Discontinuing Treatment Against Medical Advice: The Role of Perceived Autonomy Support From Providers in Relapsing-Remitting Multiple Sclerosis

Discontinuing Treatment Against Medical Advice: The Role of Perceived Autonomy Support From... Abstract Background Long-term medication adherence is problematic among patients with chronic medical conditions. To our knowledge, this was the first study to examine factors associated with nonadherence among patients with relapsing-remitting multiple sclerosis who discontinue disease-modifying treatments against medical advice. Purpose To examine differences in perceived provider autonomy support between disease-modifying treatment–adherent relapsing-remitting multiple sclerosis patients and relapsing-remitting multiple sclerosis patients who discontinued disease-modifying treatments against medical advice. Methods Self-report questionnaires and a neurologic exam were administered to demographically matched adherent (n = 50) and nonadherent (n = 79) relapsing- remitting multiple sclerosis patients from the Midwest and Northeast USA. Results Adherent patients reported greater perceived autonomy support from their treatment providers, F(1, 124) = 28.170, p < .001, partial η2 = .185. This difference persisted after controlling for current multiple sclerosis healthcare provider, education, disease duration, Expanded Disability Status Scale, perceived barriers to adherence, and prevalence of side effects, F(1, 121) = 9.61, p = .002, partial η2 = .074. Neither depressive symptoms, F(1, 124) = 1.001, p > .05, partial η2 = .009, nor the occurrence of a major depressive episode, χ2(1, N = 129) = .288, p > .05, differed between adherent and nonadherent patients. Conclusions Greater perceived autonomy support from treatment providers may increase adherence to disease-modifying treatments among patients who discontinue treatment against medical advice. Results may inform interventions for patients who discontinue treatment against medical advice. Multiple sclerosis, Medication nonadherence, Against medical advice, Autonomy support Introduction Poor medication adherence is a longstanding public health challenge to both patients and providers. This is especially problematic among patients with chronic conditions [1], as nearly half demonstrate poor adherence or nonadherence [2]. Because long-term treatments often aim to reduce the likelihood of future symptoms, not ameliorate current ones, the lack of immediate observable benefit can result in poor adherence among patients that require continuous medication regimens. Nonadherence is a complex and multifaceted construct [3, 4] that can include refusing recommended treatment, deviating from prescribed regimens, missing recommended doses, or prematurely terminating treatment against medical advice [5]. Because most adherence studies focus on patients’ commitment to treatment or suboptimal adherence, little is known about nonadherent patients who prematurely discontinue treatment and refuse to re-initiate treatment against medical advice. Self-determination theory suggests that health behavior change is more likely when patients have autonomous motivation (motivation based on interest and personal values) rather than controlled motivation (motivation driven by external controls or directives) [6]. Self-determination theory also suggests that providers can increase or diminish patient autonomous motivation to the extent that they provide autonomy support for the behavior change [6]. Research indicates that autonomy support that in turn fosters autonomous motivation and perceived competence is a key component for optimizing healthcare outcomes in chronic illnesses [7, 8]. Many studies also indicate that autonomous regulation and perceived provider autonomy support are associated with better medication adherence [7, 8]. Perceived autonomy support has been shown to promote treatment adherence, but generally focuses on improving commitment to treatment among patients who demonstrate poor or suboptimal adherence (e.g., missing doses). Physician endorsement and confidence in treatment efficacy can promote the initiation and persistence of long-term adherence [9]. However, common recommendations, such as collectively identifying and addressing barriers, devising solutions, and tracking progress [10], may not benefit patients who prematurely discontinue prescribed medications against medical advice. Consequently, little is known about perceived autonomy support among patients who prematurely discontinue treatment against medical advice. Patients with multiple sclerosis, commonly exhibit poor adherence to disease-modifying therapies that decrease future lesion formation and disease activity [11]. Up to 50% of multiple sclerosis patients prematurely discontinue disease-modifying treatments within the first 2 years of initiating treatment [12]. Despite this, nonadherence in the multiple sclerosis literature typically refers to missing one or more doses over a specified period of time [13]. However, missing one dose may not be clinically relevant because the number of doses needed to achieve therapeutic effectiveness can vary by medication and patient characteristics. Studies examining ways to improve adherence in multiple sclerosis have found that perceived support from neurologists is among the most significant factors associated with optimal medication adherence [14]. Techniques such as autonomy support can strengthen patient–provider relationships by providing opportunities for patients to express their concerns, preferences, and needs [15, 16]. This approach promotes mutual decision making between patients and providers [17–21] and increases commitment to treatment by having patients assume an active role [22]. To our knowledge, this is the first study to examine perceived provider autonomy support among nonadherent relapsing-remitting multiple sclerosis patients who have discontinued disease-modifying treatments against medical advice. We hypothesized that nonadherent patients with multiple sclerosis would endorse less provider support than adherent patients with multiple sclerosis. Methods Participants and Procedures Nonadherent patients commonly exhibit poor attendance to clinic appointments; therefore, we used a multipronged method of recruitment including sending letters to patients, advertising in the Mid America Multiple Sclerosis Newsletter, promoting study involvement through Facebook and Craigslist, and approaching patients in the neurology clinic. Altogether, three multiple sclerosis specialty clinics in the Midwest and Northeastern USA were involved in recruiting nonadherent patients (n = 79), which were used as part of a larger study examining the efficacy of talk therapy in treating nonadherence in multiple sclerosis [23]. Adherent (n = 50) subjects were recruited from a multiple sclerosis specialty clinic in the Midwest for comparison. Both groups completed the same baseline protocol (N = 129). Inclusion criteria for all participants were as follows: (a) diagnosis of relapsing-remitting multiple sclerosis by a board-certified neurologist based on established guidelines [24]; (b) physician recommendation to take a disease-modifying treatment; (c) no severe sensory, motor, physical, or neurological impairment that would make participation in the study insurmountable; (d) no history of nervous system disorder other than multiple sclerosis; (e) at least 18 years of age; and (f) English speaking. Additional inclusion criteria for the adherent participants required them to have taken at least 80% of prescribed disease-modifying treatment doses over the previous 8 weeks. We used a validated single-item self-report measure of medication adherence that asked participants the number of doses they had missed in the preceding 8 weeks [25]. In an 8-week longitudinal study examining treatment adherence in multiple sclerosis, this single-item measure was highly correlated with adherence as measured by both self-report diaries (r = 0.71, p < .001) and electronic monitoring (r = 0.70, p < .001) [4]. Three additional criteria were required to meet inclusion as a nonadherent participant: (i) The patient reported that they had discontinued and did not plan to re-initiate a disease-modifying therapy. (ii) The patient’s treatment providers nevertheless continued to recommend disease-modifying therapy treatment (which was confirmed by contacting providers when possible). (iii) A study neurologist or neurology nurse practitioner determined that the patient would benefit from disease-modifying therapy re-initiation, but the patient nevertheless declined treatment. Measures Perceived autonomy support The Health Care Climate Questionnaire assesses patients’ perceived autonomy support from healthcare providers [26]. Participants rated perceived support from their healthcare providers regarding multiple sclerosis medication using a Likert-type scale ranging from 1 (not at all true) to 7 (very true), with higher scores indicating greater perceived autonomy support from their multiple sclerosis healthcare providers. Example questions include “I feel that my multiple sclerosis doctor understands how I see things with respect to my multiple sclerosis medications” and “My multiple sclerosis doctor tries to understand how I see my multiple sclerosis medications before suggesting any changes.” Depression The Mini International Neuropsychiatric Interview: Depression module is a semistructured psychiatric interview that conforms to the Diagnostic and Statistical Manual of Mental Disorders (4th ed.; text rev.; DSM-IV-TR) standards for diagnosing mental disorders and aids in clinical diagnosis of depression in multiple sclerosis [27]. The Mental Health Inventory–depression subscale was used to assess the severity of self-reported depressive symptoms [28]. Participants rate their emotions over the prior 4 weeks on a six-point scale, with higher scores indicating fewer depressive symptoms. Clinical disease-related characteristics The Expanded Disability Status Scale [29] is a measure of multiple sclerosis disease progression and neurological impairment. A clinician assesses eight functional systems and combines the ratings for a composite score ranging from 0 (normal neurologic exam) to 10 (death due to multiple sclerosis). Barriers to adherence The Multiple Sclerosis–Treatment Adherence Question naire [30] assesses the extent to which common barriers influence patients’ decisions to take disease-modifying treatment. The questionnaire asks “How important were the following factors in deciding not to take your medications?” and includes items such as “memory problems,” “side effects of medication,” and “dissatisfaction with medication.” Adherent participants completed the same questionnaire, but responded to the instruction, “To what extent does each of the following factors make it difficult for you to adhere to your multiple sclerosis medication?” Participants respond on a four-point scale, with higher scores indicating the barrier was extremely important. This measure was included for post hoc analyses to examine how barriers impact against medical advice status. Because disease-modifying treatment side effects are one of the most commonly reported reasons for premature discontinuation [31], we also examined the difference between adherent and nonadherent relapsing-remitting multiple sclerosis patients on a self-report, open-ended question asking “Why did you stop taking each medication (please list and describe if applicable)?” Responses were coded as 1 if the term “side effects” or specific side effects were listed as the reason for discontinuing a disease-modifying treatment, and 0 if side effects were not mentioned. Data Analytic Strategy Group differences for demographic and clinical variables, such as disease duration, were assessed using t-tests and chi-square analysis; significant variables were included as covariates in subsequent analyses. Analysis of variance was used to examine group differences on measures of interest. These were followed by analysis of covariance controlling for possible confounding variables. Chi-square analysis was used to examine between-group differences in DSM-IV-TR mood diagnoses (depression). All analyses were conducted in IBM SPSS 24, and significance was set at p < .05. Results Descriptive Analysis Based on research examining predictors of nonadherence in multiple sclerosis, we had more than sufficient power (>0.80) to detect small to moderate effects (d = 0.44), with p ≤ .05 indicating statistical significance [32]. Table 1 summarizes descriptive characteristics of the adherent and nonadherent groups. There were no significant differences between adherent and nonadherent patients on age, t(127) = 0.749, p > .05; gender, χ2(1, N = 129) = 3.57, p > .05; or ethnicity, χ2(3, N = 129) = 4.11, p> .05. Adherent patients had more education than the nonadherent group, χ2(5, N = 129) = 11.74, p = .05. The nonadherent group had longer disease duration, t(127) = 2.98, p < .05, and greater disability, t(127) = 2.76, p < .05, than the adherent group. Significantly fewer nonadherent patients reported currently seeing a multiple sclerosis treatment provider on a regular basis compared with adherent patients, χ2(1, N = 129) = 13.08, p < .05. The majority of adherent participants reported more than 90% disease-modifying treatment adherence, while two reported the 80% minimum. Table 1 Descriptive characteristics of the sample Adherent subjects Nonadherent subjects n (%) or mean ± SD n (%) or mean ± SD Gender  Female 38 (76) 70 (88.6)  Male 12 (24) 9 (11.3) Race  Caucasian 42 (84) 64 (81)  African American 6 (12) 15 (19)  Hispanic/Latino 1 (2) –  Other 1 (2) 1 (1.3) Age (years) 43.94 ± 9.61 45.34 ± 10.78 Education*  Less than high school graduate 1 (2) 3 (3.7)  High school graduate 8 (16) 4 (5)  Some college 10 (20) 34 (43) Graduated 2-year college with associates degree 6 (12) 8 (10.1)  Graduated 4-year college with bachelor’s or master’s degree 22 (44) 29 (36.7) Doctoral/Professional degree or other 3 (6) 1 (1.2) Duration of diagnosis (years)* 7.55 ± 6.37 11.62 ± 8.11 EDSS* 2.35 ± 1.26 3.00 ± 1.34 DMT Current use Previous use  Copaxone 33 (66) 56 (70.9)  Avonex 10 (20) 34 (43)  Betaseron 10 (20) 23 (29.1)  Novantrone – 1 (1.3)  Rebif 6 (12) 21 (26.5)  Tysabri 3 (6) 5 (6.3)  Gilenya 7 (14) 3 (3.7)  Aubagio 5 (10) –  Extavia 2 (4) –  Tecfidera 10 (20) 1 (1.3) Current MS healthcare provider** 49 (98) 59 (74.6) DMT adherence of 90% 48 (96) – Adherent subjects Nonadherent subjects n (%) or mean ± SD n (%) or mean ± SD Gender  Female 38 (76) 70 (88.6)  Male 12 (24) 9 (11.3) Race  Caucasian 42 (84) 64 (81)  African American 6 (12) 15 (19)  Hispanic/Latino 1 (2) –  Other 1 (2) 1 (1.3) Age (years) 43.94 ± 9.61 45.34 ± 10.78 Education*  Less than high school graduate 1 (2) 3 (3.7)  High school graduate 8 (16) 4 (5)  Some college 10 (20) 34 (43) Graduated 2-year college with associates degree 6 (12) 8 (10.1)  Graduated 4-year college with bachelor’s or master’s degree 22 (44) 29 (36.7) Doctoral/Professional degree or other 3 (6) 1 (1.2) Duration of diagnosis (years)* 7.55 ± 6.37 11.62 ± 8.11 EDSS* 2.35 ± 1.26 3.00 ± 1.34 DMT Current use Previous use  Copaxone 33 (66) 56 (70.9)  Avonex 10 (20) 34 (43)  Betaseron 10 (20) 23 (29.1)  Novantrone – 1 (1.3)  Rebif 6 (12) 21 (26.5)  Tysabri 3 (6) 5 (6.3)  Gilenya 7 (14) 3 (3.7)  Aubagio 5 (10) –  Extavia 2 (4) –  Tecfidera 10 (20) 1 (1.3) Current MS healthcare provider** 49 (98) 59 (74.6) DMT adherence of 90% 48 (96) – EDSS Expanded Disability Status Scale; DMT disease-modifying treatment; MS multiple sclerosis. *p ≤ .05, **p < .01. View Large Table 1 Descriptive characteristics of the sample Adherent subjects Nonadherent subjects n (%) or mean ± SD n (%) or mean ± SD Gender  Female 38 (76) 70 (88.6)  Male 12 (24) 9 (11.3) Race  Caucasian 42 (84) 64 (81)  African American 6 (12) 15 (19)  Hispanic/Latino 1 (2) –  Other 1 (2) 1 (1.3) Age (years) 43.94 ± 9.61 45.34 ± 10.78 Education*  Less than high school graduate 1 (2) 3 (3.7)  High school graduate 8 (16) 4 (5)  Some college 10 (20) 34 (43) Graduated 2-year college with associates degree 6 (12) 8 (10.1)  Graduated 4-year college with bachelor’s or master’s degree 22 (44) 29 (36.7) Doctoral/Professional degree or other 3 (6) 1 (1.2) Duration of diagnosis (years)* 7.55 ± 6.37 11.62 ± 8.11 EDSS* 2.35 ± 1.26 3.00 ± 1.34 DMT Current use Previous use  Copaxone 33 (66) 56 (70.9)  Avonex 10 (20) 34 (43)  Betaseron 10 (20) 23 (29.1)  Novantrone – 1 (1.3)  Rebif 6 (12) 21 (26.5)  Tysabri 3 (6) 5 (6.3)  Gilenya 7 (14) 3 (3.7)  Aubagio 5 (10) –  Extavia 2 (4) –  Tecfidera 10 (20) 1 (1.3) Current MS healthcare provider** 49 (98) 59 (74.6) DMT adherence of 90% 48 (96) – Adherent subjects Nonadherent subjects n (%) or mean ± SD n (%) or mean ± SD Gender  Female 38 (76) 70 (88.6)  Male 12 (24) 9 (11.3) Race  Caucasian 42 (84) 64 (81)  African American 6 (12) 15 (19)  Hispanic/Latino 1 (2) –  Other 1 (2) 1 (1.3) Age (years) 43.94 ± 9.61 45.34 ± 10.78 Education*  Less than high school graduate 1 (2) 3 (3.7)  High school graduate 8 (16) 4 (5)  Some college 10 (20) 34 (43) Graduated 2-year college with associates degree 6 (12) 8 (10.1)  Graduated 4-year college with bachelor’s or master’s degree 22 (44) 29 (36.7) Doctoral/Professional degree or other 3 (6) 1 (1.2) Duration of diagnosis (years)* 7.55 ± 6.37 11.62 ± 8.11 EDSS* 2.35 ± 1.26 3.00 ± 1.34 DMT Current use Previous use  Copaxone 33 (66) 56 (70.9)  Avonex 10 (20) 34 (43)  Betaseron 10 (20) 23 (29.1)  Novantrone – 1 (1.3)  Rebif 6 (12) 21 (26.5)  Tysabri 3 (6) 5 (6.3)  Gilenya 7 (14) 3 (3.7)  Aubagio 5 (10) –  Extavia 2 (4) –  Tecfidera 10 (20) 1 (1.3) Current MS healthcare provider** 49 (98) 59 (74.6) DMT adherence of 90% 48 (96) – EDSS Expanded Disability Status Scale; DMT disease-modifying treatment; MS multiple sclerosis. *p ≤ .05, **p < .01. View Large Primary Analysis Adherent subjects (6.16 ± 0.92) reported significantly greater perceived autonomy support from their providers than nonadherent participants (4.81 ± 1.74) on the Health Care Climate Questionnaire, F(1, 124) = 28.170, p < .001, partial η2 = .185. Follow-up Analyses There were no significant differences in endorsement of depressive symptoms between adherent (74.20 ± 21.69) and nonadherent (70.63 ± 25.61) groups (F(1, 124) = 1.001, p > .05, partial η2 = .009). Similarly, the frequency of a major depressive episode was not significantly different between adherent (n = 10, 20%) and nonadherent (n = 19, 24%) groups, χ2(1, N = 129) = .288, p > .05. Group differences are shown in Tables 2 and 3. On the self-report measure assessing the use of the term “side-effects” or specified side effects as the primary reason for discontinuing disease-modifying treatments, nonadherent (n = 60, 76%) patients reported side effects as a reason for discontinuing disease-modifying treatments significantly more often than adherent (n = 21, 42%) patients, χ2(1, N = 129) = 15.10, p < .001. We also examined the extent to which barriers of adherence influenced patients’ decisions to adhere to disease-modifying treatments. Results revealed that nonadherent participants reported that barriers influenced their disease-modifying treatment adherence to a significantly greater extent than adherent participants, F(1, 123) = 39.79, p < .001, partial η2 = .240. Group differences are shown in Table 2. Notably, nonadherent patients endorsed side effects of injection (50.6%), side effects of medication (68.7%), and dissatisfaction with medication (55.4%) as extremely important barriers to disease-modifying therapy adherence. To determine whether group differences remained in support of adherent patients perceiving greater autonomy support from providers than nonadherent patients, barriers to adherence and prevalence of side effects were included as covariates, along with education, disease duration, Expanded Disability Status Scale, and current multiple sclerosis healthcare provider. Even with the addition of barriers to adherence and prevalence of side effects as covariates, perceived autonomy support from providers remained significantly different between groups, F(1, 121) = 9.61, p = .002, partial η2 = .074. Table 2 Comparison of perceived autonomy support, depressive symptoms, and the extent to which common barriers influence adherence to disease-modifying treatments between adherent and nonadherent relapsing-remitting multiple sclerosis (RRMS) subjects Measure Adherent RRMS mean (SD) Nonadherent RRMS mean (SD) F(df) p HCCQ 6.16 (0.92) 4.81 (1.74) 28.17 (1, 124) <.001 MHI depression subscale 74.20 (21.69) 70.63 (25.61) 1.00 (1, 124) .319 MSTAQ 8.52 (8.13) 17.21 (7.17) 38.79 (1, 123) <.001 Measure Adherent RRMS mean (SD) Nonadherent RRMS mean (SD) F(df) p HCCQ 6.16 (0.92) 4.81 (1.74) 28.17 (1, 124) <.001 MHI depression subscale 74.20 (21.69) 70.63 (25.61) 1.00 (1, 124) .319 MSTAQ 8.52 (8.13) 17.21 (7.17) 38.79 (1, 123) <.001 HCCQ Heal Care Climate Questionnaire; MHI Mental Health Inventory–depression subscale; MSTAQ Multiple Sclerosis Treatment Adherence Questionnaire. View Large Table 2 Comparison of perceived autonomy support, depressive symptoms, and the extent to which common barriers influence adherence to disease-modifying treatments between adherent and nonadherent relapsing-remitting multiple sclerosis (RRMS) subjects Measure Adherent RRMS mean (SD) Nonadherent RRMS mean (SD) F(df) p HCCQ 6.16 (0.92) 4.81 (1.74) 28.17 (1, 124) <.001 MHI depression subscale 74.20 (21.69) 70.63 (25.61) 1.00 (1, 124) .319 MSTAQ 8.52 (8.13) 17.21 (7.17) 38.79 (1, 123) <.001 Measure Adherent RRMS mean (SD) Nonadherent RRMS mean (SD) F(df) p HCCQ 6.16 (0.92) 4.81 (1.74) 28.17 (1, 124) <.001 MHI depression subscale 74.20 (21.69) 70.63 (25.61) 1.00 (1, 124) .319 MSTAQ 8.52 (8.13) 17.21 (7.17) 38.79 (1, 123) <.001 HCCQ Heal Care Climate Questionnaire; MHI Mental Health Inventory–depression subscale; MSTAQ Multiple Sclerosis Treatment Adherence Questionnaire. View Large Table 3 Chi-square test for clinical depression by disease-modifying treatment adherence Major depressive episode, current Adherent RRMS mean (SD) Nonadherent RRMS mean (SD) χ2(df, N) p Yes 10 19 0.288 (1, 129) .591 No 40 60 Major depressive episode, current Adherent RRMS mean (SD) Nonadherent RRMS mean (SD) χ2(df, N) p Yes 10 19 0.288 (1, 129) .591 No 40 60 RRMS relapsing-remitting multiple sclerosis. View Large Table 3 Chi-square test for clinical depression by disease-modifying treatment adherence Major depressive episode, current Adherent RRMS mean (SD) Nonadherent RRMS mean (SD) χ2(df, N) p Yes 10 19 0.288 (1, 129) .591 No 40 60 Major depressive episode, current Adherent RRMS mean (SD) Nonadherent RRMS mean (SD) χ2(df, N) p Yes 10 19 0.288 (1, 129) .591 No 40 60 RRMS relapsing-remitting multiple sclerosis. View Large Discussion Results of the current study show that relapsing-remitting multiple sclerosis patients who discontinue disease-modifying treatments against medical advice report less perceived autonomy support from their physicians than patients who adhere to disease-modifying therapies. This finding is independent of side effects, whether patients are actively seeing a multiple sclerosis treatment provider, the extent to which barriers to adherence influence patients’ decision to take disease-modifying treatments, education, disability, and disease duration. When taken in context with other research, including a recent clinical trial using motivational interviewing to improve adherence [32] and the use of shared decision making to increase adherence [33], the current findings emphasize the importance of perceived autonomy support from providers for optimizing disease-modifying treatment adherence [34, 35]. Notably, adherent and nonadherent relapsing-remitting multiple sclerosis patients did not show differences in depressive symptoms or frequency of a major depressive episode. This null finding contrasts with the literature showing a strong association between suboptimal adherence and depression in multiple sclerosis [36]. This suggests that patients who chose to abstain from treatment, against medical advice, may differ from nonadherent patient samples in other studies that choose to take disease-modifying treatments but demonstrate suboptimal adherence [37]. Patients who choose not to take disease-modifying therapies against medical advice may require a tailored intervention or a more patient-centered approach to improve adherence. One study examining a novel Motivational Interviewing/Cognitive Behavioral Therapy intervention found that patients who discontinued disease-modifying treatments against medical advice were significantly more likely to report an intention to re-initiate disease-modifying treatments than patients who did not receive the Motivational Interviewing/Cognitive Behavioral Therapy intervention [32]. Employing patient-centered approaches, such as Motivational Interviewing/Cognitive Behavioral Therapy, may aid in strengthening patient–provider relationships because techniques such as autonomy support and reflective listening enable providers to better understand patients’ preferences and needs [15, 16] and promote mutual decision making [17–21], which has been shown to increase patients’ commitment to treatment [22]. Consistent with the self-determination theory, these findings reiterate the significance of patients’ perceptions of autonomy support and demonstrate that interventions aimed at increasing autonomy support may aid in improving adherence in chronic medical conditions [6–8]. A primary limitation of the current study is the use of a cross-sectional design. Future studies should aim to prospectively examine perceived provider support in relapsing-remitting multiple sclerosis patients who discontinued disease-modifying treatments against medical advice. Despite this limitation, the current study is the first to examine perceived provider support in relapsing-remitting multiple sclerosis patients who prematurely discontinue disease-modifying treatments against medical advice. Our findings may inform future interventions aimed at improving treatment adherence among patients that demonstrate poor adherence, as well as those who prematurely discontinue treatment against medical advice. Future research should elucidate the role of perceived provider autonomy support between patients who demonstrate suboptimal adherence and nonadherence and explore interventions aimed at improving perceived provider autonomy support in both. Future research should also assess the feasibility of physician training in autonomy support and the clinical outcomes associated with employing such techniques. Finally, future research should include additional measures of autonomous self-regulation and perceived competency for adhering to prescribed medication regimens to more fully examine the constructs associated with the self-determination theory. Compliance with Ethical Standards Authors’ Statement of Conflict of Interest and Adherence to Ethical StandardsJared Bruce provides unbranded talks as a member of the Novartis Speaker’s Bureau. He has also received research funding from Cephalon and is a paid consultant to the National Hockey League. Morgan Glusman, Amanda Bruce, Joanie Thelen, Julia Smith, Sharon Lynch, Delwyn Catley, and Kym Bennett declare that they have no conflicts of interest. Ethical Approval All procedures followed were in accordance with ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2000. Informed Consent Informed consent was obtained from all patients included in the study. Acknowledgments This work was funded in part by the University of Missouri–Kansas City School of Graduate Studies and the Women’s Council Graduate Assistance Fund to the first author. Funding was also provided by a grant from the National Multiple Sclerosis Society (HC 0138 and HC 1411 01993) to J. Bruce. References 1. Sokol MC , McGuigan KA , Verbrugge RR , Epstein RS . Impact of medication adherence on hospitalization risk and healthcare cost . Med Care . 2005 ; 43 : 521 – 530 . Google Scholar CrossRef Search ADS PubMed 2. Sabate E. Adherence to Long-Term Therapies—Evidence for Action . Geneva, Switzerland : World Health Organization ; 2003 . 3. Wong J , Gomes T , Mamdani M , Manno M , O’Connor PW . Adherence to multiple sclerosis disease-modifying therapies in Ontario is low . Can J Neurol Sci . 2011 ; 38 : 429 – 433 . Google Scholar CrossRef Search ADS PubMed 4. Bruce JM , Hancock LM , Lynch SG . Objective adherence monitoring in multiple sclerosis: Initial validation and association with self-report . Mult Scler . 2010 ; 16 : 112 – 120 . Google Scholar CrossRef Search ADS PubMed 5. Bruce JM , Lynch SG . Multiple sclerosis: MS treatment adherence—How to keep patients on medication ? Nat Rev Neurol . 2011 ; 7 : 421 – 422 . Google Scholar CrossRef Search ADS PubMed 6. Ng JY , Ntoumanis N , Thøgersen-Ntoumani C et al. Self-determination theory applied to health contexts: A meta-analysis . Perspect Psychol Sci . 2012 ; 7 : 325 – 340 . Google Scholar CrossRef Search ADS PubMed 7. Williams GC , Rodin GC , Ryan RM , Grolnick WS , Deci EL . Autonomous regulation and long-term medication adherence in adult outpatients . Health Psychol . 1998 ; 17 : 269 – 276 . Google Scholar CrossRef Search ADS PubMed 8. Williams GC , Patrick H , Niemiec CP et al. Reducing the health risks of diabetes: How self-determination theory may help improve medication adherence and quality of life . Diabetes Educ . 2009 ; 35 : 484 – 492 . Google Scholar CrossRef Search ADS PubMed 9. Fraser C , Hadjimichael O , Vollmer T . Predictors of adherence to glatiramer acetate therapy in individuals with self-reported progressive forms of multiple sclerosis . J Neurosci Nurs . 2003 ; 35 : 163 – 170 . Google Scholar CrossRef Search ADS PubMed 10. Von Korff M , Gruman J , Schaefer J , Curry SJ , Wagner EH . Collaborative management of chronic illness . Ann Intern Med . 1997 ; 127 : 1097 – 1102 . Google Scholar CrossRef Search ADS PubMed 11. Gold R , Wolinsky JS , Amato MP , Comi G . Evolving expectations around early management of multiple sclerosis . Ther Adv Neurol Disord . 2010 ; 3 : 351 – 367 . Google Scholar CrossRef Search ADS PubMed 12. Wong J , Gomes T , Mamdani M , Manno M , O’Connor PW . Adherence to multiple sclerosis disease-modifying therapies in Ontario is low . Can J Neurol Sci . 2011 ; 38 : 429 – 433 . Google Scholar CrossRef Search ADS PubMed 13. Treadaway K , Cutter G , Salter A et al. Factors that influence adherence with disease-modifying therapy in MS . J Neurol . 2009 ; 256 : 568 – 576 . Google Scholar CrossRef Search ADS PubMed 14. Devonshire V , Lapierre Y , Macdonell R et al. ; GAP Study Group . The Global Adherence Project (GAP): A multicenter observational study on adherence to disease-modifying therapies in patients with relapsing-remitting multiple sclerosis . Eur J Neurol . 2011 ; 18 : 69 – 77 . Google Scholar CrossRef Search ADS PubMed 15. Jackson H . Motivational interviewing and HIV drug adherence . Nurs Times . 2013 ; 109 : 21 – 23 . Google Scholar PubMed 16. Remington G , Rodriguez Y , Logan D , Williamson C , Treadaway K . Facilitating medication adherence in patients with multiple sclerosis . Int J MS Care . 2013 ; 15 : 36 – 45 . Google Scholar CrossRef Search ADS PubMed 17. Caon C , Saunders C , Smrtka J , Baxter N , Shoemaker J . Injectable disease-modifying therapy for relapsing-remitting multiple sclerosis: A review of adherence data . J Neurosci Nurs . 2010 ( suppl 5 ); 42 : S5 – S9 . Google Scholar CrossRef Search ADS PubMed 18. Gance-Cleveland B . Motivational interviewing: Improving patient education . J Pediatr Health Care . 2007 ; 21 : 81 – 88 . Google Scholar CrossRef Search ADS PubMed 19. Aloia MS , Arnedt JT , Strand M , Millman RP , Borrelli B . Motivational enhancement to improve adherence to positive airway pressure in patients with obstructive sleep apnea: A randomized controlled trial . Sleep . 2013 ; 36 : 1655 – 1662 . Google Scholar PubMed 20. Falvo D. Effective Patient Education: A Guide to Increased Adherence . Burlington, Massachusetts: Jones & Bartlett Publishers ; 2010 . 21. Turner AP , Sloan AP , Kivlahan DR , Haselkorn JK . Telephone counseling and home telehealth monitoring to improve medication adherence: Results of a pilot trial among individuals with multiple sclerosis . Rehabil Psychol . 2014 ; 59 : 136 – 146 . Google Scholar CrossRef Search ADS PubMed 22. Oshima Lee E , Emanuel EJ . Shared decision making to improve care and reduce costs . N Engl J Med . 2013 ; 368 : 6 – 8 . Google Scholar CrossRef Search ADS PubMed 23. Bruce JM , Bruce AS , Catley D et al. Being kind to your future self: Probability discounting of health decision-making . Ann Behav Med . 2016 ; 50 : 297 – 309 . Google Scholar CrossRef Search ADS PubMed 24. Polman CH , Reingold SC , Edan G et al. Diagnostic criteria for multiple sclerosis: 2005 revisions to the “McDonald Criteria” . Ann Neurol . 2005 ; 58 : 840 – 846 . Google Scholar CrossRef Search ADS PubMed 25. Bruce J , Bruce A , Lynch S et al. A pilot study to improve adherence among MS patients who discontinue treatment against medical advice . J Behav Med . 2016 ; 39 : 276 – 287 . Google Scholar CrossRef Search ADS PubMed 26. Carroll JK , Fiscella K , Epstein RM et al. Physical activity counseling intervention at a federally qualified health center: Improves autonomy-supportiveness, but not patients’ perceived competence . Patient Educ Couns . 2013 ; 92 : 432 – 436 . Google Scholar CrossRef Search ADS PubMed 27. Sheehan DV , Lecrubier Y , Sheehan KH et al. The Mini-International Neuropsychiatric Interview (M.I.N.I.): The development and validation of a structured diagnostic psychiatric interview for DSM-IV and ICD-10 . J Clin Psychiatry . 1998 ; 59 ( suppl 20 ): S22 – S33 . 28. Sherbourne CD , Hays RD , Ordway L , DiMatteo MR , Kravitz RL . Antecedents of adherence to medical recommendations: Results from the medical outcomes study . J Behav Med . 1992 ; 15 : 447 – 468 . Google Scholar CrossRef Search ADS PubMed 29. Kurtzke JF . Rating neurologic impairment in multiple sclerosis: An expanded disability status scale (EDSS) . Neurology . 1983 ; 33 : 1444 – 1452 . Google Scholar CrossRef Search ADS PubMed 30. Wicks P , Massagli M , Kulkarni A , Dastani H . Use of an online community to develop patient-reported outcome instruments: The Multiple Sclerosis Treatment Adherence Questionnaire (MS-TAQ) . J Med Internet Res . 2011 ; 13 : e12 . Google Scholar CrossRef Search ADS PubMed 31. Heesen C , Bruce J , Feys P et al. Adherence in multiple sclerosis (ADAMS): Classification, relevance, and research needs. A meeting report . Mult Scler . 2014 ; 20 : 1795 – 1798 . Google Scholar CrossRef Search ADS PubMed 32. Bruce J , Bruce A , Lynch S et al. A pilot study to improve adherence among MS patients who discontinue treatment against medical advice . J Behav Med . 2016 ; 39 : 276 – 287 . Google Scholar CrossRef Search ADS PubMed 33. Heesen C , Köpke S , Solari A , Geiger F , Kasper J . Patient autonomy in multiple sclerosis—Possible goals and assessment strategies . J Neurol Sci . 2013 ; 331 : 2 – 9 . Google Scholar CrossRef Search ADS PubMed 34. Mohr DC , Classen C , Barrera M , Jr . The relationship between social support, depression and treatment for depression in people with multiple sclerosis . Psychol Med . 2004 ; 34 : 533 – 541 . Google Scholar CrossRef Search ADS PubMed 35. Saunders C , Caon C , Smrtka J , Shoemaker J . Factors that influence adherence and strategies to maintain adherence to injected therapies for patients with multiple sclerosis . J Neurosci Nurs . 2010 ( suppl 5 ); 42 : S10 – S18 . Google Scholar CrossRef Search ADS PubMed 36. DiMatteo MR , Lepper HS , Croghan TW . Depression is a risk factor for noncompliance with medical treatment: Meta-analysis of the effects of anxiety and depression on patient adherence . Arch Intern Med . 2000 ; 160 : 2101 – 2107 . Google Scholar CrossRef Search ADS PubMed 37. Rolnick SJ , Pawloski PA , Hedblom BD , Asche SE , Bruzek RJ . Patient characteristics associated with medication adherence . Clin Med Res . 2013 ; 11 : 54 – 65 . Google Scholar CrossRef Search ADS PubMed © Society of Behavioral Medicine 2018. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices) http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Annals of Behavioral Medicine Oxford University Press

Discontinuing Treatment Against Medical Advice: The Role of Perceived Autonomy Support From Providers in Relapsing-Remitting Multiple Sclerosis

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Abstract

Abstract Background Long-term medication adherence is problematic among patients with chronic medical conditions. To our knowledge, this was the first study to examine factors associated with nonadherence among patients with relapsing-remitting multiple sclerosis who discontinue disease-modifying treatments against medical advice. Purpose To examine differences in perceived provider autonomy support between disease-modifying treatment–adherent relapsing-remitting multiple sclerosis patients and relapsing-remitting multiple sclerosis patients who discontinued disease-modifying treatments against medical advice. Methods Self-report questionnaires and a neurologic exam were administered to demographically matched adherent (n = 50) and nonadherent (n = 79) relapsing- remitting multiple sclerosis patients from the Midwest and Northeast USA. Results Adherent patients reported greater perceived autonomy support from their treatment providers, F(1, 124) = 28.170, p < .001, partial η2 = .185. This difference persisted after controlling for current multiple sclerosis healthcare provider, education, disease duration, Expanded Disability Status Scale, perceived barriers to adherence, and prevalence of side effects, F(1, 121) = 9.61, p = .002, partial η2 = .074. Neither depressive symptoms, F(1, 124) = 1.001, p > .05, partial η2 = .009, nor the occurrence of a major depressive episode, χ2(1, N = 129) = .288, p > .05, differed between adherent and nonadherent patients. Conclusions Greater perceived autonomy support from treatment providers may increase adherence to disease-modifying treatments among patients who discontinue treatment against medical advice. Results may inform interventions for patients who discontinue treatment against medical advice. Multiple sclerosis, Medication nonadherence, Against medical advice, Autonomy support Introduction Poor medication adherence is a longstanding public health challenge to both patients and providers. This is especially problematic among patients with chronic conditions [1], as nearly half demonstrate poor adherence or nonadherence [2]. Because long-term treatments often aim to reduce the likelihood of future symptoms, not ameliorate current ones, the lack of immediate observable benefit can result in poor adherence among patients that require continuous medication regimens. Nonadherence is a complex and multifaceted construct [3, 4] that can include refusing recommended treatment, deviating from prescribed regimens, missing recommended doses, or prematurely terminating treatment against medical advice [5]. Because most adherence studies focus on patients’ commitment to treatment or suboptimal adherence, little is known about nonadherent patients who prematurely discontinue treatment and refuse to re-initiate treatment against medical advice. Self-determination theory suggests that health behavior change is more likely when patients have autonomous motivation (motivation based on interest and personal values) rather than controlled motivation (motivation driven by external controls or directives) [6]. Self-determination theory also suggests that providers can increase or diminish patient autonomous motivation to the extent that they provide autonomy support for the behavior change [6]. Research indicates that autonomy support that in turn fosters autonomous motivation and perceived competence is a key component for optimizing healthcare outcomes in chronic illnesses [7, 8]. Many studies also indicate that autonomous regulation and perceived provider autonomy support are associated with better medication adherence [7, 8]. Perceived autonomy support has been shown to promote treatment adherence, but generally focuses on improving commitment to treatment among patients who demonstrate poor or suboptimal adherence (e.g., missing doses). Physician endorsement and confidence in treatment efficacy can promote the initiation and persistence of long-term adherence [9]. However, common recommendations, such as collectively identifying and addressing barriers, devising solutions, and tracking progress [10], may not benefit patients who prematurely discontinue prescribed medications against medical advice. Consequently, little is known about perceived autonomy support among patients who prematurely discontinue treatment against medical advice. Patients with multiple sclerosis, commonly exhibit poor adherence to disease-modifying therapies that decrease future lesion formation and disease activity [11]. Up to 50% of multiple sclerosis patients prematurely discontinue disease-modifying treatments within the first 2 years of initiating treatment [12]. Despite this, nonadherence in the multiple sclerosis literature typically refers to missing one or more doses over a specified period of time [13]. However, missing one dose may not be clinically relevant because the number of doses needed to achieve therapeutic effectiveness can vary by medication and patient characteristics. Studies examining ways to improve adherence in multiple sclerosis have found that perceived support from neurologists is among the most significant factors associated with optimal medication adherence [14]. Techniques such as autonomy support can strengthen patient–provider relationships by providing opportunities for patients to express their concerns, preferences, and needs [15, 16]. This approach promotes mutual decision making between patients and providers [17–21] and increases commitment to treatment by having patients assume an active role [22]. To our knowledge, this is the first study to examine perceived provider autonomy support among nonadherent relapsing-remitting multiple sclerosis patients who have discontinued disease-modifying treatments against medical advice. We hypothesized that nonadherent patients with multiple sclerosis would endorse less provider support than adherent patients with multiple sclerosis. Methods Participants and Procedures Nonadherent patients commonly exhibit poor attendance to clinic appointments; therefore, we used a multipronged method of recruitment including sending letters to patients, advertising in the Mid America Multiple Sclerosis Newsletter, promoting study involvement through Facebook and Craigslist, and approaching patients in the neurology clinic. Altogether, three multiple sclerosis specialty clinics in the Midwest and Northeastern USA were involved in recruiting nonadherent patients (n = 79), which were used as part of a larger study examining the efficacy of talk therapy in treating nonadherence in multiple sclerosis [23]. Adherent (n = 50) subjects were recruited from a multiple sclerosis specialty clinic in the Midwest for comparison. Both groups completed the same baseline protocol (N = 129). Inclusion criteria for all participants were as follows: (a) diagnosis of relapsing-remitting multiple sclerosis by a board-certified neurologist based on established guidelines [24]; (b) physician recommendation to take a disease-modifying treatment; (c) no severe sensory, motor, physical, or neurological impairment that would make participation in the study insurmountable; (d) no history of nervous system disorder other than multiple sclerosis; (e) at least 18 years of age; and (f) English speaking. Additional inclusion criteria for the adherent participants required them to have taken at least 80% of prescribed disease-modifying treatment doses over the previous 8 weeks. We used a validated single-item self-report measure of medication adherence that asked participants the number of doses they had missed in the preceding 8 weeks [25]. In an 8-week longitudinal study examining treatment adherence in multiple sclerosis, this single-item measure was highly correlated with adherence as measured by both self-report diaries (r = 0.71, p < .001) and electronic monitoring (r = 0.70, p < .001) [4]. Three additional criteria were required to meet inclusion as a nonadherent participant: (i) The patient reported that they had discontinued and did not plan to re-initiate a disease-modifying therapy. (ii) The patient’s treatment providers nevertheless continued to recommend disease-modifying therapy treatment (which was confirmed by contacting providers when possible). (iii) A study neurologist or neurology nurse practitioner determined that the patient would benefit from disease-modifying therapy re-initiation, but the patient nevertheless declined treatment. Measures Perceived autonomy support The Health Care Climate Questionnaire assesses patients’ perceived autonomy support from healthcare providers [26]. Participants rated perceived support from their healthcare providers regarding multiple sclerosis medication using a Likert-type scale ranging from 1 (not at all true) to 7 (very true), with higher scores indicating greater perceived autonomy support from their multiple sclerosis healthcare providers. Example questions include “I feel that my multiple sclerosis doctor understands how I see things with respect to my multiple sclerosis medications” and “My multiple sclerosis doctor tries to understand how I see my multiple sclerosis medications before suggesting any changes.” Depression The Mini International Neuropsychiatric Interview: Depression module is a semistructured psychiatric interview that conforms to the Diagnostic and Statistical Manual of Mental Disorders (4th ed.; text rev.; DSM-IV-TR) standards for diagnosing mental disorders and aids in clinical diagnosis of depression in multiple sclerosis [27]. The Mental Health Inventory–depression subscale was used to assess the severity of self-reported depressive symptoms [28]. Participants rate their emotions over the prior 4 weeks on a six-point scale, with higher scores indicating fewer depressive symptoms. Clinical disease-related characteristics The Expanded Disability Status Scale [29] is a measure of multiple sclerosis disease progression and neurological impairment. A clinician assesses eight functional systems and combines the ratings for a composite score ranging from 0 (normal neurologic exam) to 10 (death due to multiple sclerosis). Barriers to adherence The Multiple Sclerosis–Treatment Adherence Question naire [30] assesses the extent to which common barriers influence patients’ decisions to take disease-modifying treatment. The questionnaire asks “How important were the following factors in deciding not to take your medications?” and includes items such as “memory problems,” “side effects of medication,” and “dissatisfaction with medication.” Adherent participants completed the same questionnaire, but responded to the instruction, “To what extent does each of the following factors make it difficult for you to adhere to your multiple sclerosis medication?” Participants respond on a four-point scale, with higher scores indicating the barrier was extremely important. This measure was included for post hoc analyses to examine how barriers impact against medical advice status. Because disease-modifying treatment side effects are one of the most commonly reported reasons for premature discontinuation [31], we also examined the difference between adherent and nonadherent relapsing-remitting multiple sclerosis patients on a self-report, open-ended question asking “Why did you stop taking each medication (please list and describe if applicable)?” Responses were coded as 1 if the term “side effects” or specific side effects were listed as the reason for discontinuing a disease-modifying treatment, and 0 if side effects were not mentioned. Data Analytic Strategy Group differences for demographic and clinical variables, such as disease duration, were assessed using t-tests and chi-square analysis; significant variables were included as covariates in subsequent analyses. Analysis of variance was used to examine group differences on measures of interest. These were followed by analysis of covariance controlling for possible confounding variables. Chi-square analysis was used to examine between-group differences in DSM-IV-TR mood diagnoses (depression). All analyses were conducted in IBM SPSS 24, and significance was set at p < .05. Results Descriptive Analysis Based on research examining predictors of nonadherence in multiple sclerosis, we had more than sufficient power (>0.80) to detect small to moderate effects (d = 0.44), with p ≤ .05 indicating statistical significance [32]. Table 1 summarizes descriptive characteristics of the adherent and nonadherent groups. There were no significant differences between adherent and nonadherent patients on age, t(127) = 0.749, p > .05; gender, χ2(1, N = 129) = 3.57, p > .05; or ethnicity, χ2(3, N = 129) = 4.11, p> .05. Adherent patients had more education than the nonadherent group, χ2(5, N = 129) = 11.74, p = .05. The nonadherent group had longer disease duration, t(127) = 2.98, p < .05, and greater disability, t(127) = 2.76, p < .05, than the adherent group. Significantly fewer nonadherent patients reported currently seeing a multiple sclerosis treatment provider on a regular basis compared with adherent patients, χ2(1, N = 129) = 13.08, p < .05. The majority of adherent participants reported more than 90% disease-modifying treatment adherence, while two reported the 80% minimum. Table 1 Descriptive characteristics of the sample Adherent subjects Nonadherent subjects n (%) or mean ± SD n (%) or mean ± SD Gender  Female 38 (76) 70 (88.6)  Male 12 (24) 9 (11.3) Race  Caucasian 42 (84) 64 (81)  African American 6 (12) 15 (19)  Hispanic/Latino 1 (2) –  Other 1 (2) 1 (1.3) Age (years) 43.94 ± 9.61 45.34 ± 10.78 Education*  Less than high school graduate 1 (2) 3 (3.7)  High school graduate 8 (16) 4 (5)  Some college 10 (20) 34 (43) Graduated 2-year college with associates degree 6 (12) 8 (10.1)  Graduated 4-year college with bachelor’s or master’s degree 22 (44) 29 (36.7) Doctoral/Professional degree or other 3 (6) 1 (1.2) Duration of diagnosis (years)* 7.55 ± 6.37 11.62 ± 8.11 EDSS* 2.35 ± 1.26 3.00 ± 1.34 DMT Current use Previous use  Copaxone 33 (66) 56 (70.9)  Avonex 10 (20) 34 (43)  Betaseron 10 (20) 23 (29.1)  Novantrone – 1 (1.3)  Rebif 6 (12) 21 (26.5)  Tysabri 3 (6) 5 (6.3)  Gilenya 7 (14) 3 (3.7)  Aubagio 5 (10) –  Extavia 2 (4) –  Tecfidera 10 (20) 1 (1.3) Current MS healthcare provider** 49 (98) 59 (74.6) DMT adherence of 90% 48 (96) – Adherent subjects Nonadherent subjects n (%) or mean ± SD n (%) or mean ± SD Gender  Female 38 (76) 70 (88.6)  Male 12 (24) 9 (11.3) Race  Caucasian 42 (84) 64 (81)  African American 6 (12) 15 (19)  Hispanic/Latino 1 (2) –  Other 1 (2) 1 (1.3) Age (years) 43.94 ± 9.61 45.34 ± 10.78 Education*  Less than high school graduate 1 (2) 3 (3.7)  High school graduate 8 (16) 4 (5)  Some college 10 (20) 34 (43) Graduated 2-year college with associates degree 6 (12) 8 (10.1)  Graduated 4-year college with bachelor’s or master’s degree 22 (44) 29 (36.7) Doctoral/Professional degree or other 3 (6) 1 (1.2) Duration of diagnosis (years)* 7.55 ± 6.37 11.62 ± 8.11 EDSS* 2.35 ± 1.26 3.00 ± 1.34 DMT Current use Previous use  Copaxone 33 (66) 56 (70.9)  Avonex 10 (20) 34 (43)  Betaseron 10 (20) 23 (29.1)  Novantrone – 1 (1.3)  Rebif 6 (12) 21 (26.5)  Tysabri 3 (6) 5 (6.3)  Gilenya 7 (14) 3 (3.7)  Aubagio 5 (10) –  Extavia 2 (4) –  Tecfidera 10 (20) 1 (1.3) Current MS healthcare provider** 49 (98) 59 (74.6) DMT adherence of 90% 48 (96) – EDSS Expanded Disability Status Scale; DMT disease-modifying treatment; MS multiple sclerosis. *p ≤ .05, **p < .01. View Large Table 1 Descriptive characteristics of the sample Adherent subjects Nonadherent subjects n (%) or mean ± SD n (%) or mean ± SD Gender  Female 38 (76) 70 (88.6)  Male 12 (24) 9 (11.3) Race  Caucasian 42 (84) 64 (81)  African American 6 (12) 15 (19)  Hispanic/Latino 1 (2) –  Other 1 (2) 1 (1.3) Age (years) 43.94 ± 9.61 45.34 ± 10.78 Education*  Less than high school graduate 1 (2) 3 (3.7)  High school graduate 8 (16) 4 (5)  Some college 10 (20) 34 (43) Graduated 2-year college with associates degree 6 (12) 8 (10.1)  Graduated 4-year college with bachelor’s or master’s degree 22 (44) 29 (36.7) Doctoral/Professional degree or other 3 (6) 1 (1.2) Duration of diagnosis (years)* 7.55 ± 6.37 11.62 ± 8.11 EDSS* 2.35 ± 1.26 3.00 ± 1.34 DMT Current use Previous use  Copaxone 33 (66) 56 (70.9)  Avonex 10 (20) 34 (43)  Betaseron 10 (20) 23 (29.1)  Novantrone – 1 (1.3)  Rebif 6 (12) 21 (26.5)  Tysabri 3 (6) 5 (6.3)  Gilenya 7 (14) 3 (3.7)  Aubagio 5 (10) –  Extavia 2 (4) –  Tecfidera 10 (20) 1 (1.3) Current MS healthcare provider** 49 (98) 59 (74.6) DMT adherence of 90% 48 (96) – Adherent subjects Nonadherent subjects n (%) or mean ± SD n (%) or mean ± SD Gender  Female 38 (76) 70 (88.6)  Male 12 (24) 9 (11.3) Race  Caucasian 42 (84) 64 (81)  African American 6 (12) 15 (19)  Hispanic/Latino 1 (2) –  Other 1 (2) 1 (1.3) Age (years) 43.94 ± 9.61 45.34 ± 10.78 Education*  Less than high school graduate 1 (2) 3 (3.7)  High school graduate 8 (16) 4 (5)  Some college 10 (20) 34 (43) Graduated 2-year college with associates degree 6 (12) 8 (10.1)  Graduated 4-year college with bachelor’s or master’s degree 22 (44) 29 (36.7) Doctoral/Professional degree or other 3 (6) 1 (1.2) Duration of diagnosis (years)* 7.55 ± 6.37 11.62 ± 8.11 EDSS* 2.35 ± 1.26 3.00 ± 1.34 DMT Current use Previous use  Copaxone 33 (66) 56 (70.9)  Avonex 10 (20) 34 (43)  Betaseron 10 (20) 23 (29.1)  Novantrone – 1 (1.3)  Rebif 6 (12) 21 (26.5)  Tysabri 3 (6) 5 (6.3)  Gilenya 7 (14) 3 (3.7)  Aubagio 5 (10) –  Extavia 2 (4) –  Tecfidera 10 (20) 1 (1.3) Current MS healthcare provider** 49 (98) 59 (74.6) DMT adherence of 90% 48 (96) – EDSS Expanded Disability Status Scale; DMT disease-modifying treatment; MS multiple sclerosis. *p ≤ .05, **p < .01. View Large Primary Analysis Adherent subjects (6.16 ± 0.92) reported significantly greater perceived autonomy support from their providers than nonadherent participants (4.81 ± 1.74) on the Health Care Climate Questionnaire, F(1, 124) = 28.170, p < .001, partial η2 = .185. Follow-up Analyses There were no significant differences in endorsement of depressive symptoms between adherent (74.20 ± 21.69) and nonadherent (70.63 ± 25.61) groups (F(1, 124) = 1.001, p > .05, partial η2 = .009). Similarly, the frequency of a major depressive episode was not significantly different between adherent (n = 10, 20%) and nonadherent (n = 19, 24%) groups, χ2(1, N = 129) = .288, p > .05. Group differences are shown in Tables 2 and 3. On the self-report measure assessing the use of the term “side-effects” or specified side effects as the primary reason for discontinuing disease-modifying treatments, nonadherent (n = 60, 76%) patients reported side effects as a reason for discontinuing disease-modifying treatments significantly more often than adherent (n = 21, 42%) patients, χ2(1, N = 129) = 15.10, p < .001. We also examined the extent to which barriers of adherence influenced patients’ decisions to adhere to disease-modifying treatments. Results revealed that nonadherent participants reported that barriers influenced their disease-modifying treatment adherence to a significantly greater extent than adherent participants, F(1, 123) = 39.79, p < .001, partial η2 = .240. Group differences are shown in Table 2. Notably, nonadherent patients endorsed side effects of injection (50.6%), side effects of medication (68.7%), and dissatisfaction with medication (55.4%) as extremely important barriers to disease-modifying therapy adherence. To determine whether group differences remained in support of adherent patients perceiving greater autonomy support from providers than nonadherent patients, barriers to adherence and prevalence of side effects were included as covariates, along with education, disease duration, Expanded Disability Status Scale, and current multiple sclerosis healthcare provider. Even with the addition of barriers to adherence and prevalence of side effects as covariates, perceived autonomy support from providers remained significantly different between groups, F(1, 121) = 9.61, p = .002, partial η2 = .074. Table 2 Comparison of perceived autonomy support, depressive symptoms, and the extent to which common barriers influence adherence to disease-modifying treatments between adherent and nonadherent relapsing-remitting multiple sclerosis (RRMS) subjects Measure Adherent RRMS mean (SD) Nonadherent RRMS mean (SD) F(df) p HCCQ 6.16 (0.92) 4.81 (1.74) 28.17 (1, 124) <.001 MHI depression subscale 74.20 (21.69) 70.63 (25.61) 1.00 (1, 124) .319 MSTAQ 8.52 (8.13) 17.21 (7.17) 38.79 (1, 123) <.001 Measure Adherent RRMS mean (SD) Nonadherent RRMS mean (SD) F(df) p HCCQ 6.16 (0.92) 4.81 (1.74) 28.17 (1, 124) <.001 MHI depression subscale 74.20 (21.69) 70.63 (25.61) 1.00 (1, 124) .319 MSTAQ 8.52 (8.13) 17.21 (7.17) 38.79 (1, 123) <.001 HCCQ Heal Care Climate Questionnaire; MHI Mental Health Inventory–depression subscale; MSTAQ Multiple Sclerosis Treatment Adherence Questionnaire. View Large Table 2 Comparison of perceived autonomy support, depressive symptoms, and the extent to which common barriers influence adherence to disease-modifying treatments between adherent and nonadherent relapsing-remitting multiple sclerosis (RRMS) subjects Measure Adherent RRMS mean (SD) Nonadherent RRMS mean (SD) F(df) p HCCQ 6.16 (0.92) 4.81 (1.74) 28.17 (1, 124) <.001 MHI depression subscale 74.20 (21.69) 70.63 (25.61) 1.00 (1, 124) .319 MSTAQ 8.52 (8.13) 17.21 (7.17) 38.79 (1, 123) <.001 Measure Adherent RRMS mean (SD) Nonadherent RRMS mean (SD) F(df) p HCCQ 6.16 (0.92) 4.81 (1.74) 28.17 (1, 124) <.001 MHI depression subscale 74.20 (21.69) 70.63 (25.61) 1.00 (1, 124) .319 MSTAQ 8.52 (8.13) 17.21 (7.17) 38.79 (1, 123) <.001 HCCQ Heal Care Climate Questionnaire; MHI Mental Health Inventory–depression subscale; MSTAQ Multiple Sclerosis Treatment Adherence Questionnaire. View Large Table 3 Chi-square test for clinical depression by disease-modifying treatment adherence Major depressive episode, current Adherent RRMS mean (SD) Nonadherent RRMS mean (SD) χ2(df, N) p Yes 10 19 0.288 (1, 129) .591 No 40 60 Major depressive episode, current Adherent RRMS mean (SD) Nonadherent RRMS mean (SD) χ2(df, N) p Yes 10 19 0.288 (1, 129) .591 No 40 60 RRMS relapsing-remitting multiple sclerosis. View Large Table 3 Chi-square test for clinical depression by disease-modifying treatment adherence Major depressive episode, current Adherent RRMS mean (SD) Nonadherent RRMS mean (SD) χ2(df, N) p Yes 10 19 0.288 (1, 129) .591 No 40 60 Major depressive episode, current Adherent RRMS mean (SD) Nonadherent RRMS mean (SD) χ2(df, N) p Yes 10 19 0.288 (1, 129) .591 No 40 60 RRMS relapsing-remitting multiple sclerosis. View Large Discussion Results of the current study show that relapsing-remitting multiple sclerosis patients who discontinue disease-modifying treatments against medical advice report less perceived autonomy support from their physicians than patients who adhere to disease-modifying therapies. This finding is independent of side effects, whether patients are actively seeing a multiple sclerosis treatment provider, the extent to which barriers to adherence influence patients’ decision to take disease-modifying treatments, education, disability, and disease duration. When taken in context with other research, including a recent clinical trial using motivational interviewing to improve adherence [32] and the use of shared decision making to increase adherence [33], the current findings emphasize the importance of perceived autonomy support from providers for optimizing disease-modifying treatment adherence [34, 35]. Notably, adherent and nonadherent relapsing-remitting multiple sclerosis patients did not show differences in depressive symptoms or frequency of a major depressive episode. This null finding contrasts with the literature showing a strong association between suboptimal adherence and depression in multiple sclerosis [36]. This suggests that patients who chose to abstain from treatment, against medical advice, may differ from nonadherent patient samples in other studies that choose to take disease-modifying treatments but demonstrate suboptimal adherence [37]. Patients who choose not to take disease-modifying therapies against medical advice may require a tailored intervention or a more patient-centered approach to improve adherence. One study examining a novel Motivational Interviewing/Cognitive Behavioral Therapy intervention found that patients who discontinued disease-modifying treatments against medical advice were significantly more likely to report an intention to re-initiate disease-modifying treatments than patients who did not receive the Motivational Interviewing/Cognitive Behavioral Therapy intervention [32]. Employing patient-centered approaches, such as Motivational Interviewing/Cognitive Behavioral Therapy, may aid in strengthening patient–provider relationships because techniques such as autonomy support and reflective listening enable providers to better understand patients’ preferences and needs [15, 16] and promote mutual decision making [17–21], which has been shown to increase patients’ commitment to treatment [22]. Consistent with the self-determination theory, these findings reiterate the significance of patients’ perceptions of autonomy support and demonstrate that interventions aimed at increasing autonomy support may aid in improving adherence in chronic medical conditions [6–8]. A primary limitation of the current study is the use of a cross-sectional design. Future studies should aim to prospectively examine perceived provider support in relapsing-remitting multiple sclerosis patients who discontinued disease-modifying treatments against medical advice. Despite this limitation, the current study is the first to examine perceived provider support in relapsing-remitting multiple sclerosis patients who prematurely discontinue disease-modifying treatments against medical advice. Our findings may inform future interventions aimed at improving treatment adherence among patients that demonstrate poor adherence, as well as those who prematurely discontinue treatment against medical advice. Future research should elucidate the role of perceived provider autonomy support between patients who demonstrate suboptimal adherence and nonadherence and explore interventions aimed at improving perceived provider autonomy support in both. Future research should also assess the feasibility of physician training in autonomy support and the clinical outcomes associated with employing such techniques. Finally, future research should include additional measures of autonomous self-regulation and perceived competency for adhering to prescribed medication regimens to more fully examine the constructs associated with the self-determination theory. Compliance with Ethical Standards Authors’ Statement of Conflict of Interest and Adherence to Ethical StandardsJared Bruce provides unbranded talks as a member of the Novartis Speaker’s Bureau. He has also received research funding from Cephalon and is a paid consultant to the National Hockey League. Morgan Glusman, Amanda Bruce, Joanie Thelen, Julia Smith, Sharon Lynch, Delwyn Catley, and Kym Bennett declare that they have no conflicts of interest. Ethical Approval All procedures followed were in accordance with ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2000. Informed Consent Informed consent was obtained from all patients included in the study. Acknowledgments This work was funded in part by the University of Missouri–Kansas City School of Graduate Studies and the Women’s Council Graduate Assistance Fund to the first author. Funding was also provided by a grant from the National Multiple Sclerosis Society (HC 0138 and HC 1411 01993) to J. Bruce. References 1. Sokol MC , McGuigan KA , Verbrugge RR , Epstein RS . Impact of medication adherence on hospitalization risk and healthcare cost . Med Care . 2005 ; 43 : 521 – 530 . Google Scholar CrossRef Search ADS PubMed 2. Sabate E. Adherence to Long-Term Therapies—Evidence for Action . Geneva, Switzerland : World Health Organization ; 2003 . 3. Wong J , Gomes T , Mamdani M , Manno M , O’Connor PW . Adherence to multiple sclerosis disease-modifying therapies in Ontario is low . Can J Neurol Sci . 2011 ; 38 : 429 – 433 . Google Scholar CrossRef Search ADS PubMed 4. Bruce JM , Hancock LM , Lynch SG . Objective adherence monitoring in multiple sclerosis: Initial validation and association with self-report . Mult Scler . 2010 ; 16 : 112 – 120 . Google Scholar CrossRef Search ADS PubMed 5. Bruce JM , Lynch SG . Multiple sclerosis: MS treatment adherence—How to keep patients on medication ? Nat Rev Neurol . 2011 ; 7 : 421 – 422 . Google Scholar CrossRef Search ADS PubMed 6. Ng JY , Ntoumanis N , Thøgersen-Ntoumani C et al. Self-determination theory applied to health contexts: A meta-analysis . Perspect Psychol Sci . 2012 ; 7 : 325 – 340 . Google Scholar CrossRef Search ADS PubMed 7. Williams GC , Rodin GC , Ryan RM , Grolnick WS , Deci EL . Autonomous regulation and long-term medication adherence in adult outpatients . Health Psychol . 1998 ; 17 : 269 – 276 . Google Scholar CrossRef Search ADS PubMed 8. Williams GC , Patrick H , Niemiec CP et al. Reducing the health risks of diabetes: How self-determination theory may help improve medication adherence and quality of life . Diabetes Educ . 2009 ; 35 : 484 – 492 . Google Scholar CrossRef Search ADS PubMed 9. Fraser C , Hadjimichael O , Vollmer T . Predictors of adherence to glatiramer acetate therapy in individuals with self-reported progressive forms of multiple sclerosis . J Neurosci Nurs . 2003 ; 35 : 163 – 170 . Google Scholar CrossRef Search ADS PubMed 10. Von Korff M , Gruman J , Schaefer J , Curry SJ , Wagner EH . Collaborative management of chronic illness . Ann Intern Med . 1997 ; 127 : 1097 – 1102 . Google Scholar CrossRef Search ADS PubMed 11. Gold R , Wolinsky JS , Amato MP , Comi G . Evolving expectations around early management of multiple sclerosis . Ther Adv Neurol Disord . 2010 ; 3 : 351 – 367 . Google Scholar CrossRef Search ADS PubMed 12. Wong J , Gomes T , Mamdani M , Manno M , O’Connor PW . Adherence to multiple sclerosis disease-modifying therapies in Ontario is low . Can J Neurol Sci . 2011 ; 38 : 429 – 433 . Google Scholar CrossRef Search ADS PubMed 13. Treadaway K , Cutter G , Salter A et al. Factors that influence adherence with disease-modifying therapy in MS . J Neurol . 2009 ; 256 : 568 – 576 . Google Scholar CrossRef Search ADS PubMed 14. Devonshire V , Lapierre Y , Macdonell R et al. ; GAP Study Group . The Global Adherence Project (GAP): A multicenter observational study on adherence to disease-modifying therapies in patients with relapsing-remitting multiple sclerosis . Eur J Neurol . 2011 ; 18 : 69 – 77 . Google Scholar CrossRef Search ADS PubMed 15. Jackson H . Motivational interviewing and HIV drug adherence . Nurs Times . 2013 ; 109 : 21 – 23 . Google Scholar PubMed 16. Remington G , Rodriguez Y , Logan D , Williamson C , Treadaway K . Facilitating medication adherence in patients with multiple sclerosis . Int J MS Care . 2013 ; 15 : 36 – 45 . Google Scholar CrossRef Search ADS PubMed 17. Caon C , Saunders C , Smrtka J , Baxter N , Shoemaker J . Injectable disease-modifying therapy for relapsing-remitting multiple sclerosis: A review of adherence data . J Neurosci Nurs . 2010 ( suppl 5 ); 42 : S5 – S9 . Google Scholar CrossRef Search ADS PubMed 18. Gance-Cleveland B . Motivational interviewing: Improving patient education . J Pediatr Health Care . 2007 ; 21 : 81 – 88 . Google Scholar CrossRef Search ADS PubMed 19. Aloia MS , Arnedt JT , Strand M , Millman RP , Borrelli B . Motivational enhancement to improve adherence to positive airway pressure in patients with obstructive sleep apnea: A randomized controlled trial . Sleep . 2013 ; 36 : 1655 – 1662 . Google Scholar PubMed 20. Falvo D. Effective Patient Education: A Guide to Increased Adherence . Burlington, Massachusetts: Jones & Bartlett Publishers ; 2010 . 21. Turner AP , Sloan AP , Kivlahan DR , Haselkorn JK . Telephone counseling and home telehealth monitoring to improve medication adherence: Results of a pilot trial among individuals with multiple sclerosis . Rehabil Psychol . 2014 ; 59 : 136 – 146 . Google Scholar CrossRef Search ADS PubMed 22. Oshima Lee E , Emanuel EJ . Shared decision making to improve care and reduce costs . N Engl J Med . 2013 ; 368 : 6 – 8 . Google Scholar CrossRef Search ADS PubMed 23. Bruce JM , Bruce AS , Catley D et al. Being kind to your future self: Probability discounting of health decision-making . Ann Behav Med . 2016 ; 50 : 297 – 309 . Google Scholar CrossRef Search ADS PubMed 24. Polman CH , Reingold SC , Edan G et al. Diagnostic criteria for multiple sclerosis: 2005 revisions to the “McDonald Criteria” . Ann Neurol . 2005 ; 58 : 840 – 846 . Google Scholar CrossRef Search ADS PubMed 25. Bruce J , Bruce A , Lynch S et al. A pilot study to improve adherence among MS patients who discontinue treatment against medical advice . J Behav Med . 2016 ; 39 : 276 – 287 . Google Scholar CrossRef Search ADS PubMed 26. Carroll JK , Fiscella K , Epstein RM et al. Physical activity counseling intervention at a federally qualified health center: Improves autonomy-supportiveness, but not patients’ perceived competence . Patient Educ Couns . 2013 ; 92 : 432 – 436 . Google Scholar CrossRef Search ADS PubMed 27. Sheehan DV , Lecrubier Y , Sheehan KH et al. The Mini-International Neuropsychiatric Interview (M.I.N.I.): The development and validation of a structured diagnostic psychiatric interview for DSM-IV and ICD-10 . J Clin Psychiatry . 1998 ; 59 ( suppl 20 ): S22 – S33 . 28. Sherbourne CD , Hays RD , Ordway L , DiMatteo MR , Kravitz RL . Antecedents of adherence to medical recommendations: Results from the medical outcomes study . J Behav Med . 1992 ; 15 : 447 – 468 . Google Scholar CrossRef Search ADS PubMed 29. Kurtzke JF . Rating neurologic impairment in multiple sclerosis: An expanded disability status scale (EDSS) . Neurology . 1983 ; 33 : 1444 – 1452 . Google Scholar CrossRef Search ADS PubMed 30. Wicks P , Massagli M , Kulkarni A , Dastani H . Use of an online community to develop patient-reported outcome instruments: The Multiple Sclerosis Treatment Adherence Questionnaire (MS-TAQ) . J Med Internet Res . 2011 ; 13 : e12 . Google Scholar CrossRef Search ADS PubMed 31. Heesen C , Bruce J , Feys P et al. Adherence in multiple sclerosis (ADAMS): Classification, relevance, and research needs. A meeting report . Mult Scler . 2014 ; 20 : 1795 – 1798 . Google Scholar CrossRef Search ADS PubMed 32. Bruce J , Bruce A , Lynch S et al. A pilot study to improve adherence among MS patients who discontinue treatment against medical advice . J Behav Med . 2016 ; 39 : 276 – 287 . Google Scholar CrossRef Search ADS PubMed 33. Heesen C , Köpke S , Solari A , Geiger F , Kasper J . Patient autonomy in multiple sclerosis—Possible goals and assessment strategies . J Neurol Sci . 2013 ; 331 : 2 – 9 . Google Scholar CrossRef Search ADS PubMed 34. Mohr DC , Classen C , Barrera M , Jr . The relationship between social support, depression and treatment for depression in people with multiple sclerosis . Psychol Med . 2004 ; 34 : 533 – 541 . Google Scholar CrossRef Search ADS PubMed 35. Saunders C , Caon C , Smrtka J , Shoemaker J . Factors that influence adherence and strategies to maintain adherence to injected therapies for patients with multiple sclerosis . J Neurosci Nurs . 2010 ( suppl 5 ); 42 : S10 – S18 . Google Scholar CrossRef Search ADS PubMed 36. DiMatteo MR , Lepper HS , Croghan TW . Depression is a risk factor for noncompliance with medical treatment: Meta-analysis of the effects of anxiety and depression on patient adherence . Arch Intern Med . 2000 ; 160 : 2101 – 2107 . Google Scholar CrossRef Search ADS PubMed 37. Rolnick SJ , Pawloski PA , Hedblom BD , Asche SE , Bruzek RJ . Patient characteristics associated with medication adherence . Clin Med Res . 2013 ; 11 : 54 – 65 . Google Scholar CrossRef Search ADS PubMed © Society of Behavioral Medicine 2018. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices)

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Annals of Behavioral MedicineOxford University Press

Published: May 16, 2018

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