypothesized to influence behaviour. The obtained results showed that all three assessed domains were associated with individuals’ cognitive and emotional factors. Specifically, leading a healthy everyday lifestyle was predicted by maximizing, optimism and trust and satisfaction with health provider. Next, healthy behaviours were predicted by maximizing, optimism and ability to override heuristic and biased thinking, whereas higher need for cognition and trust and satisfaction with health provider predicted medical adherence. These results extend previous reports and provide novel insights into the contributions of various aspects of cognitive and emotional factors for specific domains of health behaviour.

The aim of the study was to determine the relationship between medication adherence (MA) and selected psychological factors in a group of patients with rheumatoid arthritis (RA). The cross-sectional study was conducted in four rheumatology outpatient clinics in Silesia, Poland. The tests used were the Medication Adherence Questionnaire (MAQ), the Multidimensional Health Locus of Control Scale (MHLC), the Coping Inventory for Stressful Situations (CISS), and the Mindful Attention Awareness Scale (MAAS). The analysis involved 106 adult patients diagnosed with RA at least 6 months before, who were prescribed medication, with disease at any stage and with stable comorbidities. Software was used to perform analyses of frequency, basic descriptive statistics, including the Kolmogorov-Smirnov test, Student's -test for independent samples, intergroup univariate variance, Pearson's correlation coefficient, Spearman's rank correlation ρ coefficient, Fisher's exact test and stepwise linear regression. Powerful Others Health Locus of Control (PHLC), Internal Health Locus of Control (IHLC) and age of the subjects, (3, 102) = 8.05; < 0.001 explained 16.8% of the variation in the adherence level for the entire group. In the group of women PHLC and IHLC, (2, 80) = 10.04; < 0.001 were included in the model, which explained 18.1% of variation in MA. PHLC was the most significant factor in the group of women (β = 0.55; < 0.001) and in the entire group (β = 0.48; < 0.001). In the group of men, Social Diversion Style (SDS), (1, 21) = 5.81; = 0.02 was included in the model, which explained 17.9% of the variation in the MA level. The study identified some psychological predictors of adherence, which explained 16.8% of the variability. Factors increasing the likelihood of medication adherence in patients with rheumatoid arthritis include a strong belief in the power of others, low level of internal health locus of control, and advanced age.

Epilepsy requires continuous management and treatment to optimize patient outcomes. The advancement of digital health has led to the development of various mobile health (mHealth) tools designed to enhance treatment adherence among individuals with epilepsy. These solutions offer crucial support through features such as reminders, educational resources, personalized feedback, assistance with managing costs, shared decision-making, and access to supportive communities. To design effective medication adherence mHealth solutions, it is essential to evaluate the effectiveness of existing mHealth tools, understand the unique circumstances of different patients, and identify the roles of health care professionals within the digital care pathway. Existing studies on epilepsy primarily focus on self-management, whereas the effectiveness and usability of medical adherence mHealth solutions often remain overlooked. Furthermore, the involvement of health care professionals in digital care pathways for epilepsy as well as the impact of adherence mHealth solutions on the patient experience have not been adequately explored. This study aims to assess the effectiveness of current mHealth solutions designed to improve medical adherence among patients with epilepsy. Furthermore, the study will examine the experiences of patients using mHealth solutions for maintaining medical adherence in epilepsy care. Finally, this review intends to determine the roles of health care professionals within mHealth systems aimed at supporting adherence to medication among patients with epilepsy. A systematic literature review has been selected as the appropriate method to address the research questions, adhering to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. The inclusion and exclusion criteria have been carefully selected, and both qualitative and quantitative analyses will be used to analyze the results. The expected results will mainly focus on the comparison, classification, and analysis of the effectiveness of current medical adherence mHealth tools. Moreover, the patient experiences using available medical adherence mHealth tools for epilepsy will be assessed. Finally, the role of health care professionals in the epilepsy digital care pathway will be explored, with emphasis on medical adherence. The initial search, full-text screening, and data extraction have been carried out. Thirty-three papers were included in the final stage of the review. The study is expected to be completed by October 2024. To enhance the digital care pathway for epilepsy, a medical adherence mHealth solution should be personalized, manage medications, include an alarm system, track seizures, support consultations, and offer updated treatment plans. This study aims to understand how findings from the research questions can improve mHealth solutions for individuals with epilepsy. Insights from this research on the effectiveness of current mHealth adherence solutions will provide guidance for developing future mHealth systems, making them more efficient and effective in managing epilepsy. PROSPERO CRD4202347400; https://tinyurl.com/48mfx22e. DERR1-10.2196/55123.

Background Little is known about the self-perceived level of disability of stroke survivors in the community. We aimed to characterise Health-related quality of life (HRQoL) 1 year after stroke and investigate how sociodemographic and stroke-related factors and medical adherence explain the self-perceived level of disability in a Korean stroke population. Methods This was a multicentre cross-sectional study. A total of 382 ischaemic stroke survivors at 1 year after onset from 11 university hospitals underwent a one-session assessment, including socioeconomic variables, the modified Rankin Scale (mRS), various neurological sequelae, the Morisky, Green and Levin-Medication Adherence Questionnaire (MGL), and the World Health Organization Disability Assessment Schedule 2.0 (WHODAS 2.0) 36-items. The relationship between disability and different variables was analysed using ordinal logistic regression. Results The prevalence of disability based on global WHODAS 2.0 was 62.6% (mild, 41.6%; moderate, 16.0%; severe, 5.0%). The prevalence of severe disability was higher in participation in society (16.8%) and getting around (11.8%) than in other domains. Low MGL- motivation was the only factor determining a significant association between all six domains of disability after adjustment. Different predictors for specific domains were age, mRS, dysarthria, trouble seeing, cognition problems, and MGL-motivation for understanding and communicating ; age, recurrent stroke, mRS, hemiplegia, facial palsy, general weakness, and MGL-motivation for getting around ; age, education, mRS, hemiplegia, and MGL-motivation for self-care ; education, recurrent stroke, hemiplegia, dysarthria, and MGL-motivation for getting along with people ; age, education, income, mRS, hemiplegia, dysarthria, MGL-knowledge, and MGL-motivation for life activities ; living without a spouse, mRS, hemiplegia, dysarthria, trouble seeing, cognition problems, general weakness, and MGL-motivation for participation in society . Conclusions Self-perceived disability according to the WHODAS 2.0 at 1 year after stroke was highly prevalent. Each disability domain showed a different prevalence and associated factors. Interventions promoting medical adherence to motivation seemed to help achieve high HRQoL in all domains.

Background Patient satisfaction research in chronic pain treatment is scarce internationally and is nonexistent in Chinese communities like Hong Kong. This longitudinal study examined the relationships between medical adherence, pain treatment satisfaction, disability, and quality of life (QoL) in a sample of Chinese patients with chronic pain. Methods A total of 178 patients with chronic pain were assessed at baseline, 3 and 6 months following baseline. Medical adherence and pain treatment satisfaction were assessed by the Participant Compliance Reporting Scale and the Pain Treatment Satisfaction Scale (PTSS), respectively. QoL, depression, pain catastrophizing, and pain-related fear were assessed using SF-12, the depression subscale of the Hospital Anxiety and Depression Scale (HADS-D), the Pain Catastrophizing Scale, and the Tampa Scale for Kinesiophobia, respectively. Linear mixed effects models (LME) were fitted to identify predictors of pain treatment satisfaction, medical adherence, and QoL. Results Results of univariate LME analyses showed significant quadratic time effects on four PTSS scores and significant associations between disability grade and PTSS scores (all p < 0.05). Medical adherence was not significantly associated with satisfaction regarding pain medication (model 1). Satisfaction with medication characteristics emerged as an independent predictor of medical adherence (model 2: std β = —0.11, P < 0.05) after controlling for sociodemographic and pain variables. Neither medical adherence nor pain treatment satisfaction predicted QoL outcomes (models 3 and 4). Conclusions Distinct trajectories in pain treatment satisfaction were displayed in the current sample of Chinese patients with different disability grading chronic pain. Within pain treatment, only medication characteristics significantly impacted patients' medical adherence.