Setting the stage to quit smoking in Bipolar Disorder patients: brief advice in clinical practice/Preparando el escenario para dejar de fumar en el paciente con Trastorno Bipolar: intervencion breve en la practica clinica.
The mechanisms which may explain this increased prevalence are complex and may involve gene-environmental interaction (Gonzalez-Pinto et al., 1998). The choice of smoking as self-medication, a commonly-debated theory concerning patients with schizophrenia and depressive disorders (Al-Halabi et al., 2017), can apparently be ruled outin BD (de Leon, Diaz, Aguilar, Jurado & Gurpegui, 2006). Compared to BD non-smokers, BD patients who smoke present: more severe symptoms, a higher frequency of rapid cycling, greater risk of suicide, a greater number of hospital admissions (Heffner et al., 2011), a worse quality of life at a mental level (Gutierrez-Rojas et al., 2008) and a cardiovascular and respiratory mortality rate which is double and triple, respectively, that of the general population (Garcia-Portilla et al, 2010).
Tobacco use is the main preventable risk factor of mortality in this population (Callaghan et al., 2014), and with the increasing evidence of effective and safe of pharmacological and psychological treatments (Anthenelli et al., 2016; Garcia-Portilla et al., 2016; Almadana et al., 2017), the literature stresses the need to address this problem in everyday psychiatric care (Prochaska et al., 2011).
Evaluating the subjects' willingness to change and taking steps to raise motivation and control the factors which limit it, are the first steps in any treatment plan for quitting. In BD, the first trials in smokers have begun recently (Weinberger, Krishnan-Sarin, Mazure & McKee, 2008; Wu et al., 2012; Chengappa et al., 2014; Evins, Cather & Laffer, 2015; Anthenelli et al., 2016; Garcia-Portilla et al., 2016). These trials include patients who have expressed the desire to quit smoking but do not evaluate the initial motivation level specifically. So far, the main references have been from studies in patients with schizophrenia (Etter & Etter, 2004). Weinberguer et al. (2008) and Wu et al. (2012) were the first to report serious problems of recruitment since they could not find bipolar patients prepared to attempt to give up. Recently, however, members of our group have published results of a multi-component treatment in a clinical setting (Garcia Portilla et al., 2016), which puts the main emphasis on the preparation and motivation stage, before active treatment is started for schizophrenia and BD. (Prochaska et al. (2011)) An online survey in the USA revealed that of smokers with BD who had made an average of 4 previous attempts, up to 74% express willingness to quit smoking and only one third had been given a recommendation by their psychiatrist (Prochaska et al., 2011).
Although there are multiple theories to explain the concept of motivation and changes in behaviour (Miller, 2011), the Transtheoretical Model (TTM) developed by Prochascka and DiClemente (1983) has been the reference model for addictions. The TTM algorithm in the Stages of Change (SOC) and the Contemplation Ladder, (Biener & Abrams, 1991) have been the most commonly used scales, in the few studies that have analyzed motivation levels for quitting smoking in people with mental disorders (Siru, Hulse & Tait, 20094). The TTM has been the most widely used tool in research into smoking cessation and recognizes the SOC as starting, transit or arrival points in the process, to which a predictive value of the possibilities of change can be added (Chou et al., 2015). Despite the popularity of the TTM, the debate remains open about whether to use the SOC classification, based on arbitrary cut-off points, or to calculate a 'Readiness to Change' index (RTC) (Ceccarini, Borrello, Pietrobissa, Manzoni & Castelnuovo, 2015).
Success in giving up smoking is proportional to the intensity of the intervention (Fiore & Panel, 2008), but in the first objective of generating motivation, the brief interventions using the 5 As (Ask, Advice, Assess, Assist and Arrange) and the 3 A's (Ask, Advice and Assess) have been shown to be cost-effective in the general population (Rice & Stead, 2008) and have been endorsed by clinical practice guidelines (West, McNeill & Raw, 2000). The EPA (European Psychiatry Association) guidance on smoking cessation strategies (Ruther et al., 2014) recommends trying out these interventions in smokers with mental illness. In SMI patients, so far only DiClemente et al. (2011) have evaluated the intervention with 5 A's, and point out the importance of the number of repetitions of the first three A's in the long-term possibilities of success in SOC (DiClemente et al., 2011). In BD patients, although the possibilities of the brief intervention are not known, perhaps the first objective of the 3 A's intervention would be to try to increase the smoker's level of readiness through the different stages of motivation for change (precontemplation, contemplation, and preparation).
On the other hand, BD is associated with persistent neurocognitive deficits across the broad domains of sustained attention, verbal memory and prefrontal/executive cognition, which in turn are major predictors of patients' functional outcomes (Vieta et al., 2013). Moreover, neuropsychological status is related to decision making and attitudes such as motivation for lifestyle changes: neurocognitive dysfunction may therefore decrease the chances of progress on a motivation level (Baune & Malhi, 2015). In the same way, the anxiety symptoms present in over 50% of patients with BD (Goes, 2015), among other factors which increase comorbid addictions (Cazard & Ferreri, 2013), worsen the prognosis, require specific treatment and could influence the possibilities of this type of intervention.
The community care environment available to this population offers advantages through the frequency of contacts, the credibility of the health professionals and the regular advice given. Despite these advantages, we are faced with a serious health problem and cost-effective interventions such as the brief 3 A's intervention could serve to identify the level of motivation and raise initial willingness to quit among smokers with BD.
Aims of the study
(1) to describe and quantify the prevalence of smoking and how it is currently dealt within a representative sample of Bipolar Disorder patients studied in a community environment; (2) to learn the level of motivation of smoking bipolar patients, based on Transtheoretical Model; (3) to evaluate the effectiveness of the brief intervention that Asks, Advises and Assesses (3 As) to raise the patient's motivation level; and (4) to identify the socio-demographic, clinical or smoking variables that may influence the chances the intervention has of success.
Design and settings
The multi-centre study was conducted according to a mixed design including: (1) a cross-sectional, observational and descriptive study performed at the baseline visit and (2) a post-intervention design, in which patients who were identified as smokers at the baseline visit were given a brief anti-smoking intervention over three sessions.
The study lasted 6 months and was carried out in 8 Mental Health Centres of 4 provinces of Andalusia, in southern Spain (Cordoba, Granada, Jaen and Malaga). The study was given the approval of the Research Ethics Committee of the Reina Sofia University Hospital in Cordoba.
Recruitment according to ICD-10 criteria (1992) was carried out consecutively-when attending their scheduled review appointments-among adult patients with BD (18-65 years old) who were clinically stable and attended their scheduled review appointments. Clinical stability and the absence of suicidal behaviour in the 6 months before commencing the study were assessed by the patient's usual psychiatrist. All of them signed to give their informed consent for their data to be used in the study database.
In addition, the patients who were current smokers (1 cigarette or more a day) at the recruitment appointment were invited to participate in the pre- and post-intervention evaluation (Figure 1).
Measurements and outcomes
The total sample was subdivided into 3 groups according to their smoking habits (current smoker, former smoker and never-smoker). Socio-demographic and clinical data were collected from all of them.
Current smokers were assessed using the following instruments at the baseline visit:
The Hamilton Anxiety Scale (HARS) (Hamilton, 1959) was used to assess whether the patient suffered from anxiety symptoms. This scale is hetero-administered by a clinician after an interview in which 0 to 4 points are scored for each item, which assess both intensity and frequency of anxiety symptoms. There are no cut-off points, and a higher score indicates a greater intensity of anxiety.
Fagerstrom's Nicotine Dependence Test (FTND) (Fagestrom, 1978) the modified version of this scale (Heatherton, Kozlowski, Frecjer & Fagestrom, 1991), is hetero-administered and consists of 6 items which evaluate nicotine dependence. The scores are divided into: below 4 (low dependence), 4-7 (moderate dependence) and over 7 (high dependence) on nicotine.
The Visual Analogic Scale (VAS) from 0 to 10 was used to measure patients' perceived desire and self-perceived capacity to quit smoking, because its predictive value equivalent to scales with multiple items (Gwaltney, Metrik, Kahler &Shiffman, 2009).
The URICA (University of Rhode Island Change Assessment) (Di Clemente et al., 1991) is a 32-item self-report scale that includes 4 subcategories measuring the stage of change (SOC) based on Prochascka's TTM. Items are rated on a 5-point Likert scale ranging from 1 (strong disagreement) to 5 (strong agreement). The Spanish version of the scale (Gomez-Peha et al., 2011) has shown adequate internal consistency both globally and in each of the 4 sub-scales (between 0.69 and 0.89). Two possible means of evaluation have been described: a) discrete measurements where the highest score ranks the patient on a particular SOC and b) a continuum in which the subscales are combined arithmetically (Contemplation + Action + Maintenance - Precontemplation) to produce a second order continuous score which can be used to assess Readiness to Change (RTC). Subjects completed the URICA as a way of carrying out the third A (Assess) at baseline (URICA 1) and week 4 (URICA 2), thus giving us two continuous measurements for RTC. The difference between the two was the main variable for the analysis ([RTC.sub.2-1]).
The COBRA, disfuncion cognitiva en el trastorno bipolar (Rosa et al., 2013), is a 16-item questionnaire that measures BD patients' perception of cognitive deficits in several areas, such as: executive functions, processing speed, working memory, learning and verbal memory and attention/concentration. Responses are given on a 3-point Likert scale ranging from 0 (never limited) to 3 (always limited). The total score is a result of the sum of the items. We have used the Spanish version, with a Cronbach's alpha of 0.913.
A brief motivational intervention to quit smoking was carried out based on the idea of the 3A's (Ask -Do you smoke?, Advice--Advise the patient to quit in a clear, firm and personalized way, Assess -Would you be willing to quit smoking next month?) discussed above (Rice & Stead, 2008), distributed over 3 sessions (baseline, week 2 and week 4) lasting no more than 10 minutes each and with a total duration of 30 minutes. The first and last contacts were conducted face-to-face and the contact at week 2 by phone. At baseline and at week 4, the 3A's were completed, using URICA to measure the readiness to quit, while the phone interview was used to reinforce the advice given in the baseline intervention. To minimize any inter-observer differences, the three interventions were carried out by the same professional (psychiatrist or nurse) in each centre.
Statistical analysis was performed using the SPSS program (version 15.0). Dimensional variables and frequencies were compared by parametric or non-parametric tests, as appropriate. Continuous variables were analyzed by analysis of variance (ANOVA) or Student t tests, and categorical variables by chi-square or Fisher's exact tests. Relationships between variables were expressed as Pearson's product-moment (r) or Spaerman rank-order ([r.sub.s]) correlations. Multiple linear regression, using the conditional back procedure, was used to analyse the independent effect of each variable (pharmacological treatment, history of psychotic symptoms, current anxiety symptoms, willingness, self-perceived capacity to quit smoking and subjective perception of cognitive functioning), and the change of RTC after the intervention. For this reason we calculate a new variable (RTC after the intervention minus RTC before: [RTC.sub.2-1]), the fundamental dependent variable. The multivariate models include the statistically significant variables in the bivariate analysis or those of special clinical relevance. The statistical significance level was set at P<0.05.
Of 212 patients diagnosed with BD, 101 (47.6%) were current smokers and were included in the intervention phase. Of the remaining patients, 47 were former-smokers and 64 had never smoked. Table 1 shows the socio-demographic, and clinical data of the three groups (smokers, former smokers and never-smokers). On comparing these three groups, we found some differences: the intervention group (current smokers) contained a higher proportion of male patients, patients whose marital status either separated or divorced, unemployed and disabled patients and patients with a comorbid psychiatric disorder. We found no differences as regards level of education, length of illness, presence of psychotic symptoms or pharmacological treatment received (Table 1).
Eighty one patients (80.2%) from the intervention group completed the follow-up. All non-completer cases were due to subjects expressing their wish to abandon the study. We found no statistically significant differences between those who had completed the intervention and those who did not, in the variables analyzed (socio-demographic, clinical and smoking history, SOC, RTC, dependence and anxiety level, self-perception capacity to quit and cognitive function).
In those patients who were current smokers, 80.2% of them had never been offered a specific anti-smoking treatment during the years of follow-up at their mental health centre and 62.1% had never been asked about their smoking habits. In the 'former smokers' group, 75% reported that they had given up smoking without specific treatment and 81.8% reported no clinical worsening of their illness in any way after giving up smoking (this measures were patients self-reported).
In the intervention group, the mean age at onset of smoking behaviour was 18.9 years (SD 7.1), with a mean consumption of around 18.1 cigarettes per day (SD 11.2) for 28.5 years (SD 10.5). 16.8% of these smokers had high nicotine dependence (FTND> 7). 80.2% of the current smokers had tried to quit smoking with an average of 2.7 attempts (SD 2.5), and had achieved an average of 11.8 months without smoking (SD 21.4). The clinical and tobacco use variables are summarized in Table 2.
In the VAS, smokers obtained a score of 6.2 in their willingness to quit smoking (SD 3.4), and 4.7 on their self-perceived capacity of quitting (SD 3.1). In the URICA scale evaluation of level of motivation performed in the first contact (URICA 1), 17 (16.8%) of our patients were in the pre-contemplative phase, 76 (75.2%) in the contemplative phase and 8 (7.9%) in the action stage. 30 days after completing the brief intervention (URICA 2), 30.9% of patients had advanced in their SOC; 7 (8.6%) were in the pre-contemplative phase, 53 (65.4%) in the contemplative phase and 21 (25.9%) in the action stage (Figure 2). Baseline SOC assessed by the 'Readiness to Change' (RTC) continuous quantitative variable showed a significant increase from 53.3 at the beginning, to 59.3 after completing the intervention (P= 0.019).
A multiple linear regression model with the number of years that the patient had been smoking, the age at onset of smoking behaviour and the willingness to quit smoking all linked with a greater evolution of the RTC. Moreover, using the total score of the COBRA scale, the variables that were associated with a lower progress on the RTC were age, self-perceived capacity for quitting smoking and subjective cognitive status (Table 3).
Concurring with previous results in BD (Diaz et al., 2009; Tidey & Miller, 2015), we found smoking rates that doubled those published in the general population and the recurrent alerts in the literature about the passivity of the patient's normal health care environment have been confirmed (Evins et al., 2015).
Within the conceptual framework of stages of motivation and change process, and for the first time in BD, this study of an anti-smoking intervention conducted in a community setting shows that a low-intensity intervention based on the 3A's has a significant impact on the level of motivation for change in patient who smoke - independently of the degree of tobacco consumption. The subjective perception of the neurocognitive functioning predicted the chances of progress after the intervention.
The 30.9% of the smokers increased their SOC and there was a significant increase in the RTC index - both measurements are based on the Transtheoretical Model of Behaviour Change (Prochaska & DiClemente, 1983). This type of intervention, strongly supported in the literature for generating motivation and attempts to quit smoking in general populations (Rice & Stead, 2008), has been scarcely studied in SMI patients who smoke and to date, there is no specific works referring to BD. Di Clemente et al. (2011) reported how up to 60% of patients with psychotic disorders make progress in their SOC when followed up over a year, with a scheduled intervention using the 5 A's. In addition, patients exposed to more frequent repetition of the first 3 A's were the ones that progressed most at this stage.
The level of willingness to quit smoking expressed at the beginning of the intervention, having started smoking later in life and having smoked for more years were the variables that independently predicted a greater advance in readiness to give up after the intervention. These variables also predict the possibilities of a first attempt to give up in the general population (West et al., 2015). It showed that the younger the patient at the time of the intervention, the greater the evolution in the level of readiness. This, in theory, goes against what is usually published in the health advice given to the general population, where older people tend to be more receptive to these kind of interventions (Zolnierek & Dimatteo, 2009; Marin, Gil & Romero, 2017), but does concur with the favourable characteristics of a recently-published study on first attempt in smokers (Sharma & Szatkowski, 2014). Despite the fact that the psychiatric population has been less receptive to the campaigns to reduce smoking than the general population, those who are younger--who may feel less stigmatized and have failed on fewer occasions - may in fact benefit from them more and be more receptive to the kind of intervention mentioned above. Those smokers who expressed a higher level of self-capacity for change at the start of the intervention improved their level of readiness less. While recognizing the value of self-perceived capacity as a predictor of attempting to give up (Borland et al., 2010), we may assume that in a sample of BD where many of the patients are asked for the first time about their tobacco use and wishes, the self-capacity expressed may have an unrealistic value and offer little insight into the true depth of their problem. This is similar to the conclusion reached in the meta-analysis of smokers from the general population (Gwaltney et al., 2009), where self-capacity was evaluated prior to the attempt to quit.
A greater perception of functional limitation at the neurocognitive level meant that less progress could be made on the level of readiness. Regarding cognition, the present findings are difficult to interpret, since no previous study has examined the potential role of neurocognitive status to predict either changes in motivation or quitting smoking. Some indirect evidence, however, may be extrapolated from studies of smokers with schizophrenia or without psychiatric disorders. In a set of placebo-controlled clinical trials, baseline dysfunction, as measured by sustained attention, spatial working memory and executive cognition, has been associated with the failure of smoking cessation treatment in smokers with schizophrenia seeking treatment (Moss et al., 2009). It is worth noting that schizophrenia and BD share a core neurocognitive dysfunction, with high rates of comorbid nicotine dependence and low rates of smoking abstinence. In other clinical populations, cognitive impairments decreased response to treatment among subjects with marijuana or cocaine dependence (Aharonovich, Brooks, Nunes & Hasin, 2008). Moreover, the key cognitive predictors of attaining abstinence in smokers without psychiatric disorders are also sustained attention, working memory and executive functioning (Ashare, Falcone & Lerman, 2014). Since the prefrontal cortex is involved in all these cognitive deficits, it represents a potential target for the development of pharmacological and psychological therapies aimed at treating cognitive deficits. In turn, targeting prefrontal-related dysfunctions may enhance the success rates for giving up smoking in all these populations and possibly also in BD.
In our study, the impact generated was independent of gender, the number of cigarettes per day and the dependency level. In the results found by DiClemente et al (2011), women progressed less than men over one year, and this result was also independent of tobacco consumption and dependence. The literature leaves no room for doubt about the prognostic value of the dependence level on the rates of initial cessation and possible recurrences (Japuntich et al., 2011). Even so, the results we obtained from a BD sample seem coherent with an intervention designed to be carried out in large target populations, with the aim of generating a first approach to the motivation level without specifying the gender or the severity of the smoking addiction (Kruger, O'Halloran, Rosenthal, Babb & Fiore, 2016).
The results of the URICA scale, which positions the patient in a SOC or quantifies an RTC value and its evolution, have a predictive value regarding the possibilities of a treatment designed to break the habit (Chou et al., 2015). Moreover, this is the first study to specifically assess SOC in bipolar smokers. Our results confirm that up to two-thirds of the patients asked about their habit in a normal psychiatric care setting are placed in the contemplation stage, which means that they plan to quit in the next 6 months and therefore could be target patients for intensive treatments. These results are higher than those described in samples of patients with schizophrenia (DiClemente et al., 2011) and, as noted in the age factor, they certainly give us an insight into the greater permeability in BD for changes in tobacco consumption that have occurred in recent years in the general population.
The motivation to change when suffering an addiction plays a key role in recognizing the problem and finding solutions. Although it is urgent to know the level of motivation in this population of smokers and generate steps to increase it, how to quantify the level of motivation and its progress over time remains as a challenge. The concepts of SOC and RTC and their quantification through the URICA scale constitute a key option in addiction research, but it also has its drawbacks (DiClemente, Schlundt & Gemmell, 2004). Understanding motivation from the SOC allows us to position the patient and provides a model by which we can observe the process of change; however, quantifying it in discrete stages or as a continuum also generates debate (DiClemente & Prochaska, 1998). In tobacco addiction, a specific stage or a figure for readiness does not determine a specific treatment, but as has been stressed above, it has a predictive value about the possible success of the behaviour of change.
Considering the representativeness of the sample studied and its development in the real environment of community care, our study may illustrate the real chances of success of administering the brief anti-tobacco intervention in BD patients and shows that it can be a simple, cost-effective first step which paves the way for more specific interventions which would aim at producing change. In any case, we must be aware of the difficulty of the problem being addressed and controlled studies with larger samples are needed as a first step to confirm the effectiveness of this type of intervention in clinical practice, and as a way to assess objectively the influence of factors such as cognitive performance. We evaluated neurocognitive factors by means of a validated survey, the COBRA questionnaire. Nevertheless, subjective perception of neurocognitive functioning does not always correlate with objective performance on neuropsychological tests and this remains a matter of debate (Miskowiak et al., 2016). Future studies should use formal neuropsychological batteries to confirm or refute the results of this study.
Finally, identifying predictors of motivation/abstinence in further studies of smokers with BD may help devise novel treatments for nicotine addiction. Interventions such as cognitive and functional remediation have proved to be effective in BD (Torrent et al., 2013). Moreover, from a public health perspective, smoking cessation programs aimed at patients with BD could take into account the potential limiting effects of neurocognitive dysfunction to enhance their motivation to quit smoking.
Conflict of interest
V.B.-M. has received grants and served as consultant, advisor or continuing medical education (CME) speaker during the last 5 years for the following entities: Angelini Spain, Angelini Portugal, AstraZeneca, Bristol-Myers-Squibb, Ferrer, Janssen. Juste, Lundbeck, Nutrition Medica, and Otsuka. JA Alcala-Partera has served as advisor to: Adamed, Lundbeck, Otsuka, Janssen-Cilag, Pfizer, Rovi y Servier; and as speaker to: Janssen-Cilag, Pfizer y Servier. L. Gutierrez-Rojas has been speaker for and advisory board member of Bristol-Myers Squibb, Janssen-Cilag, Astra-Zeneca, Rovi, Lundbeck, Otsuka, GSK and Pfizer.
All other researchers report no biomedical financial interests or potential conflicts of interests.
Funding. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
The authors would like to thank the patients for participating in this study. We thank V. Millan, M. Ayora, E. Prieto, M. Toledano, MJ Notario and P. Zurita for their involvement in participants' inclusion; we thank FJ. Ayesta, R. Luque, C. Prada and L. Padilla for their assistance.
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FERNANDO SARRAMEA (*,**), MARIA JOSE JAEN-MORENO (***), VICENT BALANZA-MARTINEZ (****), MARIA ISABEL OSUNA (*****), JOSE ANGEL ALCALA (*), FRANCISCO JAVIER MONTIEL (******), CRISTINA GOMEZ (******), MARIA DOLORES SANCHEZ (******), ANA BELEN RICO (*), JUSTA REDONDO-ECIJA (*******), SUSANA GIL (*), FRANCISCA VALDIVIA (*), JAVIER CABALLERO-VILLARRASO (********), LUIS GUTIERREZ-ROJAS (*********).
(*) Clinical Unit of Mental Health. Reina Sofia University Hospital, Cordoba, Spain. (**) Centro de Investigacion Biomedica en Red de Salud Mental, CIBERSAM, 33006 Oviedo, Spain. (***) Psychiatry section, Faculty of Medicine and Nursing, University of Cordoba, Cordoba, Spain. (****) Teaching Unit of Psychiatry and Psychological Medicine, Department of Medicine, University of Valencia, CIBERSAM, Valencia, Spain. Service of Psychiatry, La Fe University and Polytechnic Hospital, Valencia, Spain. (*****) Clinical Unit of Mental Health. Virgen de la Victoria University Hospital, Malaga, Spain. (******) Clinical Unit of Mental Health. Complejo Hospitalario de Jaen, Jaen, Spain. (*******) Drugs and Addictions Unit. Instituto Provincial Bienestar Social, Diputacion de Cordoba, Cordoba, Spain. (********) Clinical Analyses Service, Reina Sofia University Hospital, Cordoba, Spain. Maimonides Biomedical Research Institute of Cordoba (IMIBIC). (*********) Psychiatry and Neurosciences Research Group (CTS-549), Institute of Neurosciences, University of Granada, Granada, Spain.
Received: August 2017; Accepted: November 2017.
Send correspondence to: Fernando Sarramea Crespo. Clinical Unit of Mental Health, Reina Sofia University Hospital. Avda. Menendez Pidal s/n 14014 Cordoba, Spain. E-mail: email@example.com.
Table 1. Description of the sample Current Smoker Former Smoker (101; 47.6%) (47; 22.2%) Mean Age (sd) 47.5 (9.8) 49.7 (8.2) Gender [n (%)] Male 45 (56.2) 21 (26.2) Female 56 (42.7) 25 (19.1) Marital status [n (%)] Never married 28 (54.9) 8 (15.7) Married or cohabiting 35 (36.5) 25 (26.0) Widowed or separated/divorced 36 (61.0) 12 (20.3) Educational level [n (%)] Without formal studies 8 (42.1) 2 (10.5) Primary school 41 (47.1) 17 (19.5) Secondary school 33 (51.6) 15 (23.4) University 18 (43.9) 13 (31.7) Work status [n (%)] Unemployed 22 (51.2) 10 (23.3) Working (full/part-time) 24 (55.8) 9 (20.9) Disabled (temporary/permanent) 46 (51.7) 21 (23.6) Others (*) 9 (24.3) 7 (18.9) Diagnosis [n (%)] Bipolar disorder I 62 (45.3) 26 (19.0) Bipolar disorder II 38 (51.4) 21 (28.4) Comorbid disorder [n (%)] Anxiety disorder 16 (55.2) 2 (6.9) Personality disorder 14 (60.9) 2 (8.7) Substance use disorder 8 (72.7) 3 (27.3) Length of illness, months 162 (536) 168 (416) [Median (range)] Psychotic symptoms, yes [n (%)] 68 (50.4) 31 (23.0) Treatment, yes [n (%)] Mood stabilizers 88 (48.9) 37 (20.6) Antipsychotics 75 (47.8) 35 (22.3) Antidepressants 37 (46.2) 16 (20.0) Benzodiazepines 53 (47.3) 21 (18.8) Hypnotics 19 (51.4) 7 (18.9) Never smoker Statistical test, p (64; 30.2%) Mean Age (sd) 50.7 (11.1) 2.159 (1). 0.118 Gender [n (%)] Male 14 (17.5) 10.056 (2). 0.007 Female 50 (38.2) Marital status [n (%)] Never married 15 (29.4) 11.295 (2). 0.023 Married or cohabiting 36 (37.5) Widowed or separated/divorced 11 (18.6) Educational level [n (%)] Without formal studies 9 (47.4) 6.753 (2). 0.344 Primary school 29 (33.3) Secondary school 16 (25.0) University 10 (24.4) Work status [n (%)] Unemployed 11 (25.6) 16.074 (2). 0.013 Working (full/part-time) 10 (23.3) Disabled (temporary/permanent) 22 (24.7) Others (*) 21 (56.8) Diagnosis [n (%)] Bipolar disorder I 49 (35.8) 6.087 (2). 0.048 Bipolar disorder II 15 (20.3) Comorbid disorder [n (%)] Anxiety disorder 11 (37.9) 13.996 (2). 0.030 Personality disorder 7 (30.4) Substance use disorder 0 (0) Length of illness, 170 (468) 0.731 (1). 0.483 months [Median (range)] Psychotic symptoms, yes [n (%)] 36 (26.7) 2.000 (2). 0.368 Treatment, yes [n (%)] Mood stabilizers 55 (30.6) 2.224 (2). 0.329 Antipsychotics 47 (29.9) 0.050 (2). 0.975 Antidepressants 27 (33.8) 0.950 (2). 0.622 Benzodiazepines 38 (33.9) 2.653 (2). 0.265 Hypnotics 11 (29.7) 0.381 (2). 0.827 Note. (*) Other includes housewife, student and retired. (1). ANOVAtest, (2). Chi-square test. Table 2. Current smokers, baseline clinical and smoking characteristics (n=81) Media (SD) HAM-A 13.6 10.4 COBRA 13.0 9.5 Self-reported CPD 18.1 11.2 FTND 4.9 2.6 Willingness to quit tobacco smoking 6.2 3.4 Self-perceived capacity to quit tobacco smoking 4.7 3.1 RTC 1 53.3 25.9 RTC 2 59.3 25.9 RTC 2-1 7.1 22.5 Number of attempts 2.7 2.5 Mean duration of attempts in months 11.9 21.4 Note. SD: Standard deviation; COBRA: Cognitive complaints in bipolar disorder rating assessment; FTND: Fagerstrom Test for Nicotine Dependence HAM-A: Hamilton anxiety rating scale. CPD: Cigarettes per day. Table 3. Multiple linear regressions of factors associated with change in Readiness to change after the intervention ([RTC.sub.2-1]) in current smokers (n=81) Coefficient of partial Variables correlation ([beta]) [t.sub.exp] Readiness to change (*) Age (years) -0.72 Length of smoking (years) 0.58 Age at onset of smoking behaviour (years) 0.48 Willingness to quit tobacco smoking 0.32 Self-perceived capacity to quit tobacco smoking -0.30 COBRA cognition scale score -0.35 Variables valor P Readiness to change (*) Age (years) -2.96 0.004 Length of smoking (years) 2.28 0.025 Age at onset of smoking behaviour (years) 2.34 0.022 Willingness to quit tobacco smoking 2.71 0.008 Self-perceived capacity to quit tobacco smoking -2.57 0.012 COBRA cognition scale score -3.28 0.002 Note. (*) Coefficient of determination (adjusted R2) = 0.16, F = 3.38, df = 1, 74, P =0.003.
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|Author:||Sarramea, Fernando; Jaen-Moreno, Maria Jose; Martinez, Vicent Balanza; Osuna, Maria Isabel; Alcala,|
|Date:||May 26, 2019|
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