Skip to main content
Erschienen in: Annals of Behavioral Medicine 3/2014

Open Access 01.12.2014 | Original Article

Does Electronic Monitoring Influence Adherence to Medication? Randomized Controlled Trial of Measurement Reactivity

verfasst von: Stephen Sutton, Ph.D., Ann-Louise Kinmonth, M.D., Wendy Hardeman, Ph.D., Dyfrig Hughes, Ph.D., Sue Boase, M.St, A. Toby Prevost, Ph.D., Ian Kellar, D.Phil., Jonathan Graffy, M.D, Simon Griffin, D.M, Andrew Farmer, D.M

Erschienen in: Annals of Behavioral Medicine | Ausgabe 3/2014

Abstract

Background

Electronic monitoring is recommended for accurate measurement of medication adherence but a possible limitation is that it may influence adherence.

Purpose

To test the reactive effect of electronic monitoring in a randomized controlled trial.

Methods

A total of 226 adults with type 2 diabetes and HbA1c ≥58 mmol/mol were randomized to receiving their main oral glucose lowering medication in electronic containers or standard packaging. The primary outcomes were self-reported adherence measured with the MARS (Medication Adherence Report Scale; range 5–25) and HbA1c at 8 weeks.

Results

Non-significantly higher adherence and lower HbA1c were observed in the electronic container group (differences in means, adjusting for baseline value: MARS, 0.4 [95 % CI −0.1 to 0.8, p = 0.11]; HbA1c (mmol/mol), −1.02 [−2.73 to 0.71, p = 0.25]).

Conclusions

Electronic containers may lead to a small increase in adherence but this potential limitation is outweighed by their advantages. Our findings support electronic monitoring as the method of choice in research on medication adherence. (Trial registration Current Controlled Trials ISRCT N30522359)

Background

Accurate measurement of adherence is a prerequisite for rigorous research on the patterns, determinants and consequences of medication adherence. Several different methods are used to measure adherence, including self-reports, pill counts, pharmacy records, plasma drug levels and electronic monitoring. Each of these methods has advantages and disadvantages [1, 2]. However, electronic monitoring (e.g., by container caps that record the date and time of each opening) is widely recommended as the method of choice in research on medication adherence [3, 4]. Assuming that there is a one-to-one correspondence between opening the container cap and ingesting the prescribed dose of tablets, electronic monitoring can provide detailed, precise and objective data on daily adherence over an extended period. In research on medication adherence, electronic monitoring is used to analyze dosing patterns [5], as the primary behavioral outcome in trials of medication adherence interventions [6] and as the gold standard comparator for validating other measures of adherence [7].
A possible limitation of electronic monitoring is that it may influence adherence. Switching research participants to electronic medication containers may disrupt established routines and lead to reduced adherence. On the other hand, participants may increase adherence because they know that the researchers will be able to tell how adherent they have been and they wish to appear maximally adherent or because the medication container acts as a novel visual prompt or cue.
There is increasing interest in the possible reactive effects of measurement on behavior and other outcomes [8, 9]. Where they occur, reactive effects on behavior are usually positive, i.e., where the behavior is desirable, measurement produces an increase. Whether positive or negative, measurement effects may threaten the validity of conclusions that are drawn from research studies. For example, in an uncontrolled single-group study designed to estimate the effect of an adherence intervention, the use of electronic monitoring to assess adherence may lead to a biased estimate of the intervention effect.
Denhaerynck and colleagues [10] identified six studies that examined the possible effect of electronic monitoring on medication adherence [1116]. The findings were inconclusive. Only two of the studies [15, 16] were randomized controlled trials. Elixhauser et al. [15] tested the effect of electronic blister packs compared with standard packaging over a period of 2–4 months in a sample of 93 psychiatric outpatients treated with lithium. Of four measures of medication adherence, only one showed a statistically significant difference between groups: the mean percentage of prescription refills obtained was 82 % in the electronically monitored group versus 69 % in the control group (p < 0.01). Wagner and Ghosh-Dastidar [16] compared monitoring using an electronic medication container with control in 120 HIV-positive patients on highly active antiretroviral therapy (HAART) over a period of 4 weeks. The adherence measure was the mean percentage of pills taken as prescribed based on a medication recall interview covering the previous 3 days. Adherence was high at baseline in both groups (electronic monitoring 93 %; control 92 %) and remained high at follow up with no difference between groups (91 % vs. 94 %, p = 0.73).
This paper reports a randomized controlled trial of the effect on adherence of dispensing medication in an electronic container that recorded the date and time of each opening (TrackCap, Aardex, Zurich, Switzerland) compared with standard packaging over an 8-week period, in a sample of patients with type 2 diabetes, using self-reported adherence as the primary behavioral outcome and HbA1c, a summary measure of recent glycemic (blood sugar) control, as the primary clinical outcome. As well as reporting the trial analysis, we examined the electronic monitoring data from the group allocated to using electronic containers to look for a pattern of daily adherence over the 8-week period that might indicate an effect of electronic monitoring. For example, a reduction in daily adherence over time would be consistent with a short-lived positive effect of electronic monitoring on adherence.

Methods

Participants

Patients were recruited from 13 primary care clinics in Oxfordshire, Buckinghamshire, Suffolk, Essex and Huntingdonshire (UK). Patients were eligible for inclusion if aged 18 years or over with type 2 diabetes of at least 3 months’ duration, able to give informed consent, currently taking any oral glucose-lowering agent and with a HbA1c ≥ 7.5 % (58 mmol/mol). (An HbA1c level of 7.5 % or above is widely used in clinical practice to indicate suboptimal blood glucose control which may in turn reflect inadequate adherence to glucose-lowering medication.) Those approached were deemed by their general practitioner to be appropriate for tight glycemic control and independent in medication taking.

Design

We used a parallel group trial design with 1:1 randomization to electronic container or standard packaging and follow up at 8 weeks (Fig. 1). Randomization was conducted before the baseline visit to the clinic (visit 1). Prior to visit 2 at 8 weeks, a second randomization was conducted to evaluate the effect of an adherence intervention delivered during that visit. The adherence intervention trial is reported elsewhere [17]; the present study focuses on the 8-week period up to and including visit 2.
Randomization of patients was carried out by the trial statistician independently of the trial co-ordination and clinic teams. A partial minimization procedure was used to dynamically adjust randomization probabilities to balance the baseline stratification variables (clinic, duration of diabetes, HbA1c result from the clinic record and self-reported adherence). After the participant had been allocated to group, both the participant and the clinic nurse who saw the participant became unblinded to the participant’s group allocation. However, the laboratory staff who measured HbA1c were blind to allocation. The London multi-centre research ethics committee reviewed and approved the protocol (06/MRE02/3).

Measures

The primary behavioral outcome was self-reported medication adherence measured by postal questionnaire sent 1 week before visit 2 (8 weeks) using the Medication Adherence Report Scale (MARS) developed by Horne and colleagues [18]. We used a five-item version of the MARS that asked about “using your diabetes medicines in the last month”, with item responses scored on a 5-point scale from “always true” [1] to “never true” [5]. Scores are summed to give a score ranging from 5 to 25 with a higher score indicating higher self-reported adherence (Table 1). In a pilot study, we found that this version of the MARS had slightly better psychometric properties than alternative versions (e.g., a version that did not specify a time frame) and compared with the similar questionnaire developed by Morisky and colleagues [19]. In the present study, MARS at follow up correlated significantly (r = 0.27; p = 0.02; n = 81) with electronically measured adherence (using only the data from the last 30 days of the 8-week follow-up period, to match the time frame of the self-report measure). Cronbach’s alpha was 0.82 at baseline and 0.67 at follow-up. The primary clinical outcome was HbA1c at 8 weeks.
Table 1
The Medication Adherence Report Scale (MARS) [14]
Many people find a way of using their medicines or tablets which suit them. This may differ from the instructions on the label or what their doctor has said. We would like to ask you a few questions about how you take your diabetes medicines (tablets).
The following statements show some ways in which other people have said they use their medicines.
For each of the statements below, please circle the number that best describes how you have taken your diabetes medicines in the last month.
1 I forget to take my diabetes medicines.
2 I alter the dose of my diabetes medicines.
3 I stop taking my diabetes medicines for a while.
4 I decide to miss out a dose of my diabetes medicines.
5 I take less of my diabetes medicines than instructed.
A secondary behavioral outcome measure, available only for participants in the group allocated to using electronic containers, was whether or not the correct number of doses of the main glucose lowering medication was taken as prescribed on each day over the 8-week follow-up period as measured by electronic monitoring.

Procedure

The clinic nurse identified eligible patients registered with the clinic. Eligible patients were sent a letter from the clinic giving details of the trial, and a questionnaire asking about basic demographics, medication regimen, medication adherence (including the MARS) and beliefs about taking diabetes medicines. Responders were telephoned by the clinic nurse to arrange a recruitment visit at the clinic (visit 1). In advance of the visit, patients eligible and willing to take part were allocated to be dispensed medication in the electronic medication-monitoring device (TrackCap, Aardex, Zurich, Switzerland) or in standard packaging. At the visit, informed consent was obtained, clinical data were collected, blood was taken, and questionnaires completed. For those patients allocated to the electronic container, the nurse briefly explained its purpose and use and gave them an information sheet explaining how to use it. The patient was told that the container counts the number of times that the lid is removed, that they should only open and close the container at the time a tablet is to be taken, and that they should only remove the amount of medication required at the time. The nurse then referred them to the clinic dispenser or pharmacist to dispense their usual prescription for metformin or alternative oral glucose lowering agent in the device. Other medication was dispensed as usual, in standard packaging. For those allocated to the other trial arm, the clinic dispenser or pharmacist dispensed their medication as usual, in standard packaging. Patients in both trial arms who used a pillbox to organize their medications were able to continue to use this method during the trial, with the proviso that those in the electronic container arm had to use the electronic containers for their main oral glucose lowering agent. A second visit was arranged in 8 weeks.
In advance of the 8-week visit (visit 2), patients were sent a questionnaire (which included the MARS) from the coordinating center and were centrally randomized to the adherence intervention or a standard care visit. The patients were not told their intervention group allocation before they attended the visit. At the beginning of the visit, patients in both groups had blood samples taken for measurement of HbA1c.

Analysis

The adherence intervention trial was planned to follow up 200 patients [20]. For the measurement effect trial, this sample size gave 80 % power to detect a difference of one point in the MARS self-reported adherence measure assuming a standard deviation of 2.5 (a small to medium effect size), and 80 % power to detect a 0.5 % difference in HbA1c assuming a standard deviation of 1.25 % (two-sided tests, 5 % significance level).
Analysis of the trial data was by intention to treat. Outcomes were analyzed using analysis of covariance adjusting for their corresponding baseline value to improve precision. The missing indicator method [21] was used so that patients with a missing baseline value could be included. Since the MARS score was skewed, the results were checked using a Mann–Whitney U test.
An exploratory subgroup analysis was conducted to examine whether the effect of using the electronic container was different among participants who used a pillbox to organize their medications compared with the rest of the sample. This was done by extending the analysis of covariance to incorporate a test for interaction between trial arm and pillbox use (yes/no). An additional analysis (suggested by one of the reviewers) was conducted to examine whether the effect of using the electronic container was different among participants who were less adherent at baseline (as measured by self-report) compared with those who were more adherent; this involved testing the interaction between trial arm and baseline adherence (treated as continuous).
Generalized estimating equations (GEE), which take account of the dependence of the observations within individuals, were used to analyze change over time in the proportion of participants in the electronic container group who were adherent according to the electronic monitoring data on each day over the 8-week follow-up period. The analysis specified an autoregressive (order 1) correlation matrix and robust standard errors and tested linear and quadratic effects of time. Analyses were conducted in Stata 11 and PASW 16.

Results

In the 13 participating clinics, 797 registered patients with type 2 diabetes potentially meeting the inclusion criteria were identified, of whom 273 responded as eligible and 226 were randomized (Fig. 1). The two groups were similar at baseline (Table 2). On average, participants were in their early 60s, had had diabetes for less than 10 years, were taking six medications daily and reported high medication adherence (scored 24 out of 25 on the MARS).
Table 2
Baseline characteristics of trial participants
 
Electronic container (n = 111)
Standard packaging (n = 115)
All participants (n = 226)
% Male (n)
62.2 % (69)
67.8 % (78)
65.0 %(147)
Age (years)
63.7 (11.2)
62.8 (10.6)
63.2 (10.9)
IMD deprivation score (0–100)a,d
10.6 (6.3)
10.0 (6.8)
10.3 (6.6)
Duration of diabetes (years)
6.5 (4.8)
7.1 (5.4)
6.8 (5.1)
Weight (kg)e
94.5 (20.2)
97.8 (21.5)
96.2 (20.9)
% Treated with Metformin (n)f
70.0 (70)
77.3 (85)
73.8 (155)
Metformin daily dose (mg)b,g
1,767 (609)
1,695 (601)
1,728 (604)
Total number of medications taken/daye
5.6 (2.6)
6.0 (2.3)
5.8 (2.5)
Self-reported adherence (MARS,c range 5–25)h
23.8 (1.8)
23.4 (3.0)
23.6 (2.5)
HbA1c (%)i
8.31 (1.28)
8.35 (1.20)
8.33 (1.24)
HbA1c (mmol/mol)i
67.33 (13.99)
67.77 (13.12)
67.55 (13.55)
Values are mean (SD) unless otherwise stated
aIndex of Multiple Deprivation
bFor those treated with Metformin
cMedication Adherence Report Scale
Number of missing values: d2, e15, f16, g72, h25, i32
Primary outcome data were available for 81 % and 85 % of those randomized for MARS and HbA1c, respectively. Missing outcome data were due to failure to return the questionnaire for MARS and non-attendance at visit 2 for HbA1c. In the main trial analysis, both outcomes showed a difference between groups, with higher adherence and lower HbA1c in the electronic container group, but the differences were not statistically significant (p = 0.11 and p = 0.25, respectively; Table 3). This was confirmed by the results of the non-parametric test. There was no evidence that the effect of using an electronic container was different among the 23 % of participants who used a pillbox to organize their medications; the tests for interaction between trial arm and pillbox use were not statistically significant for self-reported adherence (p = 0.11) or HbA1c (p = 0.71). There was also no evidence for an interaction between trial arm and baseline adherence on adherence at outcome (p = 0.93).
Table 3
Outcomes 8 weeks after randomization to electronic container or standard packaging
 
Electronic container
Standard packaging
Estimated effecta
(95 % CI)
p value
Baseline
8 weeks
Baseline
8 weeks
Self-reported adherenceb
23.8 (1.8)c
24.2 (1.1)d
23.6 (2.5)e
23.8 (1.9)f
0.4 (−0.1 to 0.8)g
0.11
HbA1c (%)
HbA1c (mmol/mol)
8.31 (1.28)h
67.33 (13.99)h
8.22 (1.30)i
66.34 (14.21)i
8.36 (1.21)j
67.87 (13.23)j
8.39 (1.16)j
68.20 (12.68)j
−0.09 (−0.25 to 0.07)k
−1.02 (−2.73 to 0.71)k
0.25
Values are Mean (SD) unless otherwise stated
aAdjusted for baseline value
bMedication Adherence Report Scale (MARS)
Number of missing values: c36, d25, e24, f17, g42, h21, i20, j13, k33
Within the electronic container arm, Fig. 2 appears to show a small reduction in the proportion adherent over time as measured by electronic monitoring. However, neither linear (p = 0.09) nor quadratic (p = 0.08) effects of time were statistically significant in the GEE analysis.

Discussion

The trial analysis showed no statistically significant difference between electronic medication container and standard packaging on either outcome measure at 8 weeks. The confidence intervals around the estimates of the effect suggest that using an electronic container did not reduce adherence but may have increased it slightly.
A null effect may mask a negative effect in one subgroup offset by a positive effect in another subgroup. We explored the possibility that the effect differed in those participants who used a pillbox to organize their tablets and whose medication-taking routine may have been disrupted by switching to an electronic container for their main glucose lowering agent, but found no evidence for this.
The data from the electronic container group appeared to show a small initial increase in adherence that reduced gradually over time, which could be interpreted as being consistent with the trial results. However, there was no significant effect of time. This differed from the pattern observed in a study of HIV patients which suggested that adherence reduced over time and stabilized after 40 days [22].
Limitations of the present study concern the outcome measures and the high level of adherence at baseline, as reported in a previous trial [16]. HbA1c is affected by a number of factors in addition to adherence to hypoglycemic medication, and 8 weeks may not be a sufficiently long period to observe a change in HbA1c arising from a change in adherence. The MARS self-report measure referred to “diabetes medicines in the last month”, so this would not have detected an initial short-term increase or decrease in adherence. The MARS score correlated significantly (r = 0.27) with electronically measured adherence, providing some evidence of validity, but this was lower than the correlation of 0.42 reported for a ten-item version of the scale in a sample of asthmatic adults [23].
For the purposes of both the adherence intervention study and the measurement effect substudy reported here, we aimed to recruit a sample of patients with type 2 diabetes who showed suboptimal blood glucose control. The findings of the intervention trial [17] showed that it was possible to increase adherence in this group, though this was with adherence measured by electronic monitoring not by self-report. However, self-reported adherence was high at baseline in the present study and this may have reduced the chances of detecting an increase in adherence (though not of detecting a reduction in adherence).
Further investigation of this possible ceiling effect revealed that only 39 % of participants had the maximum score of 25 on the MARS at baseline, and that it was theoretically possible for the present study to have shown a statistically significant positive effect on self-reported adherence. An additional exploratory analysis showed that there was no evidence that the effect of using an electronic container was different in patients who were less adherent at baseline (as measured by self-report) compared with those who were more adherent. Nevertheless, future studies should aim to recruit less adherent patients.
Strengths of the present study include the trial design, with central randomization, concealment of group allocation, baseline comparability of groups and an acceptable proportion (>0.8) of those randomized providing primary outcome data.
Unlike other measures of adherence, electronic monitoring can provide detailed, precise and objective data on daily adherence over an extended period. The findings of this trial suggest that electronic containers may lead to a small increase in adherence but this potential limitation is outweighed by their advantages. Although other problems have been identified [10] and need further investigation, our findings support electronic monitoring as the method of choice in research on medication adherence.

Acknowledgments

The trial was supported by the Medical Research Council (G0500267 to AF, ALK, WH, ATP, SG and SS), and through NHS support funding. Additional funding was provided by: the NIHR Oxford Biomedical Research Centre (AF); NIHR Senior Investigator awards (SS, ALK); an NIHR training fellowship (WH); and an NIHR Programme Grant (RP-PG-0606-1259 to SG). The clinics and pharmacies that took part in the trial are listed at http://​www.​biomedcentral.​com/​1471-2296/​13/​30

Authors’ Statement of Conflict of Interest and Adherence to Ethical Standards

Authors Stephen Sutton, Ann-Louise Kinmonth, Wendy Hardeman, Dyfrig Hughes, Sue Boase, A Toby Prevost, Ian Kellar, Jonathan Graffy, Simon Griffin and Andrew Farmer declare that they have no conflict of interest. All procedures, including the informed consent process, were conducted in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2000.
Open Access This article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.

Unsere Produktempfehlungen

e.Med Interdisziplinär

Kombi-Abonnement

Für Ihren Erfolg in Klinik und Praxis - Die beste Hilfe in Ihrem Arbeitsalltag

Mit e.Med Interdisziplinär erhalten Sie Zugang zu allen CME-Fortbildungen und Fachzeitschriften auf SpringerMedizin.de.

e.Med Neurologie & Psychiatrie

Kombi-Abonnement

Mit e.Med Neurologie & Psychiatrie erhalten Sie Zugang zu CME-Fortbildungen der Fachgebiete, den Premium-Inhalten der dazugehörigen Fachzeitschriften, inklusive einer gedruckten Zeitschrift Ihrer Wahl.

e.Med Psychiatrie

Kombi-Abonnement

Mit e.Med Psychiatrie erhalten Sie Zugang zu CME-Fortbildungen des Fachgebietes Psychiatrie, den Premium-Inhalten der psychiatrischen Fachzeitschriften, inklusive einer gedruckten Zeitschrift Ihrer Wahl.

Literatur
2.
Zurück zum Zitat Berg KM, Arnsten JH. Practical and conceptual challenges in measuring antiretroviral adherence. J Acquir Immune Defic Syndr. 2006; 43: S70-87.CrossRef Berg KM, Arnsten JH. Practical and conceptual challenges in measuring antiretroviral adherence. J Acquir Immune Defic Syndr. 2006; 43: S70-87.CrossRef
3.
Zurück zum Zitat Greenlaw SM, Yentzer BA, O’Neill JL, Balkrishnan R, Feldman SR. Assessing adherence to dermatology treatments: A review of self-report and electronic measures. Skin Res Technol. 2010; 16: 253-258.PubMedCrossRef Greenlaw SM, Yentzer BA, O’Neill JL, Balkrishnan R, Feldman SR. Assessing adherence to dermatology treatments: A review of self-report and electronic measures. Skin Res Technol. 2010; 16: 253-258.PubMedCrossRef
4.
Zurück zum Zitat Saini SD, Schoenfeld P, Kaulback K, Dubinsky MC. Effect of medication dosing frequency on adherence in chronic diseases. Am J Manag Care. 2009; 15: e22-e33.PubMed Saini SD, Schoenfeld P, Kaulback K, Dubinsky MC. Effect of medication dosing frequency on adherence in chronic diseases. Am J Manag Care. 2009; 15: e22-e33.PubMed
5.
Zurück zum Zitat Vrijens B, Vincze G, Kristanto P, Urquhart J, Burnier M. Adherence to prescribed antihypertensive drug treatments: Longitudinal study of electronically compiled dosing histories. Br Med J. 2008; 336: 1114-1117.CrossRef Vrijens B, Vincze G, Kristanto P, Urquhart J, Burnier M. Adherence to prescribed antihypertensive drug treatments: Longitudinal study of electronically compiled dosing histories. Br Med J. 2008; 336: 1114-1117.CrossRef
6.
Zurück zum Zitat Gerin W, Tobin JN, Schwartz JE, Chaplin W, Rieckmann N, et al. The medication Adherence and Blood Pressure Control (ABC) trial: A multi-site randomized controlled trial in a hypertensive, multi-cultural, economically disadvantaged population. Contemp Clin Trials. 2007; 28: 459-71.PubMedCrossRef Gerin W, Tobin JN, Schwartz JE, Chaplin W, Rieckmann N, et al. The medication Adherence and Blood Pressure Control (ABC) trial: A multi-site randomized controlled trial in a hypertensive, multi-cultural, economically disadvantaged population. Contemp Clin Trials. 2007; 28: 459-71.PubMedCrossRef
7.
Zurück zum Zitat Zeller A, Schroeder K, Peters TJ. An adherence self-report questionnaire facilitated the differentiation between nonadherence and nonresponse to antihypertensive treatment. J Clin Epidemiol. 2008; 61: 282-8.PubMedCrossRef Zeller A, Schroeder K, Peters TJ. An adherence self-report questionnaire facilitated the differentiation between nonadherence and nonresponse to antihypertensive treatment. J Clin Epidemiol. 2008; 61: 282-8.PubMedCrossRef
8.
Zurück zum Zitat French DP, Sutton S. Reactivity of measurement in health psychology: How much of a problem is it? What can be done about it? Br J Health Psychol. 2010; 15: 453-68.PubMedCrossRef French DP, Sutton S. Reactivity of measurement in health psychology: How much of a problem is it? What can be done about it? Br J Health Psychol. 2010; 15: 453-68.PubMedCrossRef
9.
Zurück zum Zitat McCambridge J, Butor-Bhavsar K, Witton J, Elbourne D. Can research assessments themselves cause bias in behaviour change trials? A systematic review of evidence from Solomon 4-group studies. PloS One. 2011; 6: e25223.PubMedCentralPubMedCrossRef McCambridge J, Butor-Bhavsar K, Witton J, Elbourne D. Can research assessments themselves cause bias in behaviour change trials? A systematic review of evidence from Solomon 4-group studies. PloS One. 2011; 6: e25223.PubMedCentralPubMedCrossRef
10.
Zurück zum Zitat Denhaerynck K, Schäfer-Keller P, Young J, Steiger J, Bock A, De Geest S. Examining assumptions regarding valid electronic monitoring of medication therapy: Development of a validation framework and its application on a European sample of kidney transplant patients. BMC Med Res Methodol. 2008; 8: 5.PubMedCentralPubMedCrossRef Denhaerynck K, Schäfer-Keller P, Young J, Steiger J, Bock A, De Geest S. Examining assumptions regarding valid electronic monitoring of medication therapy: Development of a validation framework and its application on a European sample of kidney transplant patients. BMC Med Res Methodol. 2008; 8: 5.PubMedCentralPubMedCrossRef
11.
Zurück zum Zitat Cramer JA, Ouellette VL, Mattson RH. Effect of microelectronic observation on compliance. Epilepsia. 1990; 31: 617-618. Cramer JA, Ouellette VL, Mattson RH. Effect of microelectronic observation on compliance. Epilepsia. 1990; 31: 617-618.
12.
Zurück zum Zitat Matsui D, Hermann C, Klein J, Berkovitch M, Olivieri N, Koren G. Critical comparison of novel and existing methods of compliance assessment during a clinical trial of an oral iron chelator. J Clin Pharmacol. 1994; 34: 944-949.PubMedCrossRef Matsui D, Hermann C, Klein J, Berkovitch M, Olivieri N, Koren G. Critical comparison of novel and existing methods of compliance assessment during a clinical trial of an oral iron chelator. J Clin Pharmacol. 1994; 34: 944-949.PubMedCrossRef
13.
Zurück zum Zitat Yeung M, O’Connor SA, Parry DT, Cochrane GM. Compliance with prescribed drug therapy in asthma. Respir Med. 1994; 88: 31-35.PubMedCrossRef Yeung M, O’Connor SA, Parry DT, Cochrane GM. Compliance with prescribed drug therapy in asthma. Respir Med. 1994; 88: 31-35.PubMedCrossRef
14.
Zurück zum Zitat Bertholet N, Favrat B, Fallab-Stubi CL, Brunner HR, Burnier M. Why objective monitoring of compliance is important in the management of hypertension. J Clin Hypertens (Greenwich). 2000; 2: 258-262. Bertholet N, Favrat B, Fallab-Stubi CL, Brunner HR, Burnier M. Why objective monitoring of compliance is important in the management of hypertension. J Clin Hypertens (Greenwich). 2000; 2: 258-262.
15.
Zurück zum Zitat Elixhauser A, Eisen SA, Romeis JC, Homan SM. The effects of monitoring and feedback on compliance. Med Care. 1990; 28: 882-93.PubMedCrossRef Elixhauser A, Eisen SA, Romeis JC, Homan SM. The effects of monitoring and feedback on compliance. Med Care. 1990; 28: 882-93.PubMedCrossRef
16.
Zurück zum Zitat Wagner GJ, Ghosh-Dastidar B. Electronic monitoring: Adherence assessment or intervention? HIV Clin Trials. 2002; 3: 45-51.PubMedCrossRef Wagner GJ, Ghosh-Dastidar B. Electronic monitoring: Adherence assessment or intervention? HIV Clin Trials. 2002; 3: 45-51.PubMedCrossRef
17.
Zurück zum Zitat Farmer A, Hardeman W, Hughes D, et al. An explanatory randomised controlled trial of a nurse-led, consultation-based intervention to support patients with adherence to taking glucose lowering medication for type 2 diabetes. BMC Fam Pract. 2012; 13: 30.PubMedCentralPubMedCrossRef Farmer A, Hardeman W, Hughes D, et al. An explanatory randomised controlled trial of a nurse-led, consultation-based intervention to support patients with adherence to taking glucose lowering medication for type 2 diabetes. BMC Fam Pract. 2012; 13: 30.PubMedCentralPubMedCrossRef
18.
Zurück zum Zitat Mora P, Berkowitz A, Contrada RJ, et al. Factor structure and longitudinal invariance of the Medication Adherence Report Scale—Asthma. Psychol Health. 2011; 26: 713-727.PubMedCrossRef Mora P, Berkowitz A, Contrada RJ, et al. Factor structure and longitudinal invariance of the Medication Adherence Report Scale—Asthma. Psychol Health. 2011; 26: 713-727.PubMedCrossRef
19.
Zurück zum Zitat Morisky DE, Green LW, Levine DM. Concurrent and predictive validity of a self-reported measure of medication adherence. Med Care. 1986; 24: 67-74.PubMedCrossRef Morisky DE, Green LW, Levine DM. Concurrent and predictive validity of a self-reported measure of medication adherence. Med Care. 1986; 24: 67-74.PubMedCrossRef
20.
Zurück zum Zitat Farmer AJ, Prevost AT, Hardeman W, et al. for The Support and Advice for Medication Trial Group. Protocol for SAMS (Support and Advice for Medication Study): A randomised controlled trial of an intervention to support patients with type 2 diabetes with adherence to medication. BMC Fam Pract. 2008; 9: 20.PubMedCentralPubMedCrossRef Farmer AJ, Prevost AT, Hardeman W, et al. for The Support and Advice for Medication Trial Group. Protocol for SAMS (Support and Advice for Medication Study): A randomised controlled trial of an intervention to support patients with type 2 diabetes with adherence to medication. BMC Fam Pract. 2008; 9: 20.PubMedCentralPubMedCrossRef
21.
Zurück zum Zitat White IR, Thompson SG. Adjusting for partially missing baseline measurements in randomized trials. Stat Med. 2005; 24: 993-1007.PubMedCrossRef White IR, Thompson SG. Adjusting for partially missing baseline measurements in randomized trials. Stat Med. 2005; 24: 993-1007.PubMedCrossRef
22.
Zurück zum Zitat Deschamps AE, Van Wijngaerden E, Denhaerynck K, Vandamme A-M, De Geest S. Use of electronic monitoring induces a 40-day intervention effect in HIV patients. J Acquir Immune Defic Syndr. 2006; 43: 247-249.PubMedCrossRef Deschamps AE, Van Wijngaerden E, Denhaerynck K, Vandamme A-M, De Geest S. Use of electronic monitoring induces a 40-day intervention effect in HIV patients. J Acquir Immune Defic Syndr. 2006; 43: 247-249.PubMedCrossRef
23.
Zurück zum Zitat Cohen JL, Mann DM, Wisnivesky JP, et al. Assessing the validity of self-reported medication adherence among inner-city asthmatic adults: The Medication Adherence Report Scale for Asthma. Ann Allergy Asthma Immunol. 2009; 103: 325-331.PubMedCrossRef Cohen JL, Mann DM, Wisnivesky JP, et al. Assessing the validity of self-reported medication adherence among inner-city asthmatic adults: The Medication Adherence Report Scale for Asthma. Ann Allergy Asthma Immunol. 2009; 103: 325-331.PubMedCrossRef
Metadaten
Titel
Does Electronic Monitoring Influence Adherence to Medication? Randomized Controlled Trial of Measurement Reactivity
verfasst von
Stephen Sutton, Ph.D.
Ann-Louise Kinmonth, M.D.
Wendy Hardeman, Ph.D.
Dyfrig Hughes, Ph.D.
Sue Boase, M.St
A. Toby Prevost, Ph.D.
Ian Kellar, D.Phil.
Jonathan Graffy, M.D
Simon Griffin, D.M
Andrew Farmer, D.M
Publikationsdatum
01.12.2014
Verlag
Springer US
Erschienen in
Annals of Behavioral Medicine / Ausgabe 3/2014
Print ISSN: 0883-6612
Elektronische ISSN: 1532-4796
DOI
https://doi.org/10.1007/s12160-014-9595-x

Weitere Artikel der Ausgabe 3/2014

Annals of Behavioral Medicine 3/2014 Zur Ausgabe