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1 REPORT OF RESULTS

2 Edited by: General Pharmaceutical Council of Spain Villanueva, 11, 7 th Madrid Mail: Web: Legal Deposit: M Layout and graphic production: Comuniland, S. L. Copyright of all original texts: General Pharmaceutical Council of Spain, All rights reserved. No part of this publication may be copied or reproduced in any form or by any means, electronic or mechanical, including photocopying, recording or any production system, without the written permission of the copyright holders

3 REPORT OF RESULTS General information Protocol code and version: CGC-ADH (17th September 2013, version 14). Study name: Evaluation of the pharmaceutical services programme for elderly, chronic, polymedicated and non-adherent patients (ADHIÉRETE Programme). Promoted by: General Pharmaceutical Council of Spain Sponsored by: Laboratorios Esteve, S. A. Reference CREC: Clinical Research Ethics Committee of the Basque Country

4 Report of results Table of contents 1. Abbreviations Background Objectives of the study Main objective Secondary objectives Design of the study Sample size and analysis sample Inclusion criteria Exclusion criteria Analysis sample Study schedule Description of statistical analysis Presentation/format of the results Hypotheses and statistical methods Descriptive analysis Bivariate analysis Calculation of derived variables Analysis of the main objective Analysis of the secondary objectives Significance level, multiple comparisons and multiplicity Dropouts and missing data Principal investigators and co-investigators Sample Diseases Medicines Adherence Analysis of DRP/NOM Usage problems Support systems Quality of life Resources analysis Patient satisfaction Dropout analysis Conclusions

5 1 Abbreviations AACC: Autonomous Communities AEMPS: Spanish Medicines and Medical Devices Agency BV: baseline visit CREC: Clinical Research Ethics Committee CRF: Case Report Form DRP: Drug-Related Problems EQ-5D: EuroQoL 5D Questionnaire FV: final visit M-G: Morisky-Green Test MRFU: Medicines Review with Follow-Up NNTT: New Technologies (mobile application) NOM: Negative Outcomes associated with Medicines PDS: Personalized Dosage Systems PDS+App: MDS alerting system PMI: Personalized Medicines Information V: visit VAS: Visual Analogue Scale 2 Background Associated with the increased life expectancy of recent decades is greater chronicity and greater demands placed on healthcare services and medicines. Healthcare for chronic patients is a policy priority for the Ministry of Health, Social Services and Equality (Strategy for handling chronicity in the National Health System - June 2012), which recommends a systematic review of medication in an effort to detect problems involving medicines, such as the lack of adherence to treatment. Although the adherence rate can vary greatly depending on the pathology, it is estimated that % of all patients do not take their medication as prescribed. In contrast, it has been shown that actions aimed at improving adherence lower long-term healthcare spending by reducing expenses associated with hospital stays and treatment complications. The pharmacist mission is to attend patients needs as these pertain to their medication. To fulfill this mission, pharmacists must dispense treatments individually to each patient, evaluate their results on the patients health and prevent or resolve any unexpected or undesired outcomes. 3

6 Report of results 3 Objectives of the study 3.1. Main objective To evaluate adherence to treatment in patients included in the Programme Secondary objective To detect drug related problems (DRP), in particular non-adherence, so as to diminish negative outcomes (NOM) and achieve the desired therapeutic goal while maximizing the medication s safety and effectiveness. To evaluate the impact of the adherence support services offered in the Programme. To evaluate the change in the quality of life of the patients in the Programme. To evaluate the Programme s impact in terms of cost-benefit. To assess the impact of the electronic prescription on the Programme s effectiveness and efficacy. To evaluate the patients satisfaction with the Programme. 4 Design of the study The Programme was evaluated by means of a naturalistic (pre-post), randomized, prospective, multi-center, community pharmacist interventional study with no control group. The pharmacists who agreed to take part in the Programme signed an Investigator Commitment. Associated with each pharmacy was a principal investigator and as many co-investigators as designated by the former. It was estimated that six months of field work would be needed to achieve the stated objectives, with a minimum of one monthly visit per patient in the Programme. Each community pharmacy had to recruit five patients, of whom two would use Personalized Dosage Systems (PDS) to support adherence, two would use a mobile application (NNTT) and one would use PDS along with an alert system associated with the PDS (PDS+App). These patients were assigned to each of the three adherence support systems at random by means of a stratified sample for each community pharmacy Sample size and analysis sample The sample size was calculated based on existing studies and considering those patients who adhered to medication regimens at the end of the study using the Morisky-Green (M-G) Test. This yielded an approximate percentage of patients ad- 4

7 hering to their medication at the end of the study of 70 % ± 6 %, with a two-sided confidence interval of 95 %. These assumptions yielded a required sample size of 225 patients. Assuming five patients were recruited per community pharmacy, a total of 45 pharmacies would be needed. In anticipation of the loss of community pharmacies from the study, and so as to obtain the total number of patients needed, it was decided to involve 60 community pharmacies from the provinces of Badajoz, Barcelona, Bizkaia and Cáceres Inclusion criteria Patients aged 60. Chronic and polymedicated (five or more medicines and treatment duration > 3 months) patients. Patients in whom the Dispensing Service identified a DRP due to non-adherence and/or a NOM of ineffectiveness due to non-adherence. Patients not adhering to treatments based on the Morisky-Green Test. Patients who signed the informed consent form. Patients with a mobile device (smartphone) Exclusion criteria Patients not meeting all of the inclusion criteria. Patients with a medical or psychological condition that could limit their ability to participate in the study. Patients not expected to cooperate fully Analysis sample The valid sample was defined as the one that included all the patients who met the inclusion/exclusion criteria defined in the protocol. 5

8 Report of results 5 Study schedule Start End AEMPS presentation May 13 June 13 CREC presentation June 13 September 13 AACC presentation June 13 September 13 Preparation of electronic Clinical Report Form (CRF) September 13 October 13 Recruitment period November 13 January 14 Field work November 13 June 14 Interim analysis August 14 August 14 Statistical analysis September 14 December 14 Report of final results January 15 April 15 6 Description of statistical analysis The contents of the database were transferred to SAS datasets for statistical analysis. All of the statistical analyses were carried out using the SAS Statistics Package, version 9.3. No changes were made to the analyses anticipated in the protocol Presentation/format of the results In general, the average, median, and standard deviation for the continuous variables are shown with one additional decimal place. The maximum and minimum values are shown with the same level of accuracy as the raw data. For the categorical values, the percentages are shown to one decimal place. 7 Hypotheses and statistical methods 7.1. Descriptive analysis Descriptive analyses were carried out for all of the variables. Depending on the variable type, are shown: 6

9 Frequencies and percentages for the categorical variables. The continuous variables are summarized using the measures of central tendency and dispersion: mean, average deviation, median, 25 % and 75 % percentiles (Q1 and Q3) and extreme values (minimum and maximum). All of the cases considered are listed in the open comments/remarks fields when deemed of interest Bivariate analysis When deemed of interest to the study s objectives, the relationship between variables was evaluated: Contingency tables are provided for two categorical variables with columns for the frequency of each category and percentages. Any potential relationship was analyzed using the Chi Square Test or Fisher Exact Test, with the resulting p-value as shown. Descriptive statistics by group are given for one numerical and one categorical variable. Potential associations were analyzed using the T-test, ANOVA test or the non-parametric Wilcoxon or Kruskal-Walls tests. The Pearson or Spearman correlation, as well as its p-value, is given to assess the relationship between two numerical variables Calculation of derived variables Age: (Date of birth - date of baseline visit)/ EQ-5D Questionnaire (Shaw, 2005): All patients start out with a value of 1. If the answer to any question is not 1, apply: If the answer to any question is 3, apply: Based on the answer: Answer 2 3 Question Question Question Question Question If the answer to any question is missing, the index cannot be calculated. 7

10 Report of results Patient Satisfaction Questionnaire: Questions 1, 3, 4, 5, 6, 7 and 9 were scored from 3 to 0, question 6 was only valid for patients in the PDS or PDS+App groups, and 7 only for patients in the NNTT group. Questions 2, 8 and 10 were scored as a 3 if Yes and 0 if No. The overall score was obtained by adding each score to the questions Analysis of the main objective Adherence was evaluated using the Morisky-Green test. The trend in the percentage of patients who adhered to their treatments was evaluated over the course of the different visits. The evaluation was carried out globally using a mixed-effects model and differences in adherence were analyzed between the different groups Analysis of the secondary objectives To detect DRP so as to diminish NOM and achieve more safety and effective medicines: A descriptive analysis was conducted of the DRP and NOM at the patient level and for both the DRP and NOM. A descriptive analysis was conducted for each support group. To evaluate the impact of the adherence support services offered in the Programme: A descriptive analysis was conducted of the information collected in the CRF section on assessing adherence. A descriptive analysis was conducted for each support group. To evaluate the change in the quality of life of the patients included in the Programme: The quality of life of the patients included in the Programme was evaluated using the EuroQoL 5D-3L Quality of Life Questionnaire. The progress of the quality of life was evaluated in patients based on the answers to the questionnaires given during the V1 and final visits. This was done by using the T-Test for paired data or, if the required conditions were not satisfied, the non-parametric Wilcoxon Test. Potential differences in the progression of the quality of life were also evaluated for the different support groups. To evaluate the Programme s impact in terms of cost-benefit: To evaluate the cost-benefit, the data collected in the CRF regarding communication with the doctor, healthcare resources and time devoted to the visit were described. A descriptive analysis was conducted for each group. 8

11 To assess the impact of the electronic prescription on the Programme s effectiveness and efficacy: A descriptive analysis was conducted for each type of DRP and NOM depending on whether it was corrected or not. A mixed-model effect was used to determine if these percentages varied by group. To evaluate the patients satisfaction with the Programme: The patients satisfaction with the Programme was evaluated by using the answers provided on the relevant satisfaction questionnaire. A global descriptive analysis of the answers in the satisfaction questionnaire was carried out to determine if there were differences in the answers based on the respondent s group Significance level, multiple comparisons and multiplicity The significance level adopted for all the tests was two-sided No adjustments or corrections were made for multiple comparisons or for multiplicity Dropouts and missing data No kind of data allocation was performed. Only the patients observed were analyzed, with a description provided for the number of data missing from each analysis. 9

12 Report of results 8 Principal investigators and co-investigators BADAJOZ Bermejo García, María Ángeles Caballero Ledo, Alejandra Fuentes Palacios, María Pasión Gervasini Rodríguez, Cristina Llorente Cancho, Carmen Martínez de la Concha, Juan Cachola Maldito, Vanda María Noa Ríos, José Manuel Pulgarín Moruno, Manuel Gridilla Saavedra, Javier AndrÉs Perera, Anna M.ª Verdasco Muñoz, VerÓnica Iracheta Todo, MontseRRAT BARCELONA Barau Germès, Mercè Tribo Alcobe, Gemma Bovet Pla, Tura Trabe Sánchez, Marta Carbonell Brufau, Mercè dalmases balaña, Jordi Pardo Reguant, Carme Sánchez Cepero, Montserrat Piera Serra, Gloria Carrasco López, Sandra Sajko, Jani Rius Sala, Mercè Roig MartínEZ, Marta Font Olivet, Anna M.ª Rius Font, Laura IbÁñez FernÁndeZ, JosÉ Algueró Arnal, Inés AMAYUELAS CELAYA, LOURDES Lores Santamaría, Cristina Martínez Monge, Almudena ANDRACA ITURBE, IRUNE Andraca Iturbe, Leire Gallardo Escudero, Alba Gaztelurrutia Laves, Leire Merino Alonso, Oihane Sánchez Carrillo, Lorena Solaun Bilbao, Estíbaliz Surroca RebéS, Aina Ramón Muñoz, Antonio Veciana Borràs, M.ª Concepció Rabella Foz, Eulalia Veciana Botet, Marina GABILONDO ZELAIA, ITXASNE Aureguizar Ajuriagogeascoa, Joana GONZÁLEZ ALVÁREZ, LEONARDO Carpintero Díaz, Sonia Davalillo Cadabal, Argiñe García Amann, Fátima González Cavia, María Salazar Lecue, Naiara BIZKAIA ANGOITIA GARCÍA, ÁNGELA Irastorza Hierro, Marta Ouro García, Raquel ARTECHE ARANZAMENDI, ELENA Egaña Bilbao, Maider BENITO BUTRON, M.ª PILAR Sorrigueta Gredilla, Casilda CASTIELLA LECUONA, ELENA Aburto Goiri, Avelina María Romero Valverde, Edurne GOYENECHEA UZKANGA, ITZIAR Lamela Macías, Cristina LINAZA PEÑA, IÑAKI Fernández Fernández, Mónica Galiana Gálvez, Rafael Linaza García, Amaya Nieto Hierro, M.ª Jesús LÓPEZ REGUEIRO, SOFÍA Fernández Ibáñez, Irati García Ordoñez, Alberto MARTÍNEZ AZUMENDI, GERMÁN Azpiazu Carmouze, Marta María Larrinaga Santamaría, Izaskun 10

13 CORTINA MENDIZABAL, VIRGINIA Alcalá Etayo, Arantzazu Domenech Alfonso, Eider Gómez Fernández, Beatriz MÚGICA VAN HERCKENRODE, ELSA Gómez Suárez, Janelis BIZKAIA DÍAZ RAMÓN, RUTH LUCÍA Bengoa Castellanos, Isabel Capitán Osorio, María Dávila Carmona, Vanesa Pérez Pérez, Mikel Ramón Ferrer, M.ª Lucía UNCETA SUÁREZ, MANUEL Rodríguez Ferreras, Adrián Suárez Lacalle, Paula Zuloaga Pérez, Susana ERAZO PRESSER, FLAVIA MARINA Rodrigo Gutiérrez, Jon Sáenz Álvarez, María URIARTE GARCÍA-BORREGUERO, JUAN Gallego Castañiza, Francisco Javier CÁCERES CERRO SIERRA, M.ª ISABEL CLAROS VICARIO, PEDRO Claros, Sara Cuéllar, Ruth Martínez-Ancín, Ana M.ª Reixa, Mónica FERNÁNDEZ LORO, ISABEL-FIDELA HERNÁNDEZ RINCÓN, JUAN JOSE Casero, Ana Díaz, Esperanza Hernández, Mª Pilar JARAÍZ ÁRIAS, JUAN FERMIN JARAIZ FERNÁNDEZ, FRANCISCO GARCÍA DE CASASOLA GARCÍA, JUAN FRANCISCO GÓMEZ VICENTE, JAVIER VICENTE MUÑOZ, ROSA MARIA Gómez, Mercedes Gómez, Miriang Total 51 community pharmacies 116 community pharmacists Coordinators from Pharmacy Chambers BADAJOZ BARCELONA BIZKAIA CÁCERES CGCOF Javier Gridilla Mercè Barau Mónica Gallach Blanca Díez Nerea Seisdedos Isabel Rodríguez Carmen Peña Ana Aliaga Luis Amaro Carmen Recio Laura Martín Carmen Megía Raquel Varas 11

14 Report of results 9 Sample 174 patients recruited. 114 patients suitable for analysis. 60 recruited patients excluded from the analysis for not satisfying a selection criterion or for leaving it blank. 74 patients completed the study. 40 patients completed the study early, with most withdrawing during the third visit. The problem cited most often involved the use of new technologies (22 patients %). The dropouts by group totalled 9 in the PDS group, 27 in NTTT and 4 in PDS+App. The distribution of valid patients by province was: Badajoz: 22 patients (19.3 %) Barcelona: 31 patients (27.2 %) Bizkaia: 39 patients (34.2 %) Cáceres: 22 patients (19.3 %) The distribution of valid patients based on the level of polymedication was: No. of patients taking 5 to 8 medicines: 64 patients (56.1 %) No. of patients taking 9 to 12 medicines: 39 patients (34.2 %) No. of patients taking 13 to 17 medicines: 9 patients (7.9 %) No. of patients taking over 17 medicines: 2 patients (1.8 %) The average number of medicines per patient was 8.7 (standard deviation of 2.9). 12

15 10 Diseases The distribution of the most prevalent diseases among the valid patients included in the study was 1. Hypertension 74.3 % 2. Hypercholesterolemia 55 % 3. Cardiovascular disease 52.3 % 4. Diabetes 30.3 % 5. Pain 27.5 % 6. Depression 18.3 % 7. Anxiety 15.6 % 8. Osteoporosis 14.7 % 9. Arthritis/Arthrosis 11 % 10.Renal impairment 5.5 % The remaining diseases were distributed as follows:: 1. Respiratory, thoracic and mediastinal disorders 15,6 % COPD 6,4 % Asthma 2,8 % 2. Infections: 13,8 % Respiratory infections 3,7 % Nasopharyngitis 2,8 % 3. Gastrointestinal disorders: 11,9 % Hyperchlorhydria 5,5 % Constipation 1,8 % 4. Nervous system disorders 11 % Alzheimer 3,7 % Parkinson 2,8 % 5. Breast/reproductive disorders (prostate problems) 10,1 % 6. Metabolism/nutrition disorders 8,3 % Hyperuricemia 2,8 % Obesity 1,8 % 7. Eye disorders 7,3 % Glaucoma 4,6 % 8. Blood and lymphatic system disorders (anaemias) 6,4 % 9. Medical and surgical procedures 5,5 % 10. Traumatic injuries 4,6 % Sprained ligaments 1,8 % 11. Ear and labyrinth disorders 4,6 % Vertigo 3,7 % 12. Endocrine disorders 4,6 % Hypothyroidism 3,7 % 13

16 Report of results 11 Medicines The patients used a total of 1236 medicines during the study. Their distribution, according to the Anatomical Therapeutical Chemical Classification (ATC) System, was as follows: A B ANATOMICAL GROUP No. of meds. Therapeutic subgroup No. of meds. ALIMENTARY TRACT AND METABOLISM BLOOD AND BLOOD FORMING ORGANS 257 (20.79 %) 108 (8.74 %) C CARDIOVASCULAR SYSTEM 423 (34.22 %) A01 STOMATOLOGICAL 22 (1.78 %) A02 A03 MEDICINES FOR ACID RELATED DISORDERS MEDICINES FOR FUNCTIONAL GASTROINTESTINAL DISORDERS 116 (9.38 %) 7 (0.57 %) A06 MEDICINES FOR CONSTIPATION 7 (0.57 %) A07 ANTIDIARRHEALS, INTESTINAL ANTIINFLAMMATORY/ANTIINFECTIVE AGENTS 4 (0.32 %) A10 MEDICINES USED IN DIABETES 75 (6.07 %) A11 VITAMINS 9 (0.73 %) A12 MINERAL SUPPLEMENTS 17 (1.37 %) B01 ANTITHROMBOTIC AGENTS 84 (6.79 %) B03 ANTIANEMIC PREPARATIONS 24 (1.94 %) C01 CARDIAC THERAPY 26 (2.10 %) C02 ANTIHYPERTENSIVES 17 (1.37 %) C03 DIURETICS 61 (4.93 %) C04 PERIPHERAL VASODILATORS 5 (0.40 %) C05 VASOPROTECTIVES 2 (0.16 %) C07 BETA BLOCKING AGENTS 54 (4.37 %) C08 CALCIUM CHANNEL BLOCKERS 39 (3.16 %) C09 D DERMATOLOGICALS 4 (0.32 %) D07 G GENITO-URINARY SYSTEM (INCL. SEX HORMONES) 33 (2.67 %) H HORMONE THERAPY 23 (1.86 %) J L M ANTIINFECTIVES FOR SYSTEMIC USE ANTINEOPLASTIC AND IMMUNOMODULATING AGENTS MUSCULO-SKELETAL SYSTEM AGENTS ACTING ON THE RENIN- ANGIOTENSIN SYSTEM 110 (8.90 %) C10 LIPID MODIFYING AGENTS 109 (8.82 %) G03 CORTICOSTEROIDS, DERMATOLOGICAL PREPARATIONS SEX HORMONES AND MODULATORS OF THE GENITAL SYSTEM 4 (0.32 %) 3 (0.24 %) G04 UROLOGICALS 30 (2.43 %) H01 PITUITARY AND HYPOTHALAMIC HORMONES 1 (0.08 %) H02 CORTICOSTEROIDS FOR SYSTEMIC USE 5 (0.40 %) H03 THYROID THERAPY 16 (1.30 %) H05 CALCIUM HOMEOSTASIS 1 (0.08 %) 14 (1.13 %) J01 ANTIBACTERIALS FOR SYSTEMIC USE 14 (1.13 %) 7 (0.57 %) 46 (3.72 %) L01 ANTINEOPLASTIC AGENTS 2 (0.16 %) L02 ENDOCRINE THERAPY 5 (0.40 %) M01 ANTIINFLAMMATORY AND ANTIRHEUMATIC PRODUCTS 21 (1.70 %) M03 MUSCLE RELAXANTS 1 (0.08 %) M04 ANTIGOUT PREPARATIONS 14 (1.13 %) M05 MEDICINES FOR TREATMENT OF BONE DISEASES 10 (0.81%) 14

17 ANATOMICAL GROUP No. of meds. Therapeutic subgroup No. of meds. N01 ANESTHETICS 2 (0.16 %) N02 ANALGESICS 60 (4.85 %) N03 ANTIEPILEPTICS 19 (1.54 %) N NERVOUS SYSTEM 240 (19.42 %) N04 ANTI-PARKINSON MEDICINES 9 (0.73 %) N05 PSYCHOLEPTICS 79 (6.39 %) N06 PSYCHOANALEPTICS 66 (5.33 %) N07 OTHER NERVOUS SYSTEM MEDICINES 5 (0.40 %) P ANTIPARASITIC PRODUCTS, INSECTICIDES AND 1 (0.08 %) P01 ANTIPROTOZOALS 1 (0.08 %) REPELLENTS R01 NASAL PREPARATIONS 3 (0.24 %) MEDICINES FOR OBSTRUCTIVE AIRWAYS R03 R RESPIRATORY SYSTEM 52 (4.21 %) DISEASES 43 (3.48 %) R05 COUGH AND COLD PREPARATIONS 5 (0.40 %) R06 ANTIHISTAMINES FOR SYSTEMIC USE 1 (0.08 %) S01 OPHTHALMOLOGICALS 24 (1.94 %) S SENSORY ORGANS 25 (2.02 %) S02 OTOLOGICALS 1 (0.08 %) V VARIOUS 3 (0.24 %) V03 ALL OTHER THERAPEUTIC PRODUCTS 3 (0.24 %) 12 Adherence General M-G Evolution: from 35 % of adherent patients at the V3 to 75.7 % of adherent patients at the FV. Starting from 0 % of compliant patients at the BV. Yielding a p< for both the difference between overall visits and the difference between v3 and the FV. M-G by group: A mixed-effects regression model was adjusted to evaluate the effects of the visits and the groups in patient adherence as per the M-G Test. No evidence was found of different trends over the course of the visits by group type (p=0.2870), but statistically significant differences were found between visits (p<0.0001) and between groups (p=0.0114). The percentage of adherent patients increased in all three groups with each additional visit, though these percentages were higher in the MDS group, followed closely by the PDS+App group. The NNTT group was significantly lower. The adherence trend in the PDS group went from 51.1% at V3 to 82.9 % at FV (p=0.0002), from 9.1% at V3 to 57.1 % at FV for the NNTT group (p=0.0034) and from 39.1 % to 73.7 % for the PDS+App group (p=0.0037). Checks were also carried out to see if there were differences between V3 and FV overall (p<0.001) and separately for each group. Significant differences 15

18 Report of results were found in every case, which indicated that the percentage of adherent patients at the end of the study was higher. Effects of gender, age (60-75; 76-89) and number of medicines (5-8; more than 8) in M-G: No differences were found in the groups based on gender (p=0.8071) or in the trend over the course of the visits (p=0.5563). The percentage of adherent patients was also unchanged based on gender (p=0.3849). Equivalent results were found in the analysis carried out based on age group (60-75 vs , p=0.4176; p=0.5393; p=0.8523). The number of medicines was also compared. No evidence was found of any differences between the groups based on the number of medicines taken (p=0.5767), though statistically significant differences were found in the interaction between visits and the number of medicines, indicating that the trend in the number of adherent patients over the course of the visits changed based on the number of medicines taken. Statistically significant differences existed based on visits for patients on 5-8 medicines (p<0.0001), though no significant differences were found in patients taking more than 8 medicines (p=0.1063). The percentage of patients in each group increased with each visit, though the increase for patients on more than 8 medicines was smaller, making it statistically insignificant. Relationship between variables associated with compliance (visits, support group and number of medicines taken) in M-G: No differences were found in the groups based on the number of medicines (p=0.6809) or in the trend over the course of the visits (p=0.1721). Evidence was found, however, of different trends over the course of the visits based on the number of medicines (p=0.0012). Differences were also found based on the group (p=0.0271). To understand the degree of association between these factors and adherence with the medication regime, the odds ratio (OR) of the different effects were estimated. Support group: The PDS group was 3.87 times more associated with compliant patients than the NNTT group (CI 95 % = times more associated - significant). The PDS+App group was 2.79 times more associated with adherent patients than the NNTT group (CI 95 % = times more associated - not significant). 16

19 Visits: Patients on 5-8 medicines: The adherent patients were 3.71 times more associated with V4 than with V3 (CI 95 % = times more associated - significant). The adherent patients were 5.02 times more associated with V5 than with V3 (CI 95 % = times more associated - significant). The adherent patients were 6.00 times more associated with V6 than with V3 (CI 95 % = times more associated - significant). The adherent patients were times more associated with FV than with V3 (CI 95 % = times more associated - significant). Patients on more than 8 medicines: The adherent patients were 1.36 times more associated with V4 than with V3 (CI 95 % = times more associated - not significant). The adherent patients were 2.27 times more associated with V5 than with V3 (CI 95 % = times more associated - significant). The adherent patients were 1.90 times more associated with V6 than with V3 (CI 95 % = times more associated - significant). The adherent patients were 1.83 times more associated with FV than with V3 (CI 95 % = times more associated - not significant). These results show that the relationship between visits varied based on the number of medicines taken. In the patient group with 5-8 medicines, the higher number of visits was associated with more adherent patients. In contrast, in the patient group with more than 8 medicines, the majority of the visits were approximately two times more associated with adherent patients than V3, but this association did not increase with a higher number of visits. M-G by province: The trend in the M-G Test between V3 and FV by province was: Badajoz: from 15.8 % to 71.4 % p=0.0012; Barcelona: from 13.3 % to 59. 1% p=0.0005; Bizkaia: 61.8 %-80.8 % p=0.1114; Cáceres: 40.0 % % p= Reasons for non-adherence: in the BV, the reasons for non-adherence given by patients were as follows, from highest to lowest: forgetting to take the medication, thinking it was unimportant to take the medication every day, other reasons, the medication makes the patient feel discomfort, not picking up the medication on time, using a dosage higher or lower than prescribed, difficulties using the medication. It is worth noting that no patient in the study 17

20 Report of results mentioned not being able to afford the medication as a reason for non-adherence. In the FV, the reasons for non-adherence by non-adherent patients were as follows, from highest to lowest: forgetting to take the medication, thinking it unimportant to take the medication every day, other reasons, not picking up the medication on time and difficulties using the medication. The using a dosage higher or lower than prescribed and the medication makes the patient feel discomfort reasons for non-compliance disappear from the baseline to the final visit. Evolution in medication intake: refers to a medication returned by the patient in a PDS or intakes not confirmed in the NNTT application. An improvement was noticed over the study, with values going from 62.1 % in V3 to 89.2 % in VF (p<0.0001). Although the effect of increasing adherence results from using both estimating strategies (M-G and medication intake), the specific data do not match. Additional studies would be required to evaluate this effect. Evolution in medication intake by groups: the percentage of patients who took all the medication increased with the number of visits. No evidence was found of a different trend based on the group (p=0.0678). As for the effect of the visits and the group on the percentage of patients who took all their medication, both were statistically significant (visits p=0.0003, group p=0.0029). The percentage of patients who took their medication increased overall with the number of visits, with the PDS and PDS+App groups exhibiting higher percentages than the NNTT group. Considering separately the visit effect on each group, the NNTT and PDS+App groups did not exhibit a statistically significant visit effect (p=0.4456, p=0.1291). This agrees with the previous observation that the overall visit effect was the same for all three groups and that any differences were insignificant. This is perhaps due to the presence of a significant interaction between the number of visits and the group, but it was unable to detect it. When analysing the evolution in the intake of medication by groups between V3 and FV, we see that there was an improvement in the PDS group in the percentage of patients taking their medicines, which went from 72.3 % in V3 to 95.1 % in FV (p=0.0028). A positive trend was also noted in the NNTT group, which went from 36.4 % in V3 to 64.3 % in FV, though the values between the visits were variable and the difference was not significant (p=0.0620). The PDS+App group also saw an improvement, going from 78.3 % in V3 to 94.7 % in FV, though it too was not significant, with variable figures between visits (p=0.0821). Both the initial (V3) and final (FV) percentages were higher from the start for the PDS and PDS+App groups. Their percentages were also very similar, meaning these groups exhibited an advantage over the NNT group in terms of medication intake. 18

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