Osteoarthritis (OA) is one of the leading chronic conditions in the older population. People with OA are more likely to have one or more other chronic conditions than those without. However, the temporal associations, clusters of the comorbidities, role of analgesics and the causality and variation between populations are yet to be investigated. This paper describes the protocol of a multinational study in four European countries  exploring comorbidities in people with OA.This multinational study will investigate (1) the temporal associations of 61 identified comorbidities with OA, (2) the clusters and trajectories of comorbidities in people with OA, (3) the role of analgesics on incidence of comorbidities in people with OA, (4) the potential biomarkers and causality between OA and the comorbidities, and (5) variations between countries.A combined case–control and cohort study will be conducted to find the temporal association of OA with the comorbidities using the national or regional health databases. Latent class analysis will be performed to identify the clusters at baseline and joint latent class analysis will be used to examine trajectories during the follow-up. A cohort study will be undertaken to evaluate the role of non-steroidal anti-inflammatory drugs (NSAIDs), opioids and paracetamol on the incidence of comorbidities. Mendelian randomisation will be performed to investigate the potential biomarkers for causality between OA and the comorbidities using the UK Biobank and the Rotterdam Study databases. Finally, a meta-analyses will be used to examine the variations and pool the results from different countries.Research ethics was obtained according to each database requirement. Results will be disseminated through the FOREUM website, scientific meetings, publications and in partnership with patient organisations. This is first ever multicentre study on comorbidities in osteoarthritis in Europe involving nearly 27 million electronic health records.More than 60 chronic conditions are being studied—representing a wider coverage of diseases.We will examine the causal association using Mendelian Randomisation -Phenome-Wide Association Study methods with genetic data collected from two countriesSame protocol with robust statistical methods will be used across all countries to replicate the findings and examine the variations.Possible biases may be introduced by the nature of electronic health records and length of data availability.Osteoarthritis (OA) affects 27% of people aged over 45 years at peripheral synovial joints such as knees, hips, hands and feet.A recent systematic review has confirmed that people with OA are more likely to have other diseases, especially stroke, peptic ulcer, hypertension and depression.Comorbidity in OA can occur due to multiple factors. Various hypotheses have been used to explain the existence of comorbidities in general, the most accepted of which are the concordant  and discordant  theories.OA is one of the leading conditions reported in multimorbidity research. Exploring the association of OA with other diseases would help in further explaining the burden and pattern of the comorbidity.Understanding the temporal association with comorbidity and disease trajectory is crucial for any chronic condition, and this is possible through studies using longitudinal databases.The burden of diseases in primary care often depends on the population structure, health infrastructure and individual factors such as income and education. Such factors vary between countries, because of the heterogeneity mentioned above there are no robust data comparing OA and its comorbidities between countries. Therefore, the aims of this study were to explore the burden, pattern and causal factors of comorbidities in people with OA across four European countries using national registration databases in the UK, the Netherlands, Sweden and Spain.To estimate the prevalence, incidence, and time sequence of comorbidities in OA.To examine the clusters of comorbidities and trajectories of clusters in OA and associations with death.To investigate the associations between commonly used OA drugs, such as NSAIDs and opioids, and risk of comorbidities.To identify the potential biomarkers and causal pathways between OA and the comorbidities.To examine the variations of OA comorbidities and clusters across countries.Four routinely collected national (the UK and the Netherlands) or regional (Sweden and Spain) health databases will be used for objectives 1–3. In addition, for objective 4, genomic associations of OA with comorbidities will be examined using two cohort studies from the UK (UK Biobank)People registered with the respective databases aged 18 years or above are eligible for the study.Three patient and public involvement representatives (with OA and multiple chronic conditions) were involved in this study through group meetings. Difficulties of living with multiple conditions, lack of research in causal relationship and identification of diseases to be studied and the role of drugs in comorbidity were discussed. They are constantly in touch through providing their inputs at each step of the study.OA will be defined as having at least one recorded physician diagnosis of OA for hip, knee, ankle/foot, wrist/hand or site recorded as ‘unspecified’ during the study period for the respective database. People with any previous recording of the OA prior to the start date of the study will be excluded.We defined comorbidity as the recording of diagnosis of predefined chronic conditions in individuals using either International Classification of Diseases, 10th Edition (ICD-10) or Read or international classification of primary care (ICPC) code. An extensive list of 61 chronic conditions was prepared from the Quality Outcome Framework,Age and sex will be used in all centres as covariates to adjust in regression models. Additionally, information on body mass index (BMI), smoking, alcohol use, socioeconomic variables such as education level, income, place of birth (to identify those who immigrated to the country), and residential area, marriage (or registered partner) will be included when available. For calculating severity of the comorbidities in an individual, Elixhauser comorbidity index will be used to estimate the impact of comorbidities on death.First, we carried out a code mapping exercise for identification of people with OA and other comorbidities. A list of chronic conditions was built with different codes used by different databases for the same condition to identify eligible people with the conditions, such as Read code in Clinical Practice Research Datalink (UK), ICPC2 (Rotterdam) and ICD-10 for Lund and Spanish as used in respective database. The code lists were compared and edited to maintain the uniformity. The list was screened and verified by two researchers and two GPs. We also used unform definition for inclusion of condition, for example, at least one recording of the chronic conditions. Because all the centres did not have all the listed comorbidities, a minimum number of chronic conditions and covariates common in all the database were identified to be studied. Similarly, we decided to have a minimum follow-up study of 5 years and centres with more registration period can use the entire length of data available.Summary of the study design and analysis is provided in The study has obtained the following ethics approvals: UK- Independent Scientific Advisory Council 19/30R, The Netherlands—The Integrated Primary Care Information registration no. 11/2019, Spain—the Information System for the Development of Research in Primary Care, 4R19/011, Sweden—Ethical Review Authority, Skåne Healthcare Register, ‘Dnr 2011-432, Dnr 2014-276 and Dnr 2018-233. The registry databases are made available anonymised for the research purposes. Each centre will follow the data privacy policy of respective countries.We plan to publish all the results as manuscripts in peer reviewed journals and present the findings in relevant conferences. The results would be circulated as ‘lay-person’ language and would be available at least four different international languages such as English, Dutch, Swedish and Spanish. These will be shared on appropriate patient–public forum and involved institution’s websites.A combined retrospective and prospective study of OA cases and sex, age (±2 years), first year of registration, and practice matched controls (1:1–4) without OAFor the prospective analysis participants with incident OA but without the specific comorbidity of interest at the index date  and matched controls without OA will be followed up until the date of the first diagnosis of the comorbidity, deregistration, or death whichever comes first. The cumulative incidence will be calculated for each comorbidity in OA cases and matched controls at 1, 3, 5, 10, 15, 20 years (based on the data available) after the index date to examine the dynamic change of developing comorbidities during follow-up. Kaplan-Meier survival curves will be used to display the cumulative probability in OA and non-OA groups. Proportional hazard assumption will be tested using Schoenfeld residual plots. The Cox regression model will be used to calculate HR for each comorbidity unadjusted and adjusted for age, sex, practice, BMI, smoking and alcohol consumption. This hybrid design has been previously used by us to examine the temporality of associations between other rheumatic MSK diseases  and comorbidities.28 29For each dataset, an 80%:20% split into the training and testing data will be introduced. The following analysis in objective 2 will be first employed into the training dataset and then tested its generalisability in the testing dataset. At baseline, clusters of people based on 61 comorbidities will be identified using Latent class  analysis.A cohort study will be undertaken for this objective to evaluate the contribution of common analgesics for OA to the development of comorbidity such as NSAIDs, opioids and paracetamol. We are interested in the interaction between OA and use of drugs on the incidence of comorbidities, that is, to evaluate if the drug use in persons with OA poses increased or decreased risk of comorbidities compared to persons without OA and/or analgesics. Individual comorbidity, as well as clusters of comorbidities identified from objective 2 will be examined as outcomes. The 61 comorbidities in our study will be further categorised into eight groups, specifically: MSK, respiratory, neurodegenerative, psychological/psychiatric, cancer, cardiovascular, metabolic, renal problem, liver diseases, GI and others . The pro37We will perform an MR Phenome-Wide Association Study (MR-PheWAS)We will use the Rotterdam Study and the UK Biobank jointly for this objective. This is because that the Rotterdam Study is an OA cohort with deep phenotypes and biomarkers of OA, whereas the UK Biobank is a primary cohort for cancer and multiple disease outcomes, and both have detailed genetic variants. We will use two sample MR approach, that is, to establish an association between OA and genetic variants in the Rotterdam Cohort to identify genetic IV, for example, a set of single nucleotide polymorphisms associated with OA . We will then undertake the MR-PheWAS analysis to examine the causal effects of the OA IV on comorbidities in the UK Biobank. The MR method has been widely used in real world data to examine the causal relationship between IV and specific disease, under two assumptions: (1) genetic variants are randomly assigned in the population and (2) genetic variants can only be the cause not consequence of disease.The MR-PheWAS analysis includes three steps. First, we will identify the genetic variants that are associated with OA-IV. Second, we will undertake the PheWAS analysis—a series of case control analyses to estimate the associations between the IV and other disease outcomes,44With the MR-PheWAS study, the OR can be interpreted as causal association. We are primarily interested in the causality from OA to comorbidities. We are also interested in inflammatory , metabolic  and biomechanics  biomarkers and deep phenotypes of OA such as knee, hip and hand OA with and without symptoms. This will be undertaken if it is feasible within 3 years of this funded project, otherwise will be considered as our future research agenda.2 statistic and the Q test.2 value using the fixed effects model, otherwise the reasons for the heterogeneity will be investigated. Random effects models will be used to pool the results if the reasons for the heterogeneity cannot be identified and if the overall pooling is appropriate. Individual patient data meta-analysis may be used to help identify the reasons for heterogeneity.We will use meta-analyses to examine the variation between countries and to pool the data as appropriate. Estimates from first three objectives such as prevalence, incidence, OR, HR and 95% CI for each specific comorbidity across different populations will be distributed in a forest plot. Heterogeneity will be examined using the ITo detect minimum incidence of 1% comorbidity  with a minimum clinical important difference of HR1.2, and 90% power of the study, the estimated sample size was 197 561 for 1581 events. It was calculated using STATA v.15, with a correlation=0.2, SD of 0.5, proportion of withdrawal=0.20, alpha=0.05. The initial check with the registry database revealed to have minimum required sample size for the study.This study will be the largest epidemiological study on comorbidities of OA in primary care. One of the key advantages of this multinational study is the use of the same protocol to measure the burden of comorbidities in primary care settings in four European countries to ensure reproducibility and comparison. There is scant evidence on the comorbidities in people with OA, and this approach should help to identify the leading and most important associations before and after presenting clinical OA (the index date). Further advantages of this study are the large and representative populations studied and the same/similar extensive list of chronic conditions for identifying comorbidity clusters. Often comorbidities accumulate with age over time and the large primary care databases in this study have the advantage of having long follow-up time which will enable us to detect the incidence of comorbidities. Also, longer follow-up would help to identify the picture of the trajectory of the diseases.The relationship between chronic conditions and polypharmacy is a complex area of research. The count of the medications and more importantly the nature of prescribed drugs may be responsible for developing many new comorbidities in people with OA. We aim to explore the associations of the most commonly prescribed drugs in OA, such as NSAIDs, with the incidence of a wide range of comorbidities, which will be the first time that conditions other than established comorbidities such as psychological conditions and endocrine diseases will be examined. Finally, the causality study will further explore the associations at genetic levels and phenotypes, which will be novel in OA research. Using a two sample MR approach—one for OA deep phenotypes and the other for other chronic conditions maximises the potentials of sample size, disease phenotypes and comorbidity spectrum to better explore the causal pathways between OA and comorbidity.There are some limitations to this study. First, there are inherent issues in the nature of electronic health records with respect to possible misdiagnosis, ascertainment biases, under-recording, and changes in databases due to change in coding structures. Also, the analysis will be restricted to fewer covariates in some databases due to missing information on lifestyle factors such as physical activities and diet. Even though the databases have different durations of data available, if possible we will use a common follow-up time for objective five. Another important limitation is that we do not have information on quality of life and other outcomes to measure functional limitations recorded in the database.Chronic conditions, especially comorbidities recorded in general practices, depend on multiple factors such as population structure, healthcare facilities, health policies and the nature of the national databases. A major strength of this study is that it will include medical records on approximately 27 million people in four European countries. Also, the study will cover the sequence of research questions in comorbidity or multimorbidity starting from the burden through to the causality and variation. Such a research can be used for other similar multimorbidity studies. The expected results should inform health professionals in primary care settings with respect to management of people with OA and associated comorbidities.All the centres have obtained the necessary approvals for using the database in 2020. A consensus has been made on the code mapping exercise. The statistical analysis will be explained in detail in each of the publications. The team is expected to produce results by mid-2021.


Under the continuous impact of the epidemic, online learning methods represented by MOOC have developed rapidly. The course forum area has produced a large amount of text-based unstructured data, which can reflect the potential characteristics of learners' emotional states and behavioral interactions, and has an important impact on students' learning outcomes. To this end, this paper constructs an emotional and behavioral analysis model based on online forum texts, obtains forum data from the “Python Language Programming” course on the Chinese University MOOC platform, uses domain dictionary emotion classification method to analyze learning emotions, and based on the method of cognitive behavior coding table and knowledge construction behavior coding table analyzes learners' cognitive behavior and knowledge construction behavior. It can dynamically analyze learners' emotions, behavior changes, and evolutionary trends. This research provides opinions and suggestions on the improvement of platform interactive functions for teachers' online teaching, students' online learning, and platform management, which can effectively improve the efficiency and effectiveness of online learning. In the context of the deep integration of artificial intelligence and big data in education, significant changes have taken place in the ecological environment of education in China, and the future classroom will no longer be limited to classroom teaching, but to the network, digital, virtualization, and intelligent teaching environment , 2. In tAs a platform for learners and teachers to exchange and study, the MOOC forum area is an important place to share personal views and release information and resources . HoweverThe current research on MOOC learning experience is relatively scarce , 11; parThis paper constructs an emotion and behavior analysis model based on MOOC forum text. The model includes two parts: domain dictionary-based sentiment analysis and LSA-based behavior analysis, which dynamically analyzes the characteristics and evolution of learners' emotions and behaviors.Using the emotion and behavior analysis model we proposed, we conducted an empirical analysis on the MOOC course “Python Language Programming.” Studies have shown that learning emotions are dominated by positive emotions, and they show certain evolutionary laws over time. There are also significant differences among different academic achievers; low-level cognitive behaviors and knowledge-building behaviors in learning behaviors account for a relatively large proportion and differ in academic performance. There are also significant differences among achievers.Based on the results of empirical analysis, some operable forum interaction strategies are proposed for educators, learners, and platform managers.In summary, this study has the following main contributions:Related work is divided into two categories, namely, MOOC forum text sentiment analysis and behavior analysis, which are described in detail in the following.Recent approaches to the analysis of text-based unstructured data fall into the following three main categories: sentiment lexicon based analysis methods, machine-learning based methods, and qualitative analysis methods that require software support. Current researchers usually use content analysis methods to analyze forum data in various dimensions, such as Feldmand et al. constructing themselves' sentiment dictionaries based on existing dictionaries using Bootstrapping and partial manual annotation . In ordeIn Qin Changbo's study, machine learning and natural language processing methods were used to text-mine classroom discussion posts in Chinese MOOC forum areas, classifying them into three main categories, confusion, explanation, and irrelevance, and built a data mining system for MOOC forum posts . In Hui'In recent years, neural network-based sentiment classification methods have been gradually adopted by many researchers. Unlike models that use traditional machine learning algorithms for sentiment classification, neural network-based sentiment classification methods can automatically extract text features. Although there are relatively few emotion classification methods based on neural networks in the MOOC field, this method has been widely used in emotion classification in other fields in NLP . Rao et In addition to the above three methods, it also contains studies combining multiple methods, such as Zhu and other researchers who drew on the prefatory idea of affective computing, integrated the use of affective computing, text mining, social network analysis, and multiple regression to establish an affective measure based on an effective weight dictionary for online activity texts, and used regression models to analyze the relevant influencing factors of academic emotions in online activities, providing a harmonious interpersonal environment . There aThe data of learners in the forum area often contains rich semantic information, which maps the behavior patterns and evolution rules related to learning tasks. Moreover, existing studies have shown that as far as learning analysis subjects are concerned, relatively few in China pay attention to students' behaviors , while sWang et al. classified the cognitive participation degree of learners' forum posts according to certain coding manuals, and the results showed that learners with higher-order thinking could gain more knowledge . TherefoSome researchers have also explored the learning process in terms of three dimensions: emotional, cognitive, and metacognitive, while the results showed that learning emotions affect cognitive behavior , 41, 42.In addition to this, Wang et al. conducted a visual analysis of the literature related to international educational text mining in the last decade and found that the current research methods show diversity, with research hotspots focusing on five areas: learning behaviour, cognitive behaviour, behavioural analysis of knowledge construction, writing evaluation, and development of mining tools for curriculum texts . Based oThe learner's emotion and behavior analysis model based on the MOOC forum text is shown in F value is 97%, which can better identify the emotional tendency of the forum text.In order to perform sentiment analysis on text data, this research uses the domain dictionary method to perform dynamic sentiment analysis on the processed data based on data preprocessing. First, the Dalian Polytechnic Ontology Library dictionary is selected as the basic dictionary, combined with the self-built negative dictionary and the degree adverb dictionary, a domain dictionary suitable for Python programming courses is constructed through the SO-PMI algorithm, and its effectiveness is verified; the accuracy rate is 97.1%, the recall rate is 96.8%, and the Each forum text may contain both positive and negative emotions. For this reason, this study uses sentiment values to represent forum emotions. The specific calculation process is as follows.Find the emotional words contained in each forum text. If an emotional word appears in this forum, the word polarity is assigned the corresponding value, the commendatory meaning is assigned −1, and the derogatory meaning is assigned 1. The formula for the sentiment value of a word in this dictionary isi(w) represents the emotional strength of the vocabulary; p(w) represents the emotional polarity of the vocabulary; and z(w) represents the emotional value of the vocabulary.Here, Find out negative words. If the number of negative words is odd, the polarity of the negative words will be reversed. If it is an even number, the polarity does not change.Find adverbs of degree. The various degree adverbs are integrated into 6 grades, and different weight values are assigned to them according to the gradient descent formula, as shown in T1 is the weight value of the first rank “most.”The gradient descent formula used isE represents the sentiment value of a single sentiment word class in each forum; e represents the sentiment value of a particular sentiment word; n represents the number of negatives associated with the sentiment word; (−1)n represents the total number of weights of negatives; i represents the number of degree adverbs preceding the sentiment word; ∑i=0ih represents the cumulative sum of the weights of all degree adverbs preceding the sentiment word; and P represents the relative position of negatives and degree adverbs, with the following two relationships:Calculate the sentiment value of each forum. The number of negation words and degree adverbs before and after the word and their relative positions were found for each sentiment word in the forum as shown in the following formula.j represents the total number of sentiment word categories contained in each comment; Z (j) represents the final sentiment value of each forum, if Z (j) > 0 means the forum sentiment is positive, Z (j) = 0 means the forum sentiment is neutral, and Z (j) < 0 means the forum sentiment is negative.Each comment contains multiple sentiment words, and the sentiment value of all sentiment words in the text is accumulated to the final sentiment value, calculated as shown in equation . In equaDomain Lexicon Construction. This study will use a statistical approach to construct a domain sentiment lexicon, firstly using PMI to count the similarity between two words and then expanding the base lexicon with sentiment words through the SO-PMI algorithm to select extended sentiment words from the forum comments, which in turn increases the domain applicability of the sentiment words. The sentiment value of a word in the forum is shown in the following equation:Pword and Nword are positive sentiment seed words and negative sentiment seed words. If the value is greater than zero, then it is a positive sentiment word and if it is less than zero, then it is a negative sentiment word.The basic steps of behavioral analysis are (i) to develop behavioral coding rules, (ii) to collect textual data related to behavior, (iii) to code the data one by one according to the format required by the GSEQ software, (iv) to check the consistency of the coding table, (v) to conduct behavioral analysis and obtain the corresponding behavioral conversion frequency table and the adjusted residual table, (vi) to plot the conversion sequence for significant behavior, and (vii) to interpret the questions, the behavioral analysis of which includes two main types of cognitive behavior and knowledge construction behavior. The cognitive behaviour coding table is shown in In the experimental part, our research object is the course of “Python Language Programming.” On the basis of data collection and preprocessing, we focus on the analysis of learners' emotion and behavior and get some rules. The details are described as follows.In this paper, we selected a representative course of the Python category with a high number of participants and a high overall rating among Chinese university mooted courses: the course “Python Language Programming.” Considering that the eleventh course  is the longest of all the courses offered, and this period is a critical stage in the prevention and control of the new crown epidemic, the data from the eleventh course is used as the data source for this study. With over 760,000 learners participating in this course, and in the home independent learning phase, this period has a higher number of participants and forum interaction data compared to other open periods.Firstly, a total of 17,814 threaded posts and replies were obtained from the course forum area through web crawling. Secondly, the raw data was preprocessed by de-duplication and deactivation of words. Then, all the processed data were sorted according to the number of weeks and days, which were divided into 0 to 11 weeks, a total of 12 weeks of data, and saved as 0z.xlxs-11z.xlxs, and then divided into days and all the periods of each day on a weekly basis, and saved as 2.22Saturday.xlxs. Finally, in order to analyze learners' emotions and behaviors in a fine-grained way from different dimensions, it is necessary to classify learners who have obtained excellent certificates as high academic achievers and learners who have obtained passes as medium academic achievers, and then to organize the postings of different academic achievers in chronological order.The content of the postings of the different academic achievers is then organized in chronological order. The analysis and interpretation of learning emotions and behaviors is carried out at a fine-grained level at both the overall and individual levels. On the one hand, the analysis helps teachers to observe the overall attitude of students towards the forum, and on the other hand, the individual level analysis helps teachers to detect abnormalities from the perspective of the average level and to provide timely attention and intervention.Next, it mainly analyzes and interprets learners' emotions from two levels of the whole and the individual: on the one hand, according to the order of time evolution, the study of the overall emotion transfer law and on the other hand, from the individual to explore the emotional differences between different individuals and different academic achievements.This paper analyzes the learning emotions of online learners in terms of temporal evolution, including classified statistics, visual display, and dynamic analysis. It mainly includes two types: one is the academic emotion distributed throughout the entire learning process, also known as the duration academic emotion; the other is the real-time academic emotion distributed in different time stages .(1) Periodic Sentiment Analysis. The statistics yielded a total of 57% of the total forum posts for positive emotions and 43% of the total discussion posts for negative emotions. It was observed that learners' overall evaluation of the course was relatively stable, with a high proportion of positive sentiment being expressed. The results indicate that learners generally expressed appreciation and satisfaction with the Python course, but also expressed some negative sentiment towards the course.The following reasons were identified after analyzing the current situation: as a programming course, Python programming requires learners not only to learn theoretical basics but also to write code themselves and practise their practical skills . For exa(2) Weekly Sentiment Analysis. As learning emotions are dynamic, the interval between observations of learning emotions should not be too long , as the details of emotions may be missed if the interval is too long; however, the interval should not be too short , as the observed changes in emotions may be unstable if the interval is too short. Taking these factors into account and taking into account the teaching schedule, a week's span (from 0:00 on Monday to 24:00 on Sunday) is considered to be a more stable and observable period of time and is used as a time cycle. Figures  Weekly SIn this study, the whole learning process was divided into three stages: the early learning period (week zero to week two), the middle learning period (week three to week seven), and the end learning period (week eight to week eleven), and the distribution of learning emotions throughout the course week was observed. There was a consistent trend of positive and overall mood swings.The trend of the learners' emotions in the eleventh course was as follows: as the students had just returned from their holidays at the beginning of the course, they were in the stage of exploring the online learning method, so their overall learning state was not very good and there was a certain degree of confusion, so their positive emotions were not very high. However, with the adjustment of the state and the continuous exploration of learning methods, the learning mood reaches its peak in the mid-term, which is mainly because the learners have a certain enthusiasm and interest in Python learning at this stage. The learning mood peaks in the middle stages, mainly because there is a degree of motivation and interest in learning Python. However, as the course continues, students accumulate difficulties and problems to a certain extent and dissatisfaction with learning Python exists, and negative emotions peak at this point. Through processes such as teacher guidance and peer support, students are helped to get through the difficult learning period. Towards the end of the school year, near the final exam, there are no significant ups and downs in learning emotions, but problems such as excessive revision and exam pressure are at their lowest point, so it is also important to reduce students' stress in the classroom to some extent.(3) Sentiment Analysis of Days. To analyze the trends in learners' dynamic mood changes at a more granular level, this study will observe the trends in the evolution of mood dynamics within the weekly subsessions  and positive emotions (p ≤ 0.001) between the academic achievers. In terms of means, the high-achieving learners were more active in positive emotions (13.364 > 4.10) than the middle-achieving group, while the middle-achieving group was more active in negative emotions (−19.51 > −7.57) than the high-achieving group, thus considering a correlation between academic achievement and academic emotions. To verify this relationship, the Pearson correlation coefficient in SPSS software was used for analysis. From the results, it is clear that the value of the correlation coefficient between academic achievement and positive emotions is 0.361, indicating a general positive correlation between academic achievement and positive emotions (0.361 < 0.4). The correlation coefficient between academic achievement and negative emotions was 0.539, indicating that there was also a significant positive correlation between academic achievement and negative emotions (p ≤ 0.001), and the correlation coefficient of 0.539 > 0.4 indicates that there is a strong positive correlation between academic achievement and negative emotions; i.e., the more the negative emotions, the lower the academic achievement. Based on this result, it is known that negative emotions are more likely to affect the final academic achievement; therefore, it is more important for instructors to pay attention to the abnormal individuals with poor emotions.The results of this analysis showed that there were significant differences in both negative emotions  Analysis of Overall Cognitive Behavior of Learners. In this study, the proportion of each cognitive behavior in the whole group was calculated and it was found that descriptive behaviors (43.9%) and inferential or explanatory behaviors (30.1%) were more predominant in the group. Firstly, learners preferred to describe the problems they encountered in their studies or simply state their personal opinions, so descriptive behaviors accounted for nearly half of the group, indicating that learners only thought about and explored the problems on the surface, but did not go deeper to reveal the patterns behind the problems. Also, some learners responded or commented with simple affirmative or negative statements such as “yes, what else do you want,” lacking deeper interaction. Secondly, learners also tend to use inferential or explanatory behaviors to engage in interactive discussions in the forum and also to help other learners solve problems in this way. Then, learners also used a small number of conceptual or definitional behaviors to validate their ideas. Finally, fewer learners used difference-contrast relations to argue the differences that existed between perspectives and were not very keen to explicitly point out points of cognitive difference with the object of their interaction, choosing instead to avoid talking about them. Comparing several types of cognitive behavior reveals that comparative cognitive behavior is the least frequent, but this type of questioning is often the most effective in triggering deeper exploration on both sides of the interaction and in providing a pavement for other participants to refine new ideas, making comparative cognitive behavior an important part of higher-order thinking for learners. In conclusion, the high percentage of low-level cognitive behaviors (69.9%) and the lack of cognitive behaviors that enhance higher-order thinking in the interactive communication process suggest that guidance from teachers or other instructional managers such as teaching assistants is urgently needed in the learning process.(2) Analysis of Cognitive Behavior of Learners with Different Academic Achievements. The willingness to learn, motivation, and learning goals of different academic achievers may differ, giving rise to differences in interactive behaviors. To explore the differences in learner cognitive behaviors among different academic achievers, it was necessary to eliminate the non-cognitive behavior postings and finally screen out 36 higher academic achievers and 9 middle academic achievers. The coded cognitive behavior data were subjected to chi-square testing and the results showed that there were significant differences in these four cognitive behaviors between the academic achievers (p ≤ 0.001).Higher academic achievers had the highest proportion of inferred or explained high-level cognitive behaviors (62.67%) and middle academic achievers had the highest proportion of described low-level cognitive behaviors (47.37%), indicating that higher academic achievers had higher levels of cognitive behaviors. The reason for this may be due to the difference in the total number of posts between higher academic achievers and middle academic achievers and the fact that a large number of posts without cognitive behaviors were removed from the posts of middle academic achievers. The reason for this phenomenon is most likely that these learners just want to post more content such as punch cards, flooding posts, and logistical posts to complete the requirements for the completion certificate. Comparing the descriptive behavior posts, the proportion of descriptive behavior posts made by moderate achievers was about three times higher than the proportion made by higher achievers, suggesting that moderate achievers are more willing to describe facts or phenomena. In comparing the inferential or explanatory behavior posts, the proportion of higher academic achievers was about twice as high as the proportion of intermediate achievers, suggesting that higher academic achievers are more likely to actively think about the content, explore the principles behind the knowledge, or give examples of cause-and-effect relationships between events and propose new solutions and ideas. The above analysis shows that the higher achievers tend to post definitions, inferences, or explanations, and to think in terms of definitions and continuous thinking; the middle achievers tend to post descriptions, inferences, or explanations, and to think in terms of asking a lot of questions and trying to solve problems. Teachers are also called upon to help moderate achievers build their own cognitive strategies to facilitate meaningful learning.This subsection will provide a detailed interpretation of learners' knowledge construction behaviour along two different dimensions: overall and for different academic achievers.(1) Analysis of the Overall Knowledge Construction Behavior of Learners. Firstly, the knowledge construction behavior of the whole group was measured, and it was found that 40.6% of the interactive posts were opinion sharing posts and 36.5% were meaning construction posts, which were the two largest sections, and the total ratio was 77.1%, indicating that most of the learners were in the low-level construction stage of opinion sharing and meaning negotiation. The results suggest that learners tend to simply state their opinions or ask questions about code bugs or software installation and then elaborate on the details of their questions and discuss with others the possible causes of the problems, but are not sure of the true causes of the bugs and do not agree on them over the interactions. Secondly, the proportion of questioning posts was 2.8%, indicating that only some of the learners tested their own coding process and found that the answer did not match the reference answer, expressed that the final result of their code was different from the expected result, or expressed a different view from the questioner, or even denied the other person's view directly. The reason for this may be that learners are only at the stage of acquiring knowledge in online courses, but not at the stage of thinking and reflecting on their learning. There is no doubt that the ability to question is an important learning skill, and with so many online learning resources, learners are required to learn to reflect and question rather than read a dead book. Teachers are also asked to pay more attention to the questioning posts posted by learners to help students practise their thinking skills. Finally, it was found that the proportion of test or revision posts was 17.5% and the proportion of application posts was 2.6%, both of which were relatively low overall and at a high level of construction. This suggests that it may take time and effort for learners to complete the acquisition of knowledge and move up to the revision and application stages, resulting in fewer high-level knowledge construction behaviors. Higher level knowledge construction requires a degree of creativity and innovation and is a difficult task, but it may also be due to a lack of critical thinking on the part of the learner to find the right way to correct the problem [ problem , 51. LeaThe sequential transformation of knowledge construction behavior of the learning as a whole is plotted according to the residual table, as shown in Analysis of Knowledge Construction Behavior of Learners with Different Academic Achievements. The differences in willingness to learn, motivation, and goals of the academic achievers may lead to slight differences in knowledge construction behaviors. In order to ensure the accuracy of the differences in knowledge construction behaviors between academic achievers, posters without knowledge construction behaviors were eliminated in advance, and the knowledge construction behaviors of 36 higher academic achievers and 9 middle academic achievers were finally obtained after screening, and the cardinality test was conducted to verify the level of differences, and the results showed that there were significant differences between academic achievers in these five categories (p = 0.003 < 0.05).(2) Analysis of the proportion of posts for each behavior revealed that higher academic achievers posted the largest proportion of shared views posts and middle academic achievers posted the largest proportion of negotiation of meaning posts. The analysis revealed that the higher achievers were more willing to ask questions or share their opinions and that they were not just passive learners, but also active thinkers. Therefore, teachers should encourage learners to ask more questions to help them solve their own problems while practising their own logical expression. The intermediate achievers are more willing to interact with their peers, indicating that they are more willing to participate in the search for solutions to problems, possibly because they have encountered similar problems and are trying to find solutions through meaningful negotiation, but also as a warning to teachers that they should provide more guidance on learning methods. Comparing the levels of knowledge construction between high and middle academic achievers revealed that higher academic achievers had a greater proportion of higher knowledge construction behaviors (14.76% > 7.50%), indicating that higher academic achievers had a somewhat higher level of knowledge construction.The knowledge construction behaviors of the different academic achievers were analyzed to obtain a sequence of significant behaviors transformation charts, which yielded the results shown in Comparing the sequence of cognitive behavioral transitions of the different academic achievers, it can be seen that the secondary academic achievers lack a sequence of behavioral transitions from low-level knowledge construction to higher-level knowledge construction, such as testing and revision after meaning construction (P3 ⟶ P4) and application after testing and revision (P4 ⟶ P5), and the lack of development of these behavioral levels also confirms that the transition from the secondary academic achievers to the higher academic achievers requires teachers' support. The lack of development at these levels also confirms the need for teachers to provide optimal training to help them reach a deeper level of knowledge construction as they move from middle to high academic achievement.In order to inform both teachers and learners, this study takes into account the current state of forum interaction in the Python programming course and proposes several actionable forum interaction strategies for teachers, learners, and platform administrators.The pedagogue should be personally engaging, appropriately paced to enhance knowledge, improve data literacy, provide timely feedback and take a leading role, and provide personalized support services on this basis.In the course of the data analysis, it was found that learners expressed a lot of admiration and praise for the course instructor, which shows that the instructor's teaching methods, presentation format, personal charm, and other teaching behaviors have an important impact on the construction of knowledge and deep interaction with students.In the design of the lesson, for example, the part on “Python Basic Image Drawing,” the practicality and interest of the content should be enhanced in order to stimulate students' interest in learning. In addition to this, teachers should also control the difficulty of teaching according to the level of difficulty; for example, week 0 and week 1 should increase the teaching pace on the basis of the original teaching schedule, but should also give students a buffer time to enter the learning state. After the second week, which is a critical stage of learning, you need to start paying attention to the learning mood of each student and slow down the learning pace appropriately. However, after the fourth and fifth weeks of difficult content, the pace of learning needs to be slowed down and the focus should be on the quality of teaching.As there is a lot of implicit data related to learning on the platform, in order to read and understand the data, you need to take initiative and learn the core techniques related to the application and practice of big data in education, data processing and analysis, analysis and interpretation of data results, etc., through various means. Consciously develop a sense of data awareness and adjust teaching strategies in depth through deeper digging for the purpose of optimising teaching effectiveness.Teachers or teaching assistants should give full play to their leading role, posting more open questions in the forum, scaffolding critical questions for students, encouraging students to put forward different views, promoting students' knowledge construction, and developing higher-order thinking skills. In addition to this, teaching assistants should take the lead and help the forum to develop in the right direction. When lecturers post topic posts in the forum, they should avoid leaving comments under the topic posts to suggest general ideas for students, and encourage more postings, different views, and the courage to question them to facilitate the construction of meaning by other learners . In addiTake a leading role in observing the interaction in the forum area and use the spare time to respond to simple questions. Posting supportive or favourable comments to meaningful responses encourages the construction of meaningful knowledge and promotes mutual help among students. Consider using live video streaming to answer questions online for learners when they encounter more difficult questions that most students encounter, and keep an eye on the forum interaction strategy.As a result of the above emotional analysis, teachers should pay particular attention to learners with negative emotions and provide a degree of intervention and guidance to help students get out of their learning difficulties and optimize the learning process and learning outcomes. For example, when negative emotions are high, teachers need to pay close attention to the individual learner, providing emotional cues, teacher guidance, and peer support, such as human-computer, teacher-student, or student-student interaction measures to reduce students' resistance to and fear of difficult problems.This can be done by considering different teaching modes for different groups of learners, differentiating the course design, and improving the accuracy of online learning, in order to tailor the teaching to the student's needs and achieve precise teaching. In addition to this, personalized emotional profiles can be introduced to facilitate adaptive adjustments in subsequent courses according to emotional dynamics, such as appropriate teaching resources push, so as to improve the positive emotional experience of students, so that their learning interest and initiative to achieve the best .Learners should prepare for lessons, improve their self-regulatory skills, identify cognitive strategies, and exercise higher-order thinking.It takes one to two weeks to get into the swing of things before the course starts, and within this time to prepare for the learning. Firstly, you need to have a general understanding of the course content, and only if you are familiar with the course content can you plan your subsequent time well. Secondly, you need to familiarise yourself with the various functions of the platform and the installation and use of its associated software. Finally, you need to be clear about your motivation for learning, whether it is to obtain a certificate or to acquire knowledge or skills. One particular point to note is that online learning requires not only a sense of self-awareness and active learning but also a certain level of self-learning ability. This self-learning ability requires self-monitoring, which requires learners to plan their time according to the actual situation, such as planning their learning activities, supervising the learning process appropriately, and evaluating the learning results in stages and summaries, on the premise of being active and proactive. The students are active and proactive and plan their time well according to the actual situation, such as planning their learning activities rationally, supervising the learning process appropriately, and doing stage and summative evaluations of the learning results.From the above analysis, it is clear that most learners have not yet reached the desired cognitive depth, so they need to be able to take full advantage of their initiative and have the ability to construct their knowledge, to discover the mechanisms by which knowledge occurs and to explore the laws of its evolution, to achieve knowledge-driven deep learning, and to promote meaningful learning, in addition to learning to analyze, compare, and evaluate issues from multiple perspectives, to use higher-order thinking to construct high-level knowledge, and to transform what is learned into a complete body of knowledge. During forum discussions, it is important to exercise a sense of self-reflection, not to follow the crowd, but to acquire the right learning strategies and then learn to solve difficult problems independently. You should write more hands-on operational programs when studying programming courses in order to exercise higher-order thinking such as problem-based critical thinking and creative thinking.Platform administrators should categorise the content of forum posts for display, support multiple expressions, visualise implicit behaviours, and focus on course evaluation.This learning platform currently only supports the posting of text and images, but as Python language programming is an operational course, it is difficult for students to post static forum content such as text and images to fully and accurately express the specific problems they encounter in programming, so the platform could be developed to support the transmission of dynamic content such as video or voice so that both parties can accurately express and understand each other's content. To facilitate deeper insight and analysis for both pedagogues and learners, visualisation can be considered, which can help them to dynamically identify those with learning difficulties and provide timely assistance.To address the current situation of learners posting repetitive content and simple support or approval content in order to obtain certificates, platform administrators could consider categorising the content of posts in the forum area in order to stimulate learners' higher level of knowledge construction and cognition, help students engage in high-quality interactions, and improve the level of interaction. For example, post content could be categorised into top posts (questions posted by teachers), highlight posts , repeat posts (the same content posted by learners), and stepped posts (possible class swiping posts).Observation of the courses on the current Chinese university catechism platform revealed that although the number of participants in the courses was high, the number of people who rated the courses was low and the evaluation dimension was single. In order to obtain learners' perspectives on the course and improve and enhance subsequent courses, platform managers should create a multidimensional and exclusive evaluation scale for this course only and encourage learners to systematically evaluate different aspects of the online course. Course evaluations not only provide timely feedback and suggestions for students and teachers, but also help in the design and development of online courses.To facilitate deeper insight and analysis by teachers and students, visualisation can also be considered, for example, by setting thresholds for learner emotions, with the system automatically generating warning signals to teachers or students if a certain threshold is exceeded. The visual displays explore the cognitive and knowledge construction processes of individuals or different groups and adjust discussion activities and interaction strategies. For learners, they can view their personal knowledge based on the platform's intelligent learning analytics mapping and dynamically adjust learning styles.The interactive forum area of an online course is an important place for course communication and discussion, and the text data generated in it contains a large amount of implicit information that can be used to explore the emotional state and behavioural characteristics of learners and provide important data support for relevant stakeholders. This study uses the domain dictionary emotion classification method to analyze the emotions of learning, and the cognitive behaviour and knowledge construction behaviour of learners based on the cognitive behaviour coding table and the knowledge construction behaviour coding table, so as to provide reasonable suggestions and opinions for the three major user groups, namely, learners, teachers, and administrators. In future research, the improvement of the model  is one aspect, and another important aspect is to apply the proposed model to actual teaching scenarios and integrate emotion and cognitive analysis into intelligent teaching systems. The system can provide personalized intervention to online learners, and it can also help teachers grasp the learning status of students in real time and automatically provide online learners with personalized support services to make up for the limitations of “emotional lack” in the intelligent age.


The strengthening or substitution of intravenous cytotoxic chemotherapy cycles by oral targeted anticancer therapies, such as protein kinase inhibitors (PKIs), has provided impressive clinical benefits and autonomy as well as a better quality of life for patients with cancer. Despite these advances, adverse event management at home and medication adherence remain challenging. In addition, PKI plasma concentrations vary significantly among patients with cancer receiving the same dosage, which could explain part of the observed variability in the therapeutic response.The aim of this optimizing oral targeted anticancer therapies (OpTAT) study is to optimize and individualize targeted anticancer treatments to improve patient care and self-monitoring through an interprofessional medication adherence program (IMAP) combined with measurement PKI plasma concentrations.The OpTAT study has two parts: (1) a 1:1 randomized medication adherence program, in which the intervention consists of regular motivational interviewing sessions between the patient and the pharmacist, along with the delivery of PKIs in electronic monitors, and (2) a systematic collection of blood samples and clinical and biological data for combined pharmacokinetic and pharmacodynamic analysis. On the basis of the electronic monitor data, medication adherence will be compared between groups following the three operational definitions: implementation of treatment during the persistent period, persistence with treatment and longitudinal adherence. The implementation will be described using generalized estimating equation models. The persistence of PKI use will be represented using a Kaplan-Meier survival curve. Longitudinal adherence is defined as the product of persistence and implementation. PKI pharmacokinetics will be studied using a population approach. The relationship between drug exposure and efficacy outcomes will be explored using Cox regression analysis of progression-free survival. The relationship between drug exposure and toxicity will be analyzed using a pharmacokinetic-pharmacodynamic model and by logistic regression analysis. Receiver operating characteristic analyses will be applied to evaluate the best exposure threshold associated with clinical benefits.The first patient was included in May 2015. As of June 2021, 262 patients had participated in at least one part of the study: 250 patients gave at least one blood sample, and 130 participated in the adherence study. Data collection is in process, and the final data analysis is planned to be performed in 2022.The OpTAT study will inform us about the effectiveness of the IMAP program in patients with solid cancers treated with PKIs. It will also shed light on PKI pharmacokinetic and pharmacodynamic properties, with the aim of learning how to adapt the PKI dosage at the individual patient level to increase PKI clinical suitability. The IMAP program will enable interprofessional teams to learn about patients’ needs and to consider their concerns about their PKI self-management, considering the patient as an active partner.ClinicalTrials.gov NCT04484064; https://clinicaltrials.gov/ct2/show/NCT04484064.DERR1-10.2196/30090 On the basis of the Global Cancer Observatory estimates, cancer was responsible for almost 10 million deaths globally in 2020 ; howeverFor 15 years, the strengthening or alternatively the substitution of intravenous cytotoxic chemotherapy cycles by oral, sometimes long-term, targeted anticancer therapies has offered impressive clinical benefit and autonomy as well as a better quality of life for patients with cancer. Targeted cancer therapies involve protein kinase inhibitors (PKIs) and inhibitors of growth factor receptors, which are used to treat a variety of cancers, including gastrointestinal stromal tumors; kidney, thyroid, colorectal, lung, and breast cancers; melanoma; and sarcomas. Although treatment failure and poor tolerance are common in clinical practice, they cannot all be attributed to solid tumor multidrug resistance, as they can also be triggered by suboptimal medication adherence or inadequate drug plasma concentrations.Suboptimal medication adherence is one of the causes of cancer progression and higher costs for health systems -7. Thus,Since 1995, a routine interprofessional medication adherence program (IMAP) has been implemented at the University Community Pharmacy of the Center for Primary Care and Public Health Unisanté  to support drug self-management in patients with chronic illness . Before Oral PKIs are licensed at a fixed dose despite the variable plasma concentrations observed in real-life situations ,16. ThisThe global aims of the OpTAT study are to establish the necessary knowledge to build rational monitoring strategies through medication adherence monitoring and patient-adjusted dosage in real-life conditions, which should contribute to optimizing the therapeutic benefit of PKIs, in order to improve outpatient cancer care.The primary objectives are to better characterize the prospective and longitudinal patterns of PKI adherence in patients participating in a medication adherence program versus a control group and to identify key driver-modifiable adherence factors.The secondary objectives are to quantify interpatient variability, identify sources of variability in serum PKI levels, and characterize the relationships between the serum concentration, the therapeutic response and the toxicity of these treatments in a population of patients with solid cancer.The hypothesis states that the medication adherence program will increase PKI implementation and persistence before the treatment is stopped for any reasons beyond the patient’s control or therapeutic change compared with the control group. Moreover, it is hypothesized that interpatient variability in PKI plasma concentrations plays a fundamental role in the heterogeneity of the therapeutic response and may play a role in PKI adherence.The local ethics committee  approved the OpTAT study in April 2015. Since then, two amendments have been accepted in July 2017 and October 2020. The study will be conducted in accordance with the current version of the Declaration of Helsinki and international good clinical practice principles. The protocol was peer-reviewed by five experts in April 2017 , leadingThis monocentric prospective study comprises two parts: (1) an open randomized controlled medication adherence study and (2) an observational pharmacokinetic and pharmacodynamic study. This design  exploresThis translational study will be conducted at the Medical Oncology Service of the Department of Oncology of the Lausanne University Hospital (CHUV) outpatient clinic  in collaboration with the Community Pharmacy of the Center for Primary Care and Public Health Unisanté  and the Center for Research and Innovation in Clinical Pharmacy Sciences (CHUV).Adult patients aged >18 years who were treated with a PKI for solid tumors and followed at the CHUV were eligible to participate in this study. Participants with an inability to make decisions, with cognitive disorders, under tutelage, or not fluent in French or English or without the aid of an interpreter will be excluded from the study. Patients will be excluded solely from the medication adherence part of the study if they do not self-manage oral anticancer treatment  or if they are already enrolled in an interventional study. If one PKI is switched to another PKI during the study, the patient will continue to participate in the study. However, if the PKI treatment is stopped or changed to a non-PKI anticancer treatment, the participant exits the OpTAT study; but, the data collected are kept for analysis.The OpTAT study is voluntary, and patients will need to sign an informed consent form to participate in the medication adherence study, the pharmacokinetic and pharmacodynamic study, or both. The recruitment of participants began in May 2015. It ended in March 2021 for the medication adherence part but is ongoing until December 2021 for the pharmacokinetics part.Oncologists were asked to refer eligible patients to a pharmacist PhD student or to the research staff from the Center of Experimental Therapeutics (CHUV), who will present the informed consent form to the patient after the medical visit. The participants will follow their treatment as usual, and no changes in the usual medical procedure will be made. No payment or compensation will be provided to the patients or to the oncologists as the patients are being seen as part of their routine follow-up clinical care.Belief about Medicines Questionnaire, which assesses the patient’s cognitive representation of medication [European Organization for Research and Treatment of Cancer Quality of Life Questionnaire (version 3.0), which assesses the quality of life of patients with cancer [Treatment Satisfaction with Medicines Questionnaire [European Organization for Research and Treatment of Cancer Quality of Life Questionnaire, as the latter was more adapted to patients treated with a PKI. The questionnaires were translated and validated in French, with satisfactory psychometric properties [All patients participating in the medication adherence study will use an electronic monitor  for 12 months or until PKI is stopped. The electronic monitor registers the date and time of each opening, and a liquid-crystal display monitor on top of the cap indicates the number of daily openings to the patient. All patients included in the study will be asked to complete two validated questionnaires at three timepoints: inclusion, 6 months postinclusion, and at the end of the study. The first questionnaire is the dication . The sech cancer . During ionnaire ; howeveroperties -26.Patients included in the intervention group will visit the routine IMAP of the pharmacy of Unisanté. They will attend 15- to 30-minute medication adherence interviews, which will take place after each clinical appointment over 12 months. On the basis of the information-motivation-behavior model embedded in sociocognitive theory , the phaThe electronic monitor data will be uploaded to MEDAMIGO software (MEMS and MEMS AS), which generates graphs of electronic monitor daily openings. Feedback on adherence to PKI therapy, based on the information generated by the electronic monitor, will be provided to and discussed with the patient. Interprofessionality is a cornerstone of the program, and pharmacists will summarize the intervention in a semistructured report sent to the oncologist after each intervention. During the COVID-19 pandemic, motivational interviews will be conducted by phone calls with patients considered by clinicians to be at a high risk of SARS-CoV-2 infection. All pharmacists who perform the medication adherence intervention must have attended specific training to lead motivational interviews  .Patients included in the control group will use an electronic monitor but will be considered standard-of-care patients as they will not receive any medication adherence intervention. The electronic monitor opening data of the control group will be concealed from the patients, pharmacy team, clinicians, and investigators until the end of the study. If an oncologist expressively asks for adherence data because of stringent and urgent clinical matters, the patient will be excluded from the OpTAT study and referred to the routine medication adherence program of the Community Pharmacy of the Center for Primary Care and Public Health Unisanté.The collection of adherence data patterns in both groups at baseline is crucial to check whether adherence patterns are homogeneous between the groups before the intervention starts. In both groups, patients will use an electronic monitor for at least 21 days before randomization. At the end of the baseline period, the patients will be randomized 1:1 to either attend the medication adherence program (intervention group) or be part of the standard of care .A total of 10 mL of peripheral blood samples will be collected for up to a maximum of 8 samples per patient or until PKI is stopped at an unselected time after the last PKI intake. The blood samples will be centrifuged, the plasma will be frozen and stored until PKI levels are measured, and the dry pellet will be analyzed for the exploration of genetic variants involved in PKI pharmacokinetics. Moreover, for patients who accept, 6 × 5 mL blood samples will be collected over a maximum of 12 hours during one visit to the hospital. Specific information on pharmacokinetic data will be collected at each blood draw, and all relevant clinical and biological information will be obtained from the medical files of the patients (detailed in the Sample Size section). Medication adherence will also be compared between groups following the three operational definitions defined by the EMERGE  guidelines: implementation of treatment during the persistent period , persistence with treatment (the distribution of times between enrollment and discontinuation), and longitudinal adherence (the product of persistence and implementation) [The primary outcome was global adherence, defined as the percentage of days with correct medication use of each subject. The sample size was calculated to ensure sufficient power to test the difference in the mean logit global adherence between groups (see the ntation) ,28,29.Response Evaluation Criteria In Solid Tumors [The plasma concentrations of the PKI and active metabolites, if appropriate, will be measured using validated methods of liquid chromatography-tandem mass spectrometry for each blood sample collected ,31. Inted Tumors , or deatd Tumors .t test. Differences are considered statistically significant if P<.05.We assumed that the mean longitudinal adherence will remain stable at 95% in the intervention group during the follow-up period, whereas it would decrease from 95% to 80% in the control group during the 340 days from randomization to the end of follow-up. We also assumed an SD for the logit of longitudinal adherence of 1.8, for both groups and at all timepoints. This corresponds to 95% individual adherence in the intervals 10%-99.3% and 35%-99.9% in the control and intervention groups, respectively, at the end of follow-up. A sample size calculation was performed to simulate individual series of daily medication  according to the above parameters to consider both the interindividual variability (SD 1.8) and the intraindividual variability (the measurement error). Considering that the number of measures of a subject will be 340 and assuming a 10% dropout in the middle of the year, 120 patients must be included in the study (60 in each group) to reach a power of 80% with a .05 significance level when comparing the mean of the logit of global adherence between groups using a two-tailed Student It has been empirically determined that at least 50 patients with several samples per PKI are necessary to characterize drug pharmacokinetics and variability.In collaboration with the investigators, the community pharmacy of Unisanté will manage the PKI adherence data, and the Center of Experimental Therapeutics will manage the blood samples as well as the clinical, biological, and sociodemographic data. The schedule of enrollment, intervention, and assessments is shown in Adherence to PKI will be assessed using electronic monitor, calculated pill counts, and patient reports. Medication behavior will be described with a binary variable  in each group. This coding method is commonly used; however, this code cannot discriminate overadherence, which is considered in the same way as optimal adherence. The implementation will be described in each group using generalized estimating equation models; persistence with PKI  will be represented using a Kaplan-Meier survival curve . Global A systematic computerized term search in the adherence reports of the intervention group will be performed to identify the most frequent variables affecting PKI adherence, and a regression model will be used to discover their association with adherence .Belief about Medicines Questionnaire and European Organization for Research and Treatment of Cancer Quality of Life Questionnaire or the Treatment Satisfaction with Medicines Questionnaire will be described longitudinally for the included patients and transversely per group at 6 and 12 months. Medians and IQRs or proportions will be used as appropriate. Nonparametric tests  or Fisher exact test of two proportions will be used to test the differences between groups.Sociodemographic data and quality of life and beliefs about medication of patients who refused to participate and completed both questionnaires versus those who accepted to participate in the adherence study will be compared at baseline. For patients included in the adherence study, the results of the questionnaires will be compared at randomization, 6 months, and at the end of the study. Changes in Ultimately, the adherence patterns and their relationship with drug exposure and clinical response will be evaluated in an exploratory analysis.To minimize interpretation bias, the statistician will not be aware of the group assignments during the analysis. The data will be analyzed in an intention-to-treat manner. All statistical analyses will be performed using the R statistical package  .Population pharmacokinetic analyses will be performed using nonlinear mixed regression models with the software program NONMEM (Icon Development Solutions). This approach allows for the characterization of the average pharmacokinetic profile of PKI from data pooled over all sampled individuals and the quantification of inter- and intraindividual variability. This approach allows us to analyze sparse (few drug concentrations per patient) and unbalanced data. The built models integrate active metabolites when appropriate. The impact of potential sources of variability will be evaluated using linear and nonlinear functions.The correlation between PKI exposure and efficacy will be explored using Cox regression analysis of progression-free survival. The correlation between PKI exposure and toxicity will be explored by pharmacokinetic and pharmacodynamic models using the software program NONMEM or by logistic regression analysis. Receiver operating characteristic analyses will be used to evaluate the best exposure threshold to predict clinical outcomes and performance (in terms of area under the receiver operating characteristics curve and sensitivity and specificity).Missing data will not be imputed but will be clearly identified and considered during the analysis process (nature and frequency) and described in the publication.Patients or the public will not be involved in the design, conduct, reporting, or dissemination plans of this research. The participants will be informed about the study results after the final publication using lay language.The first patient was included in May 2015. Until June 2021, 262 patients have participated in at least one part of the study . In June 2021, 51 patients treated with palbociclib have provided at least one blood sample, and the adherence study currently includes 43 participants with breast cancer, which corresponds to most of the included participants. Moreover, so far, at least one blood sample has been collected from patients treated with 31 different PKIs and their combination .Data collection is in process, and the final analysis will be performed in 2022. The results of the study will be submitted to local, national, and international conferences and to peer-reviewed open access journals for publication.The OpTAT study is based on a mixed methodology involving a randomized controlled medication adherence study, along with pharmacokinetic and pharmacodynamic data collection. The methodology is robust, as adherence data will be assessed through electronic monitoring, often considered the gold standard, in both the intervention and control groups. Participants randomized to the intervention group will be included in a routine medication adherence program (IMAP) that has previously been shown to lead to significant improvements in terms of medication adherence; clinical outcomes; and retention in care of other chronically ill patients, such as those with HIV ,14,37-39the one dosage fits all patients paradigm, which may be an important source of side effects that are potentially detrimental to the continuation of the course of the treatment and patient adherence. Hence, our ultimate and cutting-edge development is to combine an analysis of PKI adherence, pharmacokinetics, and pharmacodynamics.The second strength of this study is the pharmacokinetic analysis of a wide range of PKIs. PKIs are marketed under a single regimen according to This study has some limitations. First, the adherence data will be collected over 12 months, a time frame during which several stops and breaks in the treatment regimen can occur. This will be a challenge for data analysis; however, the adherence team and the statistician, who will perform the analysis, have already dealt with this kind of data and are experts in this field . Second,The OpTAT study will strengthen knowledge on the effectiveness of the routine IMAP medication adherence program in patients with solid cancers treated with PKIs. The IMAP will enable interprofessional teams to learn about patients’ needs and concerns about their PKI self-management. The study will also shed light on PKI pharmacokinetics and its relationship with pharmacodynamic properties, with the aim of adapting dosage regimens at the individual patient level, increasing the clinical suitability of PKIs.Adherence combined with pharmacokinetic data will inform pharmacists and clinicians on how to support patients during the entire course of their PKI treatment and how to manage adaptations of treatment efficiently, with the patient as an informed partner.


The evolutionary process of genetic recombination has the potential to rapidly change the properties of a viral pathogen, and its presence is a crucial factor to consider in the development of treatments and vaccines. It can also significantly affect the results of phylogenetic analyses and the inference of evolutionary rates. The detection of recombination from samples of sequencing data is a very challenging problem and is further complicated for SARS-CoV-2 by its relatively slow accumulation of genetic diversity. The extent to which recombination is ongoing for SARS-CoV-2 is not yet resolved. To address this, we use a parsimony-based method to reconstruct possible genealogical histories for samples of SARS-CoV-2 sequences, which enables us to pinpoint specific recombination events that could have generated the data. We propose a statistical framework for disentangling the effects of recurrent mutation from recombination in the history of a sample, and hence provide a way of estimating the probability that ongoing recombination is present. We apply this to samples of sequencing data collected in England and South Africa and find evidence of ongoing recombination. Ongoing mutation of the SARS-CoV-2 virus has received significant scientific and media attention since the start of the pandemic. The process of viral recombination has received far less coverage, but has the potential to have a drastic impact on the evolution of virulence, transmissibility, and evasion of host immunity . RecombiThe detection of ongoing recombination from a sample of genetic data is, in general, a very challenging problem. Only a fraction of recombination events significantly change the shape of a genealogy, and even then, mutations must occur on the correct branches of the genealogy in order to create patterns that are detectable in the data , where mEarly evidence of ongoing recombination in SARS-CoV-2 was presented by Phylogenetic analysis of SARS-CoV-2 data largely assumes the absence of recombination. Recombination can significantly influence the accuracy of phylogenetic inference , distortIn this article, we use KwARG , a parsiWe investigate the presence of ongoing recombination in SARS-CoV-2 using publicly available data from GISAID , collectOur method consists of two main steps. First, using KwARG, plausible genealogical histories are reconstructed for each sample under a parsimony assumption, with varying proportions of posited recombination and recurrent mutation events. Then, using simulation, we approximate the distribution of the number of recurrent mutations that might be observed in a data set of the same size as each sample. We use this to establish which of the identified genealogical histories is more plausible for the data at hand, and thus whether the presence of recombination events in the history of the given sample is likely.T is the number of recurrent mutations in the history of the data set; the null distribution of T is approximated through simulation. The observed value Tobs is the minimal number of recurrent mutations required to explain the data set in the absence of recombination, as estimated by KwARG. The ”P value” is the probability of observing a number of recurrent mutations equal to or greater than Tobs. Small P values allow us to reject the null hypothesis, providing evidence that recombination has occurred. The reconstructed genealogies then allow for the detailed examination of possible recombination events in the history of the sampled sequences.This can be framed in the language of statistical hypothesis testing. The “null hypothesis” is the absence of recombination. The test statistic P values larger (more stringent).We emphasize that we make very conservative assumptions throughout, both in processing the data and in estimating the distribution of the number of recurrent mutations. Moreover, the number of recurrent mutations required to explain a given data set computed by KwARG is (or is close to) a lower bound on the actual number of such events, and is likely to be an underestimate, making the reported ancestral recombination graph (ARG)  . As the incompatible sites in the data, which violate the four gamete test . Crucially, the parsimony approach does this without requiring the assumption of a particular generative model for the data  beyond specifying the types of events that can occur. Although this means that mutation and recombination rates cannot be inferred, it allows us to sidestep the need to specify a detailed model of population dynamics, which is particularly challenging for SARS-CoV-2 data. A parsimony-based approach is more appropriate when our focus is on interrogating the hypothesis that recombination is present at all. It also allows for the explicit reconstruction of possible events in the history of a sample, and thus allows us to identify recombinant sequences and uncover patterns consistent with the effects of sequencing errors.Both recombination and recurrent mutation can produce patterns of ete test  and coulKwARG outputs a set of possible ARGs for a given data set, including those that explain all incompatibilities through recombination events, those that only contain recurrent mutations, and those containing a combination of both event types. KwARG distinguishes between recurrent mutations that occur on the internal branches of the ARG from those can be placed on the terminal branches, which affect only one sequence in the input data set, so can be examined separately for indications that they arose due to errors in the sequencing process.We ran KwARG on four samples of data: from South Africa, collected in 1) November 2020  and 2) February 2021 , and from England, collected in 3) November 2020 , and 4) December 2020–January 2021 (40 sequences within GISAID clade GR). An overview of the identified solutions is given in homoplasy test of P values are shown in the third and fourth columns of We next determined which of the solutions identified by KwARG is more likely, by calculating the probability of observing the given number of recurrent mutations. To avoid making model-based assumptions on the genealogy of the sample, we use a nonparametric method inspired by the The accuracy of the presented method depends on an assumption that there are no highly homoplasic sites (arising either due to selection or repeated sequencing errors) that have not been masked. If this assumption were violated, the estimate H, simulating data with the resulting mutation rate map in the absence of recombination with parameters that appear reasonable for SARS-CoV-2, and checking whether our method would (incorrectly) reject the null hypothesis. The results are presented in the left panel of We investigated the validity of this assumption through simulation as described in P values. The proportion of false positives only increases significantly when a large number of extremely homoplasic sites is present, showing that our method is reasonably robust to violations of this assumption. As we apply several stringent quality filters and implement a conservative strategy in masking sites known to be homoplasic, seeing a large number of extremely hypermutable sites appears improbable, so our method is unlikely to falsely indicate the presence of recombination.False positives were seen in only 0.5% of cases when there are no highly homoplasic sites, demonstrating that our method conservatively overestimates the computed We also investigated the robustness of the method to random variation in the site-level mutation rates, which can arise for various biological reasons, such as the effects of selection on specific codons. First, as described in detail in P values were below the significance threshold of 0.05, so we did not find that using this alternative phylogeny-based method changes our findings.This suggests that the method may not be suitable in cases where only a small fraction of sites have nonnegligible mutation rates. Although this does not appear to be the case for SARS-CoV-2, based on the observed number of segregating sites, we further checked robustness of the reported results using a phylogeny-based estimate of mutation rate heterogeneity. We used a SARS-CoV-2 phylogeny from Nextstrain  to fit aρ, by simulating data sets as described in P< 0.05). The results are shown in the right panel of The power of our test in detecting the presence of recombination was investigated for a range of recombination rates All sequences collected in England in December 2020–January 2021, labeled as belonging to clade GR in GISAID, were downloaded and processed as described in Tobs = 14 or more recurrent mutations is An illustration of the sample is provided in Considering the results in It is striking that eight of the recurrent mutations seen in All sequences collected in South Africa in February 2021 were downloaded and processed as described in Initial examination of the solutions identified by KwARG show that at least eight recurrent mutations are required to construct a valid ARG for this sample in the absence of recombination. However, it was noted that three of these recurrent mutations occur at the same site 28,254. This may imply that the site is highly mutable, which could be due to repeated sequencing errors, or as a consequence of selection. We note that this demonstrates the usefulness of our approach in identifying potentially highly homoplasic sites.Tobs = 5 or more recurrent mutations is This position was masked from the sample before rerunning the analysis. The probability of observing the recalculated value of All sequences collected in South Africa in November 2020 were downloaded and processed as described in Tobs = 9 or more recurrent mutations is An initial run of KwARG demonstrated that, notably, one recurrent mutation occurs at site 28,254, further suggesting that this site is excessively prone to recurrent mutation. This site was therefore masked before rerunning the analysis. An illustration of the sample is provided in The probability of observing three or fewer recurrent mutations is 0.96, which indicates that, with high probability, at least four recombination events have occurred in the history of the data set. Indeed, The sequences SAO21 and SAO22 carry three and two of the identified nine recurrent mutations, respectively, when recombination is prohibited in reconstructing the genealogy. Both of these sequences carry some of the mutations characteristic of lineage B.1.351; this is demonstrated in not applied in this case. The resulting sample comprises 80 sequences with 363 variable sites, 40 of which belong to lineage B.1.1.7 (labeled EN1-EN40) and 40 to other lineages (labeled EO1-EO40).All sequences labeled as GISAID clade GR, collected in England in November 2020, were aligned, masked, and processed as detailed in The results showed that in the absence of recombination, at least 15 recurrent mutations were required to explain the incompatibilities observed in this sample. However, it was identified that six of these recurrent mutations could be placed in the same sequence EO40, as illustrated in Our findings prompted further investigation by the submitters of this sequence, which revealed the signal to be the result of significant contamination of the genetic sample causing multiple errors in the consensus sequence, rather than a result of intrahost recombination. The sequence has subsequently been removed from GISAID.MERS-CoV sequences collected in Saudi Arabia in January–March 2015 were downloaded from the NCBI virus database , and aliTobs = 16 recurrent mutations are required, which has probability The data set is illustrated in A group of four identical sequences , which appears to carry a mixture of mutations from the group identified above and other sequences in the sample, which is consistent with recombination. This sequence does not match any others in the data set, so it is possible that this is the result of sequencing errors or sample contamination. If this sequence is removed from the sample, at least MERS-CoV .The method presented in this article offers a clear and principled framework for recombination detection, which can be interpreted as a hypothesis testing approach. We make very conservative assumptions throughout, demonstrating on both real and simulated data that the method achieves a very low rate of false positive results (if mutation rate heterogeneity is not extreme), while offering powerful detection of recombination at even relatively low values of recombination rate. We use nonparametric techniques at each stage, to avoid making assumptions on the process generating the data, and thus circumvent issues with model misspecification. Our method allows us to gain clear insights into the evolutionary events that may have generated the given sequences, offering easily interpretable results. The method detects sequences carrying patterns consistent with recombination, demonstrating its effectiveness as a tool for flagging sequences with distinctive patterns of incompatibilities for further detailed investigation.Our results clearly indicate the presence of recombination in the history of the analyzed SARS-CoV-2 sequencing data, suggesting a recombination rate greater than around w levels  or is unw levels  even whew levels  and withw levels  making tRecombination can occur when the same host is coinfected by two different strains, which has been noted to occur in COVID-19 patients , and couWe also note that although we sought to mask any sites known to be highly homoplasic, we cannot rule out that some of the identified recurrent mutations did arise multiple times as a consequence of selection or as a result of repeated sequencing errors. However, we have demonstrated that the solutions presented by KwARG can be examined for the presence of highly mutable sites, and have identified using both samples from South Africa that this appears to be the case for site 28,254 .Our findings suggest that care should be taken when performing and interpreting the results of analysis based on the construction of phylogenetic trees for SARS-CoV-2 data. The presence of recombination, as well as other factors complicating the structure of the transmission network of the virus, strongly suggests that tree-based models are not appropriate for modeling SARS-CoV-2 genealogies, and inference of evolutionary rates based on such methods may suffer from errors due to model misspecification that are difficult to quantify.Due to the high level of homogeneity between sequences, the effects of recombination will be either undetectable or indistinguishable from recurrent mutation in the majority of cases. However, as genetic diversity builds up over longer timescales, the effects of recombination may become more pronounced. Particularly in light of the recent emergence of new variants, the rapid evolution of the virus through recombination between strains with different pathogenic properties is a crucial risk factor to consider. This highlights the need for continuous monitoring of the sequenced genomes for new variants, to enable the early detection of novel recombinant genotypes, and for further work on the quantification of recombination rates and identification of recombination hotspots along the genome.Sequences were downloaded from GISAID and aligned as described in The timing of samples was selected to coincide with periods of high transmission numbers, as this increases the probability of coinfection of the same host with multiple strains, which is a requirement for recombination to occur. Collection dates were also restricted to reasonably narrow windows, as KwARG assumes that the sequences are sampled contemporaneously.Four samples were analyzed; details of sample selection and processing are given in The first step in our approach is to use a parsimony-based method to reconstruct possible genealogical histories for the given data sets.infinite sites assumption is applied, at most one mutation can affect each site of the genome. The four gamete test . Then the allele at each site can be denoted 0 or 1. If the commonly used ete test  can thenmutation .parsimony approach to reconstructing ARGs given a sample of genetic data focuses on minimizing the number of recombination and/or recurrent mutation events. This does not necessarily produce the most biologically plausible histories, but it does provide a lower bound on the number of events that must have occurred in the evolutionary pathway generating the sample. Thus, recombination can be detected in the history of a sample by considering whether the most plausible parsimonious solutions contain at least one recombination node.A sample of genetic sequences may have many possible histories, with many different corresponding ARGs. The KwARG  is a proIn order to evaluate which of the solutions identified by KwARG is more likely, we calculate an estimate ith entry of the vector ith site of the genome. Details of our method for estimating The RS-CoV-2 . The resP values are shown in the third and fourth columns of The estimate of Molecular Biology and Evolution online.Code used in processing the data  is available at github.com/a-ignatieva/sars-cov-2-recombination.msac028_Supplementary_DataClick here for additional data file.