The LORS-Enabled Dialogue: A Collaborative Intervention to Promote Recovery From Psychotic Disorders
Abstract
Objective
This study examined the effectiveness of the LORS-Enabled Dialogue (LED) in reducing the discrepancy between clinicians’ and patients’ ratings of the severity of symptoms of psychotic disorders, improving adherence to medication, and improving functioning.
Methods
The LED intervention addresses the discrepancy between a clinician’s severity rating of 13 symptoms measured by the Levels of Recovery From Psychotic Disorders Scale (LORS-clinician) and a patient’s severity rating (LORS-patient). A discrepancy in ratings (LORS-discrepancy), which is conceptualized as a patient’s lack of awareness of his or her symptoms, is used by the clinician as the focus of a brief motivational interviewing technique, the LED, to enhance recovery. Ninety adult inpatients or outpatients with psychotic disorders were randomly assigned to the LED intervention (N=50) or a control group (N=40). They were assessed on measures of symptom awareness (LORS-discrepancy), psychopathology (LORS-clinician, LORS-patient, and the Positive and Negative Syndrome Scale [PANSS]), adherence to medication (Kemp Compliance Tool), and functioning at baseline and at four postbaseline monthly assessments. The LED intervention was provided weekly for inpatients and monthly for outpatients.
Results
Among LED intervention participants, a decrease in psychopathology, as measured by the PANSS and LORS-clinician scores, and an improvement in functioning were noted, along with a decrease in LORS-discrepancy scores.
Conclusions
The LED intervention appears to be an efficient and effective treatment to reduce the severity of psychotic symptoms and improve functioning among persons with psychotic disorders. Further study of this intervention in various populations and clinical settings is needed.
Problems associated with impaired symptom awareness (poor insight) among patients with psychotic disorders are significant. A number of studies have indicated that treatment adherence and recovery behaviors are related to multiple factors, including patients’ understanding of their illness (1–3). Insight of persons with psychiatric disorders is generally defined as an awareness of the psychiatric symptoms experienced, of the presence of a psychiatric disorder, and of the achieved effect of treatment (3).
Problems with insight into illness and awareness of symptoms of psychotic illnesses are common to several diagnostic categories, including schizophrenia, schizoaffective disorder, bipolar I disorder, and depression with psychotic features. One study found no significant differences in measures of insight between individuals with schizophrenia and those with bipolar disorder (4). Another study found significant problems with insight among veterans with bipolar disorder, which led to poor treatment adherence (5). In another study, hospitalized patients with bipolar disorder who had acute manic symptoms were found to have poor insight on discharge even though they had improved on other measures of psychopathology (6).
Despite overwhelming evidence linking patients’ difficulties understanding their symptoms with their nonadherence to medications, few interventions exist that respect resources and that can be used in various clinical settings at multiple points of care. Patients who have a better understanding of the symptoms of psychotic illness and the connection between these symptoms and medication adherence and their ability to achieve recovery goals may experience improved functioning (1). The Levels of Recovery from Psychotic Disorders Scale (LORS) (7) and the LORS-Enabled Dialogue (LED) (8) were developed because of this need.
Despite advances in pharmacological treatment of psychotic disorders, relapse remains a major problem for patients with a diagnosis of a schizophrenia spectrum disorder (9,10). Moreover, relapse is associated with higher treatment costs (11). Nonadherence to medication regimens is a major contributor to relapse (9–12). One study that used electronic monitoring found that 42% of 79 patients were nonadherent (13), a rate similar to estimates of nonadherence in other studies (12,14). These findings are consistent with those of the Clinical Antipsychotic Trials of Intervention Effectiveness, in which 75% of patients stopped phase I antipsychotic medication within 18 months (15). The researchers reported that the most common reason for stopping was simply the patient's decision to discontinue, not a lack of efficacy or the occurrence of side effects.
One important variable linked to nonadherence and to relapse among patients with psychotic disorders is patient insight (16,17). Historically, the insight construct is conceptualized as a patient's self-awareness of psychotic symptoms. The construct has received considerable attention in the past decade. In a comprehensive review of the insight literature, Chakraborty and Basu (18) found that numerous conceptual models had been applied to this construct and concluded that various dimensions of insight appear to be related to different outcomes. Another systematic review critically examined 13 published measures of insight and concluded that many of the measures did not directly assess insight into symptoms (19). One of these measures, the Scale to Assess the Unawareness of Mental Disorder, appears both reliable and valid (20). Unfortunately, administration of this measure is time-consuming, requires highly trained assessors, and is not always practical.
To be clinically useful, the insight construct needs to be operationally defined. A patient's self-awareness of psychotic symptoms seems the most relevant definition, and this definition was used in the study reported here. In addition, the difference between a clinician's view of a patient’s symptoms and the patient's awareness of his or her symptoms presents opportunities for both evaluation and intervention.
The LORS was initially developed by using features of the Positive and Negative Syndrome Scale (PANSS) (21) as a way to assess multiple aspects of psychosis and as a method of educating patients and families about psychotic disorders. The LORS was included in the Massachusetts Guideline for Schizophrenia as a method of educating patients about their illness (22). When used in a research setting, a numerical score from 1 to 5 is given to 13 items: supervision, potential for violence, voices, thinking, reality testing, knowledge regarding illness and adherence to treatment, facial expression, speech, interest in others, volunteer or work, personal hygiene care, social interaction skills, and self-advocacy. Higher scores indicate more severe symptoms. [The LORS is available online as a data supplement to this article.]
In the early development of this instrument, it became apparent that patients who appeared to have less awareness of their symptoms might benefit from feedback. The LORS was used as an empirically derived measure to quantify this awareness dimension. The LORS was designed to be easily understood. Thus it can be completed by a patient (LORS-patient) or by a clinician familiar with the patient (LORS-clinician). Moreover, a discrepancy score (LORS-discrepancy) can be computed by subtracting the LORS-patient score from the LORS-clinician score. Conceptually, the greater the discrepancy, the more unaware patients may be of their psychotic symptoms. The psychometric properties of the three LORS subscales have been examined (Corriveau D, Sousa S, unpublished manuscript, 2011). One-month test-retest reliability coefficients were exceptionally high (range .925–.977). In addition, the LORS measures showed good concurrent and discriminate validity.
The most clinically compelling use of the LORS may be as a treatment or an intervention. We developed an intervention—the LORS-Enabled Dialogue (LED)—in which the discrepancy between the clinician and patient ratings of psychotic symptoms is the focus of a collaborative discussion between the clinician and the patient. This study examined the feasibility of using a LED-focused motivational interviewing approach with patients with psychotic symptoms. The primary purpose of this study was to evaluate the LED as a method of decreasing symptoms of psychopathology, improving adherence to medication, and improving functioning.
Methods
Sample recruitment
Patients were recruited from two state-funded inpatient units associated with the Massachusetts Department of Mental Health. The average length of stay on these units ranged from several weeks to several months. Patients were also recruited from four outpatient facilities, three of which were publically funded. Control and treatment groups were drawn equally from all facilities.
All current patients at identified facilities with DSM-IV diagnoses of schizophrenia, schizoaffective disorder, bipolar I disorder, or major depression with psychotic features were considered as potentially eligible for the study. Patients with these diagnoses often have problems with insight and awareness of symptoms, which has a negative impact on functioning (1–6). Clinicians in these facilities identified individuals for the study based on these diagnoses, which were primarily made in treatment team meetings. In addition, a best-estimate diagnostic approach by two or more clinicians was used in which information from clinical interviews was supplemented by information from family informants, previous psychiatrists, and medical records. Once patients with these diagnoses were identified, they were provided with information about the study and their participation. Individuals who did not speak English or who had a developmental disability, a primary substance abuse diagnosis, or a central nervous system disease were excluded.
Participants included 90 patients from five sites. After a complete description of the study was provided to the participants, written informed consent was obtained. Approval was obtained from institutional review boards at the Massachusetts Department of Mental Health, the participating psychiatric clinics, and the authors’ universities. Using a random permutation method, 50 participants were assigned to the treatment group and 40 to the control group.
Clinical assessments and measures
Patients in the treatment and control groups identified a clinician whom they perceived to be most knowledgeable about their illness to provide ratings on the LORS. If patients were unable or unwilling to do so, a clinician was selected after consultation with clinical staff. These clinicians were trained to provide ratings on the LORS and PANSS measures. Clinicians completed the LORS-clinician by rating the severity of the 13 symptoms on the LORS. Patients also rated the severity of the same 13 symptoms on the LORS-patient. Higher scores indicate greater severity. If needed, clinicians provided their patients with assistance in completing the instrument. The LORS-discrepancy is an empirically derived score and was computed by subtracting the LORS-patient score from the LORS-clinician score. (Although the LORS-clinician score was most often higher than that patient’s LORS score, there were two instances in which the LORS-patient score exceeded the LORS-clinician score.)
For patients in both the treatment and the control groups, the study clinicians completed the PANSS, a measure of the positive and negative symptoms of schizophrenia (18). The PANSS contains 30 items based on a semistructured clinical interview; higher scores indicate greater symptom severity. For this measure, the study clinicians received considerable training. It was necessary to maintain the validity of the PANSS assessments by the study clinicians, in part because the study clinicians were not blind to treatment condition. In order to promote such validity, the PANSS ratings were randomly observed by the first author.
Assessments for both treatment and control group patients also included the Kemp Compliance Tool (KCT) (23) and a functional assessment scale. Both instruments were completed by nurse-clinicians who worked in the supervised settings where all patients in the study resided. The nurse-clinicians were blind to patients’ treatment condition. The KCT is a method for measuring the degree of adherence to medications. A patient’s adherence was assessed through multiple observations before, during, and after medication administration. A 7-point observer-rated compliance scale was used, which ranges from complete refusal of medication treatment (higher scores) to active participation with medications. The independent functional assessment included eight items that are measured on a 7-point Likert scale, ranging from total attainment of functioning to no attainment of functioning. Patient functioning is assessed in four areas: socially useful activities, personal and social relationships, self-care, and disturbing and aggressive behaviors. The eight items are based on the work of Morosini and colleagues (24).
Procedure
Treatments were scheduled to address the level of psychopathology and needs of all participants. For inpatients, clinicians met weekly to provide the LED intervention to patients in the intervention group and the “attention-control placebo” intervention to patients in the control group. For outpatients, the clinicians and patients met monthly. Discussions in the control condition were limited to general aspects of treatment, and patients were referred to their treatment team members for any questions related to treatment goals or interventions. All interventions, both LED and placebo, ranged from 30 to 45 minutes. For all patients, all assessments were completed at baseline and monthly for four consecutive months after the baseline assessment.
Four master’s-level or doctorate-level clinicians were trained in the LORS assessment and the LED intervention. The LED intervention utilizes principles of motivational interviewing as a therapeutic collaborative conversation to begin the process of educating the patient about his or her illness symptoms. Motivational interviewing was originally developed by Miller (25) to address addictive behavior and is based on the premise that motivation is enhanced by a helpful therapeutic exchange between the clinician and the patient. The clinician administering the LED asks the patient a number of open-ended questions about his or her overall recovery goals and about current symptoms that may interfere with these goals. The LORS-discrepancy scores were used to emphasize the difference between the clinician’s and patient’s perceptions of symptoms. (There was one instance in which the scores of the clinician and the patient were the same. The clinician reinforced this similarity as good awareness of symptoms, awareness which is often necessary in order to promote recovery.) The LORS produces a visual representation of the discrepancy in scores, which may help to promote greater dissonance for the patient and help to overcome resistance to change. Specific realistic patient goals, along with behaviors necessary to achieve these goals, were identified on the basis of discussions between the patient and the clinician. Patients were asked to commit to a level of effort and to work on these specific behaviors. Subsequent sessions included reviewing patient success.
Data analysis
The major hypotheses were tested by using a generalized linear model. The data analysis software included “PROC GENMOD” within SAS, Version 9.1. The analyses provided regression coefficients and standard error terms, chi square statistics, and p values for each independent variable. Initial analyses showed statistically significant differences on some of the measures at baseline. To adjust for these differences, measures at all four follow-up assessments were converted to change scores by subtracting the baseline score on each measure. For each measure, a negative change score indicated improvement. Thus the overall design was a 2 × 4 factorial design with repeated measures. Of primary interest was the regression coefficient for the interaction term to compare the change pattern between the treatment and control groups over the study sessions. To examine differences between groups on secondary variables, such as demographic characteristics or medical conditions, t tests were used for continuous variables and chi square tests were used for categorical data.
To examine whether the major findings were related to outpatient versus inpatient status or to type of antipsychotic medication, these variables were coded and entered as covariates in subsequent multivariate analyses.
Results
Data were collected between December 2006 and August 2010. Demographic data are shown in Table 1. There were 40 participants in the control group and 50 participants in the treatment group. In both groups, the largest percentage of patients had diagnoses of schizophrenia or schizoaffective disorder. No significant between-group differences were found in gender, race, diagnostic category, or type of medication. The treatment group had significantly more outpatients than the control group (p<.01).
| Variable | Control group (N=40) | Intervention group (N=50) | ||
|---|---|---|---|---|
| N | % | N | % | |
| Age | 45.1±10.8 | 41.8±11.5 | ||
| Male | 23 | 58 | 35 | 70 |
| Caucasian | 36 | 90 | 46 | 92 |
| Outpatientb | 21 | 53 | 40 | 80 |
| Illness duration >20 years | 23 | 58 | 26 | 52 |
| Diagnosis | ||||
| Schizophrenia | 19 | 48 | 24 | 48 |
| Schizoaffective disorder | 16 | 40 | 14 | 28 |
| Other | 35 | 12 | 12 | 22 |
| Current medication use | ||||
| Antipsychotic | 22 | 55 | 25 | 50 |
| Antidepressant | 9 | 23 | 14 | 28 |
| Mood stabilizer | 21 | 53 | 23 | 46 |
| Clozapine | 19 | 48 | 21 | 42 |
a
LED, LORS-Enabled Dialogue; derived from the Levels of Recovery From Psychotic Disorders Scale (LORS)
b
Significant between-group difference (p<.01)
To examine whether patients underestimated their psychotic symptoms at baseline, LORS-patient scores were compared with LORS-clinician scores for all patients. The LORS-clinician scores were significantly higher than the LORS-patient scores (t=8.07, df=89, p<.001). This finding suggested that the patients were appropriate candidates for the LED intervention.
Mean change scores and 95% confidence intervals for the two groups across the four monthly assessments for each dependent variable are shown in Table 2. [Figures presenting these data are included in the online supplement to this article.]
| Measure and group | Month 1 | Month 2 | Month 3 | Month 4 | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| N | M | 95% CI | N | M | 95% CI | N | M | 95% CI | N | M | 95% CI | ||
| LORS-clinicianb | |||||||||||||
| Intervention | 50 | −.28 | −.60 to .04 | 50 | −2.30 | −4.01 to −.59 | 50 | −3.46 | −5.32 to −1.60 | 28 | −6.75 | −9.55 to −3.95 | |
| Control | 40 | −.48 | −.91 to −.04 | 40 | −1.13 | −2.31 to .06 | 40 | −1.10 | −2.74 to .54 | 12 | −2.00 | −6.07 to 2.07 | |
| LORS-patientc | |||||||||||||
| Intervention | 50 | .54 | .04 to 1.04 | 50 | −.96 | −2.09 to .17 | 50 | −1.54 | −3.08 to .00 | 28 | −2.64 | −4.62 to −.66 | |
| Control | 40 | .48 | −.22 to 1.17 | 40 | .25 | −.54 to 1.04 | 40 | −.23 | −1.36 to .91 | 12 | −.75 | −3.41 to 1.91 | |
| LORS-discrepancyd | |||||||||||||
| Intervention | 50 | −.82 | −1.49 to −.15 | 50 | −1.34 | −3.30 to .62 | 50 | −1.92 | −4.05 to .21 | 28 | −4.11 | −7.11 to −1.10 | |
| Control | 40 | −.95 | −1.81 to −.09 | 40 | −1.38 | −2.79 to .04 | 40 | −.88 | −2.76 to 1.01 | 12 | −1.25 | −5.39 to 2.89 | |
| Functional assessment | |||||||||||||
| Intervention | 50 | −.74 | −1.15 to −.33 | 50 | −3.06 | −4.97 to −1.15 | 50 | −3.68 | −5.62 to −1.74 | 28 | −7.46 | −1.73 to −4.20 | |
| Control | 40 | −.50 | −1.10 to .10 | 40 | −.85 | −2.01 to .31 | 40 | −.70 | −2.31 to .91 | 11 | −.09 | −4.27 to 4.08 | |
| Kemp Compliance Tool | |||||||||||||
| Intervention | 50 | −.10 | −.22 to .02 | 50 | −.54 | −.86 to −.22 | 50 | −.64 | −1.00 to −.28 | 28 | −.89 | −1.43 to −.36 | |
| Control | 40 | −.08 | −.16 to .01 | 40 | −.15 | −.42 to .12 | 40 | −.23 | −.53 to .08 | 11 | −.73 | −1.82 to .37 | |
| PANSSe | |||||||||||||
| Intervention | 50 | −.06 | −.84 to .72 | 50 | −5.12 | −7.66 to −2.58 | 50 | −6.20 | −9.04 to −3.36 | 27 | −9.70 | −13.79 to −5.62 | |
| Control | 40 | −.43 | −1.44 to .59 | 40 | −1.08 | −2.76 to .61 | 40 | −.35 | −2.66 to 1.96 | 12 | −2.00 | −8.34 to 4.34 | |
a
LED, LORS-Enabled Dialogue; derived from the Levels of Recovery From Psychotic Disorders Scale (LORS)
b
Clinician’s rating of patient’s symptoms on the LORS
c
Patient’s rating of own symptoms on the LORS
d
Computed by subtracting the LORS-patient score from the LORS-clinician score
e
Positive and Negative Syndrome Scale
Results of the generalized linear regression analyses are shown in Table 3. The LORS-clinician data showed a significant treatment effect (β=2.02, p<.05), a significant interaction effect (β=–1.68, p<.01), and no significant session effect. Overall, clinicians reported a decrease in psychotic symptoms for patients in the treatment group. The LORS-patient data did not show significant main effects or an interaction effect. The LORS-discrepancy data showed a significant treatment effect (β=2.47 p<.05), a significant interaction effect (β=–1.83, p<.01), and no significant session effect. For patients in the treatment condition, discrepancies between patients and clinicians decreased over time. The functional assessment scale data indicated a significant interaction effect (β=–1.47, p<.01) and no overall treatment or session effect. As observed by the nurse-clinician raters who were blind to treatment condition, patients in the treatment group showed improvements on the functional assessment over time. KCT data suggested improved medication adherence for the treatment group at month 2 and some improvement for patients in the control group at month 4; however, results of the generalized linear regression analysis did not show any significant main effect or interaction effect. PANSS data showed a significant treatment effect (β=1.10 p<.01, a significant interaction effect (β=−1.96, p<.01), and no overall session effect. Reductions in psychotic symptoms as measured by the PANSS were observed for the patients in the treatment group but not for those in the control group.
| Dependent variable | Main effects | Interaction effect (treatment × session) | |
|---|---|---|---|
| Intervention vs. control | Session | ||
| LORS-clinicianb | 2.02* | −.04 | −1.68** |
| LORS-patientc | .13 | −.25 | −.32 |
| LORS-discrepancyd | 2.47* | .42 | −1.83** |
| Functional assessment | 1.01 | 0 | −1.47** |
| Kemp Compliance Tool | −.04 | −.07 | −.10 |
| PANSSe | 1.10** | −.46 | −1.96** |
a
LED, LORS-Enabled Dialogue; derived from the Levels of Recovery From Psychotic Disorders Scale (LORS)
b
Clinician’s rating of patient’s symptoms on the LORS
c
Patient’s rating of own symptoms on the LORS
d
Computed by subtracting the LORS-patient score from the LORS-clinician score
e
Positive and Negative Syndrome Scale
*p<.05, **p<.01
Additional analyses examined whether the results were affected by inpatient versus outpatient status or by class of antipsychotic medication. For all measures, results of the adjusted generalized linear equations remained essentially unchanged for each covariate.
Discussion
Overall, the results of this study demonstrate the feasibility of employing the LORS assessment procedure and the LED intervention with patients who have psychotic symptoms. Compared with clinicians, patients at baseline greatly underestimated their psychotic symptoms. The LORS-discrepancy score provided an assessment of this disagreement. Most important, the LORS-discrepancy score helped to direct the focus of treatment. The LED intervention was a brief motivational interviewing technique that promoted a collaborative dialogue in which the patient and clinician discussed a discrepancy. The results of this study showed that the LED intervention was effective in reducing psychotic symptoms as measured by the LORS-clinician score and the PANSS.
The clinicians who completed the PANSS and who provided the LED intervention were not blind to patients’ treatment condition. It could be argued that treatment effects were due to experimenter bias. One complication in designing this study was that the LORS assessment becomes part of the LED intervention. Therefore, including a second set of LORS-clinician raters who were blind to treatment condition might have inadvertently increased the amount of “treatment” provided to patients, and this was not done. Instead, to address the issue of experimenter bias, the study included a set of blinded clinicians who provided ratings of functional assessment and medication adherence (KCT). On the basis of these independent ratings, patients in the LED intervention showed improved functioning but no change in medication adherence.
The process by which the LED intervention led to improvements in psychotic symptoms remains poorly understood. For example, the hypothesis that the LED intervention would increase medication adherence was not supported. Although a significant decrease in the LORS-discrepancy scores was observed, it is unclear from this composite variable whether the change was due solely to the reductions in symptoms observed by the clinicians (LORS-clinician score). Although it is interesting to speculate that patients may have increased their awareness of psychotic symptoms, no statistically significant differences were observed over time in the LORS-patient scores.
It should be noted that the LORS-patient score was never considered a reliable or valid measure of psychotic symptoms. Patients from both the treatment and the control groups gave themselves very low ratings on the LORS at baseline and continued to do so throughout the study. One possible explanation is that the scale suffered from a “floor effect”—that is, as patients improved, there was little opportunity to lower their already-low scores. Another possible explanation is that the psychotic symptoms of patients in the treatment group improved and these patients accurately reported their symptoms while patients in the control group continued to underreport their symptoms. A third possibility is that the reduction in the LORS-discrepancy score was an artifact—that is, patients’ symptoms did not change but the clinicians began to agree more with their patients. However, the experimenter bias effect would not explain the functional improvement reported by the blind raters. Clearly, more research is needed to understand the process of change involved in LED-based interventions.
This study demonstrated the practical utility of employing the LORS assessment and the LED intervention in a variety of clinical settings. Acknowledging that there are multiple factors associated with limited insight, researchers have recommended customized approaches to this problem. Notably, the LED is customized to each patient. Moreover, the LED responds to the need for interventions that are not resource intensive (26). The LORS is relatively easy to complete by both clinicians and patients and takes approximately half the time required by the PANSS. The PANSS also requires significantly more training and may not be as practical in clinical settings. Most important, the LORS provides a standardized and consistent assessment that helps guide the LED intervention. Completing the LORS and the LED takes approximately 30–45 minutes.
A notable feature of the LORS is that it provides a colorful, visual representation of the discrepancies between clinician and patient ratings. These discrepancies can be seen both in the individual rated differences on symptoms as well as in the overall discrepancy score. These visual representations may motivate patients to work on behaviors aimed at realistic goal attainment, which is consistent with the importance of achieving realistic hopefulness and empowerment despite enduring illness (27). Because of the possible association in this patient population between poor awareness of symptoms and stigma and despondency (28), increasing insight is an important goal. In addition, the LED intervention may be best described as an effective method of teaching, which may be particularly relevant in light of neurocognitive research demonstrating deficits in verbal working memory and attention among patients with schizophrenia (29). Although many clinicians engage in discussions with their patients about symptoms, they may not be “on the same page.” If a patient's auditory memory is less accessible than his or her visual memory, the patient may have more difficulty perceiving and retaining this information. Providing both verbal and visual feedback may be more helpful.
This study had several limitations. First, participants included both inpatients and outpatients and each group received different amounts of treatment. Future research is needed to identify the ideal parameters (for example, length and number of sessions) for each treatment setting. Second, the study included patients from different diagnostic categories. Lack of awareness of psychotic symptoms has been shown to be common to several diagnostic categories (4–6), and more research is needed to document and clarify the effectiveness of the LED intervention within specific diagnostic categories.
A third potential limitation is that the clinicians who provided the LORS and PANSS ratings were not blind to patients’ treatment condition. Within the design of this study, this problem could not be fully avoided. An integral component of the LED technique is that clinicians and patients discuss both sets of LORS ratings (LORS-clinician and LORS-patient). Although the study circumvented experimenter bias by using blind independent raters to assess functioning and medication adherence, future researchers may want to consider a duplicate set of LORS-clinician raters—one by the clinician providing the LED intervention and another by an independent and experimentally blind clinician.
Conclusions
The LORS appears to be a clinically useful tool to assess psychotic symptoms as perceived by clinical staff and patients themselves. In particular, the LORS-discrepancy score appears to be a useful metric to demonstrate the differences between a clinician’s and a patient’s perceptions of psychotic symptoms. The LORS provides the basis for the LED intervention. The LED was shown to be an efficient means to significantly reduce psychotic symptoms and to improve functioning. More research is needed to understand how the intervention facilitated these improvements.
Acknowledgments and disclosures
This research was supported by grant FID-US-X301 from Eli Lilly and Company. The authors thank patients, clinicians, students, colleagues, and mentors for their efforts and support over multiple years. They also thank the National Alliance on Mental Illness for the original inspiration for the LORS and the need to have improved interventions to increase patient awareness of and recovery from psychotic symptoms.
Dr. XX has served as a member of the speakers’ bureau for Eli Lilly. The other authors report no competing interests.
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References
1.
Mohamed S, Rosenheck R, McEvoy J, et al.: Cross-sectional and longitudinal relationships between insight and attitudes toward medication and clinical outcomes in chronic schizophrenia. Schizophrenia Bulletin 35:336–346, 2009
2.
Rosen K, Garety P: Predicting recovery from schizophrenia: a retrospective comparison of characteristics at onset of people with single and multiple episodes. Schizophrenia Bulletin 31:735–750, 2005
3.
Amador XF, Flaum M, Andreasen NC, et al.: Awareness of illness in schizophrenia and schizoaffective and mood disorders. Archives of General Psychiatry 51:826–836, 1994
4.
Engh J, Friis S, Birkenaes AB, et al.: Measuring cognitive insight into schizophrenia and bipolar disorder: a comparative study. BMC Psychiatry 7:71, 2007. DOI
5.
Copeland LA, Zeber JE, Salloum IM, et al.: Treatment adherence and illness insight in veterans with bipolar disorder. Journal of Nervous and Mental Disease 196:16–21, 2008
6.
Ghaemi S, Stoll A, Pope HG: Lack of insight in bipolar disorder: the acute manic phase. Journal of Nervous and Disease 183:464–467, 1995
7.
Sousa S: Levels of Recovery From Psychotic Disorders chart. Journal of Psychosocial Nursing 37:19–23, 1998
8.
Sousa S: The LORS-Enabled Dialogue: improving insight in psychotic illnesses. Journal of Psychosocial Nursing and Mental Health Services 46:44–51, 2008
9.
Novick D, Haro JM, Suarez D, et al.: Predictors and clinical consequences of non-adherence with antipsychotic medication in the outpatient treatment of schizophrenia. Psychiatry Research 176:109–113, 2010
10.
Gaebel W, Schreiner A, Bergmans P, et al.: Relapse prevention in schizophrenia and schizoaffective disorder with risperidone long-acting injectable vs quetiapine: results of a long-term, open-label, randomized clinical trial. Neuropsychopharmacology 35:2367–2377, 2010
11.
Ascher-Svanum H, Zhu B, Faries DE, et al.: The cost of relapse and the predictors of relapse in the treatment of schizophrenia. BMC Psychiatry 10:2, 2010. DOI 10.1186/1471-244X-10-2
12.
Buckley PF, Wirshing DA, Bhushan P, et al.: Lack of insight in schizophrenia: impact on treatment adherence. CNS Drugs 21:129–141, 2007
13.
Acosta FJ, Bosch E, Sarmiento G, et al.: Evaluation of noncompliance in schizophrenia patients using electronic monitoring (MEMS) and its relationship to sociodemographic, clinical and psychopathological variables. Schizophrenia Research 107:213–217, 2009
14.
Cramer JA, Rosenheck R: Compliance with medication regimens for mental and physical disorders. Psychiatric Services 49:196–201, 1998
15.
Lieberman JA, Stroup TS, McEvoy JP, et al.: Effectiveness of antipsychotic drugs in patients with chronic schizophrenia. New England Journal of Medicine 353:1209–1223, 2005
16.
Beck EM, Cavelti M, Kvrgic S, et al.: Are we addressing the “right stuff” to enhance adherence in schizophrenia? Understanding the role of insight and attitudes towards medication. Schizophrenia Research 132:42–49, 2011
17.
Linden M, Godemann F: The differentiation between “lack of insight” and “dysfunctional health beliefs” in schizophrenia. Psychopathology 40:236–241, 2007
18.
Chakraborty K, Basu D: Insight in schizophrenia: a comprehensive update. German Journal of Psychiatry 13:17–30, 2010
19.
Lincoln TM, Lüllmann E, Rief W: Correlates and long-term consequences of poor insight in patients with schizophrenia: a systematic review. Schizophrenia Bulletin 33:1324–1342, 2007
20.
Amador X, Strauss D: The Scale to Assess the Unawareness of Mental Disorder (SUMD). New York, Columbia University and New York State Psychiatric Institute, 1990
21.
Kay SR, Fiszbein A, Opler LA: The Positive and Negative Syndrome Scale (PANSS) for schizophrenia. Schizophrenia Bulletin 13:261–276, 1987
22.
Clinical Practice Guidelines for the Treatment of Schizophrenia in Adults. Boston, Massachusetts Department of Mental Health, 1999. Available at www.mass.gov/eohhs/docs/dmh/publications/schizophrenia-guidelines-full.pdf
23.
Kemp R, David A, Hayward P: Compliance therapy: an intervention targeting insight and treatment adherence in psychotic patients. Behavioural and Cognitive Psychotherapy 24:331–350, 1996
24.
Morosini PL, Magliano L, Brambilla L, et al.: Development, reliability and acceptability of a new version of the DSM-IV Social and Occupational Functioning Assessment Scale (SOFAS) to access routine social functioning. Acta Psychiatrica Scandinavica 101:323–329, 2000
25.
Miller W: Motivational interviewing with problem drinkers. Behavioural Psychotherapy 11:147–172, 1983
26.
Awad GA: Antipsychotic medications: compliance and attitudes towards treatment. Current Opinion in Psychiatry 17:75–80, 2004
27.
Davidson L, Schmutte T, Dinzeo T, et al.: Remission and recovery in schizophrenia: practitioner and patient perspectives. Schizophrenia Bulletin 34:5–8, 2008
28.
Schwartz RC: Insight and suicidality in schizophrenia: a replication study. Journal of Nervous and Mental Disease 188:235–237, 2000
29.
Chen KC, Chu CL, Yang YK, et al.: The relationship among insight, cognitive function of patients with schizophrenia and their relatives’ perception. Psychiatry and Clinical Neurosciences 59:657–660, 2005
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Cover: Birdie and Joseph, by Larry Rivers, 1955. Oil on canvas; 13 × 25 inches. Museum of Fine Arts, Boston, Melvin Blake and Frank Purnell Collection (2003.44). Photograph © 2013, Museum of Fine Arts, Boston.
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Published online: 1 January 2013
Published in print: January 2013
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