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Published on in Vol 22, No 10 (2020): October

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/19179, first published .
Measurement Properties of Existing Patient-Reported Outcome Measures on Medication Adherence: Systematic Review

Measurement Properties of Existing Patient-Reported Outcome Measures on Medication Adherence: Systematic Review

Measurement Properties of Existing Patient-Reported Outcome Measures on Medication Adherence: Systematic Review

Authors of this article:

Yu Heng Kwan1, 2 Author Orcid Image ;   Si Dun Weng2 Author Orcid Image ;   Dionne Hui Fang Loh3 Author Orcid Image ;   Jie Kie Phang4 Author Orcid Image ;   Livia Jia Yi Oo2 Author Orcid Image ;   Dan V Blalock5, 6 Author Orcid Image ;   Eng Hui Chew2 Author Orcid Image ;   Kai Zhen Yap2 Author Orcid Image ;   Corrinne Yong Koon Tan7 Author Orcid Image ;   Sungwon Yoon1, 3 Author Orcid Image ;   Warren Fong4, 8, 9 Author Orcid Image ;   Truls Østbye1 Author Orcid Image ;   Lian Leng Low3, 10, 11 Author Orcid Image ;   Hayden Barry Bosworth5, 6, 12, 13 Author Orcid Image ;   Julian Thumboo1, 4, 9 Author Orcid Image
Article Authors Cited by (49) Tweetations (27) Metrics

    Review

    1Program in Health Services and Systems Research,, Duke-NUS Medical School, Singapore, Singapore

    2Department of Pharmacy, National University of Singapore, Singapore, Singapore

    3PULSES Centre Grant, SingHealth Regional Health System, Singapore, Singapore

    4Department of Rheumatology and Immunology, Singapore General Hospital, Singapore, Singapore

    5Center of Innovation to Accelerate Discovery and Practice Transformation (ADAPT), Durham Veterans Affairs Health Care System, Durham, NC, United States

    6Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, United States

    7Pharmacy Transformation Office, National Healthcare Group Pharmacy, Singapore, Singapore

    8Duke-NUS Medical School, Singapore, Singapore

    9NUS Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore

    10Department of Family Medicine and Continuing Care, Singapore General Hospital, Singapore, Singapore

    11Post Acute and Continuing Care, Outram Community Hospital, Singapore, Singapore

    12Department of Population Health Sciences, Duke University Medical Center, Durham, NC, United States

    13School of Nursing, Duke University Medical Center, Durham, NC, United States

    *these authors contributed equally

    Corresponding Author:

    Yu Heng Kwan, PhD

    Program in Health Services and Systems Research,

    Duke-NUS Medical School

    8 College Road

    Singapore, 169857

    Singapore

    Phone: 65 63778465

    Email: yuheng_02071988@hotmail.com


    Background: Medication adherence is essential for improving the health outcomes of patients. Various patient-reported outcome measures (PROMs) have been developed to measure medication adherence in patients. However, no study has summarized the psychometric properties of these PROMs to guide selection for use in clinical practice or research.

    Objective: This study aims to evaluate the quality of the PROMs used to measure medication adherence.

    Methods: This study was guided by the PRISMA (Preferred Reporting Items for Systematic Review and Meta-Analysis) guidelines. Relevant articles were retrieved from the EMBASE, PubMed, Cochrane Library, Web of Science, and CINAHL (Cumulative Index to Nursing and Allied Health Literature) databases. The PROMs were then evaluated based on the COnsensus-based Standards for the selection of health Measurement Instruments (COSMIN) guidelines.

    Results: A total of 121 unique medication adherence PROMs from 214 studies were identified. Hypotheses testing for construct validity and internal consistency were the most frequently assessed measurement properties. PROMs with at least a moderate level of evidence for ≥5 measurement properties include the Adherence Starts with Knowledge 20, Compliance Questionnaire-Rheumatology, General Medication Adherence Scale, Hill-Bone Scale, Immunosuppressant Therapy Barrier Scale, Medication Adherence Reasons Scale (MAR-Scale) revised, 5-item Medication Adherence Rating Scale (MARS-5), 9-item MARS (MARS-9), 4-item Morisky Medication Adherence Scale (MMAS-4), 8-item MMAS (MMAS-8), Self-efficacy for Appropriate Medication Adherence Scale, Satisfaction with Iron Chelation Therapy, Test of Adherence to Inhalers, and questionnaire by Voils. The MAR-Scale revised, MMAS-4, and MMAS-8 have been administered electronically.

    Conclusions: This study identified 121 PROMs for medication adherence and provided synthesized evidence for the measurement properties of these PROMs. The findings from this study may assist clinicians and researchers in selecting suitable PROMs to assess medication adherence.

    J Med Internet Res 2020;22(10):e19179

    doi:10.2196/19179

    Keywords

    systematic review; reliability and validity; medication adherence; patient reported outcome measures 


    Medication adherence is known as “the degree to which the person’s behaviour corresponds with the agreed recommendations from a healthcare provider” [1]. Medication adherence is essential when it comes to improving the health outcomes of patients, especially for chronic diseases [2,3]. However, only approximately half of the patients worldwide adhere to their prescribed medication regimen [4]. Reasons for medication nonadherence include complexity of the treatment regimen, poor communication with health care providers, and concerns about side effects from taking medications [5]. Poor medication adherence may lead to worse health outcomes and higher rates of mortality and morbidity [1,6]. Nonadherence also incurs a high cost burden to the health care system by increasing hospital visits as well as causing unnecessary escalation to more expensive treatments [1]. Therefore, improving medication adherence is key to improving treatment outcomes [7,8].

    To successfully improve medication adherence, there is a need for the accurate assessment of medication adherence. Current practices for measuring medication include direct measures such as drug assays of blood or urine as well as indirect measures of adherence such as pill count, electronic monitoring devices, and the use of big data such as review of prescription records and claims [9,10]. Some of these measures are not time efficient and are likely to be unsustainable in clinical practice. Various patient-reported outcome measures (PROMs) such as the Morisky Medication Adherence Scale (MMAS) [11], Hill-Bone Compliance Scale [12], and Medication Adherence Rating Scale (MARS) [13] have been developed to measure self-reported adherence to medications. These PROMs may be useful in clinical practice because they are easy to administer. On the basis of the patients’ PROM ratings, health care professionals may be able to provide timely feedback. Thus, underlying issues that contribute to medication noncompliance can be addressed at the point of care [14].

    A number of previous studies have been conducted to validate PROMs on medication adherence [12,15-17], and a previous systematic review found 14 PROMs that assessed adherence to inhaled asthma maintenance medication alone [18]. However, to date, there is no comprehensive review that summarizes the psychometric properties of PROMs for medication adherence, which is essential to guide the selection of suitable PROMs to evaluate medication adherence in patients. Hence, we carried out a systematic review to identify studies that investigated PROMs for medication adherence and to evaluate the quality of these PROMs.


    This study was conducted with reference to the PRISMA (Preferred Reporting Items for Systematic Review and Meta-Analysis) statement [19]. The measurement properties of each PROM were evaluated based on the COnsensus-based Standards for the selection of health Measurement Instruments(COSMIN) guidelines [20,21]. The COSMIN guidelines evaluate PROM development and the following 9 measurement properties: “content validity,” “structural validity,” “internal consistency,” “cross-cultural validity/measurement invariance,” test-retest “reliability,” “measurement error,” “criterion validity,” “hypotheses testing for construct validity,” and “responsiveness” [21,22]. Of note, the assessment of “convergent validity,” “discriminant validity,” and “known-group validity” falls under “hypotheses testing for construct validity” [23,24]. We also assumed that “concurrent validity” and “predictive validity” can be evaluated by the same measurement property, “hypothesis testing for construct validity,” and sensitivity to change can be evaluated under “responsiveness” as well [22,25,26].

    Search Strategy

    The EMBASE, PubMed, Cochrane Library, Web of Science, and CINAHL databases were searched for relevant studies published before November 1, 2019. A search strategy (Multimedia Appendix 1) consisting of adherence, PROMs, and measurement properties was used. Search filters created by Terwee et al [27], which consists of a combination of search terms, were also used to enhance the sensitivity of searches, where available. For the adherence construct, synonyms such as compliance were used in the search. Duplicates were removed from the final search library.

    Study Selection

    Articles included in this study were full-text publications in English, validating medication adherence PROMs, with the assessment of at least one measurement property listed in the COSMIN [24,28]. We excluded animal studies, case studies, narrative reviews, expert opinions, conference abstracts, and PROMs completed by proxy [29]. In total, 2 independent reviewers (SW and LO) screened the titles and abstracts of the studies according to the inclusion and exclusion criteria. Opinions from a third reviewer (YK) were sought in the event of any disagreements. The remaining full-text articles were then evaluated by the same 2 independent reviewers for inclusion and exclusion. Additional articles for inclusion into the final pool of articles were also identified from the reference list of articles during the full-text screening phase [30].

    Extraction of Data

    The following data were then extracted from the included articles by 2 independent reviewers (SW and LO), where available:

    • General characteristics of the study population: age, sample size, gender, and country.
    • Characteristics of disease or condition: disease studied, duration of illness, or treatment.
    • PROM characteristics: methods of administration, availability of electronic administration, language, response scale, domains, and number of items.

    Assessment of Methodological Quality

    The methodological quality of the studies was assessed by 2 independent reviewers (SW and LO). Any disagreement was resolved in consultation with a third reviewer (JP). Each measurement property was assessed based on a 4-point scale: “inadequate,” “doubtful,” “adequate,” or “very good” [20,22]. The item with the worst rating under each measurement property would determine the overall rating for the specific measurement property [31].

    On the basis of the COSMIN guidelines, it is recommended for the review team to determine before assessing the methodological quality of studies which outcome measurement instrument can be considered a reasonable gold standard [32]. The study team decided that there is currently no gold standard in the field of patient-reported outcomes that measure medication adherence [33,34]; thus, the assessment of criterion validity of the PROMs was not performed except when an abridged PROM was compared with the original long version, which will be regarded as the gold standard. This is in line with the consensus from the COSMIN panel that no gold standard exists for PROMs [35].

    Assessment of Psychometric Quality

    The psychometric quality for each medication adherence instrument was assessed using the quality criteria by Terwee et al [36]. For each of the measurement properties evaluated in the included studies, a “positive (+),” “indeterminate (?),” or “negative (−)” rating was assigned based on the psychometric results.For example, for the internal consistency measurement property, the rating will be “+” if “at least low evidence for sufficient structural validity AND Cronbach’s alpha(s) ≥ 0.70 for each unidimensional scale or subscale.” The rating will be “−” if there is “at least low evidence for sufficient structural validity AND Cronbach’s alpha(s) < 0.70 for each unidimensional scale or subscale.” The rating will be “?” if the “criteria for at least low evidence for sufficient structural validity is not met” [36].

    Evidence Synthesis

    For each PROM, an evidence synthesis across studies was conducted. First, we determined whether each measurement property for a PROM had overall “sufficient (+),” “insufficient (−),” “inconsistent (±),” or “indeterminate (?)” evidence for each measurement property of the PROM. For PROMs that were assessed in more than one study, the overall rating of the level of evidence for the PROM would be sufficient (+), indeterminate (?), or insufficient (–) if the individual studies were all consistently rated as positive (+), indeterminate (?), or negative (-), respectively. If the results of individual studies were inconsistent, the overall rating of the level of evidence for the PROM would be inconsistent.

    We also graded the quality of evidence for each measurement property of PROM as “high,” “moderate,” “low,” or “very low” level of evidence based on the guidelines from the modified Grading of Recommendations Assessment, Development and Evaluation approach for systematic reviews of clinical trials [22,37].


    Search Results and Study Characteristics

    In total, 51,426 articles were retrieved from the 5 databases. After removing 8286 duplicates and 42,836 articles during title and abstract screening, 304 articles remained for full-text review. A total of 98 articles were further eliminated during full-text article screening. An additional 8 relevant articles were identified through hand-searching of the reference lists from the included articles to obtain a final list of 214 articles (Figure 1). A total of 240 PROMs were evaluated across 214 studies, and 121 unique medication adherence PROMs in 32 languages from 48 countries were identified (Table 1).

    ‎
    Figure 1. Preferred Reporting Items for Systematic Reviews and Meta-Analyses flow diagram for the systematic review. CINAHL: Cumulative Index to Nursing and Allied Health Literature; PROM: patient-reported outcome measure.
    View this figure
    Table 1. Study characteristics of included articles (N=214).
    Study characteristicsValues, n (%)
    Sample sizea

    		<5017 (7.9)

    		50-9931 (14.4)

    		100-19962 (29.0)

    		200-29932 (15.0)

    		300-39923 (10.7)

    		400-49915 (7.0)

    		>50035 (16.4)
    Mean age (years)a,b

    		<209 (4.2)

    		20-3928 (13.1)

    		40-5997 (45.3)

    		60-7955 (25.7)

    		>802 (0.9)
    Proportion of males(%)a

    		0-2015 (7.0)

    		21-4047 (22.0)

    		41-6078 (36.4)

    		61-8038 (17.8)

    		81-10023 (10.7)
    Mean treatment/disease durationa (years)

    		<13 (1.4)

    		1-511 (5.1)

    		6-1027 (12.6)

    		11-1516 (7.5)

    		>152 (0.9)

    aSome values were not reported.

    bSome values were reported as median, range, or not reported.

    PROM Characteristics

    The characteristics of the medication adherence PROM are presented in Multimedia Appendix 2 [38-51]. The most extensively studied PROMs were the 8-item MMAS (MMAS-8)and 4-item MMAS(MMAS-4), which were studied in 27 and 18 studies, respectively. Among the 121 PROMs, 15 PROMs have been administered electronically—Adult AIDS Clinical Trials Group (AACTG) adherence items [52], Fredericksen et al [38], Item Response Theory-30 [53], LeastAbsolute Shrinkage and Selection Operator-10 [53], Medication Adherence Reasons Scale (MAR-Scale) [54], MAR-Scale (revised) [55], MARS [56], Medication Intake Survey-Asthma [57], MMAS-4 [54], MMAS-8 [58], Multiple Sclerosis Treatment Adherence Questionnaire [59], Probabilistic Medication Adherence Scale [56], Self-Rating Scale Item [52], visual analog scale [52], and WedAd-Q Questionnaire [60].

    Assessment of Methodological and Psychometric Quality

    Outcomes on the assessment of methodological quality and study quality of PROMs are summarized in Multimedia Appendix 3 [13,17,38-54,56,57,59,61-248]. In terms of validity, “hypotheses testing for construct validity,”“structural validity,” and “content validity” were assessed in 181, 92, and 92 studies, respectively. In terms of reliability, “internal consistency” and “reliability” were assessed in 138 and 27 studies, respectively.

    No studies assessed “measurement error” or “cross-cultural validity/measurement invariance.”Of note, 46 studies performed translation of PROMs, but none of them assessed measurement invariance or differential item functioning. Furthermore, although 75 studies assessed PROM development, only one of them obtained very good methodological quality.

    Evidence Synthesis

    The results for the evidence synthesis for the PROMs are summarized in Table 2. PROMs with at least a moderate level of evidence for ≥5 measurement properties include the Adherence Starts with Knowledge 20 (ASK-20), Compliance Questionnaire-Rheumatology (CQR), General Medication Adherence Scale (GMAS), Hill-Bone Scale, Immunosuppressant Therapy Barrier Scale (ITBS), MAR-Scale revised, MARS-5, MARS-9, MMAS-4, MMAS-8, Self-efficacy for Appropriate Medication Adherence Scale (SEAMS), Satisfaction with Iron Chelation Therapy (SICT), Test of Adherence to Inhalers (TAI), and the questionnaire by Voils.

    Table 2. Evidence synthesis of measurement properties for each patient-reported outcome measure.
    PROMaNumber of studiesPROM developmentContent validityStructural validityInternal consistencyCross-cultural validity/measurement invarianceReliabilityMeasurement errorCriterion validityHypotheses testing for construct validityResponsiveness
    Adult Asthma Adherence Questionnaire10b?c/Bd?/B−e/Cf0000−/B+g/B
    Adult AIDS Clinical Trials Group80?/Dh?/B+/Ai0000±j/C+/C
    Antidepressant Adherence Scale1D?/D0−/D0000−/C0
    Adherence Barrier Questionnaire2D0?/B+/A0000−/B0
    Adherence tool for chronic myelomonocytic leukemia1D00−/C0000−/D−/D
    Adherence Evaluation of Osteoporosis Treatment Questionnaire-12100000000+/A+/A
    Antipsychotic Medication Beliefs and Attitudes Scale1D+/C−/B+/A0000+/B0
    Adherence to Pulmonary Rehabilitation Questionnaire1C+/C?/B+/D000000
    ARMSk1C+/C?/B+/A0?/C00+/B0
    ARMS-710?/C−/A+/A0+/B0000
    ASKl-12100?/B+/A0+/B00+/A0
    ASK-203B+/B?/B+/D0+/B00±/A−/B
    Attitudes to mesalamine questionnaire1D0000000+/A+/A
    Adherence self-report questionnaire10+/C000−/D000−/B
    Axelsson et alm [39]1D0?/B+/A0000+/C0
    Basel Assessment of Adherence to Immunosuppressive Medications Scale10+/B?/B+/D—n+/A00+/B0
    Brief Evaluation of Medication Influences and Beliefs1D?/D?/C−/D0?/D00−/D+/C
    Beliefs Related to Medication Adherence1D?/D?/D+/D0000+/D0
    Brief Medication Adherence Scale1D0?/B?/D0+/B00+/C0
    Beliefs about Medication Compliance Scale1D+/C?/B+/A000000
    Brief Medication Questionnaire4D?/C000+/B00+/B+/C
    Center for Adherence Support Evaluation Adherence Index200000n?/D00+/B−/B
    Chronic Disease Compliance Instrument3A+/A?/B+/B—n000−/B0
    CEAT-VIH10+/B0+/D0000+/C0
    Chaiyachatiet alm [40]1000000000−/C
    Compliance assessment1000?/D0000+/B0
    Cohort Study of Medication Adherence Among Older Adults self-report tool1D0?/D00000+/B+/B
    CQRo4B+/B?/B+/A0n±/B00±/B+/A
    CQR-510+/A+/D+/A0000+/B−/B
    Da et alm [41]100000000−/C−/B
    DAIp1D00+/D0?/D00+/B0
    DAI-102000+/C0000±/A−/B
    DAI-9100?/C00−/D00−/D0
    Diagnostic Adherence to Medication Scale1B+/B000000+/A0
    Demirtas et alm [42]1B+/B?/A+/A0?/C00+/C0
    Danish version of Medication Adherence Report Scale-410?/D0+/C0000−/D0
    Diabetes Management Questionnaire1D?/C0+/A0−/C00+/B0
    Diabetes Medication Self-efficacy Scale2D+/B?/B+/D0n+/B00+/C−/B
    Environmental Barriers to Adherence Scale1000+/A0?/D00+/D0
    Eye-Drop Satisfaction Questionnaire2D+/C?/B+/A0000?/C0
    End-Stage Renal Disease Adherence Questionnaire2D+/B000n+/C00−/B0
    Every Visit Adherence Questionnaire100000000+/D0
    Five-dimension adherence model1D00000000+/A
    Fredericksen et alm [249]1C+/A000+/A0000
    General adherence tendency measure1D0000000−/B−/B
    General Medicine Adherence Scale3C+/B+/A+/A0n±/B00+/B+/C
    Godin et alm [43]2C?/C000000−/C+/C
    GTCATq1D?/B?/D−/B0?/D00+/B0
    GTCAT (reduced)20?/B−/C−/A0+/B00−/B0
    Hill-Bone Scale5B+/A?/B+/B0n000+/A0
    Hill-Bone Scale (modified)10?/C0−/D0n00000
    HIV Intention Measure1C+/B?/B+/A0000+/C0
    HIV Symptom Quality of Life Adherence Questionnaire1D0?/B+/C0000+/B0
    Iraqi Anti-Diabetic Medication Adherence Scale1C?/C0+/B0n?/C00+/B+/C
    Item Response Theory-301B+/B?/B00000?/C+/B
    Immunosuppressant Therapy Adherence Scale200?/B+/D0n000±/B0
    Immunosuppressant Therapy Barrier Scale1C+/B?/B+/A0000+/A+/A
    Kennedy et alm [44]1D+/B00000000
    Kerr et alm [45]1D0000000−/D−/D
    Least absolute shrinkage and selection operator-101B+/B?/B00000?/C+/B
    Long-Term Medication Behaviour Self-Efficacy Scale1D?/D00000000
    Modified Drug Adherence Work-up Tool20?/D?/D+/D0000−/B+/B
    Medication Adherence Questionnaire1D?/D0+/D0n+/D0000
    MAR-Scaler1C?/D?/B−/A0−/D00+/D0
    MAR-Scale (revised)2B+/B?/B+/A0000+/B0
    MARSs8D+/D?/B±/A0n?/D00±/B±/B
    MARS-1010?/D?/B+/A0n000+/B+/B
    MARS-580+/B?/B+/B0n±/B00±/A±/B
    MARS-920+/B?/B+/A0−/B00±/B0
    MASESt2D+/A?/B+/A0−/D00±/A0
    MASES-R100?/B+/D0−/C00−/B0
    Medication Adherence Self-Report Inventory6D00+/B0+/D00+/A+/A
    Medication adherence scale1D+/C−/A+/A0000−/B0
    Medication adherence survey1B+/B000000+/D0
    Medication Adherence Estimation and Differentiation Scale1C?/B+/A+/A0000+/D−/A
    Medication Intake Survey-Asthma1B+/B000−/B00+/B0
    MMASu-4180+/C?/B−/B0±/B00±/A−/B
    MMAS-7100000n000+/B0
    MMAS-8270+/B±/A±/A0n+/B00±/A−/B
    MMAS-9100+/A−/A0?/C00−/B0
    Medication Nonpersistence Scale1C?/C+/A+/D0000+/C−/D
    Medical Outcomes Study General Adherence Scale1000+/D0000−/C0
    Multiple Sclerosis Treatment Adherence Questionnaire1C?/B0−/A0000+/A0
    Outcome Expectations for Osteoporosis Medication Adherence Scale2D+/B±/A+/A0000−/C0
    Perceived Barriers to Antiretroviral Therapy Adherence Scale1B+/C?/B?/C0n−/C00+/C0
    Pictographic Self-Efficacy Scale1D+/B0−/A0−/D00−/B0
    Patient Rating of Compliance Scale100000000+/B0
    Patient Preference Questionnaire1D+/D0+/D000000
    Probabilistic Medication Adherence Scale1C±/C−/A+/C000000
    Number of pills taken or prescribed2000?/D0−/D00±/C0
    Questionnaire for Adherence with Topical Treatments in Psoriasis1D+/B000000±/D0
    Question of Interest10?/D000000−/D−/D
    SCIv20?/C+/A00−/B00±/A−/B
    SCI-R10+/B?/B+/A0n?/D00+/D+/D
    Strathclyde Compliance Risk Assessment Tool2000+/B0000+/B+/B
    Summary of Diabetes Self-care Activities200000000±/C0
    Self-Efficacy for Appropriate Medication Adherence Scale3B+/B?/B+/A0+/A00±/A0
    Self-efficacy scale1D?/B?/B+/A0000?/D0
    Self-report measures of adherence100000?/B00+/B0
    Self-report on adherence100000000−/C+/C
    Self-Efficacy for Osteoporosis Medication Adherence Scale2D+/B−/A+/A0000+/C0
    Self-Reported Adherence Questionnaire10D000000?/D0
    Satisfaction with Iron Chelation Therapy1B+/B?/B+/A0000+/A0
    Sidorkiewicz et alm [46]1B+/B000000+/C0
    Simplified Medication Adherence Questionnaire2D?/C0+/C0+/A00+/B−/A
    Stages of Change Model Questionnaire2D0000000+/C0
    Special Projects of National Significance Adherence Survey1000+/D0000+/C0
    Self-Rating Scale Item400000000+/C+/C
    Test of Adherence to Inhalers1B?/B?/B+/D0+/A00−/C−/A
    Tan et al [47]1C?/C?/B+/A0+/A00+/B0
    Treatment Adherence Survey-Patient Version1D0000+/B00−/C0
    Therapeutic Adherence Scale for Hypertensive Patients100+/A+/A0000+/B−/B
    Topical Therapy Adherence Questionnaire1D+/D0+/D000000
    Turcu-știolică et alm [48]1D+/D0−/D0000−/D0
    Visual analog scale400000−/D00+/B0
    Validated Hemophilia Regimen Treatment Adherence Scale—On-Demand1D+/D0+/B0+/B00±/B0
    Validated Hemophilia Regimen Treatment Regimen Treatment Adherence Scale-Prophylaxis1D+/D0+/C0+/C00−/C0
    Voils et alm [49]4C+/B+/A+/A0n−/A00±/A−/B
    Vreeman et alm [50]2C?/C000n000−/C−/C
    Web-Ad-Q Questionnaire1C+/C000+/D00+/C0
    Wilson et alm [51]3B+/B0+/A0000+/D−/A

    aPROM: patient-reported outcome measurement.

    b0: Measurement properties were not assessed by the study.

    c?: intermediate.

    dB: moderate.

    e−: insufficient.

    fC: low.

    g+: sufficient.

    hD: very low.

    iA: high.

    j±: inconsistent.

    kARMS: Adherence to Refills and Medications Scale.

    lASK: Adherence Starts with Knowledge questionnaire.

    mPROMs without proper names are labeled based on the last name of the first author who developed the instrument.

    nOnly translation was done. Cross-cultural validation was not the aim of the study.

    oCQR: Compliance Questionnaire on Rheumatology.

    pDAI: Drug Attitude Inventory.

    qGTCAT: Glaucoma Treatment Compliance Assessment Tool.

    rMAR-Scale: Medication Adherence Reasons Scale.

    sMARS: Medication Adherence Rating Scale.

    tMASES: Medication Adherence Self-efficacy Scale.

    uMMAS: Morisky Medication Adherence Scale.

    vSCI: Self-Care Inventory.


    To the best of our knowledge, this is the first systematic review that comprehensively summarized PROMs for medication adherence based on the COSMIN guidelines [21,22]. Among the 214 included articles, we identified 121 unique PROMs for medication adherence. Our study revealed the most commonly evaluated medication adherence PROMs to be the MMAS-8, MMAS-4, AACTG, MARS, and MARS-5. However, being more commonly evaluated does not mean that these PROMs have the best psychometric properties. Moreover, based on the number of studies for each PROM in Table 2, most of the PROMs have too few studies to provide a strong evidence base for their use.

    Among the 15 PROMs that have been administered electronically, 3 PROMs (MAR-Scale revised, MMAS-4, and MMAS-8) have at least a moderate level of evidence for ≥5 measurement properties. Electronic administration of PROMs to measure medication adherence may be appealing in health care settings, as it may reduce the administrative burden for data collection and data entry. In addition, as web-based interventions to improve medication adherence become increasingly commonplace [250], electronic PROMs may be incorporated into web-based platforms to assess the effectiveness of these web-based interventions.

    Despite a few studies claiming the use of certain PROMs and objective measures as the gold standard for measuring medication adherence, we deliberately omitted evaluating criterion validity for these studies. As mentioned in the introduction, although objective measures such as pill count, electronic monitoring devices, and big data may measure adherence indirectly, these measures are laborious, costly, and sometimes invasive, making them unsuitable for routine clinical use. Furthermore, all these surrogate measures, including PROMs, do not predict any real biological outcomes such as a reduction in viral load, blood pressure, or glucose concentration in determining medication adherence in patients. Hence, none of these measures can be deemed as a gold standard [10].

    Measurement error was not evaluated because none of the studies reported the standard error of measurement, smallest detectable change, or limits of agreements required by the COSMIN. In addition, although translations of PROMs were performed in 46 studies, none of these studies assessed measurement invariance or differential item functioning; therefore, cross-cultural validity was not evaluated for any of the PROMs in this study. Moreover, only one study examined the interpretability of PROMs in the form of minimal detectable change for the MMAS-8 [251]. Further study on measurement error, cross-cultural validity, and interpretability of medication adherence PROMs is warranted.

    The strengths of this study include using COSMIN guidelines, which are well regarded as a consensus-based standard for evaluating the measurement properties of PROMs [23]. The COSMIN Risk of Bias checklist employed in this study is an improvement from the original COSMIN checklist with several improvements in the standards for evaluation [21,22]. We also used sensitive search filters to retrieve and include as many potentially relevant articles as possible.

    One limitation related to this study was that the selection of articles and evaluation of psychometric properties were subjective in nature and may have been prone to judgment bias. Nevertheless, the requirement by COSMIN to have 2 independent reviewers and the need for a third reviewer to reach a consensus in the case of any discrepancy occurring has helped reduce the risk of judgment bias [22,252].

    Conclusions

    In summary, 121 unique medication adherence PROMs were identified in 214 studies. On the basis of the COSMIN guidelines, PROMs with at least a moderate level of evidence for ≥5 measurement properties include the ASK-20, CQR, GMAS, Hill-Bone Scale, ITBS, MAR-Scale revised, MARS-5, MARS-9, MMAS-4, MMAS-8, SEAMS, SICT, TAI, and questionnaire by Voils. Of these, only the GMAS has sufficient (+) ratings for at least four measurement properties. We believe this study would assist clinicians and researchers in selecting suitable PROMs to measure medication adherence among patients. Future research may consider validating measurement errors and cross-cultural validity to further improve the insights on the measurement properties of these PROMs.

    Acknowledgments

    The authors would like to thank Librarian Ms Wong Suei Nee at the National University of Singapore Medical Library for her advice on search strategies. This research was supported by the SingHealth Regional Health System Population-based, Unified, Learning System for Enhanced and Sustainable Health (PULSES) Centre Grant—Measuring Medication Adherence in Singapore (CGDec19S03), and PULSES Fellowship (CGDec19F02).

    Kwan Yu Heng, Weng Si Dun, and Dionne Hui Fang Loh contributed equally as co-first authors. Truls Østbye, Low Lian Leng, Hayden Barry Bosworth, and Julian Thumboo contributed equally as senior coauthors.

    Conflicts of Interest

    None declared.

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    Multimedia Appendix 1

    Search strategy.

    DOCX File , 33 KB
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    Multimedia Appendix 2

    Characteristics of patient reported outcome measures.

    DOCX File , 45 KB
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    Multimedia Appendix 3

    Assessment of psychometric properties of patient-reported outcome measures.

    DOCX File , 64 KB
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    ‎
    AACTG: Adult AIDS Clinical Trials Group
    ASK-20: Adherence Starts with Knowledge 20
    COSMIN: Consensus-based Standards for the selection of health Measurement INstruments
    CQR: Compliance Questionnaire-Rheumatology
    GMAS: General Medication Adherence Scale
    ITBS: Immunosuppressant Therapy Barrier Scale
    MAR-Scale: Medication Adherence Reasons Scale
    MARS: Medication Adherence Rating Scale
    MARS-5: five-item Medication Adherence Rating Scale
    MARS-9: nine-item Medication Adherence Rating Scale
    MMAS-4: four-item Morisky Medication Adherence Scale
    MMAS-8: eight-item Morisky Medication Adherence Scale
    PROM: patient-reported outcome measure
    PULSES: Population-based, Unified, Learning System for Enhances and Sustainable Health
    SEAMS: self-efficacy for appropriate medication adherence scale
    SICT: satisfaction with iron chelation therapy
    TAI: Test of Adherence to Inhalers

    Edited by CL Parra-Calderón; submitted 11.04.20; peer-reviewed by P Kardas, E Román-Villarán, S Wee; comments to author 14.06.20; revised version received 05.07.20; accepted 08.08.20; published 09.10.20

    Copyright

    ©Yu Heng Kwan, Si Dun Weng, Dionne Hui Fang Loh, Jie Kie Phang, Livia Jia Yi Oo, Dan V Blalock, Eng Hui Chew, Kai Zhen Yap, Corrinne Yong Koon Tan, Sungwon Yoon, Warren Fong, Truls Østbye, Lian Leng Low, Hayden Barry Bosworth, Julian Thumboo. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 09.10.2020.

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