Enhancing antiepileptic drug adherence: A randomized controlled trial Ian Brown a , Paschal Sheeran a , Markus Reuber b, * a Department of Psychology, University of Sheffield, Western Bank, Sheffield, UK b Department of Neurology, University of Sheffield, Royal Hallamshire Hospital Sheffield, Glossop Road, Sheffield, UK article info Article history: Received 13 July 2009 Revised 5 September 2009 Accepted 13 September 2009 Available online 27 October 2009 Keywords: Antiepileptic drugs Adherence Randomized controlled trial Compliance Epilepsy Implementation intentions abstract Suboptimal adherence to antiepileptic drug (AED) treatment is commonplace, and increases the risk of status epilepticus and sudden unexplained death in epilepsy. This randomized controlled trial was designed to demonstrate whether an implementation intention intervention involving the completion of a simple self-administered questionnaire linking the intention of taking medication with a particular time, place, and other activity can improve AED treatment schedule adherence. Of the 81 patients with epilepsy who were randomized, 69 completed a 1-month monitoring period with an objective measure of tablet taking (electronic registration of pill bottle openings, Medication Event Monitoring System [MEMS]). Intervention participants showed improved adherence relative to controls on all three out- comes: doses taken in total (93.4% vs. 79.1%), days on which correct dose was taken (88.7% vs. 65.3%), and doses taken on schedule (78.8% vs. 55.3%) (P < 0.01). The implementation intention intervention may be an easy-to-administer and effective means of promoting AED adherence. Ó 2009 Elsevier Inc. All rights reserved. 1. Introduction Approximately 60% of patients with epilepsy achieve full con- trol of their seizures with antiepileptic drugs (AEDs) [1]. Modern medical treatment aims not only to prevent seizures but also to avoid negative effects on cognitive function and emotional, physi- cal, and general well-being. People with epilepsy are most likely to achieve these aims by the regular ingestion of the lowest dose of medication and the smallest number of AEDs necessary [2]. This, in turn, depends on their taking their medication as prescribed. However, research indicates that 30% to 50% of adults with epi- lepsy adhere poorly to their AED treatment schedules [3]. Adher- ence problems may be more common in epilepsy than in other medical conditions [4]. Indeed, nonadherence has been identified as one of the most important causes of treatment failure in patients with epilepsy [5], It is possible that neurologists underestimate the extent of adherence problems in their own clinical practice because patients do not admit failing to take their medication regularly to their doc- tor [6–8]. Seventy percent of patients with epilepsy state that they never miss a dose [9,10], and the majority of patients admit to missing only one or two doses per month [7]. However, studies using objective measures have revealed much higher rates of irreg- ular AED use. For instance, a study of 33,658 Medicaid recipients showed that less than 80% of the AEDs required for full prescription adherence were picked up by participants in 26% of quarters dur- ing the observation period [11]. Two studies using the Medical Events Monitoring System (MEMS)—a pill bottle with an electronic cap that registers each occasion the bottle is opened—found that only 76% of doses were taken overall [12], and that 48% of patients took one-third or fewer of the prescribed AED doses [6]. AED blood levels in the ‘‘therapeutic range” can offer false reassurance: although complete nonadherence can be detected using AED blood levels, there is no reliable correlation between the variability of AED blood levels and irregular medication intake [12]. Poor adherence has been shown to affect important treatment outcomes: in the large Medicaid study mentioned above, the num- bers of hospital admissions, inpatient treatment days, and emer- gency room visits were higher in ‘‘noncompliant” quarters, resulting in increased total health care spending [11]. Another re- cent study based on more than 10,000 individuals with epilepsy found that 39% picked up less than 80% of the medication required to cover their AED prescription and that hospital admission rates and health care costs were higher in the nonadherent group [13]. Other studies have shown that patients who miss doses may expe- rience additional seizures [7,14], and may be slower to achieve full seizure control [15]. Lack of adherence to AED treatment has been identified as a potential precipitating cause in 31% of epileptic sei- zures for which ambulances were called and 13% of seizures requiring emergency hospital admission [16,17]. Patients whose medication intake is irregular are also at increased risk of sudden unexplained death in epilepsy (SUDEP) [18]. After demographic and clinical covariates in the Medicaid study were controlled for, 1525-5050/$ - see front matter Ó 2009 Elsevier Inc. All rights reserved. doi:10.1016/j.yebeh.2009.09.014 * Corresponding author. Address: Department of Neurology, University of Sheffield, Royal Hallamshire Hospital Sheffield, Glossop Road, Sheffield S10 2JF, UK. Fax: +44 (0) 114 2713158. E-mail address: [email protected](M. Reuber). Epilepsy & Behavior 16 (2009) 634–639 Contents lists available at ScienceDirect Epilepsy & Behavior journal homepage: www.elsevier.com/locate/yebeh
Transcript
Epilepsy & Behavior 16 (2009) 634–639
Contents lists available at ScienceDirect
Epilepsy & Behavior
journal homepage: www.elsevier .com/locate /yebeh
Enhancing antiepileptic drug adherence: A randomized controlled trial
Ian Brown a, Paschal Sheeran a, Markus Reuber b,*
a Department of Psychology, University of Sheffield, Western Bank, Sheffield, UKb Department of Neurology, University of Sheffield, Royal Hallamshire Hospital Sheffield, Glossop Road, Sheffield, UK
a r t i c l e i n f o a b s t r a c t
Article history:Received 13 July 2009Revised 5 September 2009Accepted 13 September 2009Available online 27 October 2009
Suboptimal adherence to antiepileptic drug (AED) treatment is commonplace, and increases the risk ofstatus epilepticus and sudden unexplained death in epilepsy. This randomized controlled trial wasdesigned to demonstrate whether an implementation intention intervention involving the completionof a simple self-administered questionnaire linking the intention of taking medication with a particulartime, place, and other activity can improve AED treatment schedule adherence. Of the 81 patients withepilepsy who were randomized, 69 completed a 1-month monitoring period with an objective measureof tablet taking (electronic registration of pill bottle openings, Medication Event Monitoring System[MEMS]). Intervention participants showed improved adherence relative to controls on all three out-comes: doses taken in total (93.4% vs. 79.1%), days on which correct dose was taken (88.7% vs. 65.3%),and doses taken on schedule (78.8% vs. 55.3%) (P < 0.01). The implementation intention interventionmay be an easy-to-administer and effective means of promoting AED adherence.
� 2009 Elsevier Inc. All rights reserved.
1. Introduction
Approximately 60% of patients with epilepsy achieve full con-trol of their seizures with antiepileptic drugs (AEDs) [1]. Modernmedical treatment aims not only to prevent seizures but also toavoid negative effects on cognitive function and emotional, physi-cal, and general well-being. People with epilepsy are most likely toachieve these aims by the regular ingestion of the lowest dose ofmedication and the smallest number of AEDs necessary [2]. This,in turn, depends on their taking their medication as prescribed.However, research indicates that 30% to 50% of adults with epi-lepsy adhere poorly to their AED treatment schedules [3]. Adher-ence problems may be more common in epilepsy than in othermedical conditions [4]. Indeed, nonadherence has been identifiedas one of the most important causes of treatment failure in patientswith epilepsy [5],
It is possible that neurologists underestimate the extent ofadherence problems in their own clinical practice because patientsdo not admit failing to take their medication regularly to their doc-tor [6–8]. Seventy percent of patients with epilepsy state that theynever miss a dose [9,10], and the majority of patients admit tomissing only one or two doses per month [7]. However, studiesusing objective measures have revealed much higher rates of irreg-ular AED use. For instance, a study of 33,658 Medicaid recipients
ll rights reserved.
f Neurology, University ofsop Road, Sheffield S10 2JF,
ber).
showed that less than 80% of the AEDs required for full prescriptionadherence were picked up by participants in 26% of quarters dur-ing the observation period [11]. Two studies using the MedicalEvents Monitoring System (MEMS)—a pill bottle with an electroniccap that registers each occasion the bottle is opened—found thatonly 76% of doses were taken overall [12], and that 48% of patientstook one-third or fewer of the prescribed AED doses [6]. AED bloodlevels in the ‘‘therapeutic range” can offer false reassurance:although complete nonadherence can be detected using AED bloodlevels, there is no reliable correlation between the variability ofAED blood levels and irregular medication intake [12].
Poor adherence has been shown to affect important treatmentoutcomes: in the large Medicaid study mentioned above, the num-bers of hospital admissions, inpatient treatment days, and emer-gency room visits were higher in ‘‘noncompliant” quarters,resulting in increased total health care spending [11]. Another re-cent study based on more than 10,000 individuals with epilepsyfound that 39% picked up less than 80% of the medication requiredto cover their AED prescription and that hospital admission ratesand health care costs were higher in the nonadherent group [13].Other studies have shown that patients who miss doses may expe-rience additional seizures [7,14], and may be slower to achieve fullseizure control [15]. Lack of adherence to AED treatment has beenidentified as a potential precipitating cause in 31% of epileptic sei-zures for which ambulances were called and 13% of seizuresrequiring emergency hospital admission [16,17]. Patients whosemedication intake is irregular are also at increased risk of suddenunexplained death in epilepsy (SUDEP) [18]. After demographicand clinical covariates in the Medicaid study were controlled for,
I. Brown et al. / Epilepsy & Behavior 16 (2009) 634–639 635
the mortality risk in nonadherent patients was more than threetimes higher than that of adherent patients [11].
For these reasons, interventions that can improve AED treat-ment adherence are of great clinical interest. Previous studies havetested the effectiveness of a range of strategies including the sim-plification of AED regimens to no more than two doses per day[12], discussion of serum level measurements with patients [19],and shorter intervals between clinic visits [20]. The most intensiveintervention (incorporating counseling, medication containers,self-monitoring, and mailed reminders for prescriptions andappointments) improved adherence in the treatment group andhalved seizure frequency [14]. However, this sort of intensive sup-port may not be practical (especially for patients who also missclinic appointments) or easy to integrate into routine clinicalpractice.
This study tests whether a simple and self-administered work-sheet consisting of an implementation intention intervention (III)can increase AED adherence [21,22]. In this III, patients are askedto write down exactly when and where they will take their medi-cation, using the format of an if–then plan (‘‘If it is time X in place Yand I am doing Z, then I will take my pill dose”). IIIs target the prob-lem that holding a strong goal intention (‘‘I intend to take my tab-lets regularly”) does not guarantee goal achievement, becausepeople may fail to deal effectively with self-regulatory problemsduring goal striving. Evidence indicates that the act of writingdown an if–then plan can help to ‘‘automate” triggering of the in-tended behavior by sensitizing people to the cues they have writ-ten down [23]. This means that they become more likely tocomplete the intended activity when these cues are encountered[21]. IIIs reduce the burden of having to think about and rememberwhen to act by using environmental cues to trigger the desiredbehavior. IIIs are not merely a theoretical construct, but have al-ready been proven effective in promoting a range of health behav-iors in other areas of medicine including cancer screening, physicalactivity, and psychotherapy attendance [21].
2. Methods
2.1. Participants
Five consultant neurologists recruited patients consecutivelyfrom their outpatient clinic at the Royal Hallamshire Hospital inSheffield, United Kingdom, between January and June 2007. All pa-tients had a clinical diagnosis of epilepsy, were taking antiepilepticdrugs once or twice daily, and were attending the neurology clinicfor a follow-up visit. The diagnoses had been made by a consultantneurologist on the basis of clinical history and neurological exam-ination. Neuroimaging, EEG, or video/EEG telemetry had been car-ried out if clinically indicated. Patients were included only if theywere: taking at least one of the AEDs that could be dispensed inthe monitoring bottle once or twice daily (carbamazapine, clonaze-pam, gabapentin, lamotrigine, levetiracetam, oxcarbazepine, phe-nytoin, topiramate, or zonisamide); at least 16 years of age; ableto read and write English; and responsible for taking their ownmedication. Patients were excluded if they indicated that theywere already using a method of ensuring adherence that couldbe compromised if they took part in the study (e.g., weekly tabletdispensers), if they were receiving a diagnosis of epilepsy for thefirst time, or if they had learning difficulties. The study was ap-proved by the North Sheffield Ethics Review Committee, and all pa-tients gave written informed consent.
2.2. Study design
We randomized patients to the intervention or control groupusing a computerized random number generator (http://www.
randomizer.org). All patients completed a 14-page packet of self-report measures after they had seen the neurologist (baseline).
The intervention consisted of one additional intention imple-mentation worksheet (up to six items on two pages dependingon daily dose) which was included in the questionnaire packet thatpatients randomized to the intervention were asked to complete(i.e., patients in this group completed a total of 15 pages of ques-tionnaires rather than 14). This additional worksheet is shown inFig. 1.
Neither the neurologist nor the clinic or pharmacy staff wereaware of the patient’s group allocation. Following the proceduredescribed by Gollwitzer and Sheeran [21], the III asked participantsto specify environmental cues for tablet taking using the format ofan ”if–then” plan (i.e., participants wrote down exactly when andwhere they were intending to take their antiepileptic medicationevery day, and what they would be doing at the moment theywould take their AEDs).
All patients picked up a 1-month supply of one of their antiep-ileptic drugs in an electronic pill-monitoring bottle, which re-corded the number and timing of bottle openings (MEMS AardexLtd., Switzerland). The electronic monitoring caps can be con-nected to a personal computer that reads the data from the pillcaps’ microprocessor and generates a printout of the patient’s bot-tle openings.
One month after the initial clinic visit (follow-up), we ap-proached patients by letter and asked them to complete an addi-tional set of questionnaires and return the electronic pill-monitoring device. In line with previous studies using this methodof monitoring, we measured adherence using three different out-come measures counting each opening as a presumptive dose[12]: percentage of doses taken, percentage of days on which thecorrect number of doses was taken, and percentage of doses takenon schedule. We designated doses as having been taken on sche-dule if the MEMS bottle was opened within a ±3-hour target timewindow for each dose.
2.3. Self-report measures
We administered self-report measures to ensure the equiva-lence of control and experimental groups and to identify factorsthat could moderate the impact of the III. At baseline, participantscompleted the following measures: Theory of Planned Behaviour(TPB, 24 items, five scales), Brief Illness Perception Questionnaire(BIPQ, 9 items) [24], Multiple Ability Self-Report Questionnaire(MASQ, 38 items, five scales) [25], Hospital Anxiety and DepressionScale (HADS, 14 items, two scales) [26], Liverpool Seizure SeverityScale (LSSS, 12 items, one scale) [27,28], and a single-item self-esti-mate of the number of missed doses during the preceding month.At follow-up, participants completed the HADS, LSSS, and Prospec-tive and Retrospective Memory Questionnaire (PRMQ, 7 items, onescale) [29].
2.4. Statistical analysis
We conducted an analysis of variance (ANOVA) on the continu-ous, cognitive, clinical, and demographic variables measured atbaseline to ensure the equivalence of (1) participants who com-pleted both baseline and follow-up measures and participantswho completed the baseline measures only (representativenesscheck), and (2) participants in the intervention and control groups(randomization check). We used v2 tests to compare the respectivegroups on categorical variables. We used an ANOVA to determinethe effect of intervention on the three measures of AED adherence.
Because adherence is measured using continuous (0–100%)scales, moderated regression analysis is the appropriate test foridentifying possible interactions between condition (intervention
636 I. Brown et al. / Epilepsy & Behavior 16 (2009) 634–639
vs. control) and patient characteristics in predicting adherence[30], that is, for discovering what factors moderate the effects ofthe intervention on adherence outcomes. The three outcome mea-sures (doses taken, days on which the correct number of doses wastaken, and doses taken on schedule) were consolidated into a reli-able overall index of adherence (Cronbach’s a = 0.95) to reduce thenumber of statistical comparisons and the risk of making type 2 er-rors. We accomplished this consolidation by standardizing andthen averaging the three measures. For each potential moderatorvariable (i.e., all demographic, clinical, and cognitive variables),adherence was regressed on condition (coded 0 = control, 1 = inter-vention) and the focal moderator variable (standardized) at step 1,and the interaction term (condition multiplied by the standardizedmoderator variable) was added to the model at step 2. If the inter-action term improved the fit of the model according to the DR2 andDF statistics and the b coefficient for the interaction term was sig-nificant, moderation was demonstrated (i.e., the impact of theintervention on adherence varies according to scores on the mod-erator variable). The nature of the interaction was probed usingsimple slopes analysis. In particular, we computed the b coeffi-cients describing the effect of the intervention on adherence athigh levels of the moderator (conventionally defined as themean + 1SD) and at low levels of the moderator (mean � 1SD).Simple slopes analyses indicate precisely what types of patientsobtain greater (vs. less) benefit from the intervention.
3. Results
3.1. Participants
We recruited 81 patients with epilepsy from the outpatientclinic at baseline. Of these, 79 (98%) collected their medication inMEMS pill monitors from the hospital pharmacy; 2 patients inthe control group left the hospital without collecting their medica-tion. At follow-up, 11 individuals (14%) did not return their bottles(3 (4%) could not be contacted, and 8 (10%) said they had returnedtheir bottle but the bottles were not received at the pharmacy).Comparisons between participants who completed both the base-line and follow-up measures (n = 69) and participants who com-pleted the baseline questionnaire only (n = 12) did not reveal anyrelevant clinical or demographic differences. This indicates thatthe final sample (n = 69) satisfactorily represents the populationfrom which it was drawn.
Table 1 lists the demographic and clinical characteristics of thefinal sample. Comparisons between participants in the interven-tion and control conditions on demographic, clinical, and cognitivevariables revealed significant differences on only two variables:intervention participants reported more frequent symptoms andexpressed greater concern about epilepsy on the BIPQ comparedwith control participants (P < 0.02 for both variables). Type 2 errorcannot plausibly account for these findings (d = 0.03).
Table 1Demographic and clinical characteristics of the intervention and control groups(N = 69).
EducationNo secondary school 12 (40) 5 (23)Secondary school to 16 years 9 (30) 9 (41)Secondary school to 18 years 3 (10) 4 (18)Degree 6 (20) 4 (18) v2 = 0.52, ns
Mean age (SD) 41.9 (15.4) 44.10 (16.4) F = 0.33, ns
Last seizure <1 year 29 (78) 26 (81) v2 = 0.09, nsPolytreatment 22 (60) 14 (44) v2 = 1.70, nsComedication 21 (57) 11 (34) v2 = 3.46, nsDuration of seizures, years 20.3 (18.1) 18.6 (15.4) F = 0.19, nsHADS depression score 11.2 (4.6) 12.6 (3.3) F = 1.98, nsHADS anxiety score 15.8 (3.9) 16.8 (3.0) F = 1.23, ns
Note. There were missing data for 8, 17, and 3 cases for employment, education, andgender, respectively, because of incomplete records.
0
5
35
< 30
31-40
41-50
51-60
61-70
71-80
81-90
91-95
96-10
0
Control
Intervention
Perc
enta
ge o
f Par
ticip
ants
Percentage of Doses Taken on Schedule
Fig. 2. Distribution of doses taken on schedule in the control and implementationintention intervention conditions.
I. Brown et al. / Epilepsy & Behavior 16 (2009) 634–639 637
3.2. Intervention effects on AED adherence
Table 2 indicates that the intervention had a significant positiveimpact on all three measures of AED adherence. Relative to con-trols, participants in the intervention group took 18.1% more pre-scribed doses, took the correct number of doses on 35.9% moredays, and took 42.5% more of their doses in the correct 6-hour timewindow. Fig. 2 illustrates the impact of the intervention on takingAED medication on schedule. Whereas adherence exhibited a V-shaped curve among control participants (18.8% took <30% of doseson schedule, and 24.9% took >90% of doses on schedule), the distri-bution of responses for intervention participants is highly skewedin the direction of greater adherence (2.7% took <30% of doses onschedule, and 48.6% took >90% of doses on schedule) (Fig. 2).
Table 3Moderators of the effect of the implementation intention intervention on AEDadherence.
Step Variable entered b R2 ModelF
DR2 DF
Step 1 Step 2
1 Prospective memory 0.40a 0.61a
3.3. Moderators of the intervention effects on AED adherence
Moderated regression analyses that tested interactions betweencondition (intervention vs. control) and demographic, clinical, andcognitive variables revealed three significant effects: prospectivememory scores, participant-estimated number of missed doses atbaseline, and one item from the BIPQ (illness concern) each mod-erated the impact of the intervention on the overall index of AED
Table 2Effects of the implementation intention intervention on three measures of AEDadherence.
Measure Intervention Control F
Percentage of doses taken 93.4 (12.3) 79.1 (28.1) 7.90a
Percentage of days correct dosestaken
88.7 (15.1) 65.3 (35.6) 13.19b
Percentage of doses taken on schedule 78.8 (23.5) 55.3 (34.8) 10.98a
Overall adherence 0.35 (0.55) �0.40(1.15)
12.17a
Note. Values are means (SD). Overall adherence scores were generated by stan-dardizing and then averaging the three percentage measures.
a P < 0.01.b P < 0.001.
adherence. Simple slopes analyses showed that the implementa-tion intention intervention did not benefit adherence when partic-ipants had high scores on prospective memory (B = 0.31, SE = 0.27,P = 0.26), estimated having missed few doses at baseline (B = 0.27,SE = 0.36, P = 0.46), or were concerned about their epilepsy(B = 0.37, SE = 0.30, P = 0.22. Conversely, the III had a significant po-sitive impact on adherence when participants had poor memoryfor intended actions (B = 1.17, SE = 0.27, P < 0.001), when partici-pants reported having missed many doses at baseline (B = 1.49,SE = 0.43, P < 0.001), and among participants who were less con-cerned about their epilepsy (B = 1.14, SE = 0.30, P < 0.001). Thus,the III had particular benefit for AED adherence among participantswho were at greatest risk of poor adherence (see Table 3).
4. Discussion
4.1. Main findings
Although 85% of patients with epilepsy in our study stated thatthey considered it ‘‘very important” to take their AEDs regularly
Condition 0.39a 0.39a 0.31 14.78a
2 Prospectivememory � condition
�0.31b 0.36 5.07b 0.05 5.07b
1. Missed doses atbaseline
�0.27b �0.83b
Condition 0.43a 0.46a 0.22 9.48a
2. Misseddoses � condition
0.59c 0.26 7.58a 0.04 3.17b,c
1. Illness concern 0.17 0.36b
Condition 0.43a 0.40a 0.18 7.28a
2. Illnessconcern � condition
�0.39b 0.23 6.44b 0.05 4.08b
a P < 0.001.b P < 0.05.c One-tailed test.
638 I. Brown et al. / Epilepsy & Behavior 16 (2009) 634–639
and 90% indicated that they ‘‘definitely” intended to take theirmedication, our study confirmed that many patients do not adhereto their AED treatment schedules. In our control group, only 55.3%of all doses were taken on schedule, the correct number of doseswas taken on 65.3% of days, and only 79.1% of doses were takenoverall. These high rates of nonadherence are not unusual. In fact,they fall in the middle of the range suggested by studies in epilepsyusing other assessment methods [10], and are virtually identical torates obtained in a previous study using the same methodology[12].
The findings from the intervention arm of our study suggestthat implementation intentions can produce substantial improve-ments in AED adherence. In fact, the effect size for our intervention(d = 0.78) compares very favorably with that of adherenceimprovement initiatives for other diseases [31]. Importantly, theIII benefited mainly those participants who were less concernedabout their illness, who had missed taking their medication inthe past, and who had poor prospective memory—in other words,the patient groups at greatest risk of missing doses [32]. Thesefindings are line with previous studies demonstrating the positiveimpact of IIIs on action initiation and goal attainment [21], andwith evidence that this type of planning is especially advantageousfor people with cognitive and emotional difficulties [33,34]. Thelevels of the depression, anxiety, and memory functioning ob-served in this study document how serious the cognitive and emo-tional problems are in this particular patient group.
4.2. Limitations
This study has a number of limitations. Most importantly, thisfirst study of an III in patients with epilepsy did not include a per-iod of baseline monitoring prior to the intervention. This meansthat we cannot rule out with certainty that the different adherencelevels we found in the intervention and control groups are not re-lated to the documented differences in illness perceptions. In viewof our positive findings, future studies of IIIs involving a more com-plex design (including a baseline monitoring period) are nowjustified.
Next, tablet taking was monitored for only 1 month after the III.In view of the fact that the intervention aimed to make tablet tak-ing more ‘‘automatic” and that each successful implementation ofthe formed intention would strengthen the link between the envi-ronmental clues and the act of tablet taking, it would be reasonableto hypothesize that the intervention effect should be maintained.Indeed, IIIs in other areas have shown sustained improvementsin performance over periods of 1 year [35]. Nevertheless, thelong-term effectiveness of our intervention remains unproven. Afurther limitation of our findings to date is that our study wasnot designed or powered to detect differences in important out-comes such as the frequency of seizures, the percentage of patientsachieving full seizure control, and the frequency or severity of sideeffects. However, a link between increased adherence and reduc-tion in symptomatology has been suggested by other studies[7,14,36,37], and the present study also obtained a significant cor-relation between the extent of adherence and scores on the Liver-pool Seizure Severity Scale at follow-up (r = �0.23, P < 0.03, one-tailed).
Although the MEMS system is considered the best method foradherence research [38], and has been shown to be superior to pillcounting, the coefficient of variability of AED blood levels, and self-report measures in patients with epilepsy [12,39], it is associatedwith some important drawbacks. For instance, choosing the MEMSsystem for this study meant that patients taking large hydrophilictablets (such as the most important brands containing sodium val-proate) had to be excluded. However, there is every reason to sup-pose that the III used here would be effective in promoting tablet
taking no matter whether tablets are dispensed via a bottle, a blis-ter pack, or another type of dispenser.
Another potential drawback is the lack of additional biologicalmeasures of adherence. The use of MEMS pill bottles does notprove that tablets were actually taken, only that the medicationbottle was opened at a particular time. It would make sense in fu-ture studies to combine the MEMS system with a measure such ashair analysis that can provide biochemical evidence of medicationingestion over time [40].
5. Conclusion
Despite these limitations the present study confirms that non-adherence to AED treatment is a common problem. Our data sug-gest that cognitive or memory problems are a more significantreason for nonadherence than are unwillingness to take medica-tion and carelessness. The overwhelming majority of patients werekeen to take their medication regularly. In this context, our simpleintention implementation tool is a promising intervention and wasassociated with a large treatment effect in this randomized trial.Further research should look at treatment schedules with more fre-quent doses where adherence has been shown to be a much great-er problem [39]. Sufficiently powered future studies should includea period of baseline monitoring, be designed to provide furtherconfirmation that the intervention really is effective, and use alonger period of postintervention monitoring to demonstrate thatthe benefits are sustained. Future studies also need to prove thatimproved adherence really produces better seizure control, fewerside effects, and, ultimately, reduced mortality. It also remains tobe established how best to deliver the intervention: in the physi-cian’s office, at the pharmacy, or incorporated into medicationpackage design.
Acknowledgments
The authors thank the following organizations that providedfinancial support to purchase equipment and consumables for thisstudy: Janssen Cilag provided $6582, Epilepsy Action provided$2950, and the University of Sheffield provided $985. The fundershad no role in study design; the collection, analysis, and interpre-tation of data; the writing of the report; and the decision to submitthe article for publication. The authors also thank the staff and pa-tients at the Royal Hallamshire Hospital outpatient neurologyclinic, especially Dr. Grünewald, Dr. Howell, Dr. Hills, and Dr. Saras-ama; the inpatient pharmacy staff, Helen Bowler and Edna Web-ster; and Brian Parkinson for his design advice on the graphic forthe implementation intention.
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