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Original Research: RESPIRATORY CARE |

Accurate Assessment of AdherenceAssessment of Adherence to Nebulizer Treatments: Self-Report and Clinician Report vs Electronic Monitoring of Nebulizers FREE TO VIEW

Tracey Daniels, MSc; Lynne Goodacre, PhD; Chris Sutton, PhD; Kim Pollard, BSc(hons); Steven Conway, MBBS; Daniel Peckham, MD
Author and Funding Information

From the York Adult Cystic Fibrosis Unit (Ms Daniels), York Teaching Hospital Foundation NHS Trust, York Hospital, York; School of Public Health and Clinical Sciences (Drs Goodacre and Sutton), University of Central Lancashire, Preston; and Leeds Regional Adult Cystic Fibrosis Unit (Ms Pollard and Drs Conway and Peckham), The Leeds Teaching Hospitals NHS Trust, St James’s Hospital, Leeds, England.

Correspondence to: Tracey Daniels, MSc, York Adult Cystic Fibrosis Unit, Ward 34, York Teaching Hospital Foundation NHS Trust, York Hospital, Wigginton Rd, York YO31 8HE, England; e-mail: traceydaniels1@nhs.net


Parts of this article have been presented in abstract form (Hughes TE, Pollard K, Black A, Conway SP, Peckham DG. Reported and objective adherence to nebulised therapy in adults with cystic fibrosis [Abstract S64]. J Cyst Fibros. 2008;7[suppl 2]:256 and Hughes TE, Pollard K, Goodacre L, Sutton C, Conway SP, Peckham D. Variation in bias of self-reported adherence to nebulizers in adults with cystic fibrosis [Abstract S92]. J Cyst Fibros. 2009;8[suppl 2]:370).

Funding/Support: The authors have reported to CHEST that no funding was received for this study.

Reproduction of this article is prohibited without written permission from the American College of Chest Physicians (http://www.chestpubs.org/site/misc/reprints.xhtml).


© 2011 American College of Chest Physicians


Chest. 2011;140(2):425-432. doi:10.1378/chest.09-3074
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Published online

Background:  People with cystic fibrosis have a high treatment burden. While uncertainty remains about individual patient level of adherence to medication, treatment regimens are difficult to tailor, and interventions are difficult to evaluate. Self- and clinician-reported measures are routinely used despite criticism that they overestimate adherence. This study assessed agreement between rates of adherence to prescribed nebulizer treatments when measured by self-report, clinician report, and electronic monitoring suitable for long-term use.

Methods:  Seventy-eight adults with cystic fibrosis were questioned about their adherence to prescribed nebulizer treatments over the previous 3 months. Self-report was compared with clinician report and stored adherence data downloaded from the I-Neb nebulizer system. Adherence measures were expressed as a percentage of the prescribed regimen, bias was estimated by the paired difference in mean (95% CI) patient and clinician reported and actual adherence. Agreement between adherence measures was calculated using intraclass correlation coefficients (95% CI), and disagreements for individuals were displayed using Bland-Altman plots.

Results:  Patient-identified prescriptions matched the medical record prescription. Median self-reported adherence was 80% (interquartile range, 60%-95%), whereas median adherence measured by nebulizer download was 36% (interquartile range, 5%-84.5%). Nine participants overmedicated and underreported adherence. Median clinician report ranged from 50% to 60%, depending on profession. Extensive discrepancies between self-report and clinician report compared with nebulizer download were identified for individuals.

Conclusions:  Self- and clinician-reporting of adherence does not provide accurate measurement of adherence when compared with electronic monitoring. Using inaccurate measures has implications for treatment burden, clinician prescribing practices, cost, and accuracy of trial data.

Figures in this Article

People with cystic fibrosis (CF) may require large amounts of medication to control symptoms and slow disease progression.1 Studies suggest that the median number of daily treatments prescribed for patients with CF is seven (interquartile range [IQR], 5-9).2 Administering these treatments takes a mean time of 108 min/d (SD, 58 min/d),2 posing significant challenges in terms of scheduling treatment around education, work, and families.3 In view of this treatment burden, poor adherence has been identified as the greatest cause of treatment failure,4 and monitoring adherence to prescribed therapy has been identified as a priority.5

Variable rates of adherence have been reported depending on the treatment: 65% of all prescribed treatment6; 80% to 95% with oral and IV antibiotics7; 65% to 80% with nebulized therapy and pancreatic enzymes8; 40% with airway clearance9; and 40% to 55% with vitamins, dietary changes, exercise, and physiotherapy.8 These studies used self-report, physician report, or medical record review, which are subject to reporting bias, errors, and intentional manipulation and are likely to overestimate the extent of adherence.4,10,11

Challenges in measuring adherence have led to uncertainty about accurate levels of adherence to medication.3 Daily diary methods have been considered more accurate,12 but for long-term use, they are costly in terms of clinician time and increased treatment burden. All adherence measures to date have struggled to measure competence (effective device use) and contrivance (intentional suboptimal device use), both of which contribute to a comprehensive assessment of adherence.13,14

Clinician assessment of patient adherence plays an important role in the care of patients with CF. Perceptions of adherence may affect the types of treatment offered, such as home vs hospital IV antibiotics, through to suitability for transplantation. Many members of the multidisciplinary team are involved in clinical decision making and express different opinions with regard to adherence. These differences have not been quantified in the literature.

Devices incorporating electronic monitoring may improve adherence data accuracy and reliability15,16 and are established in terms of their psychometric properties.4 New nebulizer technologies, such as the I-Neb adaptive aerosol delivery (AAD) nebulizer system (Philips Respironics; Chichester, England), overcome many limitations of older electronic monitoring methods. The I-Neb stores data, such as treatment date, time, duration, and completeness of dose, that can be downloaded to provide accurate and detailed longitudinal adherence data.17,18

Such improved accuracy provides new opportunities to assess adherence to nebulizer therapy. A number of abstracts and articles demonstrate the ability of I-Neb technology to store detailed adherence data17-22 and to compare adherence levels between those who received early and late diagnoses of CF.20 Two studies using electronic monitoring through devices such as the I-Neb have demonstrated lower and more variable adherence levels to nebulizer therapy in a pediatric CF population21 and in an adult CF population22 than do studies using reported measures.7,9,12,15,23 To our knowledge, no studies have directly compared standard measurement techniques, such as self-report and clinician report, with electronic monitoring to establish the accuracy with which patients with CF report their adherence to nebulizer therapy. Studies in diseases other than CF show an overestimation of adherence when reported adherence is compared with electronic monitoring methods.24 We, therefore, hypothesized that self-report and clinician report would overestimate adherence to nebulizer therapy when compared with electronic monitoring data using the I-Neb.

The aim of this study was first to assess rates of adherence to all prescribed nebulizer treatments in adults with CF using three measures: self-report, clinician report, and electronic monitoring with the I-Neb. The second aim was to compare these rates of adherence to determine the nature, extent, and direction of discrepancies among these measures. The relationship between adherence levels and participant demographics also was assessed.

A cross-sectional comparison of three approaches to measuring adherence (self-report, clinician report, and electronic monitoring through the I-Neb) was undertaken at the Leeds regional CF unit in England. Ethical approval for this study was obtained from the relevant National Health Service local research ethics committee.

Participants

At the time the study commenced, 105 adults with CF25 were using the I-Neb for all maintenance nebulizer treatments (Fig 1). All were asked to bring their I-Neb to their routine outpatient clinic appointment. A sample size of approximately 75 was required in order to give reasonable precision in the estimation of the intraclass correlation coefficient (ICC) between any pair of assessments of adherence. The specific criterion used was to give a 95% CI for the ICC no wider than 0.3, assuming the true ICC to be at least 0.6.

Recruitment

Discussion of adherence to nebulizer therapy is a routine event at clinic visits. Ethical approval, therefore, was granted to ask participants to identify their prescribed nebulizer regimen and to assess adherence to this regimen, using self-report, prior to the study information being given and consent being obtained. Providing information about the study before asking these questions had the potential to influence the answers given. Participation is detailed in Figure 1.

Self-Reported Adherence

Participants were asked to identify their prescribed nebulizer regimen by medication, dose, and frequency. They were then asked two questions about their adherence to nebulizer therapy: (1) “On an average week, how often do you take your…” (each medication the patient identified was questioned separately) and (2) “Overall, what percentage of your nebulizers do you think you have taken over the last 3 months?” Each estimate was converted into a percentage of nebulizer treatments taken relative to those prescribed over a 3-month period. The rationale for using two forms of questioning was to explore potential variation in reporting according to the wording of the question.

Clinician-Assessed Adherence

Many members of the multidisciplinary team are involved in clinical decision making, expressing varying opinions regarding adherence. To assess this variation, estimates of adherence for each participant were obtained from the physician, dietician, physiotherapist, ward nurse (responsible for inpatient care), liaison nurse (responsible for outpatient and home care), and pharmacist. The clinicians were asked to complete a questionnaire assessing adherence for each participant over the preceding 3 months. The questionnaire asked the clinician to estimate how often in an average week each medication had been taken by the participant and overall what percentage of nebulizer treatments had been taken. These clinicians had regular contact with the participants, and all had a minimum of 5 years experience in CF care within this cohort of patients. They were blinded to data from the I-Neb and to all other reports of adherence.

Electronic Monitoring of Adherence

The I-Neb uses vibrating mesh technology and AAD to decrease treatment times and optimize deposition. Vibrating mesh technology aerosolizes medication using a vibrating horn that forces liquid through holes in a mesh to produce homogenous particles. AAD analyzes pressure changes related to airflow and delivers timed pulses of aerosol, during inspiration only, until the preset dose is delivered, thus overcoming issues of presumptive dosing. Inhalation technique is assessed, and the device will not operate unless correctly set up and used at the appropriate angle, thereby minimizing the need for skill in carrying out treatments and dismissing issues of competence and contrivance. The I-Neb is currently available for long-term clinical use in the United Kingdom, Spain, Germany, and Italy.

The I-Neb was used for this study because it gives accurate and detailed adherence data. During analysis of downloaded data in trials, no corrupt data were found.18 Adherence data were downloaded and data for the 3 months prior to the study visit were analyzed, ensuring that the reported data and downloaded data assessed the same time period. The total number of completed nebulizer doses as recorded by the I-Neb was noted and adjusted to allow for agreed missed doses. Adherence to a dose was defined as a complete dose taken at any time during the day.

Demographic Data and Statistical Methods

Sex, age, FEV1 (%), FVC (%), oxygen saturation as measured by pulse oximetry (%), BMI (kg/m2), prescribed nebulizer treatments (type and frequency), and agreed missed doses (eg, omission of nebulized antibiotics during a course of IV antibiotic therapy) were recorded from the participant’s electronic medical record (Egton Medical Information Systems Ltd; Leeds, England).

Data analyses were undertaken using SPSS, version 15 (SPSS Inc; Chicago, Illinois) and R, version 2.7.0 (The R Foundation for Statistical Computing; Vienna, Austria). Metric data were expressed as median (IQR) because most variables demonstrated substantial skewness, and categorical data were expressed as frequencies and percentages. Adherence measures were expressed as a percentage of the prescribed regimen. Patient characteristics affecting the downloaded adherence level were explored with multiple linear regression using a forward selection strategy and a 5% significance level for inclusion. Characteristics considered were age, sex (as a dummy variable), BMI, FEV1, and number of prescribed nebulizer doses per day.

The assumption was made that the I-Neb had no systematic error, so bias was defined as the difference, on average, between the estimate of adherence for each participant from a patient or multidisciplinary team member and that downloaded from the I-Neb. Bias, therefore, was used to measure the degree of any systematic underestimation or overestimation by patient or clinician. It was estimated as the average paired difference between patient- and clinician-reported and actual adherence (with 95% CI) based on a paired t test. Agreement was defined as the level of consistency between adherence measures reported by the patient and clinician and adherence downloaded from the I-Neb, having adjusted for any bias (ie, systematic disagreement). Agreement was measured using the ICC with 95% CI.

Bland-Altman plots,26 in which the difference between two assessments of the same characteristic is plotted against the average for each participant, were used to display patient, clinician, and electronic monitoring assessments. Plots were inspected to determine whether, by how much, and for whom the results given by pairs of methods measuring adherence differed. If one of the measurement methods is viewed as being without systematic error (a property we have assumed the I-Neb to hold), a Bland-Altman plot illustrates whether the other method is biased. The plot also was used to assess whether the difference in terms of both average (ie, bias when comparison is made with the I-Neb) and variation (ie, agreement) between the measurements depends on the (average) magnitude of two measurements in the same participant.

Eighty-one patients brought their I-Neb systems to the clinic within the study period, and 80 gave informed consent to take part in the study. Downloaded data could not be obtained in one case because the I-Neb was an older model without the capability to download data and in one case because of malfunction following ingress of water. Data for 78 participants, therefore, were analyzed (Fig 1). Complete data were collected for 63 participants; 15 participants had missing data for the ward nurse assessment of adherence because the participants were unknown to the nurse. All other data for these participants were complete and were included in the analysis.

Participant characteristics are displayed in Table 1. The median prescription was three nebulizer doses a day, ranging between one and seven and including combinations of colistin, tobramycin for inhalation (TOBI; Novartis Pharmaceuticals Corp; East Hanover, New Jersey), dornase alpha (Pulmozyme; Roche; Welwyn Garden City, England), salbutamol, ipratropium bromide, and hypertonic saline.

Table Graphic Jump Location
Table 1 —Participant Characteristics

Data are presented as No. (%) or median (interquartile range).

The median adherence level demonstrated variation depending on measurement method. The lowest was identified using electronic monitoring at 36% (IQR, 5%-84.5%) of nebulized treatment prescribed, and the highest was 80% (IQR, 57.5%-95%) identified by self-report. The IQR and range of measurements were much greater with electronic monitoring than with self-report and clinician report (Fig 2). Patients with higher adherence measured by electronic monitoring were older (P = .001) and more likely to be men (P = .044). They did not differ with respect to their FEV1 (P = .95), BMI (P = .31), or number of prescribed nebulizer doses per day (P = .98).

Figure Jump LinkFigure 2. Box-and-whisker plot of percentage adherence according to different approaches to assessment.Grahic Jump Location

Very strong agreement (ICC > 0.89) was found between the responses to the two different questions used for self-report and clinician report. Therefore, results are displayed only for the self-report question, “Overall, what percentage of your nebulizers do you think you have taken over the last 3 months,” and for clinician report, “What percentage of nebulizers was taken by the participant over the previous 3 months?”

Agreement Between Self-Reported and Electronically Monitored Adherence

A scatterplot and Bland-Altman plot (Fig 3) demonstrated upward bias (overestimation of adherence, on average) in self-report for those with lower levels of adherence measured by electronic monitoring, particularly < 60% adherence (demonstrated by differences typically being above zero). Participant bias was less pronounced at the lower extreme of electronically monitored adherence levels and even tended toward a negative bias, demonstrating underestimation at the higher extreme of downloaded adherence. Overall, the bias of patient report was 25.3% (95% CI, 18.7%-31.9%).

Figure Jump LinkFigure 3. A, Patient-reported adherence compared with downloaded adherence data (scatterplot). B, Patient-reported adherence compared with degree of bias (Bland-Altman plot with 95% limits of agreement [dotted lines]).Grahic Jump Location

Subgrouping the data by downloaded adherence of 0% to 25%, 26% to 50%, 51% to 75%, 76% to 100%, and 101% to 125% (Fig 4) showed a high tendency to overestimate adherence and to have greater upward bias in report where the downloaded adherence level was low (< 50%). This tendency continued but decreased as downloaded adherence improved and was minimal in those with downloaded adherence levels of 76% to 100%. A group of patients (nine out of 78) reported 100% adherence, but their adherence levels were > 100% on download (Figs 3, 5). These participants had a significant and consistent tendency to underestimate adherence.

Figure Jump LinkFigure 4. Degree of patient overestimation of adherence (bias) within subgroups of objective adherence (mean with 95% CI).Grahic Jump Location
Figure Jump LinkFigure 5. A, Physiotherapist-reported adherence compared with downloaded adherence data (scatterplot). Physiotherapist-reported adherence compared with degree of bias (Bland-Altman plot with 95% limits of agreement [dotted lines]).Grahic Jump Location
Agreement Between Adherence Assessed by Clinician Report and Electronic Monitoring

Individual clinician agreement was moderate (ICC, 0.55; 95% CI, 0.44-0.66). Comparison of individual clinician agreement with downloaded adherence levels ranged from 0.28 (pharmacist) to 0.54 (physiotherapist). Significant overestimation of adherence was demonstrated (range, 12%-19%) by all clinicians except for the physiotherapist (Table 2). Although the physiotherapist had no overall tendency to overestimate participant adherence, there was an indication that overestimation of adherence by the physiotherapist was greater where adherence was low. There was also extreme inaccuracy for individual participants (Fig 5). For example, in one participant, adherence downloaded from the I-Neb was 109% of prescribed treatment, whereas physiotherapist estimation was 7%. In another participant, the downloaded adherence was 0%, whereas physiotherapist estimation was 71%. Physiotherapist report also varied in agreement with self-report and showed some large positive and negative discrepancies (Fig 6).

Table Graphic Jump Location
Table 2 —Bias of Clinician Report and Agreement Between Clinician Report and Objective Adherence

ICC = intraclass correlation coefficient.

Figure Jump LinkFigure 6. Degree of bias (Bland-Altman plot) of patient-reported adherence compared with physiotherapist-reported adherence, with 95% limits of agreement (dotted lines).Grahic Jump Location

Previous studies have shown important differences in the level of patient adherence to treatments when adherence was assessed by reported methods or by electronic monitoring. Nonetheless, report-based estimates of adherence continue to inform important treatment decisions, such as whether to intensify treatment or whether a patient is suitable for further interventions, including transplantation. The present study was designed to directly assess the commonly used adherence measures of self-report and clinician report against new nebulizer technology, which provides accurate and detailed longitudinal electronic monitoring data. The results show the extent of discrepancies between reported and electronic monitoring measures. The latter method, when available in routine clinical practice, allows open and honest discussion between patient and clinician.

Treatment decisions based on reported measures of adherence or on adherence data from electronic monitoring are likely to be very different. Adherence levels to nebulized therapy were low (median, 36% of prescribed treatment) when measured by electronic monitoring and were consistent with other studies using AAD technology22 where age is taken into account.21 The median rate of adherence measured by self- or clinician report was 80% and 50% to 60%, respectively, consistent with studies that described adherence rates of 65% to 84% when using reported measures or compressor loggers (a basic form of electronic monitoring that records for how long the nebulizer is switched on).7,15,23

Variation was seen in the accuracy of patient reporting across different levels of adherence as measured by electronic monitoring, with bias of patient report dropping with increasing adherence. Extreme inaccuracy was observed for individual patients by clinician and self report of adherence. Similar discrepancies between self- and clinician-reporting and other adherence measures have been demonstrated,7,15,23 although not to the extent and magnitude observed in our study. Burrows et al23 identified higher clinician accuracy (50%-70% accuracy) when adherence was classified as good, moderate, or poor. This rating scale might have led to greater accuracy by chance, and such wide ranges may be less clinically relevant. In practice, members of the CF multidisciplinary teams, patients with CF, and families are likely to always have individual beliefs and perceptions about adherence levels. This study shows that no individual’s assessment of patient adherence should influence treatment decisions. They may be useful, however, in exploring differing perceptions and their effects on adherence, negotiating treatment, and patient-clinician relationships.

Some of the adherence patterns identified by electronic monitoring in the present study have not been previously described. For example, nine patients overmedicated, with adherence rates of 105% to 119% of prescribed treatment, which highlights the improvement in detail provided by electronic monitoring methods and recognizes an aspect of adherence behavior that may have implications for medication toxicity and incorrect assumptions about clinical status.

Limitations to the present study include the fact that participants were asked to recall their adherence over the past 3 months. Patient recall may be poor over such long time periods, although in routine practice, questions about adherence are asked at interval outpatient consultations, usually every 6 to 12 weeks. Poor patient understanding of the treatment regimen or inaccurate medical records have been suggested as causes for mismatch between patient-reported and alternative adherence measures.12,27,28 However, participants in the present study were able to accurately recall their prescribed regimen. Finally, novel nebulizer systems (the I-Neb and the Akita inhalation system [Activaero GmbH; Gemuenden, Germany]) that allow review of detailed longitudinal adherence data are not yet available worldwide.

Electronic monitoring feedback can enhance adherence and has been shown to be successful in diabetes management29 and in maintaining or enhancing adherence to inhaled medications.15,21 Since the completion of the study, participants who have voluntarily returned their I-Nebs for continuing review of their adherence have shown a greater use of nebulized therapy. Research is needed to assess how this continuous feedback is best delivered and in whom it would most benefit. Home- and hospital-linked access to adherence data from the I-Neb through the software Insight (Philips) is now available, providing the opportunity to deliver electronic monitoring feedback in a setting, format, and frequency that may facilitate future research. Other directions for research include investigating the causes and implications of poor adherence and the variable reporting of adherence, demonstrating the link between detailed adherence data and clinical outcome, and assessing parental report vs electronic monitoring data, as the results of the present study will not necessarily be applicable to the pediatric population.

In conclusion, this study has demonstrated low levels of adherence to nebulized therapy and inaccurate reporting of adherence when assessed by clinicians or patients vs electronic monitoring. We suggest that detailed electronic monitoring be used to provide an accurate long-term record of adherence levels for individual patients, which may aid the clinician to tailor treatment to the individual, minimize the health-care burden and cost, and assess new treatments as well as may help to develop a more honest relationship between clinician and patient.

Author contributions: Ms Daniels had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Ms Daniels: contributed to the concept for this research; wrote the proposal and submitted it to the ethics committee; made amendments for ethics committee acceptance; carried out data collection; analyzed data with guidance, correction, and assistance from Dr Sutton; and wrote and made amendments to this manuscript based on guidance from the other authors.

Dr Goodacre: contributed academic supervision and reviewed and contributed to the final manuscript.

Dr Sutton: contributed to statistical supervision, including creating plots for Bland-Altman figures, and reviewed and contributed to the final manuscript.

Ms Pollard: contributed to the data collection and reviewed and contributed to the final manuscript.

Dr Conway: contributed clinical supervision, commented on the proposal and approved its submission to the ethics committee, and reviewed and contributed to the final manuscript.

Dr Peckham: contributed clinical supervision, commented on the proposal and approved its submission to the ethics committee, and reviewed and contributed to the final manuscript.

Financial/nonfinancial disclosures: The authors report to CHEST the following conflicts of interest: Ms Daniels provides advice as a consultant to Philips regarding nebulizer and associated technology development, to Novartis Pharmaceuticals Corporation and Pharmaxis regarding inhaled therapies, and to Air products regarding home oxygen delivery. These posts were all commenced following the completion of the present work. Ms Pollard has received assistance with travel and accommodation for a meeting from Novartis Pharmaceuticals Corporation. Dr Conway is a member of an advisory board that provides advice to Philips regarding nebulizer development. This board also has provided advice to Medic-Aid Limited and Respironics who developed AAD technology prior to being taken over by Philips. This work involves an honorarium payment and began prior to this study. Drs Goodacre, Sutton, and Peckham have reported that no potential conflicts of interest exist with any companies/organizations whose products or services may be discussed in this article.

Other contributions: This work was completed at the Leeds Regional Adult Cystic Fibrosis Unit, Leeds, England. It was part of a master of science thesis and was unfunded. We thank Helen White, MSc (Leeds Regional Adult Cystic Fibrosis Unit, St James Hospital and Leeds Metropolitan University) who reviewed and extensively contributed to the final manuscript and Ruth Watson, BSc(hons) (Leeds Teaching Hospitals NHS Trust, St James Hospital, Leeds, England) who assisted with data collection.

AAD

adaptive aerosol delivery

CF

cystic fibrosis

ICC

intraclass correlation coefficient

IQR

interquartile range

Halfhide C, Evans HJ, Couriel J. Inhaled bronchodilators for cystic fibrosis. Cochrane Database Syst Rev. 2005;44:CD003428. [PubMed]
 
Sawicki GS, Sellers DE, Robinson WM. High treatment burden in adults with cystic fibrosis: challenges to disease self-management. J Cyst Fibros. 2009;82:91-96. [CrossRef] [PubMed]
 
Dodd ME, Webb AK. Understanding non-compliance with treatment in adults with cystic fibrosis. J R Soc Med. 2000;93suppl 38:2-8. [PubMed]
 
Quittner AL, Modi AC, Lemanek KL, Ievers-Landis CE, Rapoff MA. Evidence-based assessment of adherence to medical treatments in pediatric psychology. J Pediatr Psychol. 2008;339:916-936 discussion 937-938.. [CrossRef] [PubMed]
 
Glasscoe CA, Quittner AL. Psychological interventions for people with cystic fibrosis and their families. Cochrane Database Syst Rev. 2008;33:CD003148. [PubMed]
 
Koocher GP, McGrath ML, Gudas LJ. Typologies of nonadherence in cystic fibrosis. J Dev Behav Pediatr. 1990;116:353-358. [CrossRef] [PubMed]
 
Conway SP, Pond MN, Hamnett T, Watson A. Compliance with treatment in adult patients with cystic fibrosis. Thorax. 1996;511:29-33. [CrossRef] [PubMed]
 
Abbott J, Dodd M, Bilton D, Webb AK. Treatment compliance in adults with cystic fibrosis. Thorax. 1994;492:115-120. [CrossRef] [PubMed]
 
Passero MA, Remor B, Salomon J. Patient-reported compliance with cystic fibrosis therapy. Clin Pediatr (Phila). 1981;204:264-268. [CrossRef] [PubMed]
 
Lask B. Non-adherence to treatment in cystic fibrosis. J R Soc Med. 1994;87suppl 21:25-27. [PubMed]
 
Kettler LJ, Sawyer SM, Winefield HR, Greville HW. Determinants of adherence in adults with cystic fibrosis. Thorax. 2002;575:459-464. [CrossRef] [PubMed]
 
Modi AC, Lim CS, Yu N, Geller D, Wagner MH, Quittner ALA. A multi-method assessment of treatment adherence for children with cystic fibrosis. J Cyst Fibros. 2006;53:177-185. [CrossRef] [PubMed]
 
Brennan VK, Osman LM, Graham H, Critchlow A, Everard ML. True device compliance: the need to consider both competence and contrivance. Respir Med. 2005;991:97-102. [CrossRef] [PubMed]
 
Everard ML. Regimen and device compliance: key factors in determining therapeutic outcomes. J Aerosol Med. 2006;191:67-73. [CrossRef] [PubMed]
 
Dodd ME, Haworth CS, Webb AK. The medical and economic consequences of non-compliance in clinical trials and in primary care [Abstract]. Thorax. 1998;53suppl 4:A18
 
Riekert KA, Rand CS. Electronic monitoring of medication adherence: when is high tech best? J Clin Psychol Med Settings. 2002;91:25-34. [CrossRef]
 
Denyer J, Dyche T. The adaptive aerosol delivery (AAD) technology: past, present and future. J Aerosol Med Pulm Drug Deliv. 2010;23suppl 1:s1-s10. [CrossRef] [PubMed]
 
Nikander K, Prince I, Hinch S. Use of I-Neb Insight to examine patient logging system data from a 3-month evaluation of the I-Neb AAD system. Respir Drug Del. 2008;3:735-738
 
Prince I, Denyer J, Nikander K. I-Neb Insight: a new tool for monitoring inhaled medication use. Eur Respir J. 2006;28suppl 50:261s
 
Archer MJ, Williams V, Rayner RJ. An adherence study using data from an AAD device in cystic fibrosis comparing early and late diagnosis [Abstract]. J Cyst Fibros. 2010;9suppl 1:S12. [CrossRef]
 
McNamara PS, McCormack P, McDonald AJ, Heaf L, Southern KW. Open adherence monitoring using routine data download from an adaptive aerosol delivery nebuliser in children with cystic fibrosis. J Cyst Fibros. 2009;84:258-263. [CrossRef] [PubMed]
 
Latchford G, Duff A, Quinn J, Conway S, Conner M. Adherence to nebulised antibiotics in cystic fibrosis. Patient Educ Couns. 2009;751:141-144. [CrossRef] [PubMed]
 
Burrows JA, Bunting JP, Masel PJ, Bell SC. Nebulised dornase alpha: adherence in adults with cystic fibrosis. J Cyst Fibros. 2002;14:255-259. [CrossRef] [PubMed]
 
Wagner GJ, Ghosh-Dastidar B. Electronic monitoring: adherence assessment or intervention? HIV Clin Trials. 2002;31:45-51. [CrossRef] [PubMed]
 
Rosenstein BJ, Cutting GR. Cystic Fibrosis Foundation Consensus Panel Cystic Fibrosis Foundation Consensus Panel The diagnosis of cystic fibrosis: a consensus statement. J Pediatr. 1998;1324:589-595. [CrossRef] [PubMed]
 
Bland JM, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet. 1986;18476:307-310. [CrossRef] [PubMed]
 
Rand CS. Patient adherence with COPD therapy. Eur Resp Rev. 2005;1496:97-101. [CrossRef]
 
Modi AC, Quittner AL. Barriers to treatment adherence for children with cystic fibrosis and asthma: what gets in the way? J Pediatr Psychol. 2006;318:846-858. [CrossRef] [PubMed]
 
Cramer JA. A systematic review of adherence with medications for diabetes. Diabetes Care. 2004;275:1218-1224. [CrossRef] [PubMed]
 

Figures

Figure Jump LinkFigure 2. Box-and-whisker plot of percentage adherence according to different approaches to assessment.Grahic Jump Location
Figure Jump LinkFigure 3. A, Patient-reported adherence compared with downloaded adherence data (scatterplot). B, Patient-reported adherence compared with degree of bias (Bland-Altman plot with 95% limits of agreement [dotted lines]).Grahic Jump Location
Figure Jump LinkFigure 4. Degree of patient overestimation of adherence (bias) within subgroups of objective adherence (mean with 95% CI).Grahic Jump Location
Figure Jump LinkFigure 5. A, Physiotherapist-reported adherence compared with downloaded adherence data (scatterplot). Physiotherapist-reported adherence compared with degree of bias (Bland-Altman plot with 95% limits of agreement [dotted lines]).Grahic Jump Location
Figure Jump LinkFigure 6. Degree of bias (Bland-Altman plot) of patient-reported adherence compared with physiotherapist-reported adherence, with 95% limits of agreement (dotted lines).Grahic Jump Location

Tables

Table Graphic Jump Location
Table 1 —Participant Characteristics

Data are presented as No. (%) or median (interquartile range).

Table Graphic Jump Location
Table 2 —Bias of Clinician Report and Agreement Between Clinician Report and Objective Adherence

ICC = intraclass correlation coefficient.

References

Halfhide C, Evans HJ, Couriel J. Inhaled bronchodilators for cystic fibrosis. Cochrane Database Syst Rev. 2005;44:CD003428. [PubMed]
 
Sawicki GS, Sellers DE, Robinson WM. High treatment burden in adults with cystic fibrosis: challenges to disease self-management. J Cyst Fibros. 2009;82:91-96. [CrossRef] [PubMed]
 
Dodd ME, Webb AK. Understanding non-compliance with treatment in adults with cystic fibrosis. J R Soc Med. 2000;93suppl 38:2-8. [PubMed]
 
Quittner AL, Modi AC, Lemanek KL, Ievers-Landis CE, Rapoff MA. Evidence-based assessment of adherence to medical treatments in pediatric psychology. J Pediatr Psychol. 2008;339:916-936 discussion 937-938.. [CrossRef] [PubMed]
 
Glasscoe CA, Quittner AL. Psychological interventions for people with cystic fibrosis and their families. Cochrane Database Syst Rev. 2008;33:CD003148. [PubMed]
 
Koocher GP, McGrath ML, Gudas LJ. Typologies of nonadherence in cystic fibrosis. J Dev Behav Pediatr. 1990;116:353-358. [CrossRef] [PubMed]
 
Conway SP, Pond MN, Hamnett T, Watson A. Compliance with treatment in adult patients with cystic fibrosis. Thorax. 1996;511:29-33. [CrossRef] [PubMed]
 
Abbott J, Dodd M, Bilton D, Webb AK. Treatment compliance in adults with cystic fibrosis. Thorax. 1994;492:115-120. [CrossRef] [PubMed]
 
Passero MA, Remor B, Salomon J. Patient-reported compliance with cystic fibrosis therapy. Clin Pediatr (Phila). 1981;204:264-268. [CrossRef] [PubMed]
 
Lask B. Non-adherence to treatment in cystic fibrosis. J R Soc Med. 1994;87suppl 21:25-27. [PubMed]
 
Kettler LJ, Sawyer SM, Winefield HR, Greville HW. Determinants of adherence in adults with cystic fibrosis. Thorax. 2002;575:459-464. [CrossRef] [PubMed]
 
Modi AC, Lim CS, Yu N, Geller D, Wagner MH, Quittner ALA. A multi-method assessment of treatment adherence for children with cystic fibrosis. J Cyst Fibros. 2006;53:177-185. [CrossRef] [PubMed]
 
Brennan VK, Osman LM, Graham H, Critchlow A, Everard ML. True device compliance: the need to consider both competence and contrivance. Respir Med. 2005;991:97-102. [CrossRef] [PubMed]
 
Everard ML. Regimen and device compliance: key factors in determining therapeutic outcomes. J Aerosol Med. 2006;191:67-73. [CrossRef] [PubMed]
 
Dodd ME, Haworth CS, Webb AK. The medical and economic consequences of non-compliance in clinical trials and in primary care [Abstract]. Thorax. 1998;53suppl 4:A18
 
Riekert KA, Rand CS. Electronic monitoring of medication adherence: when is high tech best? J Clin Psychol Med Settings. 2002;91:25-34. [CrossRef]
 
Denyer J, Dyche T. The adaptive aerosol delivery (AAD) technology: past, present and future. J Aerosol Med Pulm Drug Deliv. 2010;23suppl 1:s1-s10. [CrossRef] [PubMed]
 
Nikander K, Prince I, Hinch S. Use of I-Neb Insight to examine patient logging system data from a 3-month evaluation of the I-Neb AAD system. Respir Drug Del. 2008;3:735-738
 
Prince I, Denyer J, Nikander K. I-Neb Insight: a new tool for monitoring inhaled medication use. Eur Respir J. 2006;28suppl 50:261s
 
Archer MJ, Williams V, Rayner RJ. An adherence study using data from an AAD device in cystic fibrosis comparing early and late diagnosis [Abstract]. J Cyst Fibros. 2010;9suppl 1:S12. [CrossRef]
 
McNamara PS, McCormack P, McDonald AJ, Heaf L, Southern KW. Open adherence monitoring using routine data download from an adaptive aerosol delivery nebuliser in children with cystic fibrosis. J Cyst Fibros. 2009;84:258-263. [CrossRef] [PubMed]
 
Latchford G, Duff A, Quinn J, Conway S, Conner M. Adherence to nebulised antibiotics in cystic fibrosis. Patient Educ Couns. 2009;751:141-144. [CrossRef] [PubMed]
 
Burrows JA, Bunting JP, Masel PJ, Bell SC. Nebulised dornase alpha: adherence in adults with cystic fibrosis. J Cyst Fibros. 2002;14:255-259. [CrossRef] [PubMed]
 
Wagner GJ, Ghosh-Dastidar B. Electronic monitoring: adherence assessment or intervention? HIV Clin Trials. 2002;31:45-51. [CrossRef] [PubMed]
 
Rosenstein BJ, Cutting GR. Cystic Fibrosis Foundation Consensus Panel Cystic Fibrosis Foundation Consensus Panel The diagnosis of cystic fibrosis: a consensus statement. J Pediatr. 1998;1324:589-595. [CrossRef] [PubMed]
 
Bland JM, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet. 1986;18476:307-310. [CrossRef] [PubMed]
 
Rand CS. Patient adherence with COPD therapy. Eur Resp Rev. 2005;1496:97-101. [CrossRef]
 
Modi AC, Quittner AL. Barriers to treatment adherence for children with cystic fibrosis and asthma: what gets in the way? J Pediatr Psychol. 2006;318:846-858. [CrossRef] [PubMed]
 
Cramer JA. A systematic review of adherence with medications for diabetes. Diabetes Care. 2004;275:1218-1224. [CrossRef] [PubMed]
 
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