Title page - University of East Anglia



Abstract Background: Understanding the magnitude of non-adherence in pre-marketing clinical trials and clinical practice is essential. However, accurately measuring non-adherence to medication is problematic and the variety of adherence measures and/or calculation methods has led to highly variable results.Purpose: To compare commonly used methods for measuring adherence to eye drop therapy in order to assess which methods achieve the most complete data sets over an eight month monitoring period; to quantify the magnitude of variance in adherence estimations; and to review the different methods used to calculate or interpret adherence data.Methods: Adherence was measured electronically for eight months by participants administering eye drops using a Travalert? dosing aid. The mean number of Travelert dosing aid recorded doses administered over the monitoring period was used to calculate a percentage adherence score. In addition, the value of graphically presenting Travalert dosing aid data to classify patterns of adherence behaviour was explored. The validated Morisky Measure of Adherence Scale and questions requesting participants to report the frequency of missed doses, were two measures of self-reported adherence calculated for each participant. Finally, medication possession ratio was calculated from expected repeat prescription orders compared with actual repeat prescription orders. Results: For the 208 recruited participants, self-reported adherence was the most reliable method of collecting complete data sets over the eight month period; 16% of self-reported adherence data were missing compared with 45% of Travalert dosing aid data missing at 8 months. The mean adherence measured over the monitoring period by the Travalert dosing aid was 77%. When adherence measures were dichotomised into adherent and non-adherent groups the Travalert dosing aid found 54% of participants were adherent, compared to 60% Morisky Measure of Adherence Scale and 57% frequency of missed dose self-report measures. However, there was poor agreement between the Travalert dosing aid measured adherence and self-report measures. Medication possession ratio was not a reliable measure of persistence with medication. Graphical Travalert dosing aid data presentation provided additional information about participant behaviour by indicating that most non-adherence was due to participants taking drug holidays rather than missing occasional doses.Conclusions: The analysis provided evidence about the inconsistencies between different monitoring strategies and adherence measures. Furthermore, the analysis highlighted the difficulties in collecting complete data for studies investigating chronic, slowly progressive conditions that require long-term follow-up. Future adherence studies could use multiple methods for quantifying and classifying adherence in parallel, both to maximise precision of adherence estimates and facilitate comparison between studies. However, the authors are cautious of the effect of using multiple adherence measures on participant behaviour and their potential reactivity effects is an area for future research.IntroductionFor marketing authorisation, clinical trials must be carried out to ascertain the safety, quality and efficacy of any medicine. Poor adherence to any treatment protocol can result in an underestimation of therapeutic efficacy and under-reporting of potential side effects. Such uncertainties and diminished observed treatment effects can result in failure to ascertain the optimal dosage for maximum efficacy, reduced statistical study power, the requirement for larger sample sizes and, in turn, increased study costs. ADDIN EN.CITE <EndNote><Cite><Author>Burke</Author><Year>1997</Year><RecNum>2249</RecNum><DisplayText><style face="superscript">1</style></DisplayText><record><rec-number>2249</rec-number><foreign-keys><key app="EN" db-id="xxs0xespbdasduevdr2xw0sq90z9v0trzwr5">2249</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Burke, L. E.</author><author>Dunbar-Jacob, J. M.</author><author>Hill, M. N.</author></authors></contributors><auth-address>University of Pittsburgh, School of Medicine, Department of Psychiatry, PA 15213, USA.</auth-address><titles><title>Compliance with cardiovascular disease prevention strategies: a review of the research</title><secondary-title>Ann Behav Med</secondary-title><alt-title>Annals of behavioral medicine : a publication of the Society of Behavioral Medicine</alt-title></titles><periodical><full-title>Ann Behav Med</full-title><abbr-1>Annals of behavioral medicine : a publication of the Society of Behavioral Medicine</abbr-1></periodical><alt-periodical><full-title>Ann Behav Med</full-title><abbr-1>Annals of behavioral medicine : a publication of the Society of Behavioral Medicine</abbr-1></alt-periodical><pages>239-63</pages><volume>19</volume><number>3</number><keywords><keyword>Behavior Therapy</keyword><keyword>Cardiovascular Agents/administration &amp; dosage</keyword><keyword>Cardiovascular Diseases/*prevention &amp; control/psychology</keyword><keyword>Diet, Fat-Restricted/psychology</keyword><keyword>Exercise/psychology</keyword><keyword>Humans</keyword><keyword>Patient Compliance/*psychology</keyword><keyword>Risk Factors</keyword><keyword>Smoking Cessation/psychology</keyword><keyword>Treatment Outcome</keyword></keywords><dates><year>1997</year><pub-dates><date>Summer</date></pub-dates></dates><isbn>0883-6612 (Print)&#xD;0883-6612 (Linking)</isbn><accession-num>9603699</accession-num><urls><related-urls><url> Furthermore, in routine clinical practice non-adherence can have a significant adverse effect on the apparent success of medication to control chronic conditions.Thus, understanding the magnitude of non-adherence in pre-marketing clinical trials and clinical practice is essential. However, accurately measuring non-adherence to medication is problematic. The literature reports a variety of adherence measures and calculation methods that have led to considerable variation in results. Currently, there is no consensus as to how non-adherence is reported or defined and gold-standard methodology for measuring adherence does not exist.PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5XYXRlcm1hbjwvQXV0aG9yPjxZZWFyPjIwMTM8L1llYXI+

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ADDIN EN.CITE.DATA 11, 12 A further proposed advantage of electronic monitoring devices is that they can be used to compare individual and collective ‘behaviour patterns’, thus potentially providing a more refined measure of complex adherence behaviours to enable identification of clinically significant non-adherent patient behaviour.PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5SaWNoYXJkc29uPC9BdXRob3I+PFllYXI+MjAxMzwvWWVh

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ADDIN EN.CITE.DATA 12-15 Short persistence accounts can take many different forms, and can jeopardize treatment effectiveness which leads to treatment discontinuation. ADDIN EN.CITE <EndNote><Cite><Author>Blaschke</Author><Year>2012</Year><RecNum>2317</RecNum><DisplayText><style face="superscript">16</style></DisplayText><record><rec-number>2317</rec-number><foreign-keys><key app="EN" db-id="xxs0xespbdasduevdr2xw0sq90z9v0trzwr5">2317</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Blaschke, T. F.</author><author>Osterberg, L.</author><author>Vrijens, B.</author><author>Urquhart, J.</author></authors></contributors><auth-address>Department of Medicine, Stanford University School of Medicine, Stanford, California 94305, USA. blaschke@stanford.edu</auth-address><titles><title>Adherence to medications: insights arising from studies on the unreliable link between prescribed and actual drug dosing histories</title><secondary-title>Annu Rev Pharmacol Toxicol</secondary-title><alt-title>Annual review of pharmacology and toxicology</alt-title></titles><periodical><full-title>Annu Rev Pharmacol Toxicol</full-title><abbr-1>Annual review of pharmacology and toxicology</abbr-1></periodical><alt-periodical><full-title>Annu Rev Pharmacol Toxicol</full-title><abbr-1>Annual review of pharmacology and toxicology</abbr-1></alt-periodical><pages>275-301</pages><volume>52</volume><keywords><keyword>*Dose-Response Relationship, Drug</keyword><keyword>Drug Administration Schedule</keyword><keyword>Drug Design</keyword><keyword>Humans</keyword><keyword>*Patient Compliance</keyword><keyword>Pharmaceutical Preparations/*administration &amp; dosage</keyword><keyword>Pharmacokinetics</keyword></keywords><dates><year>2012</year></dates><isbn>1545-4304 (Electronic)&#xD;0362-1642 (Linking)</isbn><accession-num>21942628</accession-num><urls><related-urls><url> For example if therapeutic benefit is reliant upon medication administration at a particular time of day, timing accuracy will better reflect clinical outcome than measuring the number of doses taken if they are at the wrong time of day. An 80% adherence rate is widely recognised as ‘acceptable’ for many systemic medications ADDIN EN.CITE <EndNote><Cite><Author>J.</Author><Year>1979</Year><RecNum>1052</RecNum><DisplayText><style face="superscript">17</style></DisplayText><record><rec-number>1052</rec-number><foreign-keys><key app="EN" db-id="xxs0xespbdasduevdr2xw0sq90z9v0trzwr5">1052</key></foreign-keys><ref-type name="Book Section">5</ref-type><contributors><authors><author>Sackett, D. </author><author>Snow, J.</author></authors><secondary-authors><author>Haynes, B.</author><author>Wayne Taylor, D. </author><author>Sacket, D. </author></secondary-authors></contributors><titles><title>Magnitude and Measurement of Compliance </title><secondary-title>Compliance in Healthcare</secondary-title></titles><dates><year>1979</year></dates><pub-location>Baltimore and London</pub-location><publisher>The John Hopkins University Press</publisher><urls></urls></record></Cite></EndNote>17 but to be clinically relevant, an 'acceptable' adherence level should be determined by its impact on clinical outcome.PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5PbHRob2ZmPC9BdXRob3I+PFllYXI+MjAwNTwvWWVhcj48

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ADDIN EN.CITE.DATA 20 Regular use of eye drops to control the intraocular pressure can prevent the onset or reduce the rate of progression of manifest glaucoma. ADDIN EN.CITE <EndNote><Cite><Author>Heijl</Author><Year>2002</Year><RecNum>2238</RecNum><DisplayText><style face="superscript">21</style></DisplayText><record><rec-number>2238</rec-number><foreign-keys><key app="EN" db-id="xxs0xespbdasduevdr2xw0sq90z9v0trzwr5">2238</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Heijl, A.</author><author>Leske, M. C.</author><author>Bengtsson, B.</author><author>Hyman, L.</author><author>Hussein, M.</author></authors></contributors><auth-address>Department of Ophthalmology, Malmo University Hospital, SE-20502 Malmo, Sweden. anders.heijl@oftal.mas.lu.se</auth-address><titles><title>Reduction of intraocular pressure and glaucoma progression: results from the Early Manifest Glaucoma Trial</title><secondary-title>Archives of ophthalmology</secondary-title></titles><periodical><full-title>Archives of ophthalmology</full-title><abbr-1>Arch Ophthalmol</abbr-1></periodical><pages>1268-79</pages><volume>120</volume><number>10</number><edition>2002/10/09</edition><keywords><keyword>Adrenergic beta-Antagonists/therapeutic use</keyword><keyword>Aged</keyword><keyword>Aged, 80 and over</keyword><keyword>Betaxolol/therapeutic use</keyword><keyword>Disease Progression</keyword><keyword>Female</keyword><keyword>Glaucoma, Open-Angle/*physiopathology/*therapy</keyword><keyword>Humans</keyword><keyword>*Intraocular Pressure/drug effects</keyword><keyword>Laser Therapy</keyword><keyword>Male</keyword><keyword>Middle Aged</keyword><keyword>Single-Blind Method</keyword><keyword>Trabeculectomy</keyword><keyword>Treatment Outcome</keyword></keywords><dates><year>2002</year><pub-dates><date>Oct</date></pub-dates></dates><isbn>0003-9950 (Print)&#xD;0003-9950 (Linking)</isbn><accession-num>12365904</accession-num><urls><related-urls><url> [pii]</electronic-resource-num><language>eng</language></record></Cite></EndNote>21 Although the present study was limited to patients using glaucoma medications, the relevance of these analyses may be applicable to adherence measurement for other medicines used in the management of chronic disease.The purpose of the present analysis was to compare commonly used methods for measuring adherence to glaucoma medication using data collected from the Norwich Adherence Glaucoma Study. ADDIN EN.CITE <EndNote><Cite><Author>Cate</Author><Year>2012</Year><RecNum>2146</RecNum><DisplayText><style face="superscript">22</style></DisplayText><record><rec-number>2146</rec-number><foreign-keys><key app="EN" db-id="xxs0xespbdasduevdr2xw0sq90z9v0trzwr5">2146</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Cate, H.</author><author>Bhattacharya, D.</author><author>Clark, A.</author><author>Fordham, R.</author><author>Notley, C.</author><author>Broadway, D. C.</author></authors></contributors><titles><title>Protocol for a randomised controlled trial to estimate the effects and costs of a patient centred educational intervention in glaucoma management</title><secondary-title>BMC Ophthalmol</secondary-title></titles><periodical><full-title>BMC Ophthalmol</full-title></periodical><pages>57</pages><volume>12</volume><number>1</number><edition>2012/11/23</edition><dates><year>2012</year><pub-dates><date>Nov 22</date></pub-dates></dates><isbn>1471-2415 (Electronic)&#xD;1471-2415 (Linking)</isbn><accession-num>23171166</accession-num><urls></urls><electronic-resource-num>10.1186/1471-2415-12-57</electronic-resource-num><remote-database-provider>NLM</remote-database-provider><language>Eng</language></record></Cite></EndNote>22 The objectives were to assess which methods achieved the greatest complete datasets over an eight month monitoring period; compare the results from different data collection methods; and review the different methods used to calculate or interpret non-adherence data.MethodsData were drawn from the Norwich Adherence Glaucoma Study which was a randomized controlled trial conducted in the Glaucoma Clinic of a UK National Health Service (NHS) teaching hospital to compare effects of an educational and supportive intervention on adherence behaviour over that achieved with standard care. ADDIN EN.CITE <EndNote><Cite><Author>Cate</Author><Year>2012</Year><RecNum>2146</RecNum><DisplayText><style face="superscript">22</style></DisplayText><record><rec-number>2146</rec-number><foreign-keys><key app="EN" db-id="xxs0xespbdasduevdr2xw0sq90z9v0trzwr5">2146</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Cate, H.</author><author>Bhattacharya, D.</author><author>Clark, A.</author><author>Fordham, R.</author><author>Notley, C.</author><author>Broadway, D. C.</author></authors></contributors><titles><title>Protocol for a randomised controlled trial to estimate the effects and costs of a patient centred educational intervention in glaucoma management</title><secondary-title>BMC Ophthalmol</secondary-title></titles><periodical><full-title>BMC Ophthalmol</full-title></periodical><pages>57</pages><volume>12</volume><number>1</number><edition>2012/11/23</edition><dates><year>2012</year><pub-dates><date>Nov 22</date></pub-dates></dates><isbn>1471-2415 (Electronic)&#xD;1471-2415 (Linking)</isbn><accession-num>23171166</accession-num><urls></urls><electronic-resource-num>10.1186/1471-2415-12-57</electronic-resource-num><remote-database-provider>NLM</remote-database-provider><language>Eng</language></record></Cite></EndNote>22 The study was conducted in accordance with the protocol that received ethical approval from the Norfolk Research Ethics Committee, UK and for which participants gave their written consent. The methods for collecting adherence data and social demographic data have been described previously. ADDIN EN.CITE <EndNote><Cite><Author>Cate</Author><Year>2012</Year><RecNum>2146</RecNum><DisplayText><style face="superscript">22</style></DisplayText><record><rec-number>2146</rec-number><foreign-keys><key app="EN" db-id="xxs0xespbdasduevdr2xw0sq90z9v0trzwr5">2146</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Cate, H.</author><author>Bhattacharya, D.</author><author>Clark, A.</author><author>Fordham, R.</author><author>Notley, C.</author><author>Broadway, D. C.</author></authors></contributors><titles><title>Protocol for a randomised controlled trial to estimate the effects and costs of a patient centred educational intervention in glaucoma management</title><secondary-title>BMC Ophthalmol</secondary-title></titles><periodical><full-title>BMC Ophthalmol</full-title></periodical><pages>57</pages><volume>12</volume><number>1</number><edition>2012/11/23</edition><dates><year>2012</year><pub-dates><date>Nov 22</date></pub-dates></dates><isbn>1471-2415 (Electronic)&#xD;1471-2415 (Linking)</isbn><accession-num>23171166</accession-num><urls></urls><electronic-resource-num>10.1186/1471-2415-12-57</electronic-resource-num><remote-database-provider>NLM</remote-database-provider><language>Eng</language></record></Cite></EndNote>22 Adherence measures Travalert Dosing Aid?: Participants were given a Travalert? dosing aid (TDA, Alcon Laboratories, Inc., Forth Worth, TX, USA) with which to administer their travoprost eye drops once daily in the evening, for the duration of the eight month study. The mean number of doses administered over the monitoring period, recorded by the TDA, was used to calculate a percentage adherence score using an adjusted adherence calculator PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5DYXRlPC9BdXRob3I+PFllYXI+MjAxMTwvWWVhcj48UmVj

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ADDIN EN.CITE.DATA 22, 23 as presented in Figure 1. If patients stopped using the travoprost as part of their clinical care, the reason for this was recorded. The TDA data were also used to produce each individual’s dosing history using a chronology plot to display the days (24-hours) between each dosing event. The resulting graphical representation of adherence can be used to visualise and classify patterns of adherence behaviour as described previously by Ajit et al. ADDIN EN.CITE <EndNote><Cite><Author>Ajit</Author><Year>2010</Year><RecNum>1930</RecNum><DisplayText><style face="superscript">14</style></DisplayText><record><rec-number>1930</rec-number><foreign-keys><key app="EN" db-id="xxs0xespbdasduevdr2xw0sq90z9v0trzwr5">1930</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Ajit, R. R.</author><author>Fenerty, C. H.</author><author>Henson, D. B.</author></authors></contributors><auth-address>Manchester Royal Eye Hospital, Manchester, UK.</auth-address><titles><title>Patterns and rate of adherence to glaucoma therapy using an electronic dosing aid</title><secondary-title>Eye (Lond)</secondary-title></titles><periodical><full-title>Eye (Lond)</full-title></periodical><pages>1338-43</pages><volume>24</volume><number>8</number><edition>2010/03/27</edition><keywords><keyword>Adult</keyword><keyword>Aged</keyword><keyword>Aged, 80 and over</keyword><keyword>Antihypertensive Agents/*administration &amp; dosage</keyword><keyword>Cloprostenol/administration &amp; dosage/*analogs &amp; derivatives</keyword><keyword>Cohort Studies</keyword><keyword>Drug Monitoring/*instrumentation</keyword><keyword>Female</keyword><keyword>Glaucoma/*drug therapy</keyword><keyword>Humans</keyword><keyword>Male</keyword><keyword>Medication Adherence/*statistics &amp; numerical data</keyword><keyword>Middle Aged</keyword><keyword>Ophthalmic Solutions/administration &amp; dosage</keyword><keyword>Prospective Studies</keyword></keywords><dates><year>2010</year><pub-dates><date>Aug</date></pub-dates></dates><isbn>1476-5454 (Electronic)&#xD;0950-222X (Linking)</isbn><accession-num>20339390</accession-num><urls><related-urls><url> [pii]&#xD;10.1038/eye.2010.27</electronic-resource-num><language>eng</language></record></Cite></EndNote>14 ,Cate et al. ADDIN EN.CITE <EndNote><Cite><Author>Cate</Author><Year>2013</Year><RecNum>2182</RecNum><DisplayText><style face="superscript">15</style></DisplayText><record><rec-number>2182</rec-number><foreign-keys><key app="EN" db-id="xxs0xespbdasduevdr2xw0sq90z9v0trzwr5">2182</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Cate, H.</author><author>Bhattacharya, D.</author><author>Clark, A.</author><author>Holland, R.</author><author>Broadway, D. C.</author></authors></contributors><auth-address>1] Eye Department, Norfolk and Norwich University Hospital, NHS Foundation Trust, Norwich, UK [2] School of Pharmacy, University of East Anglia, Norwich, UK.</auth-address><titles><title>Patterns of adherence behaviour for patients with glaucoma</title><secondary-title>Eye</secondary-title><alt-title>Eye (Lond)</alt-title></titles><alt-periodical><full-title>Eye (Lond)</full-title></alt-periodical><edition>2013/01/19</edition><dates><year>2013</year><pub-dates><date>Jan 18</date></pub-dates></dates><isbn>1476-5454 (Electronic)&#xD;0950-222X (Linking)</isbn><accession-num>23328800</accession-num><urls><related-urls><url> and Blaschke et al. ADDIN EN.CITE <EndNote><Cite><Author>Blaschke</Author><Year>2012</Year><RecNum>2317</RecNum><DisplayText><style face="superscript">16</style></DisplayText><record><rec-number>2317</rec-number><foreign-keys><key app="EN" db-id="xxs0xespbdasduevdr2xw0sq90z9v0trzwr5">2317</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Blaschke, T. F.</author><author>Osterberg, L.</author><author>Vrijens, B.</author><author>Urquhart, J.</author></authors></contributors><auth-address>Department of Medicine, Stanford University School of Medicine, Stanford, California 94305, USA. blaschke@stanford.edu</auth-address><titles><title>Adherence to medications: insights arising from studies on the unreliable link between prescribed and actual drug dosing histories</title><secondary-title>Annu Rev Pharmacol Toxicol</secondary-title><alt-title>Annual review of pharmacology and toxicology</alt-title></titles><periodical><full-title>Annu Rev Pharmacol Toxicol</full-title><abbr-1>Annual review of pharmacology and toxicology</abbr-1></periodical><alt-periodical><full-title>Annu Rev Pharmacol Toxicol</full-title><abbr-1>Annual review of pharmacology and toxicology</abbr-1></alt-periodical><pages>275-301</pages><volume>52</volume><keywords><keyword>*Dose-Response Relationship, Drug</keyword><keyword>Drug Administration Schedule</keyword><keyword>Drug Design</keyword><keyword>Humans</keyword><keyword>*Patient Compliance</keyword><keyword>Pharmaceutical Preparations/*administration &amp; dosage</keyword><keyword>Pharmacokinetics</keyword></keywords><dates><year>2012</year></dates><isbn>1545-4304 (Electronic)&#xD;0362-1642 (Linking)</isbn><accession-num>21942628</accession-num><urls><related-urls><url> A perfectly adherent participant would have dosing event recorded at 1 day intervals. However, participants who had missed one dose would have had a 2 day interval between doses and so forth, thus showing the number of days that elapsed between dosing events. Therefore, whilst the aggregate percentage of dosing events during the total monitoring period may be the same, the dosing history data reveal different patterns of behaviour. Seven categories were used to classify adherence; (1) Discontinued after a short time due to (1a) side effects or (1b) no response to treatment, (2) adherence greater than 80% and subdivided into (2a) greater than 97% and type (2b) less than 96%, (3) had less than 80% adherence over the course of the study due to ‘drug holidays’ (participants who had a break in eye drop use for 7 or more days) and were subdivided into type (3a) ‘Drug holidays’ mixed with a variable dosing pattern and type (3b) drug holidays only, and (4) partients who had less than 80% adherence with variable and frequent missed doses. Self-reported adherence: A participant self-administered questionnaire was completed at two months and then at eight months after randomisation and intervention to capture self-reported adherence data. Two self-report adherence measures were utilised: the Frequency of Missed Dose (FMD) which is a one-question parameter developed for the present study, inviting participants to report the number of eye drop doses missed each month (twice a week or more, once a week, once a month, or never) and the Morisky Measure of Adherence Scale ADDIN EN.CITE <EndNote><Cite><Author>Morisky</Author><Year>1986</Year><RecNum>567</RecNum><DisplayText><style face="superscript">24</style></DisplayText><record><rec-number>567</rec-number><foreign-keys><key app="EN" db-id="xxs0xespbdasduevdr2xw0sq90z9v0trzwr5">567</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Morisky, D. E.</author><author>Green, L. W.</author><author>Levine, D. M.</author></authors></contributors><titles><title>Concurrent and predictive validity of a self-reported measure of medication adherence</title><secondary-title>Med Care</secondary-title></titles><periodical><full-title>Medical care</full-title><abbr-1>Med Care</abbr-1></periodical><pages>67-74</pages><volume>24</volume><number>1</number><edition>1986/01/01</edition><keywords><keyword>Humans</keyword><keyword>Hypertension/*drug therapy</keyword><keyword>Medical History Taking</keyword><keyword>Outpatient Clinics, Hospital</keyword><keyword>*Patient Compliance</keyword><keyword>Patient Education as Topic</keyword><keyword>Physician-Patient Relations</keyword><keyword>Probability</keyword><keyword>Psychometrics</keyword></keywords><dates><year>1986</year><pub-dates><date>Jan</date></pub-dates></dates><isbn>0025-7079 (Print)&#xD;0025-7079 (Linking)</isbn><accession-num>3945130</accession-num><urls><related-urls><url> (MMAS) which is a four-item validated measure. For the FMD, participants reporting having missed their medication more than once a month were considered non-adherent.Self-reported adherence using the MMAS score was calculated for each participant. Participants that answered ‘yes’ to a question scored 1: thus scores ranged from 0-3. Participants who scored 0 were considered to be “adherent”, participants scoring 1-3 were considered “non-adherent” a classification used in a previous glaucoma adherence study. ADDIN EN.CITE <EndNote><Cite><Author>Cate</Author><Year>2013</Year><RecNum>2182</RecNum><DisplayText><style face="superscript">15</style></DisplayText><record><rec-number>2182</rec-number><foreign-keys><key app="EN" db-id="xxs0xespbdasduevdr2xw0sq90z9v0trzwr5">2182</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Cate, H.</author><author>Bhattacharya, D.</author><author>Clark, A.</author><author>Holland, R.</author><author>Broadway, D. C.</author></authors></contributors><auth-address>1] Eye Department, Norfolk and Norwich University Hospital, NHS Foundation Trust, Norwich, UK [2] School of Pharmacy, University of East Anglia, Norwich, UK.</auth-address><titles><title>Patterns of adherence behaviour for patients with glaucoma</title><secondary-title>Eye</secondary-title><alt-title>Eye (Lond)</alt-title></titles><alt-periodical><full-title>Eye (Lond)</full-title></alt-periodical><edition>2013/01/19</edition><dates><year>2013</year><pub-dates><date>Jan 18</date></pub-dates></dates><isbn>1476-5454 (Electronic)&#xD;0950-222X (Linking)</isbn><accession-num>23328800</accession-num><urls><related-urls><url> Medication Possession Ratio (MPR): In the UK, patients repeat prescription history is maintained by their General Practitioner. In order to retrieve this information, with participant consent, the researcher wrote to each individual participant’s General Practitioner to ask for their repeat prescribing records including, drug name, date of issue, and prescribed days of treatment. Payment was made for this administrative task via an existing Research Payment Contract held by the Primary Care Research Network and participating General Practitioners. From these data a measure of expected repeat prescription orders and actual repeat prescription orders were used to calculate two MPR values: MPRa HYPERLINK \l "_ENREF_25" \o "Friedman, 2007 #591" PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5GcmllZG1hbjwvQXV0aG9yPjxZZWFyPjIwMDc8L1llYXI+

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ADDIN EN.CITE.DATA 25 which used the average travoprost drop count ADDIN EN.CITE <EndNote><Cite><Author>Fiscella</Author><Year>2003</Year><RecNum>1905</RecNum><DisplayText><style face="superscript">26</style></DisplayText><record><rec-number>1905</rec-number><foreign-keys><key app="EN" db-id="xxs0xespbdasduevdr2xw0sq90z9v0trzwr5">1905</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Fiscella, R. G.</author><author>Green, A.</author><author>Patuszynski, D. H.</author><author>Wilensky, J.</author></authors></contributors><auth-address>Department of Pharmacy Practice, University of Illinois at Chicago, Illinois 60612, USA. Fisc@UIC.EDU</auth-address><titles><title>Medical therapy cost considerations for glaucoma</title><secondary-title>Am J Ophthalmol</secondary-title></titles><periodical><full-title>Am J Ophthalmol</full-title></periodical><pages>18-25</pages><volume>136</volume><number>1</number><edition>2003/07/02</edition><keywords><keyword>Adrenergic alpha-Agonists/economics</keyword><keyword>Adrenergic beta-Antagonists/economics</keyword><keyword>Antihypertensive Agents/*economics</keyword><keyword>Carbonic Anhydrase Inhibitors/economics</keyword><keyword>Costs and Cost Analysis</keyword><keyword>*Drug Costs</keyword><keyword>Drug Prescriptions/economics</keyword><keyword>Drugs, Generic/economics</keyword><keyword>Glaucoma/drug therapy/*economics</keyword><keyword>Humans</keyword><keyword>Ophthalmic Solutions/*economics</keyword><keyword>Prospective Studies</keyword><keyword>Prostaglandins/economics</keyword><keyword>United States</keyword></keywords><dates><year>2003</year><pub-dates><date>Jul</date></pub-dates></dates><isbn>0002-9394 (Print)&#xD;0002-9394 (Linking)</isbn><accession-num>12834665</accession-num><urls><related-urls><url> [pii]</electronic-resource-num><language>eng</language></record></Cite></EndNote>26; and MPRb which used the UK general prescribing instruction to renew eye drop prescriptions every 28 days (Figure 1).Statistical analysisDescriptive statistics were used to characterise the demographics of the study population. Adherence measured by the TDA was calculated for the mean of the total monitoring period and the mean of months one and two. Additionally, a binary classification of adherence score was used (‘adherent’ if the score was 80% and ‘non-adherent’ if the score was <80%) for the same time periods (total period, months one and two). A repeated measures analysis-of-variance was carried out (with time measured in months) to assess for any difference in adherence over time. A Cohen’s Kappa test was used to compare (i) agreement between TDA data at month two and eight for participants with missing data in order to determine if non-adherence scores may have been effected by missing data; the assumption being made that participants with missing data might have been more likely to have poor adherence, being less motivated to complete questionnaires, or return their TDAs to the research team, (ii) agreement between self-report data at month two and eight for participants with missing TDA data, and (iii) agreement between self-report measures (MMAS and FMD) and TDA dichotomised scores of adherence at two months and eight months to determine if non-adherence was the same between measures and at the different time points that adherence was measured. The mean and median MPRa and MPRb scores were calculated and compared to TDA adherence scores using a Spearman’s rank co-efficient test to compare measures. A binary classification of persistence score was used (‘adherent’ if the score was 1.0 or above and ‘non-adherent’ if the score was below 1.0) for the total period of the study. ResultsA total of 208 participants were recruited of which 50.5% were male and the mean age was 70.4 (SD 11.1) years. Further demographic data about the Norwich Adherence Glaucoma Study cohort has been published previously. ADDIN EN.CITE <EndNote><Cite><Author>Cate</Author><Year>2014</Year><RecNum>2271</RecNum><DisplayText><style face="superscript">27</style></DisplayText><record><rec-number>2271</rec-number><foreign-keys><key app="EN" db-id="xxs0xespbdasduevdr2xw0sq90z9v0trzwr5">2271</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Cate, H.</author><author>Bhattacharya, D.</author><author>Clark, A.</author><author>Fordham, R.</author><author>Holland, R.</author><author>Broadway, D. C.</author></authors></contributors><auth-address>Norfolk &amp; Norwich University Hospital NHS Trust, Glaucoma Research Unit, Level 2, West Block, Colney Lane, Norwich NR4 7UY, UK. heidi.cate@nnuh.nhs.uk.</auth-address><titles><title>Improving adherence to glaucoma medication: a randomised controlled trial of a patient-centred intervention (The Norwich Adherence Glaucoma Study)</title><secondary-title>BMC Ophthalmol</secondary-title><alt-title>BMC ophthalmology</alt-title></titles><periodical><full-title>BMC Ophthalmol</full-title></periodical><pages>32</pages><volume>14</volume><dates><year>2014</year></dates><isbn>1471-2415 (Electronic)&#xD;1471-2415 (Linking)</isbn><accession-num>24655814</accession-num><urls><related-urls><url> Missing data The reasons for incomplete adherence data are summarised in Table 1. Questionnaire data provided the most complete datasets over the eight month period relative to health centre and TDA data which were comparable in terms of completeness at the eight month data collection point. The reason for the failure of the TDA was not always possible to establish. In some cases the internal mechanics of the device appeared to have been tampered with by the participant but it was not possible to determine in which cases the device had failed due to deliberate tampering or genuine device malfunction. MPR data were missing where health centres refused to provide the prescribing data when requested. Adherence measuresThe mean adherence rate measured by the TDA was 73.7% (standard deviation, 26.4) for the first two months of monitoring; and 76.6% (standard deviation 20.7) for the total eight month period. A repeated measures analysis-of-variance of TDA percentage adherence for each month found no significant differences between months. The number of individuals with <80% adherence for the first two months and therefore considered to be non-adherent was 78 (46.7%) compared with 52 (45.6%) for the eight month period. Assuming that participants with missing data could more likely be non-adherent, further analysis of missing data found that of the 78 participants that were non-adherent at month two, only 25 (32%) had missing data at any time point during the period of monitoring and there was no statistically significant association between participants with non-adherence and missing data during the study (Kappa 0.006, p=0.935). Mean adherence at month two for those with missing data at month eight was 73% (standard deviation, 26) compared with 74% (standard deviation, 26) for those with complete data at month eight. Table 2 presents the adherence status for each of the three measures of adherence and their agreement between two and eight months. Little change was observed between patients reporting being adherent at month two but later at month eight reporting that they were non-adherent as demonstrated by the moderate agreement ADDIN EN.CITE <EndNote><Cite><Author>Landis</Author><Year>1977</Year><RecNum>2295</RecNum><DisplayText><style face="superscript">28</style></DisplayText><record><rec-number>2295</rec-number><foreign-keys><key app="EN" db-id="xxs0xespbdasduevdr2xw0sq90z9v0trzwr5">2295</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Landis, J. R.</author><author>Koch, G. G.</author></authors></contributors><titles><title>The measurement of observer agreement for categorical data</title><secondary-title>Biometrics</secondary-title><alt-title>Biometrics</alt-title></titles><periodical><full-title>Biometrics</full-title><abbr-1>Biometrics</abbr-1></periodical><alt-periodical><full-title>Biometrics</full-title><abbr-1>Biometrics</abbr-1></alt-periodical><pages>159-74</pages><volume>33</volume><number>1</number><keywords><keyword>Humans</keyword><keyword>Multiple Sclerosis/*diagnosis</keyword><keyword>*Statistics as Topic</keyword></keywords><dates><year>1977</year><pub-dates><date>Mar</date></pub-dates></dates><isbn>0006-341X (Print)&#xD;0006-341X (Linking)</isbn><accession-num>843571</accession-num><urls><related-urls><url> for MMAS and FMD. The change was even less with the TDA dichotomised score analysis as indicated by the substantial agreement. ADDIN EN.CITE <EndNote><Cite><Author>Landis</Author><Year>1977</Year><RecNum>2295</RecNum><DisplayText><style face="superscript">28</style></DisplayText><record><rec-number>2295</rec-number><foreign-keys><key app="EN" db-id="xxs0xespbdasduevdr2xw0sq90z9v0trzwr5">2295</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Landis, J. R.</author><author>Koch, G. G.</author></authors></contributors><titles><title>The measurement of observer agreement for categorical data</title><secondary-title>Biometrics</secondary-title><alt-title>Biometrics</alt-title></titles><periodical><full-title>Biometrics</full-title><abbr-1>Biometrics</abbr-1></periodical><alt-periodical><full-title>Biometrics</full-title><abbr-1>Biometrics</abbr-1></alt-periodical><pages>159-74</pages><volume>33</volume><number>1</number><keywords><keyword>Humans</keyword><keyword>Multiple Sclerosis/*diagnosis</keyword><keyword>*Statistics as Topic</keyword></keywords><dates><year>1977</year><pub-dates><date>Mar</date></pub-dates></dates><isbn>0006-341X (Print)&#xD;0006-341X (Linking)</isbn><accession-num>843571</accession-num><urls><related-urls><url> determine if missing data had an effect on participants self-reporting, a further analysis was carried out which found that 15% of participants with missing TDA data reported to be non-adherent at month eight compared to 25% with full TDA data (similar results for both were found using both FMD and MMAS measures). Tables 3 and 4 present agreement between FMD, MMAS and TDA measured adherence; Table 3 at month two and Table 4 at month eight. Adherence using the two self-report measures, MMAS and FMD, had substantial agreement ADDIN EN.CITE <EndNote><Cite><Author>Landis</Author><Year>1977</Year><RecNum>2295</RecNum><DisplayText><style face="superscript">28</style></DisplayText><record><rec-number>2295</rec-number><foreign-keys><key app="EN" db-id="xxs0xespbdasduevdr2xw0sq90z9v0trzwr5">2295</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Landis, J. R.</author><author>Koch, G. G.</author></authors></contributors><titles><title>The measurement of observer agreement for categorical data</title><secondary-title>Biometrics</secondary-title><alt-title>Biometrics</alt-title></titles><periodical><full-title>Biometrics</full-title><abbr-1>Biometrics</abbr-1></periodical><alt-periodical><full-title>Biometrics</full-title><abbr-1>Biometrics</abbr-1></alt-periodical><pages>159-74</pages><volume>33</volume><number>1</number><keywords><keyword>Humans</keyword><keyword>Multiple Sclerosis/*diagnosis</keyword><keyword>*Statistics as Topic</keyword></keywords><dates><year>1977</year><pub-dates><date>Mar</date></pub-dates></dates><isbn>0006-341X (Print)&#xD;0006-341X (Linking)</isbn><accession-num>843571</accession-num><urls><related-urls><url> at months two and eight however they were only in slight agreement with the TDA dichotomised scores at months two and eight. TDA and MMAS were not statistically significant at month two. Figures 2 and 3 show the distribution of FMD and MMAS categorical data compared with TDA measured adherence for the total study period.Figure 4 presents adherence behaviour patterns derived from the TDA data. Most participants’ had good adherence (>80% but <97%; 2b). Non-adherent behaviour was primarily due to participants taking drug holidays rather than just missing occasional doses.The mean possession ratio was 1.89 (standard deviation 0.66) using the MPRa calculation (refill on expiration of bottle) and 0.94 (standard deviation 0.34) using MPRb (expected refill bottle every 28 days). There was a strong positive correlation between MPRa and MPRb calculations (Spearman’s co-efficient 0.901, p<0.0001) and therefore further analysis was carried out using MPRb data. The majority of participants (71%) had the correct prescription count to remain persistent with medication over the course of the eight month study period as indicated in Figure 5 by the column assigned an MPR of 1 or higher. There was only a weak positive correlation between MPRb and adherence calculated by the TDA (Spearman’s co-efficient 0.216, p=0.041) displayed in Figure 6. However, transcription of the raw data for analysis highlighted differences in the way eye drops were prescribed and dispensed which created inaccuracies in the data. Prescribing practices resulted in the following anomalies: As recommended, regardless of whether patients were prescribed unilateral or bilateral dosing, all patients received the same size bottle of eye drops. Thus, patients prescribed unilateral dosing had double the amount of medication required compared to those prescribed bilateral dosing. Patients did not consistently receive one bottle of drops per month. When multiple bottles were given it was not possible to establish the reason but common explanations from participants were: that the pharmacist dispensed two bottles of eye drops for bilateral dosing, one to be used for each eye (thus, patients had double the amount of medication required with a more complex dosing regimen requiring the opening and use of separate bottles for each eye); multiple refills were given on request if patients found they were running out of drops; patients reported stockpiling before going on vacation or because it was difficult to visit the pharmacy to collect refills; or patients reported losing drops or forgetting them if they had been staying away from home and had ordered replacements.The dichotomised scores of adherence persistence were not statistically significant when compared with FMD, MMAS and TDA dichotomised adherence scores. Since the quality of data was questionable because of prescribing practices, which led to participants possessing more medication then required, the dichotomised MPR scores were not compared further.Discussion Discordance between the different methods of measurement compared in the present study and differing challenges associated with each method of data collection prohibits determination of a definitive measure of adherence. Self-report questionnaires provided the most complete datasets for the eight month period of study. Conversely, almost a quarter of the MPR data were missing due to health centres not providing prescribing data for research purposes despite being offered payment for this administrative task. Only one similar UK study using prospective collection of prescribing data has been identified from the literaturePEVuZE5vdGU+PENpdGU+PEF1dGhvcj5HcmF5PC9BdXRob3I+PFllYXI+MjAxMjwvWWVhcj48UmVj

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ADDIN EN.CITE.DATA 30-33 of which missing or inaccurate data was often noted as a possible limitation, although the magnitude of the problem was not quantified.Previous studies have reported that the TDA accurately recorded drop administration, but the longest of these studies was only for a three month period.PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5Pa2VrZTwvQXV0aG9yPjxZZWFyPjIwMDk8L1llYXI+PFJl

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ADDIN EN.CITE.DATA 14, 34, 35 The present study found that the TDA was relatively successful at measuring adherence for the initial two month period. However, by eight months of follow-up, data attrition was high. Retrieval of data from the TDA due to device failure, which was outside of the control of either patient or researcher, accounted for the greatest loss of daily electronic data for the eight month period. In addition, when treatment was changed, it rendered the TDA useless since the TDA aperture only holds Travatan shaped bottles. Therefore, calculating the average adherence score using TDA over the total monitoring period was the least successful of the three studied methods. The TDA data, however, did provide the additional benefit of being able to graphically present participant patterns of drop usage.PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5Baml0PC9BdXRob3I+PFllYXI+MjAxMDwvWWVhcj48UmVj

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ADDIN EN.CITE.DATA 14, 15 Comparing patterns of drop usage determined that ‘drug holidays’ were the predominate type of non-adherent behaviour. In contrast to incidental missed doses, patterns of drug holidays may be indicative of a more intentional non-adherent behaviour trait when a patient chooses not to use their medication for longer periods of time.PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5MYWNleTwvQXV0aG9yPjxZZWFyPjIwMDk8L1llYXI+PFJl

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ADDIN EN.CITE.DATA 36, 37 Thus, reviewing usage patterns rather than average adherence scores may be more useful in establishing the cause(s) of observed non-adherence. Adherence pattern observation in turn can provide invaluable information for guiding the selection of intervention(s) most appropriate for supporting patients to adhere to their prescribed therapy.Patient self-reporting remains a popular method for collection of adherence information in both clinical practice and research. However, it is of concern that a discrepancy was observed between self-reported non-adherence, which was greater at eight months than two months, and TDA measured non-adherence which did not increase at any time during the eight month follow-up even when accounting for participants with missing data. If non-adherent participants had failed to return their TDA devices, thus creating more missing TDA data, this could have been a reactivity bias. However, magnitude of adherence at two months was comparable for participants with full 8-month data and those who subsequently went on to have missing TDA data. Therefore those who had missing TDA data but still provided self-reported adherence data were not likely to have been a less adherent cohort in comparison with those who had full TDA data. If one were to accept that the level of electronic monitoring accuracy remained constant over the monitoring period, then participants were poor reporters of adherence since their reporting became less accurate over time or they were inclined to over-report adherence within the first two months of observation. The electronic monitoring used in the present study may have caused a reactivity bias not previously identified in previous studies. Adherence measured electronically did not diminish over time as previously reportedPEVuZE5vdGU+PENpdGU+PEF1dGhvcj5HcmF5PC9BdXRob3I+PFllYXI+MjAwOTwvWWVhcj48UmVj

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ADDIN EN.CITE.DATA 9, 10 and the results did not replicate previous theories of the likely short term reactivity bias time found in observational studies. Either participants in the present study were naturally very adherent, or there may have been a more long term reactivity bias caused by study participation and/or the monitoring effects of the TDA that improved adherence behaviour for the duration of follow-up. These phenomena, often referred to as mere measurement effects, are proposed to cause behavioural changes in intervention studies and are the subject of ongoing debate. ADDIN EN.CITE <EndNote><Cite><Author>Mankarious</Author><Year>2014</Year><RecNum>2294</RecNum><DisplayText><style face="superscript">38</style></DisplayText><record><rec-number>2294</rec-number><foreign-keys><key app="EN" db-id="xxs0xespbdasduevdr2xw0sq90z9v0trzwr5">2294</key></foreign-keys><ref-type name="Electronic Article">43</ref-type><contributors><authors><author>Mankarious, E.</author><author>Kothe, E.</author></authors></contributors><titles><title>A meta-analysis of the effects of measuring theory of planned behaviour constructs on behaviour within prospective studies</title><secondary-title>Health Psychology Review</secondary-title></titles><periodical><full-title>Health Psychol Rev</full-title><abbr-1>Health psychology review</abbr-1></periodical><section>07 Jul 2014</section><dates><year>2014</year><pub-dates><date>08 July 2014</date></pub-dates></dates><publisher>Routledge</publisher><urls></urls><electronic-resource-num>10.1080/17437199.2014.927722</electronic-resource-num></record></Cite></EndNote>38 The multiple measures of adherence may have had an effect on participants self-reporting of adherence. When participants are aware that multiple measures are being used, a synergistic relationship may occur which emphasises adherent behaviour and can cause a greater study reactivity bias. A future study may be able to assess if self-report of adherence changes when participants are being monitored with an electronic monitor.The calculation of MPRs with eye drops was found to be complex due to inaccuracies in the data caused by variation in prescribing conduct. In addition, unlike tablets or syrups, it is not possible to determine the volume of liquid correctly instilled in the eye as it relies upon the dexterity of the patient to instil the correct amount of liquid into the eye on the first attempt. If a patient were to miss the eye for example, they may need to apply another drop whilst with prolonged squeezing excessive liquid flows from the bottle resulting in the administration of more than one measured drop. Thus, absence of a fixed dose measure with respect to eye drops and differences in prescribing protocols can easily lead to erroneous MPR calculations. Inaccurate calculation of MPR may have been the reason why the present study failed to identify a strong correlation between TDA and MPR measures. In a previous UK study using MPR calculations, the same 28-day calculation for refills was used but the authors did not describe any of the potential inaccuracies or limitations in collecting the data as described in the present study.PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5HcmF5PC9BdXRob3I+PFllYXI+MjAxMjwvWWVhcj48UmVj

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ADDIN EN.CITE.DATA 29 The present study observations are worth further consideration since the complexity of repeat prescription administration itself, and variation in health care professional practice with respect to prescribing medication, may have a role to play in patient attitudes to medication use. The present analysis provided further evidence of the potential bias induced by using self-report and electronic tools to measure adherence and the difficulties of using routine data to calculate MPRs, particularly with respect to eye drops within the UK prescribing system. Furthermore, whilst electronic monitoring is often acclaimed to be the preferred method of adherence measure, the present study found that data were difficult to collect for the long monitoring period appropriate for the study of this chronic condition and might be sensitive to study reactivity bias. No gold-standard method for measuring adherence currently exists and it is likely that the practicalities of data collection will continue to govern what is ultimately chosen as an appropriate measure of adherence for each individual study. Unfortunately, whilst a standardised and accurate measure for adherence remains undefined, studies will continue to produce heterogeneous adherence results. Equally, multiple methods of adherence measurement used in parallel to quantify and classify adherence may maximise precision of adherence estimates and facilitate comparisons between studies, the effect that multiple measures of adherence have on patient behaviour is unknown. AcknowledgementsThe present paper presents independent research commissioned by the National Institute for Health Research (NIHR) under its Research for Patient Benefit (RfPB) Programme (Grant Reference Number PB-PG-1207-14119). The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health. Neither the sponsor nor funding organization had any role in the design or conduct of this research.Alcon Laboratories Inc., Fort Worth, TX, USA, supplied the Travalert? Dosing Aid but the organization had no role in the design or conduct of this research.Conflict of interest statementThe authors declare that there are no conflicts of interest. References ADDIN EN.REFLIST 1.Burke LE, Dunbar-Jacob JM, Hill MN. Compliance with cardiovascular disease prevention strategies: a review of the research. Annals of behavioral medicine : a publication of the Society of Behavioral Medicine. 1997; 19: 239-263.2.Waterman H, Evans JR, Gray TA, et al. Interventions for improving adherence to ocular hypotensive therapy. The Cochrane database of systematic reviews. 2013; 4: CD006132.3.DiMatteo MR. 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Health psychology review: 2014, (accessed 08 July 2014).Reason for missing dataNumber of participants with missing data*MMAS Questionnaire #FMD Questionnaire Prescribing record~TDA2 months8 months2 months8 monthsTotal period2 months8 monthsQuestionnaire or ~TDA not returned / used / fully completed by patient1713118-812Data not provided by health centre----49--Device / battery failure-----1232Participant withdrawn or stopped or changed treatment118118241948Lost to follow-up / Deceased2222222Total Missing20 (10%)33(16%)14(7%)28(13%)75(36%)41(20%)94(45%)*MMAS (Morisky Medication Adherence Score)#FMD (Frequency Missed Dose)~TDA (Travalert Dosing Aid)Table 1. Reason for missing data compared between measures. Agreement between month 2 and month 8Kappa (p)*MMASDichotomised0.402 (p<0.0001)n = 166Month 8Non-AdherentAdherentMonth 2Non-Adherent3312Adherent3388#FMDDichotomised0.556 (p<0.0001)n=172Month 8Non-AdherentAdherentMonth 2Non-Adherent4610Adherent2690~TDA Dichotomised0.629 (p<0.0001)n=114Month 8Non-AdherentAdherentMonth 2Non-Adherent4211Adherent1051* MMAS (Morisky Medication Adherence Score)# FMD (Frequency Missed Dose)~ TDA (Travalert Dosing Aid)Table 2.Participant adherence status at month two compared with month eight.Adherence score % (n)Agreement between measures Kappa (P)*MMAS#FMD*MMASDichotomisedTotal n=18872.3(136)--#FMDDichotomisedTotal n=19467.5(131)0.632 (p<0.0001)n=186-Non- AdherentAdherentNon-Adherent4219Adherent10115~TDA DichotomisedTotal n=16753.3(89)0.117 (p=0.124)n=1560.233 (p=0.002)n=161Non- AdherentAdherentNon- AdherentAdherentNon-Adherent2521Non-Adherent3319Adherent4565Adherent4168* MMAS (Morisky Medication Adherence Score)# FMD (Frequency Missed Dose)~ TDA (Travalert Dosing Aid)Table 3.Agreement between, MMAS, FMDA and TDA dichotomised score at two months.Adherence score % (n)Agreement between measures Kappa (P)*MMAS#FMD*MMASDichotomisedTotal n=17560.0(105)--#FMDDichotomisedTotal n=18057.2(103)0.724 (p<0.0001)n=172-Non- AdherentAdherentNon-Adherent599Adherent1490~TDA DichotomisedTotal n=11454.4(62)0.180 (p=0.059)n=1090.289 (p=0.002)n=111Non- AdherentAdherentNon- AdherentAdherentNon-Adherent2519Non-Adherent3019Adherent2540Adherent2042* MMAS (Morisky Medication Adherence Score)# FMD (Frequency Missed Dose)~ TDA (Travalert Dosing Aid)Table 4.Agreement between, MMAS, FMD, and TDA dichotomised scores at eight months. ................
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