Braining, Structured Physical Activity in Specialized Psychiatry for Patients with Substance Use Disorders - A Feasibility Study

Braining, Structured Physical Activity in Specialized Psychiatry for Patients with Substance Use Disorders - A Feasibility Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Braining, Structured Physical Activity in Specialized Psychiatry for Patients with Substance Use Disorders - A Feasibility Study Åsa Anger, Leida Kaaman, Rebecka Mac, Johan Holmberg, Nitya Jayaram-Lindström, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7409582/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Individuals with substance use disorders (SUD) have poor metabolic and cardiovascular health as well as high prevalence of symptoms of depression and anxiety. Regular physical activity can markedly improve these and other somatic and mental health conditions. The present study aimed to evaluate the feasibility and preliminary effects of an aerobic physical activity intervention termed Braining, in psychiatric care specialized in SUD. Methods For this uncontrolled open trial with pre-, post- and 12-month follow-up measures, 22 patients undergoing treatment for SUD and comorbid psychiatric disorders, participated in a 12-week physical activity intervention. Feasibility was assessed by adherence to exercise sessions and measurement procedures. Acceptability, credibility and negative treatment effects were measured through self-report questionnaires and reported adverse events. Physical activity was measured via accelerometry (Actigraph GT3x) and self-reporting. Psychiatric symptoms including substance use, functional status, and quality of life, were measured with self-assessments. Somatic health was evaluated through physical examinations and metabolic blood markers. Preliminary effects of psychiatric symptoms were analyzed via linear mixed models, and effects on metabolic outcomes were analyzed via t-tests. Results The mean number of exercise sessions attended was 9 (SD = 7.7). Adherence to Actigraph measurements was 73% at baseline, 46% at mid-intervention, and 32% post-intervention. Completion of self-assessments was 100% at baseline, 86% at post-intervention, and 77% at the 12-month follow-up. The mean score for the Client Satisfaction Questionnaire (CSQ-8) was 27 (SD = 3.9). No serious adverse events were reported. Significant positive changes were detected in 6 out of 8 self-assessment scales regarding psychiatric symptoms and quality of life, with effect sizes ranging from 0.28–0.77. Mean systolic blood pressure 131 (SD = 2.6) and mean diastolic blood pressure 83.5 (SD = 2.2), was significantly reduced at post-measurement (diastolic) and at follow-up (systolic). Conclusions The participants were satisfied with the Braining intervention and participated in exercise sessions and measurements, although adherence to the Actigraph measurements was low. With specified adjustments, the method was deemed feasible for future studies. alcohol use disorder anxiety depression exercise feasibility metabolic disorder physical activity psychiatry substance use disorder Figures Figure 1 Background Individuals affected by mental disorders suffer from higher rates of poor somatic health, have shorter life expectancy, and are less physically active than the average population ( 1 ). Within mental health disorders, substance use disorders (SUD) are among the leading causes of disability, representing a significant public health issue as well as having substantial effects on mortality ( 2 – 4 ). For instance, Scandinavian data show a more than 20-year reduction in life expectancy in people admitted to hospital care for alcohol use disorder (AUD) ( 5 ). Moreover, chronic as well as irregular heavy use of alcohol have detrimental effects on somatic health, including cardiovascular diseases, cancer, dementia, and injuries ( 3 ). Notably, psychiatric comorbidity is common in relation to SUD, often bidirectionally impacting disorder severity and leading to a worsened clinical course and treatment adherence ( 6 – 9 ). Major depressive disorder and anxiety disorders have a significant and consistent association with SUD ( 9 , 10 ), in addition to increasing vulnerability to substance abuse ( 8 ) and risk of suicidal behavior ( 8 , 9 ). There is also high comorbidity with attention deficit hyperactivity disorder (ADHD) ( 6 , 11 ), with reports of almost 25% of patients with substance use having the diagnosis ( 12 ). In patients with bipolar disorder, previous studies indicate a 40 to 70% ( 13 ) prevalence for AUD and a 25% prevalence for other SUDs ( 14 ), with a risk of adverse outcomes such as mixed episodes and suicide ( 7 , 15 ). The treatment for SUD includes psychosocial, psychological, and pharmacological approaches, often in combination ( 4 ). With psychiatric comorbidity, treatment recommendations differ from those of individual diagnoses, and consistent evidence is lacking. The recommended treatments focus primarily on addressing the symptoms with no focus on targeting deficits in somatic or cognitive problems experienced ( 6 – 9 ). The World Health Organization (WHO) has highlighted the importance of physical activity ( 16 ), and the key health benefits of physical activity include primary and secondary prevention of cardiometabolic disorders and improvements in mental health ( 16 – 18 ). Aerobic physical activity, particularly at moderate to vigorous intensity, is most effective for these benefits ( 18 ). The evidence supporting the positive effects of physical activity on mental health is expanding ( 19 ). A recent review, encompassing over 120,000 adult participants from specialized psychiatry, somatic care, primary care, and the general population, revealed that physical activity has medium-sized effects on depression, anxiety, and psychological distress compared with usual care and should be a cornerstone in the treatment of these disorders ( 20 ). Physical activity has been studied as a potential adjunct treatment for patients with SUD. Moderate-intensity aerobic exercise programs with durations of 12–13 weeks have been most common in this population ( 21 – 23 ). Mixed results have been reported regarding the beneficial effects related to alcohol and substance use outcomes, such as consumption, abstinence and relapse. Some studies suggest no effect on consumption rates ( 23 , 24 ) and that the effect on abstinence is unclear ( 21 ). Others suggest that a physical activity intervention could decrease consumption ( 22 ). However, the benefits in terms of improved physical fitness, depressive symptoms, anxiety and overall quality of life have been more consistent ( 21 – 25 ). These findings are of vital importance, considering the previously mentioned prevalence of psychiatric comorbidity and metabolic complications in this population. Furthermore, studies conclude that improved quality of life is an important outcome of treatment ( 21 , 25 ) and that physical exercise programs could be included in treatment programs to improve the recovery process ( 21 ). However, physical activity as an additive treatment has not yet been implemented in the standard treatment for SUD. Existing tools to motivate patients within the general healthcare sphere, such as Physical Activity on Prescription ( 26 ) and Physical Activity Referral Scheme (27), seem to be insufficiently studied in the treatment of psychiatric symptoms ( 28 ). To motivate patients in specialized psychiatric care to initiate and perform moderate to vigorous physical activity, we developed a structured clinical method, Braining, which was previously described in a retrospective study ( 29 ). To evaluate the effect of the Braining method, a randomized controlled trial (RCT) is planned. In order to prepare for this RCT, the present study was performed to evaluate feasibility and preliminary effects of the Braining method. The primary objectives of the study were to assess feasibility via a) adherence to exercise sessions; b) adherence to measurements of physical activity, psychiatric symptoms, quality of life, and metabolic variables; and c) acceptability, credibility, safety, and perceived negative effects of the intervention. The secondary objectives were to investigate the preliminary effects of the intervention on physical activity, psychiatric symptoms, quality of life, and metabolic health. Methods Where applicable, this report follows the Consolidated Standards of Reporting Trials (CONSORT) statement for pilot and feasibility trials ( 30 ) and the “Guidelines for reporting nonrandomized pilot and feasibility studies” ( 31 ). Study design, setting and participants The study was designed as an uncontrolled open trial, where participants underwent a 12-week physical activity intervention -Braining ( 29 ). Before and after the intervention and at the 12-month follow-up the participants were assessed including measurements of physical activity, psychiatric self-assessments and metabolic health assessments. The study was carried out in Region Stockholm, Sweden, during 2022, with a 12-month follow-up conducted during 2023. The study was performed at two psychiatric outpatient clinics, Liljeholmsberget (Clinic A) and Livsstilsmottagningen (Clinic B), which specialize in treatment of substance use disorders and are incorporated in the regional specialized psychiatric healthcare. These two outpatient clinics were located in the same building, with shared reception and waiting room areas, but differed in terms of organization and patient profile. Clinic A was the larger unit with approximately 1500 unique patients in 2022 and offered pharmacological and psychological treatment, e.g., relapse prevention. Clinic B provided specialized substance use treatment for individuals aged 18–25 years and had 300 unique patients in 2022. Clinic B focused on psychological treatments and used pharmacological treatment that focused mainly on psychiatric comorbidities. Eligible participants were 18 years or older and were currently receiving treatment at either clinic A or B for alcohol and other substance use disorders and comorbid psychiatric disorders. No psychiatric diagnoses per se were excluded from the study; however, individuals who experienced acute episodes of mania, psychosis or severe eating disorders; those presenting with a high risk of violence or suicide; and those experiencing acute withdrawal or intoxication were excluded. Medical conditions where physical activity is contraindicated, such as uncontrolled heart or lung diseases, infection, or severe physical impairment, were also criteria for exclusion. The intervention - Braining Braining was developed as a structured clinical intervention for physical exercise as adjunct treatment in specialized psychiatry ( 29 ). In brief, Braining consists of scheduled 30–45-minute aerobic group exercise sessions (Braining sessions) and is carried out by regular psychiatric staff at the outpatient clinics. Within this study, a standardized manual, the Braining Box , was used as a tool by the staff to deliver safe and effective exercise at moderate to vigorous intensity levels ( 32 ). The Borg Rating of Perceived Exertion Scale 6–20 was used as a recommendation for level of exertion ( 33 ), and participants were instructed to reach and maintain intensity at 11–17 during the sessions. Furthermore, Braining also consists of regular individual visits. Primarily, before and after the intervention, a clinical visit is scheduled with a nurse for risk evaluation, assessments of metabolic and psychiatric health and individual goal setting. Second, each Braining session is preceded by a short individual visit with staff for a brief assessment of mental and physical status and for motivational support. In this setting, four Braining session times per week were offered, and the participants were informed of a recommended frequency of three sessions per week. Prior to recruitment, the outpatient clinics underwent an implementation process for the Braining method. The implementation based on the model developed by Fixsen et al. involves several stages: exploration, program installation, initial implementation, and full operation ( 34 ). The exploration stage included assessing prerequisites and needs, anchoring with management and staff, and making the final decision on implementation. Furthermore, a multidisciplinary Braining team was created with staff members from both clinics. The program installation stage involved preparing resources and educating the team about the Braining method, structure, and materials. During the initial implementation stage, Braining was launched, the structure was put into operation, weekly meetings were held, and Braining classes were scheduled. Finally, the full operation stage involved recruiting patients, having them participate in Braining classes, and completing their 12-week treatment. The research team provided structured support during weekly meetings with the Braining team. Procedure Patients were recruited during a predetermined time-period, from January to April 2022. A goal was set at up to 50 recruited patients. Patients were informed about the study in connection with clinical visits. Additional information regarding the study was available on posters in the waiting area. Interested patients received detailed written and oral information about the study at weekly start-up meetings held from March to April 2022. After signing the informed consent form, the participants received a QR code to create an account on a secure web platform provided by the Karolinska Institutet (KI). Upon registration, the participants were automatically assigned a unique study ID. The participants completed a health screening questionnaire, which was developed for this study based on current national and European guidelines regarding health risk assessment and exercise ( 35 , 36 ). The health screening questionnaire included body weight, height, age, and current physical activity as well as 12 items covering risk factors for congenital or genetic heart disease, risk factors for cardiovascular events, ongoing infectious disease, chronic respiratory disease and neuromusculoskeletal diseases. Submitted answers were evaluated by the study physician. If the screening revealed potential risk factors for participation in an exercise intervention, the patient provided more detailed responses to the screening questions orally, before the physician decided on eligibility. The patient was excluded and recommended to seek health care if the risk was deemed too high. Patients with a low health risk who fulfilled the inclusion criteria were included in the study, and premeasurements were initiated. An overview of the participant flow is presented in Fig. 1. Following inclusion, the Braining intervention was initiated with a scheduled clinical visit to trained nurses experienced in motivational interviewing ( 37 ). The participants received the weekly exercise schedule as well as instructions regarding the booking procedure and were asked to set individual goals as an initial motivational strategy. Physical activity was measured via the ActiGraph GT3x accelerometer (ActiGraph, Pensacola, FL). The accelerometers were initialized by the researchers and handed directly to the participant or via the clinic reception to be picked up during clinical visits. The participants were instructed to wear the accelerometer on the lower back ( 38 ) during waking hours for seven consecutive days at three time points (pre-, mid- and postintervention). Additionally, participants were instructed to log their daily wear time in a diary used to verify wear time and number of valid days. Another accelerometer measurement was planned at the 12-month follow-up but was cancelled because of insufficient adherence at mid- and postintervention. The outcome measures in the study protocol were amended accordingly. Self-assessment instruments were administered before, at 4 and 8 weeks, after the intervention and at the 12-month follow-up. All self-assessments were completed on the KI online platform. The participants received an automatically generated monthly text message from the KI platform, with a link leading to the questionnaires. If the participant failed to fill out the forms in time, a series of reminder text messages were generated. Additionally, a research nurse or assistant called the participant on up to 3 occasions. Physical examinations were performed during the clinical visits at inclusion (see Patient-centered outcomes ) postintervention and at the 12-month follow-up. Fasting blood samples were obtained either by nurses at the clinic or with a referral to a lab of the participant’s choice at the same time points. Physical examinations at the 12-month follow-up were performed by a research nurse. Baseline data regarding age and gender was provided at registration on the KI platform. Data regarding current primary and secondary psychiatric diagnoses were extracted from patient medical records and noted at inclusion by an appointed staff member. Data from medical records regarding participation in Braining sessions and clinical visits including metabolic measures and blood samples were collected post-intervention and after the 12-month follow-up. Data from self-assessments were extracted from the KI platform. Outcome measurements Feasibility outcomes Adherence to the physical activity intervention was assessed by the number of Braining sessions attended during the intervention period and from postintervention until the 12-month follow-up. Adherence to measurements was assessed by completion rates at group level of Actigraph measurements, questionnaires, clinical visits, and blood samples at different time points. On the basis of the rule of thumb that a loss to follow-up of more than 20% puts validity at risk, an adherence to measurements of 80% or more was considered feasible ( 39 ). Acceptability was measured by the Client Satisfaction Questionnaire (CSQ-8), which is designed to rate global satisfaction with treatment. The total score ranges from 8 to 32, where higher values indicate greater satisfaction. The CSQ-8 has high internal consistency and has a correlation coefficient of 0.4 with reported changes in symptoms ( 40 ). Expectancy and credibility were measured by the Credibility and Expectancy Questionnaire (CEQ) ( 41 , 42 ), a scale commonly used in clinical outcome studies. The scale consists of six questions, three questions exploring credibility and three covering expectancy. For credibility, a mean can be calculated. With respect to expectancy, the question “By the end of the treatment, how much improvement in your symptoms do you think will occur?” is considered representative and was therefore chosen for analysis. The questions are rated on a 0–100% scale with 10-point increments. The questionnaire has high internal consistency between the two factors and adequate test‒retest reliability. The Negative Effects Questionnaire (NEQ) ( 43 ) evaluates negative effects during psychological treatment. The items explore six validated factors: symptoms, quality, dependency, stigma, hopelessness and failure. The full instrument as well as individual factors have yielded adequate internal consistency. The questionnaire differentiates between negative effects attributed to treatment and those possibly caused by other circumstances. Safety was measured as the number and severity of reported adverse events during the exercise intervention period. This information was collected through oral reports during weekly Braining staff meetings, where adverse events were one of the standing items. Physical activity outcomes In clinical studies and settings, accelerometry is considered the “gold standard” for measuring physical activity ( 44 ). In this work, the Actigraph GT3X accelerometer was used. The Actigraph accelerations were sampled at 30 Hz and summed over 60 seconds via ActiLife v.6.13.4 software. The non-wear time was set to > 90 minutes of consecutive zero counts, allowing for 2 min of nonzero counts ( 45 ). Data were deemed feasible for analysis in measurements with ≥ 4 days and ≥ 10 h per day of valid wear time ( 46 ). Wear time was allocated into steps per day and activity categories in minutes per day based on count-based thresholds: sedentary behavior < 100 counts per min (cpm) ( 47 ), low-intensity physical activity 100–1951 cpm and moderate to vigorous intensity physical activity ≥ 1952 cpm ( 48 ). The International Physical Activity Questionnaire (IPAQ) is an internationally used self-assessment instrument for indirect measures of physical activity divided into different levels of intensity and time spent sitting. The short 7-question form was used. The IPAQ has acceptable criterion validity and specificity in the general population, although its sensitivity has been shown to be low ( 49 ). Patient-centered outcomes The Alcohol Use Disorders Identification Test Consumption (AUDIT-C) ( 50 ) is a 3-item clinical questionnaire that measures alcohol consumption and has high test-retest reliability. The Drug Use Disorder Identification Test (DUDIT) ( 51 ) is an 11-item reliable and valid questionnaire covering patterns of current drug use and drug-related problems. The 9-item Patient Health Questionnaire-9 (PHQ-9) ( 52 , 53 ) is designed to screen for depression as well as a measure of depression severity. Its sensitivity for detecting major depressive disorder has been thoroughly tested, and it has yielded good results. The 7-item Generalized Anxiety Disorder Questionnaire (GAD-7) ( 54 ) is one of the most frequently used measures for general anxiety because of its validity and diagnostic reliability as well as sensitivity to change during treatment. The Insomnia Severity Index (ISI) ( 55 ) is a reliable and valid self-rating questionnaire for evaluating perceived sleep difficulties. It is sensitive to treatment response and has satisfactory internal consistency. The Brunnsviken Brief Quality of Life Scale (BBQ) ( 56 ) was developed to measure self-rated subjective quality of life and covers six identified life areas, such as leisure, learning and self-view. It has been tested and validated in both nonclinical and clinical subjects, including psychiatric populations. Moreover, it has shown adequate reliability and consistency. The World Health Organization Disability Assessment Schedule 2.0 (WHODAS-2) ( 57 ) measures health and disability. The brief version used in this study consists of 12 items evaluating function over six life domains, including cognition, mobility, self-care, getting along, life activities and participation. The WHODAS-2 has shown high validity and reliability. The EQ5D-5 L ( 58 ) is a well-known, reliable, and valid instrument for measuring health-related quality of life. It consists of five dimensions—mobility, self-care, usual activities, pain/discomfort and anxiety/depression—and a visual analog scale for overall health ranging from 0—100. The Treatment Inventory of Costs in Patients with Psychiatric Disorders (TiC-P) ( 59 ) covers healthcare consumption and production losses in patients with mental disorders. It consists of two parts that can be used separately. In this study, the second part, measuring production losses through absence from and reduced efficiency of work, was administered. It has been regarded as feasible and reliable, with adequate consistency. Somatic health was evaluated via physical examination and metabolic blood markers. During physical examination at clinical visits, blood pressure (BP), heart rate, waist circumference (WC) and body mass index (BMI) were measured. To minimize interindividual variability, the measurements were performed in accordance with a predetermined protocol. Blood pressure was measured while the participants were in a seated position with either an automatic or manual blood pressure cuff. WC was measured according to the WHO WC-mid ( 60 ). To evaluate the metabolic profile of the participants and the possible preliminary effects of the intervention, blood lipids (triglycerides, low-density lipoprotein, high-density lipoprotein), blood glucose and HbA1c were measured. Statistical analysis Statistical analyses were conducted via Jamovi, version 2.3.18 (The jamovi project, 2024) ( 61 ). The specific packages used were gamlj – General Analyses for Linear Models in jamovi 2.6.6, and jmv – Analyses bundled with jamovi 2.3.1. All data were retrieved from https://www.jamovi.org/ . Descriptive statistics of psychiatric ratings and metabolic measurements were used to display the means, standard deviations and proportion scoring above a clinical cutoff before, after and 12 months after treatment. Changes in psychiatric self-assessments and metabolic measures from pre- to posttreatment and follow-up were tested for significance, and effect sizes were evaluated via Cohen’s d and 95% confidence intervals of estimates. Since the sample size was small, the power to detect changes was limited, and all the statistical analyses were preliminary. The effects of time on changes in psychiatric self-report measures were evaluated by calculating the significance of fixed effect estimates of time in a linear mixed model. Linear mixed model analysis was chosen to maximize the size of the sample by including all possible data and fulfilling the criteria for an intention-to-treat analysis in a future RCT. Time was treated as a categorical variable in the models, resulting in distinct estimates of the time effects from the start of intervention for each of the next four measurements instead of linear average trends. The effects of time on changes in metabolic measures were evaluated with dependent pairwise t tests. In the planned RCT, imputation methods will be used for missing data to enable intention-to-treat analyses of effects on metabolic variables. Results The participant flowchart is presented in Fig. 1. In total, 26 patients attended the information meeting. During the recruitment process, two participants were excluded due to somatic health risks, and one was excluded because consent was withdrawn, leading to a total sample of 23 participants. During the pre-assessment, one participant was excluded because of the development of an acute episode of severe mental disorder, thus leading to a final study population of 22. This was regarded as a sufficient population size. At the 12-month follow-up, 7 participants had ongoing medical care contact at clinics A or B. Overview of the participation process, detailing the number of included and excluded patients as well as the completion rates of the study protocol components among the final study population. Table 1 shows the baseline characteristics of the final study population. Registered diagnoses extracted from medical records revealed that 20 out of 22 participants had at least one registered diagnosis of alcohol or other substance use disorders. Alcohol use was the most common (n = 13). With respect to other psychiatric diagnoses, hyperkinetic disorders were the most common (n = 5), followed by anxiety disorders (n = 3). Table 1 Baseline characteristics of the study population . Demographics Participants (N = 22) Age, mean (SD) 38.7 (12.3) Male, n (%) 18 (81.8) Employment status and work absence rate Participants (N = 19) Current employment, n (%) 8 (42.1) Sickness absence last 4 weeks, n (%) 6 (31.6) Diagnoses Participants (N = 22) Alcohol use disorders (F101, F102), n 13 Cannabis use disorder (F121), n 2 Stimulant use disorder (F152), n 1 Polydrug use disorders (F191, F192), n 5 Hyperkinetic disorders (F900, F900B, F900C), n 5 Bipolar disorders (F319), n 1 Depressive disorders F321), n 1 Anxiety disorders (F401, F412), n 3 No specified diagnosis (Z032), n 1 Singular diagnosis, n 11 Baseline physical activity Participants (N = 16) Sedentary time, minutes mean (SD) 504 (127) Moderate physical activity, minutes mean (SD) 52.1 (42.5) Vigorous physical activity, minutes mean (SD) Steps per day, mean (SD) 2.4 (8.0) 9510 (4870) Demographic data and employment, diagnostic groups and baseline physical activity. Note: Diagnostic groups are classified according to the 2019 version of the International Classification of Diseases (ICD-10). Diagnoses were extracted from medical records and are displayed in a summarized manner, meaning that a participant can contribute to more than one diagnosis. Baseline physical activity is measured with Actigraph and presented as minutes or steps per valid day. Feasibility outcomes The exercise participation rate during the 12-week intervention period was a mean of 9 and a median of 8 Braining sessions (SD = 7.7, range = 1–30). Eighteen participants attended at least 3 sessions. The two most active participants attended 29 and 30 sessions, respectively. At the 12-month follow-up, 27% (n = 6) had continued to participate in Braining sessions after the intervention period ended. For these participants, the number of sessions attended ranged from 2–90, with a mean of 20.7 (SD = 34.7) and a median of 4 sessions. Adherence to the study protocol assessment components included Actigraph measurements, questionnaires, clinical visits, and blood samples and are reported in Fig. 1. Adherence to Actigraph measurements varied and diminished with time, with 16 (72.7%) valid measurements at baseline, 10 (45.5%) at week 6, and 7 (31.8%) at week 12. Out of the 16 valid baseline measurements, 2 contained 4 valid days, 1 had 5 valid days, 5 had 6 valid days and 8 measurements had 7 valid days. On valid days, the mean wear time was 802.6 (SD = 130.5) minutes. The rate of completed questionnaires varied but was generally high, with 22 (100%) at baseline, 19 (86%) after treatment and 17 (77.3%) at the 12-month follow-up. The rate was the lowest at week 8 (45.5%, n = 10). The rates of completed clinical visits and blood samples were generally high at baseline and declined over time: 22 (100%) to 14 (63.6%) and 21 (95.5%) to 11 (50%), respectively. The CSQ-8 was completed by 17 participants, and the mean value was 26.8 (SD = 3.9), out of 32. This corresponds with response 3, mostly satisfied, or 4, very satisfied, for each item. Item 3, “did the treatment meet your need,” received the lowest mean score of 2.99 (SD = 0.66), whereas item 4, “would you recommend the treatment to a friend,” received the highest mean score of 3.71 (SD = 0.47). Among the 22 participants, 21 completed the CEQ. The mean value of the first 3 items representing treatment credibility was 7.3 (SD = 1.18), out of 9. The mean value of expected improvement in symptoms was 51.9% (SD = 21.2). The NEQ was completed by 19 participants. Overall reported negative effects were few and low. All the scores for negative effects were 1 (out of 4). The most reported items were as follows: increased sense of stress (n = 4, 21.1%), resurfacing of unpleasant memories (n = 3, 15.8%), thinking that the problem they were seeking help for could not improve (n = 3, 15.8%), not always understanding the treatment (n = 3, 15.8%), feeling that the treatment did not suit them (n = 3, 15.8%) and thinking that they developed a dependency on the treatment (n = 2, 10.5%). The reported negative effects were not reported to affect the participants in a negative way. During the 12-week study period, there were no reports of any serious adverse events. One minor adverse event, a migraine episode, was reported. Physical activity outcomes Owing to low Actigraph-adherence at week 6 and 12, no meaningful analysis of changes could be conducted. Due to a technical error in the digital administration of the IPAQ instrument, resulting in high levels of uncertainty, the data was deemed unusable. Patient centered outcomes The results of linear mixed model analyses of the fixed effects of time on self-reported measures are shown in Table 2 . The effect sizes of the changes in the variables ranged from 0.276–0.774 (Cohen’s d ). Six out of eight variables significantly changed from pre- to post measurement. Six out of eight variables significantly changed from pre- to follow-up measurements. With respect to the psychiatric symptom ratings, the PHQ-9 score was above the clinical cutoff of 10 for depression ( 62 ) in 64% of the participants before treatment, 37% after treatment, and 29% at the 12-month follow-up. The GAD-7 score was above the clinical cutoff of 8 for generalized anxiety ( 62 ) in 40% of participants before treatment, 21% after treatment, and 18% at the 12-month follow-up. The ISI was above the clinical cutoff of 11 for detecting insomnia ( 55 ): 55% before treatment started, 32% after treatment and 28% at the 12-month follow-up. Table 2 Preliminary effects on psychiatric symptoms including substance use, functional status, and quality of life. Measure Pre M(SE) 4 weeks M(SE) 8 weeks M(SE) Post M(SE) Follow- Up M(SE) Pre-Post p value Cohen’s d (95% CI) Pre-FU p value Cohens’ d (95% CI) AUDIT-C 3.76 (0.80) 2.57 (0.85) 2.01 0.96) 1.99 (0.81) 2.47 (0.82) .006 0.505 (0.0204;0.978) .650 0.344 (-0.166;0.844) DUDIT 12.59 (2.77) 9.87 (2.89) 8.70 (3.12) 8.10 (2.82) 5.24 (2.71) .011 0.541 (0.0523;1.018) .002 0.457 (-0.063;0.966) PHQ-9 11.55 (1.21) 9.56 (1.32) 9.42 (1.53) 8.48 (1.26) 7.14 (1.28) .009 0.519 (0.0328;0.993) < .001 0.612 (0.0674;1.139) GAD-7 7.70 (0.995) 8.23 (1.007) 7.22 (1.247) 6.15 (1.025) 5.33 (1.049) .098 0.324 (-0.1422;0.781) .021 0.527 (-0.0047;1.04) ISI 12.36 (1.32) 10.79 (1.43) 10.73 (1.65) 8.94 (1.36) 8.74 (1.40) .007 0.597 (0.1002;1.079) .006 0.704 (0.145;1.24) BBQ 38.8 (5.03) 48.1 (5.47) 53.2 (6.39) 52.5 (5.20) 54.7 (5.37) .007 -0.774 (-1.28;-0.251) .003 -0.538 (-1.041;-0.020) WHODAS-2 30.0 (3.70) 20.6 (4.03) 20.6 (4.71) 20.7 (3.77) 14.7 (3.94) .024 0.360 (-0.109;0.820) < .001 -0.330 (-0.829;0.179) EQ5D-5 L VAS EQ5D-5 L Index 58.5 (3.50) 0.894 (0.013) 60.2 (3.78) 0.909 (0.014) 58.9 (4.47) 0.903 (0.017) 62.6 (3.57) 0.918 (0.013) 65.1 (4.22) 0.917 (0.013) .247 -0.276 (-0.792;0.210) .146 -0.348 (-0.808;0.120) .160 -0.411 (-0.901;0.090) .043 -0.532 (-1.034;-0.015) Estimated marginal means (M), standard errors (SE), test for significance, and estimation of effect size on each outcome measure. Tests for significance and effect sizes are evaluated and presented for two separate time periods, from Pre to Post and Pre to Follow-Up. Note. AUDIT-C = Alcohol Use Disorders Identification Test for Consumption, DUDIT = Drug Use Disorders Identification Test, PHQ-9 = Patient Health Questionnaire-9, GAD-7 = General Anxiety Disorder 7-item scale, ISI = Insomnia Severity Index, BBQ = Brunnsviken Brief Quality of Life Scale, WHODAS-2 = World Health Organization Disability Assessment Schedule 2.0 EQ-5D = EuroQol-5 dimension, VAS = Visual Analogue Scale. The results of linear mixed model analyses of the fixed effects of time on metabolic measures are shown in Table 3 . At baseline, the mean systolic BP was 131 mmHg, and 14 of 22 individuals had a systolic BP ≥ 130 mmHg, which was considered to be above the normal range ( 63 ). There was a significant decrease in diastolic BP from pre- to post-intervention and a significant decrease in systolic blood pressure from pre-intervention to follow up. Mean BMI was at overweight levels associated with increased health risk ( 64 ) at baseline and did not change significantly with time. At baseline, the mean WC was 91.6 cm (range 82–101) in women and 100 cm (range 66–136) in men. Similarly, 50% of women and 44% of men had WC at levels where weight reduction is recommended ( 65 ). Metabolic blood marker levels were within the normal range at baseline and did not change significantly from baseline to post-intervention. Table 3 Preliminary effects on metabolic outcomes. Measure Pre M(SD) Post M(SD) Follow-up M(SD) Pre-Post p value Cohen’s d (95% CI) Pre-FU p value Cohen’s d (95% CI) Blood pressure Systolic 131 (2.57) 128 (2.65) 122 (3.02) .364 0.145 (-0.298;0.583) .006 0.846 (0.220;1.45) Blood pressure Diastolic 83.5 (2.23) 78.2 (2.29) 79.2 (2.58) .019 0.581 (0.099;1.050) .086 0.780 (0.167;1.371) Pulse 76.0 (2.81) 74.5 (2.86) 74.5 (3.28) .624 0.142 (-0.312;0.592) .667 0.131 (0.418;0.675) Waist circumference 97.4 (3.56) 96.0 (3.60) 94.8 (3.62) .204 0.646 (0.775;1.195) .070 0.355 (0.213;0.910) BMI 26.5 (1.29) 26.7 (1.30) 26.0 (1.31) .473 -0.229 (-0.681;0.230) .275 0.167 (0.364;0.691) Total cholesterol 5.04 (0.25) 5.00 (0.27) 4.89 (0.28) .863 0.024 (-0.521;0.567) .495 0.140 (0.432;0.706) LDL 3.19 (0.21) 3.05 (0.23) 3.06 (0.24) .479 0.216 (-0.339;0.762) .551 0.198 (0.378;0.765) HDL 1.34 (0.06) 1.31 (0.06) 1.38 (0.06) .553 0.134 (-0.415;0.679) .460 0.228 (0.796;0.351) LDL/HDL Quota 2.43 (0.17) 2.39 (0.19) 2.30 (0.20) .822 0.046 (-0.499;0.589) .494 0.223 (0.355;0.791) Triglycerides 1.20 (0.16) 1.43 (0.18) 1.07 (0.18) .220 -0.321 (-0.873;0.243) .494 0.044 (0.523;0.609) Blood glucose 5.52 (0.12) 5.47 (0.14) 5.62 (0.14) .734 0.240 (-0.317;0.787) .547 0.091 (0.656;0.478) HbA1c 33.8 (0.71) 34.1 (0.76) 35.0 (0.76) .664 -0.063 (-0.653;0.530) .032 0.539 (1.163;0.107) Means (M), standard deviations (SD), test for significance, and estimation of effect size on each outcome measure. Tests for significance and effect size are evaluated and presented. for two separate time periods, from Pre to Post and Pre to Follow-up. Note . BMI = body mass index, LDL = low-density lipoprotein, HDL = high-density lipoprotein. Discussion In this feasibility study, the physical activity intervention Braining was tested in a clinical setting for substance use disorders for the first time, and several crucial aspects of feasibility were evaluated. Our findings suggest that the participants found Braining to be acceptable and safe on the basis of measures of satisfaction, credibility, expectancy, and adverse events. While adherence to the intervention, questionnaires, and metabolic measurements was considered acceptable, adherence to Actigraph measures was notably low and did not result in sufficient data on changes in physical activity. Participant ratings imply potential reductions in alcohol and drug use, alongside potential improvements in symptoms of depression and insomnia over the treatment period. Similarly, there were indications of enhanced functionality and quality of life, with effect sizes ranging from small to moderate. Notably, changes in anxiety and health-related quality of life ratings did not reach statistical significance. These findings should be interpreted cautiously, given the exploratory nature of the study and the limitations associated with a small, uncontrolled sample. The recommended frequency of three Braining exercise sessions per week was not reached at the group level. On average, patients attended one Braining session per week. However, the participation level was optional, and the participants were invited to choose between four scheduled sessions per week. This result is in line with other physical activity intervention studies in comparable populations ( 66 – 68 ), as well as our evaluation of clinical data from the first years of implementation of Braining in regular care ( 29 ). Other studies that use external resources such as fitness centers and exercise specialists have managed to reach higher attendance rates, with two sessions per week ( 69 ). In addition to exceeding the resources of regular care, these studies often exclude participants with comorbidities of alcohol and other substance use disorders. One could argue that for this population, with the resources of a naturalistic setting, a slower increase in the exercise dosage with more frequent follow-ups might be favorable. Moreover, the individual number of attended sessions varied greatly, which could point to the existence of subgroups with different participation behaviors. Understanding the reasons behind these behaviors is key to adapting the method to suit a larger proportion of the target population. The adherence to the accelerometry measurements was insufficient at baseline (73%) and continued to decrease at each measurement point (46–32%). Furthermore, the Actigraph diaries could not be used to guide the data analysis as intended because of insufficient compliance. This contributed to the high rate of invalid measurements. Comparatively, a study using Actigraphs in participants with depression and anxiety disorders was limited by adherence ( 70 ). In that sample, only 54% were able to contribute a sufficient number of valid days. Compared with other psychiatric populations, the baseline Actigraph measurements revealed a high mean level of moderate-intensity physical activity compared to other psychiatric populations ( 70 – 72 ). Furthermore, the mean steps per day were greater than expected and are in line with previous studies on healthy populations ( 73 ). In terms of adherence to distributed questionnaires and to clinical visits, 100% pre- and 86% postintervention were regarded as adequate. However, adherence to blood sampling, 95% before and 68% after intervention, was less than sufficient. A possible explanation could be that, in most cases, participants had to visit an external laboratory to provide blood samples, thus requiring an additional procedure. Adherence to questionnaires at weeks four and eight was low (73–45%). We hypothesize that a smaller number of questionnaires as well as a more targeted selection of questionnaires could increase completion in this population. There was a significant decline in participation at the 12-month follow-up compared with that at the post-intervention visit (clinical visits, 64%; blood samples, 50%). This was not unexpected, since only one-third of the participants were still registered patients at the clinics at that point and were available for reminders. Moreover, completion of questionnaires remained relatively high (77%), indicating that the digital administration of questionnaires via the KI platform was a feasible and acceptable method. Evaluating treatment credibility and satisfaction as well as negative effects and adverse events was crucial in this study. At inclusion, participants rated the intervention as credible, and scores were in line with perceived credibility in similar types of studies ( 74 , 75 ). With respect to expectancy, participants' rating of 52% expected improvement was lower than the expected improvement in a comparable CBT study ( 41 ). Satisfaction with the intervention at the postintervention measurement was regarded as high. On average, participants scored 3 out of 4 on each item. For example, this corresponds to rating the intervention quality as good and meeting most of the participants’ needs. Further qualitative information from participants' experiences was obtained in the in-depth interviews and presented separately. Self-reported negative effects are scarce, but one should take into account that, to our knowledge, the NEQ scale has not yet been validated in studies on physical activity. Considering the reported satisfaction scores in conjunction with a low rate of negative effects, there are indications that the intervention is feasible for further implementation. No serious adverse events were reported, indicating a sufficient level of safety regarding both the exercise intervention and the recruitment process. This further motivates a continued use of the health screening procedure in future studies. Reported minor adverse events were rare but could be underreported given that previous studies on exercise therapy ( 76 ) reported a 19% increase in minor adverse events. The patient-centered outcomes showed interesting preliminary results. The psychiatric assessments were found to be capable of detecting changes, which motivates their further use. In addition, a majority of the changes were statistically significant. The self-assessments regarding substance use disorders revealed that the mean alcohol use was low pre-intervention and decreased over time, which could be due to ongoing treatment. In contrast, the level of reported drug use in DUDIT pre-intervention was high, possibly because DUDIT includes drug use over a 12-month period. Given this, it is difficult to interpret the decrease in DUDIT over time. To evaluate effects on substance use in upcoming studies, the Timeline Follow-Back method which allows for daily recording of consumption ( 77 ), could be a more appropriate tool. In the PHQ-9, the change was also clinically relevant, with a mean score of 8.5 post-treatment and 7.1 at long-term follow-up, which was below the cutoff for depression. The effect sizes were 0.52 pre- to post-intervention and 0.61 pre- to follow-up was in line with the vast literature on physical activity and depressive symptoms ( 20 ). However, the validity of these preliminary results is impacted by the absence of a control group as well as the small sample size and should therefore be interpreted with caution. The metabolic outcome measures were less capable of detecting changes. The mean BMI and WC were above the recommended levels at baseline and did not change with time, and metabolic blood tests remained within the normal range post-intervention and at the 12-month follow-up. However, a significant decrease in blood pressure was detected. Our findings indicate the need for subgroup analyses in the planned RCT. Strengths and limitations This study has several strengths. First, the naturalistic design successfully implemented the method within specialized psychiatry, with health care personnels providing the intervention. Second, the wide inclusion criteria enabled unique participation for a range of patients, including individuals with current substance use and comorbid psychiatric diagnoses, commonly excluded from studies ( 22 ). Third, the multitude of assessments provided a wide range of data and hereby provided comprehensive information of the participants. Fourth, the fact that patients voluntarily signed up for the study and were motivated to engage in activities beyond their regular treatment could be seen as an additional strength. However, there are also a number of limitations to this study. First, the study design with an open trial lacks a control group as well as randomization. Thus, testing the feasibility of the randomization and control process for the future trial is not possible. The interpretation of effect measure findings is also limited by the study design. Furthermore, the small sample size also affects the statistical strength. However, since this was a feasibility pilot study, the effect measures were not primary outcomes. Second, since the methods of measuring physical activity proved not feasible, no conclusions on overall changes in physical activity patterns were drawn. In the same manner, self-reported physical activity via the International Physical Activity Questionnaire (IPAQ) was planned to be used, but data could not be interpreted due to a technical error. Nevertheless, the findings from measuring physical activity in this population provide useful information for upcoming studies. Third, at the group level, the recommended number of 3 exercise sessions per week was not reached. Nevertheless, in line with recommendations ( 16 ), even a small increase in moderate- to vigorous-intensity physical activity in this population has positive effects. Participants’ individual experiences with a focus on motivation will be explored in the qualitative analysis of the conducted interviews and should lead to improvements in the intervention. Fourth, diagnoses from medical records were used and not verified by diagnostic interviews, which makes the findings less valid. In future studies, participants should undergo a verifying diagnostic interview as part of premeasurements. Clinical implications and future studies Given that this was a feasibility study, the primary implications relate to developing the method for a planned randomized trial. Owing to the great variance in exercise participation, we recommend that the motivational components of the Braining method be further developed and individualized for upcoming trials. For a better understanding of the motivational factors impacting exercise behavior, participants' experiences will be explored via qualitative analysis of interviews conducted with the participants adjacent to the intervention. Our interpretation was that the Actigraph procedure with a diary and an on-off routine was too demanding in this population. A smaller accelerometer that enables a 24-hour wear time and does not require a diary could be more feasible. To avoid losing comprehensive data regarding overall physical activity, easily accessible self-reported measurements should be distributed regularly as a complement to accelerometers. Overall, the chosen assessments for psychiatric and somatic variables were feasible and capable of detecting changes. A more precise selection of psychiatric self-assessment scales could be favorable for reducing overlap and increasing participation. Stricter inclusion/exclusion criteria regarding pre-study physical activity levels and metabolic health measures could provide a better opportunity for observing possible effects on these outcomes. Overall, the health screening procedure was considered useful both in upcoming clinical trials and in implementing physical activity in psychiatric care. Conclusions This pilot study tested the feasibility of Braining, a novel physical activity intervention in a specialized psychiatry setting focused on substance use. The participants were satisfied with the method and participated in the exercise intervention and measurements but were not fully adherent to the Actigraph measurements. With developed motivational components and adjustments regarding measurements of physical activity, psychiatric assessments and inclusion criteria, the Braining method would be feasible to evaluate in larger patient groups within psychiatric services in conjunction to their ongoing treatment. Abbreviations ADHD: Attention Deficit Hyperactivity Disorder AUDIT-C: Alcohol Use Disorders Identification Test Consumption BBQ: Brunnsviken Brief Quality of life Scale BMI: Body Mass Index BP: Blood Pressure CEQ: Credibility and Expectancy Questionnaire CSQ-8: 8-item Client Satisfaction Questionnaire DUDIT: Drug Use Disorders Identification Test EQ5D-5 L: The EQ5D™ is a trademark of the EuroQol Group and a self-assessment instrument for describing and valuing health status GAD-7: 7-item Generalized Anxiety Disorder Questionnaire IPAQ: International Physical Activity Questionnaire ISI: Insomnia Severity Index KI: Karolinska Institutet NEQ: Negative Effects Questionnaire PHQ-9: 9-item Patient Health Questionnaire RCT: Randomized Controlled Trial WC: Waist Circumference WHO: World Health Organization WHODAS-2: The World Health Organization Disability Assessment Schedule 2.0 Declarations Ethics approval and consent to participate The study was ethically approved by the Swedish Ethical Review Authority, the Ethics Approval Committee for Medicine, Gothenburg under IDs 2021-04037, 2021-05728-02, 2022-00188-02, and 2023-02746-02. Written informed consent was obtained from the participants. All methods were carried out in accordance with relevant guidelines and regulations in the Declaration of Helsinki. Consent for publication Not applicable. Availability of data and materials The datasets generated and analyzed during the current study are not publicly available owing to individual privacy even though they are pseudonymized but are available from the corresponding author upon reasonable request. Data are located in controlled access data storage at KI. Competing interests The authors declare that they have no competing interests. Funding Open access funding was provided by the Karolinska Institute. The study was funded by the regional agreement on medical training and clinical research (ALF) between the Stockholm Regional Council, Stockholm Health Care Services and Karolinska Institute (grant ID FoUI-955166), LJ Boethius Foundation, Systembolaget’s Alcohol Research Council, as well as the Centre for Psychiatry Research, Stockholm. Authors' contributions LM, SS, ÅA, RM and CJS designed the research plan. LM acquired the funding and supervised the project with SS. ÅA, LM, SS and CJS obtained ethical approval. All authors contributed to the conceptualization and design of the paper. RM and ÅA provided education and implementation of the Braining method at the participating clinics. ÅA conducted the medical screening at inclusion. ÅA, LK and RM conducted the data collection. ÅA and LK wrote the original draft and were responsible for the rewriting process. JH, ÅA, LK and SS conducted the statistical analyses. MH provided Actigraphs and analyzed and interpreted the Actigraph measurements together with ÅA. RM, LM, SS, CJS, JH, TL, NJ, and MH contributed to editing. All authors have read and agreed to the published version of the manuscript. Acknowledgments We are most grateful to the participating patients. We would also like to express our thankfulness to the staff at the clinics Liljeholmsberget and Livsstilsmottagningen, particularly the appointed team leaders Greta Schettini, Peter Margulies and Sandra Carneholm. We also wish to thank Karin Linderståhl, research nurse, for organizing and collecting the data. This work used the BASS platform from the eHealth Core Facility at Karolinska Institutet. Authors' information LM and ÅA founded the Braining method in 2017 as clinicians in Region Stockholm. References Firth J, Siddiqi N, Koyanagi A, Siskind D, Rosenbaum S, Galletly C, et al. The Lancet Psychiatry Commission: a blueprint for protecting physical health in people with mental illness. Lancet Psychiatry. 2019 Aug;6(8):675–712. Rehm J, Shield KD. Global Burden of Disease and the Impact of Mental and Addictive Disorders. Curr Psychiatry Rep. 2019 Feb 7;21(2):10. Rehm J, Gmel GE, Gmel G, Hasan OSM, Imtiaz S, Popova S, et al. The relationship between different dimensions of alcohol use and the burden of disease—an update. Addict Abingdon Engl. 2017 Jun;112(6):968–1001. Carvalho AF, Heilig M, Perez A, Probst C, Rehm J. Alcohol use disorders. Lancet Lond Engl. 2019 Aug 31;394(10200):781–92. Westman J, Wahlbeck K, Laursen TM, Gissler M, Nordentoft M, Hällgren J, et al. Mortality and life expectancy of people with alcohol use disorder in Denmark, Finland and Sweden. Acta Psychiatr Scand. 2015 Apr;131(4):297–306. Cunill R, Castells X, González-Pinto A, Arrojo M, Bernardo M, Sáiz PA, et al. Clinical practice guideline on pharmacological and psychological management of adult patients with attention deficit and hyperactivity disorder and comorbid substance use. Adicciones. 2022 Apr 1;34(2):168–78. González-Pinto A, Goikolea JM, Zorrilla I, Bernardo M, Arrojo M, Cunill R, et al. Clinical practice guideline on pharmacological and psychological management of adult patients with bipolar disorder and comorbid substance use. Adicciones. 2022 Apr 1;34(2):142–56. Sáiz PA, Flórez G, Arrojo M, Bernardo M, González-Pinto A, Goikolea JM, et al. Clinical practice guideline on pharmacological and psychological management of adult patients with an anxiety disorder and comorbid substance use. Adicciones. 2022 Apr 1;34(2):157–67. Torrens M, Tirado-Muñoz J, Fonseca F, Farré M, González-Pinto A, Arrojo M, et al. Clinical practice guideline on pharmacological and psychological management of adult patients with depression and a comorbid substance use disorder. Adicciones. 2022 Apr 1;34(2):128–41. Grant BF, Stinson FS, Dawson DA, Chou SP, Dufour MC, Compton W, et al. Prevalence and co-occurrence of substance use disorders and independent mood and anxiety disorders: results from the National Epidemiologic Survey on Alcohol and Related Conditions. Arch Gen Psychiatry. 2004 Aug;61(8):807–16. Soyka M, Mutschler J. Treatment-refractory substance use disorder: Focus on alcohol, opioids, and cocaine. Prog Neuropsychopharmacol Biol Psychiatry. 2016 Oct 3;70:148–61. van Emmerik-van Oortmerssen K, van de Glind G, van den Brink W, Smit F, Crunelle CL, Swets M, et al. Prevalence of attention-deficit hyperactivity disorder in substance use disorder patients: A meta-analysis and meta-regression analysis. Drug Alcohol Depend. 2012 Apr 1;122(1):11–9. Grunze H, Schaefer M, Scherk H, Born C, Preuss UW. Comorbid Bipolar and Alcohol Use Disorder-A Therapeutic Challenge. Front Psychiatry. 2021;12:660432. Preuss UW, Schaefer M, Born C, Grunze H. Bipolar Disorder and Comorbid Use of Illicit Substances. Medicina (Mex). 2021 Nov 17;57(11):1256. Grant BF, Stinson FS, Hasin DS, Dawson DA, Chou SP, Ruan WJ, et al. Prevalence, Correlates, and Comorbidity of Bipolar I Disorder and Axis I and II Disorders: Results From the National Epidemiologic Survey on Alcohol and Related Conditions. J Clin Psychiatry. 2005 Oct 14;66(10):15387. Bull FC, Al-Ansari SS, Biddle S, Borodulin K, Buman MP, Cardon G, et al. World Health Organization 2020 guidelines on physical activity and sedentary behaviour. Br J Sports Med. 2020 Dec;54(24):1451–62. Physical Activity Guidelines Advisory Committee. 2018 Physical Activity Guidelines Advisory Committee Scientific Report. Washington, DC: U.S. Department of Health and Human Services, 2018. 2018; Available from: http://2018 Physical Activity Guidelines Advisory Committee Scientific Report (health.gov) Kramer A. An Overview of the Beneficial Effects of Exercise on Health and Performance. Adv Exp Med Biol. 2020;1228:3–22. Noetel M, Sanders T, Gallardo-Gómez D, Taylor P, Del Pozo Cruz B, van den Hoek D, et al. Effect of exercise for depression: systematic review and network meta-analysis of randomised controlled trials. BMJ. 2024 Feb 14;384:e075847. Singh B, Olds T, Curtis R, Dumuid D, Virgara R, Watson A, et al. Effectiveness of physical activity interventions for improving depression, anxiety and distress: an overview of systematic reviews. Br J Sports Med. 20230216th ed. 2023 Feb 16. Giménez-Meseguer J, Tortosa-Martínez J, Cortell-Tormo JM. The Benefits of Physical Exercise on Mental Disorders and Quality of Life in Substance Use Disorders Patients. Systematic Review and Meta-Analysis. Int J Environ Res Public Health. 2020 May 23;17(10):3680. Piché F, Daneau C, Plourde C, Girard S, Romain AJ. Characteristics and impact of physical activity interventions during substance use disorder treatment excluding tobacco: A systematic review. PloS One. 2023;18(4):e0283861. Hallgren M, Vancampfort D, Giesen ES, Lundin A, Stubbs B. Exercise as treatment for alcohol use disorders: systematic review and meta-analysis. Br J Sports Med. 2017 Jul;51(14):1058–64. Alessi SM, Rash CJ, Pescatello LS. Reinforcing exercise to improve drug abuse treatment outcomes: A randomized controlled study in a substance use disorder outpatient treatment setting. Psychol Addict Behav J Soc Psychol Addict Behav. 2020 Feb;34(1):52–64. Shreffler J, Genova G, Huecker M. Physical activity and exercise interventions for individuals with opioid use disorder: a scoping review. J Addict Dis. 2022;40(4):452–62. Onerup A, Arvidsson D, Blomqvist Å, Daxberg EL, Jivegård L, Jonsdottir IH, et al. Physical activity on prescription in accordance with the Swedish model increases physical activity: a systematic review. Br J Sports Med. 20181109th ed. 2019 Mar;53(6):383–8. Mino E, Hanson CL, Naber I, Weissenfels A, McHale S, Saftig J, et al. A systematic review and narrative synthesis of physical activity referral schemes’ components. Int J Behav Nutr Phys Act. 2023 Nov 27;20(1):140. National Institute for Health and Care Excellency. Recommendations. In: Physical activity: exercise referral schemes. NICE. 2024. https://www.nice.org.uk/guidance/ph54/resources/physical-activity-exercise-referral-schemes-pdf-1996418406085. Accessed 10 August 2024. Anger Å, Wallerblad A, Kaaman L, Broman R, Holmberg J, Lundgren T, et al. Introducing Braining-physical exercise as adjunctive therapy in psychiatric care: a retrospective cohort study of a new method. BMC Psychiatry. 2023 Aug 7;23(1):566. Eldridge SM, Chan CL, Campbell MJ, Bond CM, Hopewell S, Thabane L, et al. CONSORT 2010 statement: extension to randomised pilot and feasibility trials. BMJ. 2016 Oct 24;355:i5239. Lancaster GA, Thabane L. Guidelines for reporting non-randomised pilot and feasibility studies. Pilot Feasibility Stud. 2019 Oct 6;5(1):114. Anger Å, Sundelin S, Lithman M, Martinsson L. Brainingboxen [Internet]. 2023. Available from: https://www.braining.nu/brainingboxen. Accessed 18 Jun 2023. Borg GA. Psychophysical bases of perceived exertion. Med Sci Sports Exerc. 1982;14(5):377–81. Fixsen, D. L., Naoom, S. F., Blase, K. A., Friedman, R. M., Wallace, F. Implementation Reserch: A synthesis of the Literature. Tampa, FL: University of South Florida, Louis de la Parte Florida Mental Health Institute, The National Implementation Research Network (FMHI Publication #231); 2005. Börjesson M. Riskbedömning vid fysisk aktivitet. In: Professional Associations for Physical Activity. Dohrn IM, Jansson E, Börjesson M, Hagströmer M, eds. Physical Activity in the Prevention and Treatment of Disease, FYSS 2021. (in Swedish: Fysisk aktivitet i sjukdomsprevention och sjukdomsbehandling, FYSS 2021). Stockholm: Läkartidningen förlag AB; 2021. p. 180-187. Pelliccia A, Sharma S, Gati S, Bäck M, Börjesson M, Caselli S, et al. 2020 ESC Guidelines on sports cardiology and exercise in patients with cardiovascular disease. Eur Heart J. 2021 Jan 1;42(1):17–96. Miller WR, Rollnick S. Motivational interviewing: Helping people change, 3rd edition. New York, NY, US: Guilford Press; 2013. xii, 482 p. Rosenberger ME, Haskell WL, Albinali F, Mota S, Nawyn J, Intille S. Estimating activity and sedentary behavior from an accelerometer on the hip or wrist. Med Sci Sports Exerc. 2013 May;45(5):964–75. Dettori JR. Loss to follow-up. Evid-Based Spine-Care J. 2011 Feb;2(1):7–10. Attkisson CC, Zwick R. The client satisfaction questionnaire. Psychometric properties and correlations with service utilization and psychotherapy outcome. Eval Program Plann. 1982;5(3):233–7. Thompson-Hollands J, Bentley KH, Gallagher MW, Boswell JF, Barlow DH. Credibility and outcome expectancy in the unified protocol: Relationship to outcomes. J Exp Psychopathol. 2014;5(1):72–82. Devilly GJ, Borkovec TD. Psychometric properties of the credibility/expectancy questionnaire. J Behav Ther Exp Psychiatry. 2000 Jun;31(2):73–86. Rozental A, Kottorp A, Forsström D, Månsson K, Boettcher J, Andersson G, et al. The Negative Effects Questionnaire: psychometric properties of an instrument for assessing negative effects in psychological treatments. Behav Cogn Psychother. 2019 Sep;47(5):559–72. Arvidsson D, Fridolfsson J, Börjesson M. Measurement of physical activity in clinical practice using accelerometers. J Intern Med. 2019 Aug;286(2):137–53. Troiano RP, Berrigan D, Dodd KW, Mâsse LC, Tilert T, McDowell M. Physical activity in the United States measured by accelerometer. Med Sci Sports Exerc. 2008 Jan;40(1):181–8. Hart TL, Swartz AM, Cashin SE, Strath SJ. How many days of monitoring predict physical activity and sedentary behaviour in older adults? Int J Behav Nutr Phys Act. 2011 Jun 16;8:62. Matthews CE, Chen KY, Freedson PS, Buchowski MS, Beech BM, Pate RR, et al. Amount of time spent in sedentary behaviors in the United States, 2003-2004. Am J Epidemiol. 2008 Apr 1;167(7):875–81. Freedson PS, Melanson E, Sirard J. Calibration of the Computer Science and Applications, Inc. accelerometer. Med Sci Sports Exerc. 1998 May;30(5):777–81. Ekelund U, Sepp H, Brage S, Becker W, Jakes R, Hennings M, et al. Criterion-related validity of the last 7-day, short form of the International Physical Activity Questionnaire in Swedish adults. Public Health Nutr. 2006 Apr;9(2):258–65. Simon CB, McCabe CJ, Matson TE, Oliver M, Bradley KA, Hallgren KA. High test-retest reliability of the Alcohol Use Disorders Identification Test-Consumption (AUDIT-C) questionnaire completed by primary care patients in routine care. Alcohol Clin Exp Res. 2024 Feb;48(2):302–8. Hildebrand M. The Psychometric Properties of the Drug Use Disorders Identification Test (DUDIT): A Review of Recent Research. J Subst Abuse Treat. 2015 Jun;53:52–9. Levis B, Benedetti A, Thombs BD, Collaboration DepresSD (DEPRESSD). Accuracy of Patient Health Questionnaire-9 (PHQ-9) for screening to detect major depression: individual participant data meta-analysis. BMJ. 20190409th ed. 2019 Apr;365:l1476. Kroenke K, Spitzer RL, Williams JB. The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med. 2001 Sep;16(9):606–13. Toussaint A, Hüsing P, Gumz A, Wingenfeld K, Härter M, Schramm E, et al. Sensitivity to change and minimal clinically important difference of the 7-item Generalized Anxiety Disorder Questionnaire (GAD-7). J Affect Disord. 20200115th ed. 2020 Mar 15;265:395–401. Morin CM, Belleville G, Bélanger L, Ivers H. The Insomnia Severity Index: psychometric indicators to detect insomnia cases and evaluate treatment response. Sleep. 20110501st ed. 2011 May 1;34(5):601–8. Lindner P, Frykheden O, Forsström D, Andersson E, Ljótsson B, Hedman E, et al. The Brunnsviken Brief Quality of Life Scale (BBQ): Development and Psychometric Evaluation. Cogn Behav Ther. 2016 Apr;45(3):182–95. Ustun TB, Kostanjesek N, Chatterji S, Rehm J, World Health Organization. Measuring health and disability : manual for WHO Disability Assessment Schedule (WHODAS 2.0) / edited by T.B. Üstün, N. Kostanjsek, S. Chatterji, J.Rehm. 2010;88. Payakachat N, Ali MM, Tilford JM. Can The EQ-5D Detect Meaningful Change? A Systematic Review. Pharmacoeconomics. 2015 Nov;33(11):1137–54. Bouwmans C, De Jong K, Timman R, Zijlstra-Vlasveld M, Van der Feltz-Cornelis C, Tan Swan S, et al. Feasibility, reliability and validity of a questionnaire on healthcare consumption and productivity loss in patients with a psychiatric disorder (TiC-P). BMC Health Serv Res. 2013 Jun 15;13:217. Ma WY, Yang CY, Shih SR, Hsieh HJ, Hung CS, Chiu FC, et al. Measurement of Waist Circumference: midabdominal or iliac crest? Diabetes Care. 2013 Jun;36(6):1660–6. The jamovi project (2024). jamovi (Version 2.5) [Microsoft]. Retrieved from https://www.jamovi.org. The National Collaborating Centre for Mental Health. NHS Talking Therapies for anxiety and depression Manual. June 2018, updated March 2024. Available from: https://www.england.nhs.uk/wp-content/uploads/2018/06/NHS-talking-therapies-manual-v7-1.pdf. Accessed 16 Jul 2024. Whelton PK, Carey RM, Aronow WS, Casey DE, Collins KJ, Dennison HC, et al. 2017 ACC/AHA/AAPA/ABC/ACPM/AGS/APhA/ASH/ASPC/NMA/PCNA Guideline for the Prevention, Detection, Evaluation, and Management of High Blood Pressure in Adults. J Am Coll Cardiol. 2018 May 15;71(19):e127–248. Global BMI Mortality Collaboration, Di Angelantonio E, Bhupathiraju S, Wormser D, Gao P, Kaptoge S, et al. Body-mass index and all-cause mortality: individual-participant-data meta-analysis of 239 prospective studies in four continents. Lancet Lond Engl. 2016 Aug 20;388(10046):776–86. Lean MEJ, Han TS, Morrison CE. Waist circumference as a measure for indicating need for weight management. BMJ. 1995 Jul 15;311(6998):158–61. Gunillasdotter V, Andréasson S, Jirwe M, Ekblom Ö, Hallgren M. Effects of exercise in non-treatment seeking adults with alcohol use disorder: A three-armed randomized controlled trial (FitForChange). Drug Alcohol Depend. 2022 Mar 1;232:109266. Rawson RA, Chudzynski J, Mooney L, Gonzales R, Ang A, Dickerson D, et al. Impact of an exercise intervention on methamphetamine use outcomes post-residential treatment care. Drug Alcohol Depend. 2015 Nov 1;156:21–8. Trivedi MH, Greer TL, Rethorst CD, Carmody T, Grannemann BD, Walker R, et al. Randomized Trial Comparing Exercise to Health Education for Stimulant Use Disorder: Results from STimulant Reduction Intervention using Dosed Exercise (CTN-0037; STRIDE). J Clin Psychiatry. 2017;78(8):1075–82. Henriksson M, Wall A, Nyberg J, Adiels M, Lundin K, Bergh Y, et al. Effects of exercise on symptoms of anxiety in primary care patients: A randomized controlled trial. J Affect Disord. 2022 Jan 15;297:26–34. Helgadóttir B, Forsell Y, Ekblom Ö. Physical Activity Patterns of People Affected by Depressive and Anxiety Disorders as Measured by Accelerometers: A Cross-Sectional Study. PLoS ONE. 2015 Jan 13;10(1):e0115894. Schuch F, Vancampfort D, Firth J, Rosenbaum S, Ward P, Reichert T, et al. Physical activity and sedentary behavior in people with major depressive disorder: A systematic review and meta-analysis. J Affect Disord. 20161129th ed. 2017 Mar;210:139–50. Vancampfort D, Firth J, Schuch FB, Rosenbaum S, Mugisha J, Hallgren M, et al. Sedentary behavior and physical activity levels in people with schizophrenia, bipolar disorder and major depressive disorder: a global systematic review and meta-analysis. World Psychiatry Off J World Psychiatr Assoc WPA. 2017 Oct;16(3):308–15. Tudor-Locke C, Craig CL, Brown WJ, Clemes SA, De Cocker K, Giles-Corti B, et al. How many steps/day are enough? for adults. Int J Behav Nutr Phys Act. 2011 Jul 28;8(1):79. Smits JAJ, Berry AC, Rosenfield D, Powers MB, Behar E, Otto MW. Reducing anxiety sensitivity with exercise. Depress Anxiety. 2008;25(8):689–99. Andersen TE, Ravn SL, Armfield N, Maujean A, Requena SS, Sterling M. Trauma-focused cognitive behavioural therapy and exercise for chronic whiplash with comorbid posttraumatic stress disorder: a randomised controlled trial. PAIN. 2021 Apr;162(4):1221. Niemeijer A, Lund H, Stafne SN, Ipsen T, Goldschmidt CL, Jørgensen CT, et al. Adverse events of exercise therapy in randomised controlled trials: a systematic review and meta-analysis. Br J Sports Med. 2020 Sep;54(18):1073–80. Sobell LC, Sobell MB. Timeline Follow-Back. In: Litten RZ, Allen JP, editors. Measuring Alcohol Consumption: Psychosocial and Biochemical Methods [Internet]. Totowa, NJ: Humana Press; 1992. p. 41–72. Available from: https://doi.org/10.1007/978-1-4612-0357-5_3. Accessed 16 Jul 2024. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7409582","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":510778739,"identity":"7856d8de-d795-4666-8dbb-18b6919f9eee","order_by":0,"name":"Åsa Anger","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+ElEQVRIiWNgGAWjYGgA9jaGAyRq4TlGshaJNPzy/P2HHzD+bKuTN5dufsDwc0etnPnMZ4kHGP7Y4DbyRpoBM2/bYcOdc44ZMPaeOW4sczvtwAHGNjxW3WAwYGZsO8C44UaCAQNv27HEGdLpDQcYGw7j1CF//vgHkMPsN9xI/8D4t+1Y/QzJ4w1Ah/3HqcXgQA7IcObEDTdyQC6sSZCQYDtwgIHtAE4thjdyCg7znDucvAHEkG07YDiDJy3hQGJbMk4tcuePb3z4o6zOFuiwjQ/fAoNOgv2Y8YcPf+xwex8IDiAxoL5OwKsBFdSRoHYUjIJRMApGCgAA9mNcBInQfLIAAAAASUVORK5CYII=","orcid":"","institution":"Karolinska Institutet","correspondingAuthor":true,"prefix":"","firstName":"Åsa","middleName":"","lastName":"Anger","suffix":""},{"id":510778740,"identity":"8ba9e9e2-d53f-4b39-8c02-034901e859c7","order_by":1,"name":"Leida Kaaman","email":"","orcid":"","institution":"Region Stockholm","correspondingAuthor":false,"prefix":"","firstName":"Leida","middleName":"","lastName":"Kaaman","suffix":""},{"id":510778741,"identity":"66d059a5-e50c-4402-a901-b6ea36626b16","order_by":2,"name":"Rebecka Mac","email":"","orcid":"","institution":"Karolinska Institutet","correspondingAuthor":false,"prefix":"","firstName":"Rebecka","middleName":"","lastName":"Mac","suffix":""},{"id":510778742,"identity":"04845a79-a3f1-415c-89be-296ddcb3721d","order_by":3,"name":"Johan Holmberg","email":"","orcid":"","institution":"Karolinska Institutet","correspondingAuthor":false,"prefix":"","firstName":"Johan","middleName":"","lastName":"Holmberg","suffix":""},{"id":510778743,"identity":"e76c817b-71b6-4f3f-a7ac-cdbf5cac880f","order_by":4,"name":"Nitya Jayaram-Lindström","email":"","orcid":"","institution":"Karolinska Institutet","correspondingAuthor":false,"prefix":"","firstName":"Nitya","middleName":"","lastName":"Jayaram-Lindström","suffix":""},{"id":510778744,"identity":"f0f0fbbe-38b7-4fe7-8e3c-67794864923d","order_by":5,"name":"Tobias Lundgren","email":"","orcid":"","institution":"Karolinska Institutet","correspondingAuthor":false,"prefix":"","firstName":"Tobias","middleName":"","lastName":"Lundgren","suffix":""},{"id":510778745,"identity":"2b15e0a9-8e61-48da-be3b-66b65348b9e8","order_by":6,"name":"Maria Hagströmer","email":"","orcid":"","institution":"Karolinska Institutet","correspondingAuthor":false,"prefix":"","firstName":"Maria","middleName":"","lastName":"Hagströmer","suffix":""},{"id":510778746,"identity":"305e3dd1-97ae-401c-a5c5-eb8b070f3144","order_by":7,"name":"Carl Johan Sundberg","email":"","orcid":"","institution":"Karolinska Institutet","correspondingAuthor":false,"prefix":"","firstName":"Carl","middleName":"Johan","lastName":"Sundberg","suffix":""},{"id":510778747,"identity":"22112e98-ba0e-4845-8f58-4e7955dd7448","order_by":8,"name":"Lina Martinsson","email":"","orcid":"","institution":"Karolinska Institutet","correspondingAuthor":false,"prefix":"","firstName":"Lina","middleName":"","lastName":"Martinsson","suffix":""},{"id":510778748,"identity":"9a43eba5-4cb0-44fa-b020-ef6dac5713f0","order_by":9,"name":"Sigrid Salomonsson","email":"","orcid":"","institution":"Karolinska Institutet","correspondingAuthor":false,"prefix":"","firstName":"Sigrid","middleName":"","lastName":"Salomonsson","suffix":""}],"badges":[],"createdAt":"2025-08-19 14:23:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7409582/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7409582/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90907410,"identity":"30e508a5-97c2-49a8-acb9-d0f2d1d40d2c","added_by":"auto","created_at":"2025-09-09 13:23:00","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":487942,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlow chart.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"FlowchartBrainingFeasibility2508191.png","url":"https://assets-eu.researchsquare.com/files/rs-7409582/v1/aaad5878c9caa6affa82b102.png"},{"id":96252545,"identity":"ba89b4b9-d332-4d65-8ec3-8993289f42ac","added_by":"auto","created_at":"2025-11-19 07:41:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1223309,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7409582/v1/f3c8b205-430c-46e0-aadc-edcad45157e5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Braining, Structured Physical Activity in Specialized Psychiatry for Patients with Substance Use Disorders - A Feasibility Study","fulltext":[{"header":"Background","content":"\u003cp\u003eIndividuals affected by mental disorders suffer from higher rates of poor somatic health, have shorter life expectancy, and are less physically active than the average population (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Within mental health disorders, substance use disorders (SUD) are among the leading causes of disability, representing a significant public health issue as well as having substantial effects on mortality (\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e–\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). For instance, Scandinavian data show a more than 20-year reduction in life expectancy in people admitted to hospital care for alcohol use disorder (AUD) (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Moreover, chronic as well as irregular heavy use of alcohol have detrimental effects on somatic health, including cardiovascular diseases, cancer, dementia, and injuries (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eNotably, psychiatric comorbidity is common in relation to SUD, often bidirectionally impacting disorder severity and leading to a worsened clinical course and treatment adherence (\u003cspan additionalcitationids=\"CR7 CR8\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e–\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Major depressive disorder and anxiety disorders have a significant and consistent association with SUD (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e), in addition to increasing vulnerability to substance abuse (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e) and risk of suicidal behavior (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). There is also high comorbidity with attention deficit hyperactivity disorder (ADHD) (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e), with reports of almost 25% of patients with substance use having the diagnosis (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). In patients with bipolar disorder, previous studies indicate a 40 to 70% (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e) prevalence for AUD and a 25% prevalence for other SUDs (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e), with a risk of adverse outcomes such as mixed episodes and suicide (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe treatment for SUD includes psychosocial, psychological, and pharmacological approaches, often in combination (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). With psychiatric comorbidity, treatment recommendations differ from those of individual diagnoses, and consistent evidence is lacking. The recommended treatments focus primarily on addressing the symptoms with no focus on targeting deficits in somatic or cognitive problems experienced (\u003cspan additionalcitationids=\"CR7 CR8\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e–\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe World Health Organization (WHO) has highlighted the importance of physical activity (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e), and the key health benefits of physical activity include primary and secondary prevention of cardiometabolic disorders and improvements in mental health (\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e–\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Aerobic physical activity, particularly at moderate to vigorous intensity, is most effective for these benefits (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). The evidence supporting the positive effects of physical activity on mental health is expanding (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). A recent review, encompassing over 120,000 adult participants from specialized psychiatry, somatic care, primary care, and the general population, revealed that physical activity has medium-sized effects on depression, anxiety, and psychological distress compared with usual care and should be a cornerstone in the treatment of these disorders (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e).\u003c/p\u003e\u003cp\u003ePhysical activity has been studied as a potential adjunct treatment for patients with SUD. Moderate-intensity aerobic exercise programs with durations of 12–13 weeks have been most common in this population (\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e–\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Mixed results have been reported regarding the beneficial effects related to alcohol and substance use outcomes, such as consumption, abstinence and relapse. Some studies suggest no effect on consumption rates (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e) and that the effect on abstinence is unclear (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Others suggest that a physical activity intervention could decrease consumption (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). However, the benefits in terms of improved physical fitness, depressive symptoms, anxiety and overall quality of life have been more consistent (\u003cspan additionalcitationids=\"CR22 CR23 CR24\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e–\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). These findings are of vital importance, considering the previously mentioned prevalence of psychiatric comorbidity and metabolic complications in this population. Furthermore, studies conclude that improved quality of life is an important outcome of treatment (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e) and that physical exercise programs could be included in treatment programs to improve the recovery process (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eHowever, physical activity as an additive treatment has not yet been implemented in the standard treatment for SUD. Existing tools to motivate patients within the general healthcare sphere, such as Physical Activity on Prescription (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e) and Physical Activity Referral Scheme (27), seem to be insufficiently studied in the treatment of psychiatric symptoms (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). To motivate patients in specialized psychiatric care to initiate and perform moderate to vigorous physical activity, we developed a structured clinical method, Braining, which was previously described in a retrospective study (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). To evaluate the effect of the Braining method, a randomized controlled trial (RCT) is planned. In order to prepare for this RCT, the present study was performed to evaluate feasibility and preliminary effects of the Braining method.\u003c/p\u003e\u003cp\u003eThe primary objectives of the study were to assess feasibility via a) adherence to exercise sessions; b) adherence to measurements of physical activity, psychiatric symptoms, quality of life, and metabolic variables; and c) acceptability, credibility, safety, and perceived negative effects of the intervention. The secondary objectives were to investigate the preliminary effects of the intervention on physical activity, psychiatric symptoms, quality of life, and metabolic health.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eWhere applicable, this report follows the Consolidated Standards of Reporting Trials (CONSORT) statement for pilot and feasibility trials (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e) and the “Guidelines for reporting nonrandomized pilot and feasibility studies” (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eStudy design, setting and participants\u003c/p\u003e\u003cp\u003eThe study was designed as an uncontrolled open trial, where participants underwent a 12-week physical activity intervention -Braining (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Before and after the intervention and at the 12-month follow-up the participants were assessed including measurements of physical activity, psychiatric self-assessments and metabolic health assessments. The study was carried out in Region Stockholm, Sweden, during 2022, with a 12-month follow-up conducted during 2023. The study was performed at two psychiatric outpatient clinics, Liljeholmsberget (Clinic A) and Livsstilsmottagningen (Clinic B), which specialize in treatment of substance use disorders and are incorporated in the regional specialized psychiatric healthcare. These two outpatient clinics were located in the same building, with shared reception and waiting room areas, but differed in terms of organization and patient profile. Clinic A was the larger unit with approximately 1500 unique patients in 2022 and offered pharmacological and psychological treatment, e.g., relapse prevention. Clinic B provided specialized substance use treatment for individuals aged 18–25 years and had 300 unique patients in 2022. Clinic B focused on psychological treatments and used pharmacological treatment that focused mainly on psychiatric comorbidities.\u003c/p\u003e\u003cp\u003eEligible participants were 18 years or older and were currently receiving treatment at either clinic A or B for alcohol and other substance use disorders and comorbid psychiatric disorders. No psychiatric diagnoses per se were excluded from the study; however, individuals who experienced acute episodes of mania, psychosis or severe eating disorders; those presenting with a high risk of violence or suicide; and those experiencing acute withdrawal or intoxication were excluded. Medical conditions where physical activity is contraindicated, such as uncontrolled heart or lung diseases, infection, or severe physical impairment, were also criteria for exclusion.\u003c/p\u003e\u003cp\u003eThe intervention - Braining\u003c/p\u003e\u003cp\u003eBraining was developed as a structured clinical intervention for physical exercise as adjunct treatment in specialized psychiatry (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). In brief, Braining consists of scheduled 30–45-minute aerobic group exercise sessions (Braining sessions) and is carried out by regular psychiatric staff at the outpatient clinics. Within this study, a standardized manual, the \u003cem\u003eBraining Box\u003c/em\u003e, was used as a tool by the staff to deliver safe and effective exercise at moderate to vigorous intensity levels (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). The Borg Rating of Perceived Exertion Scale 6–20 was used as a recommendation for level of exertion (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e), and participants were instructed to reach and maintain intensity at 11–17 during the sessions. Furthermore, Braining also consists of regular individual visits. Primarily, before and after the intervention, a clinical visit is scheduled with a nurse for risk evaluation, assessments of metabolic and psychiatric health and individual goal setting. Second, each Braining session is preceded by a short individual visit with staff for a brief assessment of mental and physical status and for motivational support. In this setting, four Braining session times per week were offered, and the participants were informed of a recommended frequency of three sessions per week.\u003c/p\u003e\u003cp\u003ePrior to recruitment, the outpatient clinics underwent an implementation process for the Braining method. The implementation based on the model developed by Fixsen et al. involves several stages: exploration, program installation, initial implementation, and full operation (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). The exploration stage included assessing prerequisites and needs, anchoring with management and staff, and making the final decision on implementation. Furthermore, a multidisciplinary Braining team was created with staff members from both clinics. The program installation stage involved preparing resources and educating the team about the Braining method, structure, and materials. During the initial implementation stage, Braining was launched, the structure was put into operation, weekly meetings were held, and Braining classes were scheduled. Finally, the full operation stage involved recruiting patients, having them participate in Braining classes, and completing their 12-week treatment. The research team provided structured support during weekly meetings with the Braining team.\u003c/p\u003e\u003cp\u003eProcedure\u003c/p\u003e\u003cp\u003ePatients were recruited during a predetermined time-period, from January to April 2022. A goal was set at up to 50 recruited patients. Patients were informed about the study in connection with clinical visits. Additional information regarding the study was available on posters in the waiting area. Interested patients received detailed written and oral information about the study at weekly start-up meetings held from March to April 2022. After signing the informed consent form, the participants received a QR code to create an account on a secure web platform provided by the Karolinska Institutet (KI). Upon registration, the participants were automatically assigned a unique study ID. The participants completed a health screening questionnaire, which was developed for this study based on current national and European guidelines regarding health risk assessment and exercise (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). The health screening questionnaire included body weight, height, age, and current physical activity as well as 12 items covering risk factors for congenital or genetic heart disease, risk factors for cardiovascular events, ongoing infectious disease, chronic respiratory disease and neuromusculoskeletal diseases. Submitted answers were evaluated by the study physician. If the screening revealed potential risk factors for participation in an exercise intervention, the patient provided more detailed responses to the screening questions orally, before the physician decided on eligibility. The patient was excluded and recommended to seek health care if the risk was deemed too high. Patients with a low health risk who fulfilled the inclusion criteria were included in the study, and premeasurements were initiated.\u003c/p\u003e\u003cp\u003eAn overview of the participant flow is presented in Fig.\u0026nbsp;1. Following inclusion, the Braining intervention was initiated with a scheduled clinical visit to trained nurses experienced in motivational interviewing (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). The participants received the weekly exercise schedule as well as instructions regarding the booking procedure and were asked to set individual goals as an initial motivational strategy.\u003c/p\u003e\u003cp\u003ePhysical activity was measured via the ActiGraph GT3x accelerometer (ActiGraph, Pensacola, FL). The accelerometers were initialized by the researchers and handed directly to the participant or via the clinic reception to be picked up during clinical visits. The participants were instructed to wear the accelerometer on the lower back (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e) during waking hours for seven consecutive days at three time points (pre-, mid- and postintervention). Additionally, participants were instructed to log their daily wear time in a diary used to verify wear time and number of valid days. Another accelerometer measurement was planned at the 12-month follow-up but was cancelled because of insufficient adherence at mid- and postintervention. The outcome measures in the study protocol were amended accordingly.\u003c/p\u003e\u003cp\u003eSelf-assessment instruments were administered before, at 4 and 8 weeks, after the intervention and at the 12-month follow-up. All self-assessments were completed on the KI online platform. The participants received an automatically generated monthly text message from the KI platform, with a link leading to the questionnaires. If the participant failed to fill out the forms in time, a series of reminder text messages were generated. Additionally, a research nurse or assistant called the participant on up to 3 occasions.\u003c/p\u003e\u003cp\u003ePhysical examinations were performed during the clinical visits at inclusion (see \u003cem\u003ePatient-centered outcomes\u003c/em\u003e) postintervention and at the 12-month follow-up. Fasting blood samples were obtained either by nurses at the clinic or with a referral to a lab of the participant’s choice at the same time points. Physical examinations at the 12-month follow-up were performed by a research nurse.\u003c/p\u003e\u003cp\u003eBaseline data regarding age and gender was provided at registration on the KI platform. Data regarding current primary and secondary psychiatric diagnoses were extracted from patient medical records and noted at inclusion by an appointed staff member. Data from medical records regarding participation in Braining sessions and clinical visits including metabolic measures and blood samples were collected post-intervention and after the 12-month follow-up. Data from self-assessments were extracted from the KI platform.\u003c/p\u003e\u003cp\u003eOutcome measurements\u003c/p\u003e\n\u003ch3\u003eFeasibility outcomes\u003c/h3\u003e\n\u003cp\u003eAdherence to the physical activity intervention was assessed by the number of Braining sessions attended during the intervention period and from postintervention until the 12-month follow-up. Adherence to measurements was assessed by completion rates at group level of Actigraph measurements, questionnaires, clinical visits, and blood samples at different time points. On the basis of the rule of thumb that a loss to follow-up of more than 20% puts validity at risk, an adherence to measurements of 80% or more was considered feasible (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAcceptability was measured by the Client Satisfaction Questionnaire (CSQ-8), which is designed to rate global satisfaction with treatment. The total score ranges from 8 to 32, where higher values indicate greater satisfaction. The CSQ-8 has high internal consistency and has a correlation coefficient of 0.4 with reported changes in symptoms (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eExpectancy and credibility were measured by the Credibility and Expectancy Questionnaire (CEQ) (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e), a scale commonly used in clinical outcome studies. The scale consists of six questions, three questions exploring credibility and three covering expectancy. For credibility, a mean can be calculated. With respect to expectancy, the question \u0026ldquo;By the end of the treatment, how much improvement in your symptoms do you think will occur?\u0026rdquo; is considered representative and was therefore chosen for analysis. The questions are rated on a 0\u0026ndash;100% scale with 10-point increments. The questionnaire has high internal consistency between the two factors and adequate test‒retest reliability.\u003c/p\u003e\u003cp\u003eThe Negative Effects Questionnaire (NEQ) (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e) evaluates negative effects during psychological treatment. The items explore six validated factors: symptoms, quality, dependency, stigma, hopelessness and failure. The full instrument as well as individual factors have yielded adequate internal consistency. The questionnaire differentiates between negative effects attributed to treatment and those possibly caused by other circumstances.\u003c/p\u003e\u003cp\u003eSafety was measured as the number and severity of reported adverse events during the exercise intervention period. This information was collected through oral reports during weekly Braining staff meetings, where adverse events were one of the standing items.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003ePhysical activity outcomes\u003c/h2\u003e\u003cp\u003eIn clinical studies and settings, accelerometry is considered the \u0026ldquo;gold standard\u0026rdquo; for measuring physical activity (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). In this work, the Actigraph GT3X accelerometer was used. The Actigraph accelerations were sampled at 30 Hz and summed over 60 seconds via ActiLife v.6.13.4 software. The non-wear time was set to \u0026gt;\u0026thinsp;90 minutes of consecutive zero counts, allowing for 2 min of nonzero counts (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). Data were deemed feasible for analysis in measurements with \u0026ge;\u0026thinsp;4 days and \u0026ge;\u0026thinsp;10 h per day of valid wear time (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e). Wear time was allocated into steps per day and activity categories in minutes per day based on count-based thresholds: sedentary behavior\u0026thinsp;\u0026lt;\u0026thinsp;100 counts per min (cpm) (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e), low-intensity physical activity 100\u0026ndash;1951 cpm and moderate to vigorous intensity physical activity\u0026thinsp;\u0026ge;\u0026thinsp;1952 cpm (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e). The International Physical Activity Questionnaire (IPAQ) is an internationally used self-assessment instrument for indirect measures of physical activity divided into different levels of intensity and time spent sitting. The short 7-question form was used. The IPAQ has acceptable criterion validity and specificity in the general population, although its sensitivity has been shown to be low (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003ePatient-centered outcomes\u003c/h3\u003e\n\u003cp\u003eThe Alcohol Use Disorders Identification Test Consumption (AUDIT-C) (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e) is a 3-item clinical questionnaire that measures alcohol consumption and has high test-retest reliability. The Drug Use Disorder Identification Test (DUDIT) (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e) is an 11-item reliable and valid questionnaire covering patterns of current drug use and drug-related problems.\u003c/p\u003e\u003cp\u003eThe 9-item Patient Health Questionnaire-9 (PHQ-9) (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e) is designed to screen for depression as well as a measure of depression severity. Its sensitivity for detecting major depressive disorder has been thoroughly tested, and it has yielded good results. The 7-item Generalized Anxiety Disorder Questionnaire (GAD-7) (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e) is one of the most frequently used measures for general anxiety because of its validity and diagnostic reliability as well as sensitivity to change during treatment. The Insomnia Severity Index (ISI) (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e) is a reliable and valid self-rating questionnaire for evaluating perceived sleep difficulties. It is sensitive to treatment response and has satisfactory internal consistency.\u003c/p\u003e\u003cp\u003eThe Brunnsviken Brief Quality of Life Scale (BBQ) (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e) was developed to measure self-rated subjective quality of life and covers six identified life areas, such as leisure, learning and self-view. It has been tested and validated in both nonclinical and clinical subjects, including psychiatric populations. Moreover, it has shown adequate reliability and consistency. The World Health Organization Disability Assessment Schedule 2.0 (WHODAS-2) (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e) measures health and disability. The brief version used in this study consists of 12 items evaluating function over six life domains, including cognition, mobility, self-care, getting along, life activities and participation. The WHODAS-2 has shown high validity and reliability. The EQ5D-5 L (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e) is a well-known, reliable, and valid instrument for measuring health-related quality of life. It consists of five dimensions\u0026mdash;mobility, self-care, usual activities, pain/discomfort and anxiety/depression\u0026mdash;and a visual analog scale for overall health ranging from 0\u0026mdash;100. The Treatment Inventory of Costs in Patients with Psychiatric Disorders (TiC-P) (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e) covers healthcare consumption and production losses in patients with mental disorders. It consists of two parts that can be used separately. In this study, the second part, measuring production losses through absence from and reduced efficiency of work, was administered. It has been regarded as feasible and reliable, with adequate consistency.\u003c/p\u003e\u003cp\u003eSomatic health was evaluated via physical examination and metabolic blood markers. During physical examination at clinical visits, blood pressure (BP), heart rate, waist circumference (WC) and body mass index (BMI) were measured. To minimize interindividual variability, the measurements were performed in accordance with a predetermined protocol. Blood pressure was measured while the participants were in a seated position with either an automatic or manual blood pressure cuff. WC was measured according to the WHO WC-mid (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTo evaluate the metabolic profile of the participants and the possible preliminary effects of the intervention, blood lipids (triglycerides, low-density lipoprotein, high-density lipoprotein), blood glucose and HbA1c were measured.\u003c/p\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eStatistical analyses were conducted via Jamovi, version 2.3.18 (The jamovi project, 2024) (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e). The specific packages used were gamlj \u0026ndash; General Analyses for Linear Models in jamovi 2.6.6, and jmv \u0026ndash; Analyses bundled with jamovi 2.3.1. All data were retrieved from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.jamovi.org/\u003c/span\u003e\u003cspan address=\"https://www.jamovi.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eDescriptive statistics of psychiatric ratings and metabolic measurements were used to display the means, standard deviations and proportion scoring above a clinical cutoff before, after and 12 months after treatment.\u003c/p\u003e\u003cp\u003eChanges in psychiatric self-assessments and metabolic measures from pre- to posttreatment and follow-up were tested for significance, and effect sizes were evaluated via Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e and 95% confidence intervals of estimates. Since the sample size was small, the power to detect changes was limited, and all the statistical analyses were preliminary. The effects of time on changes in psychiatric self-report measures were evaluated by calculating the significance of fixed effect estimates of time in a linear mixed model. Linear mixed model analysis was chosen to maximize the size of the sample by including all possible data and fulfilling the criteria for an intention-to-treat analysis in a future RCT. Time was treated as a categorical variable in the models, resulting in distinct estimates of the time effects from the start of intervention for each of the next four measurements instead of linear average trends. The effects of time on changes in metabolic measures were evaluated with dependent pairwise t tests. In the planned RCT, imputation methods will be used for missing data to enable intention-to-treat analyses of effects on metabolic variables.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThe participant flowchart is presented in Fig. 1. In total, 26 patients attended the information meeting. During the recruitment process, two participants were excluded due to somatic health risks, and one was excluded because consent was withdrawn, leading to a total sample of 23 participants. During the pre-assessment, one participant was excluded because of the development of an acute episode of severe mental disorder, thus leading to a final study population of 22. This was regarded as a sufficient population size. At the 12-month follow-up, 7 participants had ongoing medical care contact at clinics A or B.\u003c/p\u003e\n\u003cp\u003eOverview of the participation process, detailing the number of included and excluded patients as well as the completion rates of the study protocol components among the final study population.\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e shows the baseline characteristics of the final study population. Registered diagnoses extracted from medical records revealed that 20 out of 22 participants had at least one registered diagnosis of alcohol or other substance use disorders. Alcohol use was the most common (n\u0026thinsp;=\u0026thinsp;13). With respect to other psychiatric diagnoses, hyperkinetic disorders were the most common (n\u0026thinsp;=\u0026thinsp;5), followed by anxiety disorders (n\u0026thinsp;=\u0026thinsp;3).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cstrong\u003eBaseline characteristics of the study population\u003c/strong\u003e.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDemographics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eParticipants (N\u0026thinsp;=\u0026thinsp;22)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge, mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.7 (12.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18 (81.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEmployment status\u0026nbsp;and work absence rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eParticipants (N\u0026thinsp;=\u0026thinsp;19)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCurrent employment, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8 (42.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSickness absence last 4 weeks, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 (31.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiagnoses\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eParticipants (N\u0026thinsp;=\u0026thinsp;22)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlcohol use disorders (F101, F102), n\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCannabis use disorder (F121), n\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStimulant use disorder (F152), n\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePolydrug use disorders (F191, F192), n\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHyperkinetic disorders (F900, F900B, F900C), n\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBipolar disorders (F319), n\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDepressive disorders F321), n\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnxiety disorders (F401, F412), n\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo specified diagnosis (Z032), n\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSingular diagnosis, n\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBaseline physical activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eParticipants (N\u0026thinsp;=\u0026thinsp;16)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSedentary time, minutes mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e504 (127)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerate physical activity, minutes mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52.1 (42.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVigorous physical activity, minutes mean (SD)\u003c/p\u003e\n \u003cp\u003eSteps per day, mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.4 (8.0)\u003c/p\u003e\n \u003cp\u003e9510 (4870)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eDemographic data and employment, diagnostic groups and baseline physical activity. Note: Diagnostic groups are classified according to the 2019 version of the International Classification of Diseases (ICD-10). Diagnoses were extracted from medical records and are displayed in a summarized manner, meaning that a participant can contribute to more than one diagnosis. Baseline physical activity is measured with Actigraph and presented as minutes or steps per valid day.\u003c/p\u003e\n\u003cp\u003eFeasibility outcomes\u003c/p\u003e\n\u003cp\u003eThe exercise participation rate during the 12-week intervention period was a mean of 9 and a median of 8 Braining sessions (SD\u0026thinsp;=\u0026thinsp;7.7, range\u0026thinsp;=\u0026thinsp;1\u0026ndash;30). Eighteen participants attended at least 3 sessions. The two most active participants attended 29 and 30 sessions, respectively. At the 12-month follow-up, 27% (n\u0026thinsp;=\u0026thinsp;6) had continued to participate in Braining sessions after the intervention period ended. For these participants, the number of sessions attended ranged from 2\u0026ndash;90, with a mean of 20.7 (SD\u0026thinsp;=\u0026thinsp;34.7) and a median of 4 sessions.\u003c/p\u003e\n\u003cp\u003eAdherence to the study protocol assessment components included Actigraph measurements, questionnaires, clinical visits, and blood samples and are reported in Fig.\u0026nbsp;1. Adherence to Actigraph measurements varied and diminished with time, with 16 (72.7%) valid measurements at baseline, 10 (45.5%) at week 6, and 7 (31.8%) at week 12. Out of the 16 valid baseline measurements, 2 contained 4 valid days, 1 had 5 valid days, 5 had 6 valid days and 8 measurements had 7 valid days. On valid days, the mean wear time was 802.6 (SD\u0026thinsp;=\u0026thinsp;130.5) minutes.\u003c/p\u003e\n\u003cp\u003eThe rate of completed questionnaires varied but was generally high, with 22 (100%) at baseline, 19 (86%) after treatment and 17 (77.3%) at the 12-month follow-up. The rate was the lowest at week 8 (45.5%, n\u0026thinsp;=\u0026thinsp;10).\u003c/p\u003e\n\u003cp\u003eThe rates of completed clinical visits and blood samples were generally high at baseline and declined over time: 22 (100%) to 14 (63.6%) and 21 (95.5%) to 11 (50%), respectively.\u003c/p\u003e\n\u003cp\u003eThe CSQ-8 was completed by 17 participants, and the mean value was 26.8 (SD\u0026thinsp;=\u0026thinsp;3.9), out of 32. This corresponds with response 3, mostly satisfied, or 4, very satisfied, for each item. Item 3, \u0026ldquo;did the treatment meet your need,\u0026rdquo; received the lowest mean score of 2.99 (SD\u0026thinsp;=\u0026thinsp;0.66), whereas item 4, \u0026ldquo;would you recommend the treatment to a friend,\u0026rdquo; received the highest mean score of 3.71 (SD\u0026thinsp;=\u0026thinsp;0.47). Among the 22 participants, 21 completed the CEQ. The mean value of the first 3 items representing treatment credibility was 7.3 (SD\u0026thinsp;=\u0026thinsp;1.18), out of 9. The mean value of expected improvement in symptoms was 51.9% (SD\u0026thinsp;=\u0026thinsp;21.2).\u003c/p\u003e\n\u003cp\u003eThe NEQ was completed by 19 participants. Overall reported negative effects were few and low. All the scores for negative effects were 1 (out of 4). The most reported items were as follows: increased sense of stress (n\u0026thinsp;=\u0026thinsp;4, 21.1%), resurfacing of unpleasant memories (n\u0026thinsp;=\u0026thinsp;3, 15.8%), thinking that the problem they were seeking help for could not improve (n\u0026thinsp;=\u0026thinsp;3, 15.8%), not always understanding the treatment (n\u0026thinsp;=\u0026thinsp;3, 15.8%), feeling that the treatment did not suit them (n\u0026thinsp;=\u0026thinsp;3, 15.8%) and thinking that they developed a dependency on the treatment (n\u0026thinsp;=\u0026thinsp;2, 10.5%). The reported negative effects were not reported to affect the participants in a negative way.\u003c/p\u003e\n\u003cp\u003eDuring the 12-week study period, there were no reports of any serious adverse events. One minor adverse event, a migraine episode, was reported.\u003c/p\u003e\n\u003cp\u003ePhysical activity outcomes\u003c/p\u003e\n\u003cp\u003eOwing to low Actigraph-adherence at week 6 and 12, no meaningful analysis of changes could be conducted.\u003c/p\u003e\n\u003cp\u003eDue to a technical error in the digital administration of the IPAQ instrument, resulting in high levels of uncertainty, the data was deemed unusable.\u003c/p\u003e\n\u003cp\u003ePatient centered outcomes\u003c/p\u003e\n\u003cp\u003eThe results of linear mixed model analyses of the fixed effects of time on self-reported measures are shown in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. The effect sizes of the changes in the variables ranged from 0.276\u0026ndash;0.774 (Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e). Six out of eight variables significantly changed from pre- to post measurement. Six out of eight variables significantly changed from pre- to follow-up measurements. With respect to the psychiatric symptom ratings, the PHQ-9 score was above the clinical cutoff of 10 for depression (\u003cspan class=\"CitationRef\"\u003e62\u003c/span\u003e) in 64% of the participants before treatment, 37% after treatment, and 29% at the 12-month follow-up. The GAD-7 score was above the clinical cutoff of 8 for generalized anxiety (\u003cspan class=\"CitationRef\"\u003e62\u003c/span\u003e) in 40% of participants before treatment, 21% after treatment, and 18% at the 12-month follow-up. The ISI was above the clinical cutoff of 11 for detecting insomnia (\u003cspan class=\"CitationRef\"\u003e55\u003c/span\u003e): 55% before treatment started, 32% after treatment and 28% at the 12-month follow-up.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePreliminary effects on psychiatric symptoms including substance use, functional status, and quality of life.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMeasure\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePre\u003c/p\u003e\n \u003cp\u003eM(SE)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e4 weeks\u003c/p\u003e\n \u003cp\u003eM(SE)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e8 weeks\u003c/p\u003e\n \u003cp\u003eM(SE)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePost\u003c/p\u003e\n \u003cp\u003eM(SE)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFollow- Up\u003c/p\u003e\n \u003cp\u003eM(SE)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePre-Post\u003c/p\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e(95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePre-FU\u003c/p\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value Cohens\u0026rsquo; \u003cem\u003ed\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e(95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAUDIT-C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.76\u003c/p\u003e\n \u003cp\u003e(0.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.57\u003c/p\u003e\n \u003cp\u003e(0.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.01\u003c/p\u003e\n \u003cp\u003e0.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.99\u003c/p\u003e\n \u003cp\u003e(0.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.47\u003c/p\u003e\n \u003cp\u003e(0.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.006 \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.505\u003c/p\u003e\n \u003cp\u003e(0.0204;0.978)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.650 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;0.344\u003c/p\u003e\n \u003cp\u003e(-0.166;0.844)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDUDIT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.59\u003c/p\u003e\n \u003cp\u003e(2.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.87\u003c/p\u003e\n \u003cp\u003e(2.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.70\u003c/p\u003e\n \u003cp\u003e(3.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.10\u003c/p\u003e\n \u003cp\u003e(2.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.24\u003c/p\u003e\n \u003cp\u003e(2.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.011 \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.541\u003c/p\u003e\n \u003cp\u003e(0.0523;1.018)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.002 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;0.457\u003c/p\u003e\n \u003cp\u003e(-0.063;0.966)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePHQ-9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.55\u003c/p\u003e\n \u003cp\u003e(1.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.56\u003c/p\u003e\n \u003cp\u003e(1.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.42\u003c/p\u003e\n \u003cp\u003e(1.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.48\u003c/p\u003e\n \u003cp\u003e(1.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.14\u003c/p\u003e\n \u003cp\u003e(1.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.009 \u0026nbsp; \u0026nbsp; 0.519\u003c/p\u003e\n \u003cp\u003e(0.0328;0.993)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;0.612\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(0.0674;1.139)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGAD-7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.70\u003c/p\u003e\n \u003cp\u003e(0.995)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.23\u003c/p\u003e\n \u003cp\u003e(1.007)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.22\u003c/p\u003e\n \u003cp\u003e(1.247)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.15\u003c/p\u003e\n \u003cp\u003e(1.025)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.33\u003c/p\u003e\n \u003cp\u003e(1.049)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.098 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.324\u003c/p\u003e\n \u003cp\u003e(-0.1422;0.781)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.021 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;0.527\u003c/p\u003e\n \u003cp\u003e(-0.0047;1.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eISI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.36\u003c/p\u003e\n \u003cp\u003e(1.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.79\u003c/p\u003e\n \u003cp\u003e(1.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.73\u003c/p\u003e\n \u003cp\u003e(1.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.94\u003c/p\u003e\n \u003cp\u003e(1.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.74\u003c/p\u003e\n \u003cp\u003e(1.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.007 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.597\u003c/p\u003e\n \u003cp\u003e(0.1002;1.079)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.006 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;0.704\u003c/p\u003e\n \u003cp\u003e(0.145;1.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBBQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.8\u003c/p\u003e\n \u003cp\u003e(5.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48.1\u003c/p\u003e\n \u003cp\u003e(5.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53.2\u003c/p\u003e\n \u003cp\u003e(6.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52.5\u003c/p\u003e\n \u003cp\u003e(5.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54.7\u003c/p\u003e\n \u003cp\u003e(5.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.007 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;-0.774\u003c/p\u003e\n \u003cp\u003e(-1.28;-0.251)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.003 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; -0.538\u003c/p\u003e\n \u003cp\u003e(-1.041;-0.020)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWHODAS-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.0\u003c/p\u003e\n \u003cp\u003e(3.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.6\u003c/p\u003e\n \u003cp\u003e(4.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.6\u003c/p\u003e\n \u003cp\u003e(4.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.7\u003c/p\u003e\n \u003cp\u003e(3.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.7\u003c/p\u003e\n \u003cp\u003e(3.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.024 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.360\u003c/p\u003e\n \u003cp\u003e(-0.109;0.820)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; -0.330\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(-0.829;0.179)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEQ5D-5 L VAS\u003c/p\u003e\n \u003cp\u003eEQ5D-5 L Index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58.5\u003c/p\u003e\n \u003cp\u003e(3.50)\u003c/p\u003e\n \u003cp\u003e0.894\u003c/p\u003e\n \u003cp\u003e(0.013)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60.2\u003c/p\u003e\n \u003cp\u003e(3.78)\u003c/p\u003e\n \u003cp\u003e0.909\u003c/p\u003e\n \u003cp\u003e(0.014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58.9\u003c/p\u003e\n \u003cp\u003e(4.47)\u003c/p\u003e\n \u003cp\u003e0.903\u003c/p\u003e\n \u003cp\u003e(0.017)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62.6\u003c/p\u003e\n \u003cp\u003e(3.57)\u003c/p\u003e\n \u003cp\u003e0.918\u003c/p\u003e\n \u003cp\u003e(0.013)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65.1\u003c/p\u003e\n \u003cp\u003e(4.22)\u003c/p\u003e\n \u003cp\u003e0.917\u003c/p\u003e\n \u003cp\u003e(0.013)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.247 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;-0.276\u003c/p\u003e\n \u003cp\u003e(-0.792;0.210)\u003c/p\u003e\n \u003cp\u003e.146 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;-0.348\u003c/p\u003e\n \u003cp\u003e(-0.808;0.120)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.160 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; -0.411\u003c/p\u003e\n \u003cp\u003e(-0.901;0.090)\u003c/p\u003e\n \u003cp\u003e.043 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; -0.532\u003c/p\u003e\n \u003cp\u003e(-1.034;-0.015)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eEstimated marginal means (M), standard errors (SE), test for significance, and estimation of effect size on each outcome measure. Tests for significance and effect sizes are evaluated and presented for two separate time periods, from Pre to Post and Pre to Follow-Up. \u003cem\u003eNote.\u003c/em\u003e AUDIT-C\u0026thinsp;=\u0026thinsp;Alcohol Use Disorders Identification Test for Consumption, DUDIT\u0026thinsp;=\u0026thinsp;Drug Use Disorders Identification Test, PHQ-9\u0026thinsp;=\u0026thinsp;Patient Health Questionnaire-9, GAD-7\u0026thinsp;=\u0026thinsp;General Anxiety Disorder 7-item scale, ISI\u0026thinsp;=\u0026thinsp;Insomnia Severity Index, BBQ\u0026thinsp;=\u0026thinsp;Brunnsviken Brief Quality of Life Scale, WHODAS-2\u0026thinsp;=\u0026thinsp;World Health Organization Disability Assessment Schedule 2.0 EQ-5D\u0026thinsp;=\u0026thinsp;EuroQol-5 dimension, VAS\u0026thinsp;=\u0026thinsp;Visual Analogue Scale.\u003c/p\u003e\n\u003cp\u003eThe results of linear mixed model analyses of the fixed effects of time on metabolic measures are shown in Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. At baseline, the mean systolic BP was 131 mmHg, and 14 of 22 individuals had a systolic BP\u0026thinsp;\u0026ge;\u0026thinsp;130 mmHg, which was considered to be above the normal range (\u003cspan class=\"CitationRef\"\u003e63\u003c/span\u003e). There was a significant decrease in diastolic BP from pre- to post-intervention and a significant decrease in systolic blood pressure from pre-intervention to follow up. Mean BMI was at overweight levels associated with increased health risk (\u003cspan class=\"CitationRef\"\u003e64\u003c/span\u003e) at baseline and did not change significantly with time. At baseline, the mean WC was 91.6 cm (range 82\u0026ndash;101) in women and 100 cm (range 66\u0026ndash;136) in men. Similarly, 50% of women and 44% of men had WC at levels where weight reduction is recommended (\u003cspan class=\"CitationRef\"\u003e65\u003c/span\u003e). Metabolic blood marker levels were within the normal range at baseline and did not change significantly from baseline to post-intervention.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePreliminary effects on metabolic outcomes.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMeasure\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePre\u003c/p\u003e\n \u003cp\u003eM(SD)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePost\u003c/p\u003e\n \u003cp\u003eM(SD)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFollow-up\u003c/p\u003e\n \u003cp\u003eM(SD)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePre-Post\u003c/p\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value \u0026nbsp; \u0026nbsp; Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e(95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePre-FU\u003c/p\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value \u0026nbsp; \u0026nbsp; Cohen\u0026rsquo;s d\u003c/p\u003e\n \u003cp\u003e(95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBlood pressure Systolic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e131\u003c/p\u003e\n \u003cp\u003e(2.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e128\u003c/p\u003e\n \u003cp\u003e(2.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e122\u003c/p\u003e\n \u003cp\u003e(3.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.364 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.145\u003c/p\u003e\n \u003cp\u003e(-0.298;0.583)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.006 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.846\u003c/p\u003e\n \u003cp\u003e(0.220;1.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBlood pressure\u003c/p\u003e\n \u003cp\u003eDiastolic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e83.5\u003c/p\u003e\n \u003cp\u003e(2.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e78.2\u003c/p\u003e\n \u003cp\u003e(2.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e79.2\u003c/p\u003e\n \u003cp\u003e(2.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.019 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.581\u003c/p\u003e\n \u003cp\u003e(0.099;1.050)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.086 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.780\u003c/p\u003e\n \u003cp\u003e(0.167;1.371)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePulse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76.0\u003c/p\u003e\n \u003cp\u003e(2.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74.5\u003c/p\u003e\n \u003cp\u003e(2.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74.5\u003c/p\u003e\n \u003cp\u003e(3.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.624 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;0.142\u003c/p\u003e\n \u003cp\u003e(-0.312;0.592)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.667 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.131\u003c/p\u003e\n \u003cp\u003e(0.418;0.675)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWaist circumference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97.4\u003c/p\u003e\n \u003cp\u003e(3.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e96.0\u003c/p\u003e\n \u003cp\u003e(3.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94.8\u003c/p\u003e\n \u003cp\u003e(3.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.204 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;0.646\u003c/p\u003e\n \u003cp\u003e(0.775;1.195)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.070 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.355\u003c/p\u003e\n \u003cp\u003e(0.213;0.910)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.5\u003c/p\u003e\n \u003cp\u003e(1.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.7\u003c/p\u003e\n \u003cp\u003e(1.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.0\u003c/p\u003e\n \u003cp\u003e(1.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.473 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; -0.229\u003c/p\u003e\n \u003cp\u003e(-0.681;0.230)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.275 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.167\u003c/p\u003e\n \u003cp\u003e(0.364;0.691)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal cholesterol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.04\u003c/p\u003e\n \u003cp\u003e(0.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.00\u003c/p\u003e\n \u003cp\u003e(0.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.89\u003c/p\u003e\n \u003cp\u003e(0.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.863 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;0.024\u003c/p\u003e\n \u003cp\u003e(-0.521;0.567)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.495 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.140\u003c/p\u003e\n \u003cp\u003e(0.432;0.706)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLDL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.19\u003c/p\u003e\n \u003cp\u003e(0.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.05\u003c/p\u003e\n \u003cp\u003e(0.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.06\u003c/p\u003e\n \u003cp\u003e(0.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.479 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.216\u003c/p\u003e\n \u003cp\u003e(-0.339;0.762)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.551 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.198\u003c/p\u003e\n \u003cp\u003e(0.378;0.765)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHDL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.34\u003c/p\u003e\n \u003cp\u003e(0.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.31\u003c/p\u003e\n \u003cp\u003e(0.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.38\u003c/p\u003e\n \u003cp\u003e(0.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.553 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;0.134\u003c/p\u003e\n \u003cp\u003e(-0.415;0.679)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.460 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.228\u003c/p\u003e\n \u003cp\u003e(0.796;0.351)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLDL/HDL\u003c/p\u003e\n \u003cp\u003eQuota\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.43\u003c/p\u003e\n \u003cp\u003e(0.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.39\u003c/p\u003e\n \u003cp\u003e(0.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.30\u003c/p\u003e\n \u003cp\u003e(0.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.822 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;0.046\u003c/p\u003e\n \u003cp\u003e(-0.499;0.589)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.494 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.223\u003c/p\u003e\n \u003cp\u003e(0.355;0.791)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTriglycerides\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.20\u003c/p\u003e\n \u003cp\u003e(0.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.43\u003c/p\u003e\n \u003cp\u003e(0.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003cp\u003e(0.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.220 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;-0.321\u003c/p\u003e\n \u003cp\u003e(-0.873;0.243)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.494 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.044\u003c/p\u003e\n \u003cp\u003e(0.523;0.609)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBlood glucose\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.52\u003c/p\u003e\n \u003cp\u003e(0.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.47\u003c/p\u003e\n \u003cp\u003e(0.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.62\u003c/p\u003e\n \u003cp\u003e(0.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.734 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.240\u003c/p\u003e\n \u003cp\u003e(-0.317;0.787)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.547 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.091\u003c/p\u003e\n \u003cp\u003e(0.656;0.478)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHbA1c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.8\u003c/p\u003e\n \u003cp\u003e(0.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.1\u003c/p\u003e\n \u003cp\u003e(0.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.0\u003c/p\u003e\n \u003cp\u003e(0.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.664 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;-0.063\u003c/p\u003e\n \u003cp\u003e(-0.653;0.530)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.032 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.539\u003c/p\u003e\n \u003cp\u003e(1.163;0.107)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eMeans (M), standard deviations (SD), test for significance, and estimation of effect size on each outcome measure. Tests for significance and effect size are evaluated and presented. for two separate time periods, from Pre to Post and Pre to Follow-up. \u003cem\u003eNote\u003c/em\u003e. BMI\u0026thinsp;=\u0026thinsp;body mass index, LDL\u0026thinsp;=\u0026thinsp;low-density lipoprotein, HDL\u0026thinsp;=\u0026thinsp;high-density lipoprotein.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this feasibility study, the physical activity intervention Braining was tested in a clinical setting for substance use disorders for the first time, and several crucial aspects of feasibility were evaluated. Our findings suggest that the participants found Braining to be acceptable and safe on the basis of measures of satisfaction, credibility, expectancy, and adverse events. While adherence to the intervention, questionnaires, and metabolic measurements was considered acceptable, adherence to Actigraph measures was notably low and did not result in sufficient data on changes in physical activity. Participant ratings imply potential reductions in alcohol and drug use, alongside potential improvements in symptoms of depression and insomnia over the treatment period. Similarly, there were indications of enhanced functionality and quality of life, with effect sizes ranging from small to moderate. Notably, changes in anxiety and health-related quality of life ratings did not reach statistical significance. These findings should be interpreted cautiously, given the exploratory nature of the study and the limitations associated with a small, uncontrolled sample.\u003c/p\u003e\u003cp\u003eThe recommended frequency of three Braining exercise sessions per week was not reached at the group level. On average, patients attended one Braining session per week. However, the participation level was optional, and the participants were invited to choose between four scheduled sessions per week. This result is in line with other physical activity intervention studies in comparable populations (\u003cspan additionalcitationids=\"CR67\" citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e), as well as our evaluation of clinical data from the first years of implementation of Braining in regular care (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Other studies that use external resources such as fitness centers and exercise specialists have managed to reach higher attendance rates, with two sessions per week (\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e). In addition to exceeding the resources of regular care, these studies often exclude participants with comorbidities of alcohol and other substance use disorders. One could argue that for this population, with the resources of a naturalistic setting, a slower increase in the exercise dosage with more frequent follow-ups might be favorable. Moreover, the individual number of attended sessions varied greatly, which could point to the existence of subgroups with different participation behaviors. Understanding the reasons behind these behaviors is key to adapting the method to suit a larger proportion of the target population.\u003c/p\u003e\u003cp\u003eThe adherence to the accelerometry measurements was insufficient at baseline (73%) and continued to decrease at each measurement point (46\u0026ndash;32%). Furthermore, the Actigraph diaries could not be used to guide the data analysis as intended because of insufficient compliance. This contributed to the high rate of invalid measurements. Comparatively, a study using Actigraphs in participants with depression and anxiety disorders was limited by adherence (\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e). In that sample, only 54% were able to contribute a sufficient number of valid days.\u003c/p\u003e\u003cp\u003eCompared with other psychiatric populations, the baseline Actigraph measurements revealed a high mean level of moderate-intensity physical activity compared to other psychiatric populations (\u003cspan additionalcitationids=\"CR71\" citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e). Furthermore, the mean steps per day were greater than expected and are in line with previous studies on healthy populations (\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn terms of adherence to distributed questionnaires and to clinical visits, 100% pre- and 86% postintervention were regarded as adequate. However, adherence to blood sampling, 95% before and 68% after intervention, was less than sufficient. A possible explanation could be that, in most cases, participants had to visit an external laboratory to provide blood samples, thus requiring an additional procedure. Adherence to questionnaires at weeks four and eight was low (73\u0026ndash;45%). We hypothesize that a smaller number of questionnaires as well as a more targeted selection of questionnaires could increase completion in this population. There was a significant decline in participation at the 12-month follow-up compared with that at the post-intervention visit (clinical visits, 64%; blood samples, 50%). This was not unexpected, since only one-third of the participants were still registered patients at the clinics at that point and were available for reminders. Moreover, completion of questionnaires remained relatively high (77%), indicating that the digital administration of questionnaires via the KI platform was a feasible and acceptable method.\u003c/p\u003e\u003cp\u003eEvaluating treatment credibility and satisfaction as well as negative effects and adverse events was crucial in this study. At inclusion, participants rated the intervention as credible, and scores were in line with perceived credibility in similar types of studies (\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e). With respect to expectancy, participants' rating of 52% expected improvement was lower than the expected improvement in a comparable CBT study (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). Satisfaction with the intervention at the postintervention measurement was regarded as high. On average, participants scored 3 out of 4 on each item. For example, this corresponds to rating the intervention quality as good and meeting most of the participants\u0026rsquo; needs. Further qualitative information from participants' experiences was obtained in the in-depth interviews and presented separately. Self-reported negative effects are scarce, but one should take into account that, to our knowledge, the NEQ scale has not yet been validated in studies on physical activity. Considering the reported satisfaction scores in conjunction with a low rate of negative effects, there are indications that the intervention is feasible for further implementation. No serious adverse events were reported, indicating a sufficient level of safety regarding both the exercise intervention and the recruitment process. This further motivates a continued use of the health screening procedure in future studies. Reported minor adverse events were rare but could be underreported given that previous studies on exercise therapy (\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e) reported a 19% increase in minor adverse events. The patient-centered outcomes showed interesting preliminary results. The psychiatric assessments were found to be capable of detecting changes, which motivates their further use. In addition, a majority of the changes were statistically significant. The self-assessments regarding substance use disorders revealed that the mean alcohol use was low pre-intervention and decreased over time, which could be due to ongoing treatment. In contrast, the level of reported drug use in DUDIT pre-intervention was high, possibly because DUDIT includes drug use over a 12-month period. Given this, it is difficult to interpret the decrease in DUDIT over time. To evaluate effects on substance use in upcoming studies, the Timeline Follow-Back method which allows for daily recording of consumption (\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e), could be a more appropriate tool. In the PHQ-9, the change was also clinically relevant, with a mean score of 8.5 post-treatment and 7.1 at long-term follow-up, which was below the cutoff for depression. The effect sizes were 0.52 pre- to post-intervention and 0.61 pre- to follow-up was in line with the vast literature on physical activity and depressive symptoms (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). However, the validity of these preliminary results is impacted by the absence of a control group as well as the small sample size and should therefore be interpreted with caution. The metabolic outcome measures were less capable of detecting changes. The mean BMI and WC were above the recommended levels at baseline and did not change with time, and metabolic blood tests remained within the normal range post-intervention and at the 12-month follow-up. However, a significant decrease in blood pressure was detected. Our findings indicate the need for subgroup analyses in the planned RCT.\u003c/p\u003e\u003cp\u003eStrengths and limitations\u003c/p\u003e\u003cp\u003eThis study has several strengths. First, the naturalistic design successfully implemented the method within specialized psychiatry, with health care personnels providing the intervention. Second, the wide inclusion criteria enabled unique participation for a range of patients, including individuals with current substance use and comorbid psychiatric diagnoses, commonly excluded from studies (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Third, the multitude of assessments provided a wide range of data and hereby provided comprehensive information of the participants. Fourth, the fact that patients voluntarily signed up for the study and were motivated to engage in activities beyond their regular treatment could be seen as an additional strength.\u003c/p\u003e\u003cp\u003eHowever, there are also a number of limitations to this study. First, the study design with an open trial lacks a control group as well as randomization. Thus, testing the feasibility of the randomization and control process for the future trial is not possible. The interpretation of effect measure findings is also limited by the study design. Furthermore, the small sample size also affects the statistical strength. However, since this was a feasibility pilot study, the effect measures were not primary outcomes. Second, since the methods of measuring physical activity proved not feasible, no conclusions on overall changes in physical activity patterns were drawn. In the same manner, self-reported physical activity via the International Physical Activity Questionnaire (IPAQ) was planned to be used, but data could not be interpreted due to a technical error. Nevertheless, the findings from measuring physical activity in this population provide useful information for upcoming studies. Third, at the group level, the recommended number of 3 exercise sessions per week was not reached. Nevertheless, in line with recommendations (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e), even a small increase in moderate- to vigorous-intensity physical activity in this population has positive effects. Participants\u0026rsquo; individual experiences with a focus on motivation will be explored in the qualitative analysis of the conducted interviews and should lead to improvements in the intervention. Fourth, diagnoses from medical records were used and not verified by diagnostic interviews, which makes the findings less valid. In future studies, participants should undergo a verifying diagnostic interview as part of premeasurements.\u003c/p\u003e\u003cp\u003eClinical implications and future studies\u003c/p\u003e\u003cp\u003eGiven that this was a feasibility study, the primary implications relate to developing the method for a planned randomized trial. Owing to the great variance in exercise participation, we recommend that the motivational components of the Braining method be further developed and individualized for upcoming trials. For a better understanding of the motivational factors impacting exercise behavior, participants' experiences will be explored via qualitative analysis of interviews conducted with the participants adjacent to the intervention.\u003c/p\u003e\u003cp\u003eOur interpretation was that the Actigraph procedure with a diary and an on-off routine was too demanding in this population. A smaller accelerometer that enables a 24-hour wear time and does not require a diary could be more feasible. To avoid losing comprehensive data regarding overall physical activity, easily accessible self-reported measurements should be distributed regularly as a complement to accelerometers.\u003c/p\u003e\u003cp\u003eOverall, the chosen assessments for psychiatric and somatic variables were feasible and capable of detecting changes. A more precise selection of psychiatric self-assessment scales could be favorable for reducing overlap and increasing participation. Stricter inclusion/exclusion criteria regarding pre-study physical activity levels and metabolic health measures could provide a better opportunity for observing possible effects on these outcomes.\u003c/p\u003e\u003cp\u003eOverall, the health screening procedure was considered useful both in upcoming clinical trials and in implementing physical activity in psychiatric care.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis pilot study tested the feasibility of Braining, a novel physical activity intervention in a specialized psychiatry setting focused on substance use. The participants were satisfied with the method and participated in the exercise intervention and measurements but were not fully adherent to the Actigraph measurements. With developed motivational components and adjustments regarding measurements of physical activity, psychiatric assessments and inclusion criteria, the Braining method would be feasible to evaluate in larger patient groups within psychiatric services in conjunction to their ongoing treatment.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eADHD: Attention Deficit Hyperactivity Disorder\u003c/p\u003e\n\u003cp\u003eAUDIT-C: Alcohol Use Disorders Identification Test Consumption\u003c/p\u003e\n\u003cp\u003eBBQ: Brunnsviken Brief Quality of life Scale\u003c/p\u003e\n\u003cp\u003eBMI: Body Mass Index\u003c/p\u003e\n\u003cp\u003eBP: Blood Pressure\u003c/p\u003e\n\u003cp\u003eCEQ: Credibility and Expectancy Questionnaire\u003c/p\u003e\n\u003cp\u003eCSQ-8: 8-item Client Satisfaction Questionnaire\u003c/p\u003e\n\u003cp\u003eDUDIT: Drug Use Disorders Identification Test\u003c/p\u003e\n\u003cp\u003eEQ5D-5 L: The EQ5D™ is a trademark of the EuroQol Group and a self-assessment instrument for describing and valuing health status\u003c/p\u003e\n\u003cp\u003eGAD-7: 7-item Generalized Anxiety Disorder Questionnaire\u003c/p\u003e\n\u003cp\u003eIPAQ: International Physical Activity Questionnaire\u003c/p\u003e\n\u003cp\u003eISI: Insomnia Severity Index\u003c/p\u003e\n\u003cp\u003eKI: Karolinska Institutet\u003c/p\u003e\n\u003cp\u003eNEQ: Negative Effects Questionnaire\u003c/p\u003e\n\u003cp\u003ePHQ-9: 9-item Patient Health Questionnaire\u003c/p\u003e\n\u003cp\u003eRCT: Randomized Controlled Trial\u003c/p\u003e\n\u003cp\u003eWC: Waist Circumference\u003c/p\u003e\n\u003cp\u003eWHO: World Health Organization\u003c/p\u003e\n\u003cp\u003eWHODAS-2: The World Health Organization Disability Assessment Schedule 2.0\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was ethically approved by the Swedish Ethical Review Authority, the Ethics Approval Committee for Medicine, Gothenburg under IDs 2021-04037, 2021-05728-02, 2022-00188-02, and 2023-02746-02. Written informed consent was obtained from the participants. All methods were carried out in accordance with relevant guidelines and regulations in the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analyzed during the current study are not publicly available owing to individual privacy even though they are pseudonymized but are available from the corresponding author upon reasonable request. Data are located in controlled access data storage at KI.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOpen access funding was provided by the Karolinska Institute. The study was funded by the regional agreement on medical training and clinical research (ALF) between the Stockholm Regional Council, Stockholm Health Care Services and Karolinska Institute (grant ID FoUI-955166), LJ Boethius Foundation, Systembolaget\u0026rsquo;s Alcohol Research Council, as well as the Centre for Psychiatry Research, Stockholm.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLM, SS, \u0026Aring;A, RM and CJS designed the research plan. LM acquired the funding and supervised the project with SS. \u0026Aring;A, LM, SS and CJS obtained ethical approval. All authors contributed to the conceptualization and design of the paper. RM and \u0026Aring;A provided education and implementation of the Braining method at the participating clinics. \u0026Aring;A conducted the medical screening at inclusion. \u0026Aring;A, LK and RM conducted the data collection. \u0026Aring;A and LK wrote the original draft and were responsible for the rewriting process. JH, \u0026Aring;A, LK and SS conducted the statistical analyses. MH provided Actigraphs and analyzed and interpreted the Actigraph measurements together with \u0026Aring;A. RM, LM, SS, CJS, JH, TL, NJ, and MH contributed to editing. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are most grateful to the participating patients. We would also like to express our thankfulness to the staff at the clinics Liljeholmsberget and Livsstilsmottagningen, particularly the appointed team leaders Greta Schettini, Peter Margulies and Sandra Carneholm. We also wish to thank Karin Linderst\u0026aring;hl, research nurse, for organizing and collecting the data. This work used the BASS platform from the eHealth Core Facility at Karolinska Institutet.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLM and \u0026Aring;A founded the Braining method in 2017 as clinicians in Region Stockholm.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eFirth J, Siddiqi N, Koyanagi A, Siskind D, Rosenbaum S, Galletly C, et al. The Lancet Psychiatry Commission: a blueprint for protecting physical health in people with mental illness. Lancet Psychiatry. 2019 Aug;6(8):675\u0026ndash;712.\u003c/li\u003e\n\u003cli\u003eRehm J, Shield KD. Global Burden of Disease and the Impact of Mental and Addictive Disorders. Curr Psychiatry Rep. 2019 Feb 7;21(2):10.\u003c/li\u003e\n\u003cli\u003eRehm J, Gmel GE, Gmel G, Hasan OSM, Imtiaz S, Popova S, et al. The relationship between different dimensions of alcohol use and the burden of disease\u0026mdash;an update. Addict Abingdon Engl. 2017 Jun;112(6):968\u0026ndash;1001.\u003c/li\u003e\n\u003cli\u003eCarvalho AF, Heilig M, Perez A, Probst C, Rehm J. Alcohol use disorders. Lancet Lond Engl. 2019 Aug 31;394(10200):781\u0026ndash;92. \u003c/li\u003e\n\u003cli\u003eWestman J, Wahlbeck K, Laursen TM, Gissler M, Nordentoft M, H\u0026auml;llgren J, et al. Mortality and life expectancy of people with alcohol use disorder in Denmark, Finland and Sweden. Acta Psychiatr Scand. 2015 Apr;131(4):297\u0026ndash;306.\u003c/li\u003e\n\u003cli\u003eCunill R, Castells X, Gonz\u0026aacute;lez-Pinto A, Arrojo M, Bernardo M, S\u0026aacute;iz PA, et al. Clinical practice guideline on pharmacological and psychological management of adult patients with attention deficit and hyperactivity disorder and comorbid substance use. Adicciones. 2022 Apr 1;34(2):168\u0026ndash;78.\u003c/li\u003e\n\u003cli\u003eGonz\u0026aacute;lez-Pinto A, Goikolea JM, Zorrilla I, Bernardo M, Arrojo M, Cunill R, et al. Clinical practice guideline on pharmacological and psychological management of adult patients with bipolar disorder and comorbid substance use. Adicciones. 2022 Apr 1;34(2):142\u0026ndash;56.\u003c/li\u003e\n\u003cli\u003eS\u0026aacute;iz PA, Fl\u0026oacute;rez G, Arrojo M, Bernardo M, Gonz\u0026aacute;lez-Pinto A, Goikolea JM, et al. Clinical practice guideline on pharmacological and psychological management of adult patients with an anxiety disorder and comorbid substance use. Adicciones. 2022 Apr 1;34(2):157\u0026ndash;67.\u003c/li\u003e\n\u003cli\u003eTorrens M, Tirado-Mu\u0026ntilde;oz J, Fonseca F, Farr\u0026eacute; M, Gonz\u0026aacute;lez-Pinto A, Arrojo M, et al. Clinical practice guideline on pharmacological and psychological management of adult patients with depression and a comorbid substance use disorder. Adicciones. 2022 Apr 1;34(2):128\u0026ndash;41.\u003c/li\u003e\n\u003cli\u003eGrant BF, Stinson FS, Dawson DA, Chou SP, Dufour MC, Compton W, et al. Prevalence and co-occurrence of substance use disorders and independent mood and anxiety disorders: results from the National Epidemiologic Survey on Alcohol and Related Conditions. Arch Gen Psychiatry. 2004 Aug;61(8):807\u0026ndash;16.\u003c/li\u003e\n\u003cli\u003eSoyka M, Mutschler J. Treatment-refractory substance use disorder: Focus on alcohol, opioids, and cocaine. Prog Neuropsychopharmacol Biol Psychiatry. 2016 Oct 3;70:148\u0026ndash;61. \u003c/li\u003e\n\u003cli\u003evan Emmerik-van Oortmerssen K, van de Glind G, van den Brink W, Smit F, Crunelle CL, Swets M, et al. Prevalence of attention-deficit hyperactivity disorder in substance use disorder patients: A meta-analysis and meta-regression analysis. Drug Alcohol Depend. 2012 Apr 1;122(1):11\u0026ndash;9.\u003c/li\u003e\n\u003cli\u003eGrunze H, Schaefer M, Scherk H, Born C, Preuss UW. Comorbid Bipolar and Alcohol Use Disorder-A Therapeutic Challenge. Front Psychiatry. 2021;12:660432.\u003c/li\u003e\n\u003cli\u003ePreuss UW, Schaefer M, Born C, Grunze H. Bipolar Disorder and Comorbid Use of Illicit Substances. Medicina (Mex). 2021 Nov 17;57(11):1256.\u003c/li\u003e\n\u003cli\u003eGrant BF, Stinson FS, Hasin DS, Dawson DA, Chou SP, Ruan WJ, et al. Prevalence, Correlates, and Comorbidity of Bipolar I Disorder and Axis I and II Disorders: Results From the National Epidemiologic Survey on Alcohol and Related Conditions. J Clin Psychiatry. 2005 Oct 14;66(10):15387.\u003c/li\u003e\n\u003cli\u003eBull FC, Al-Ansari SS, Biddle S, Borodulin K, Buman MP, Cardon G, et al. World Health Organization 2020 guidelines on physical activity and sedentary behaviour. Br J Sports Med. 2020 Dec;54(24):1451\u0026ndash;62.\u003c/li\u003e\n\u003cli\u003ePhysical Activity Guidelines Advisory Committee. 2018 Physical Activity Guidelines Advisory Committee Scientific Report. Washington, DC: U.S. Department of Health and Human Services, 2018. 2018; Available from: http://2018 Physical Activity Guidelines Advisory Committee Scientific Report (health.gov)\u003c/li\u003e\n\u003cli\u003eKramer A. An Overview of the Beneficial Effects of Exercise on Health and Performance. Adv Exp Med Biol. 2020;1228:3\u0026ndash;22. \u003c/li\u003e\n\u003cli\u003eNoetel M, Sanders T, Gallardo-G\u0026oacute;mez D, Taylor P, Del Pozo Cruz B, van den Hoek D, et al. Effect of exercise for depression: systematic review and network meta-analysis of randomised controlled trials. BMJ. 2024 Feb 14;384:e075847.\u003c/li\u003e\n\u003cli\u003eSingh B, Olds T, Curtis R, Dumuid D, Virgara R, Watson A, et al. Effectiveness of physical activity interventions for improving depression, anxiety and distress: an overview of systematic reviews. Br J Sports Med. 20230216th ed. 2023 Feb 16.\u003c/li\u003e\n\u003cli\u003eGim\u0026eacute;nez-Meseguer J, Tortosa-Mart\u0026iacute;nez J, Cortell-Tormo JM. The Benefits of Physical Exercise on Mental Disorders and Quality of Life in Substance Use Disorders Patients. Systematic Review and Meta-Analysis. Int J Environ Res Public Health. 2020 May 23;17(10):3680.\u003c/li\u003e\n\u003cli\u003ePich\u0026eacute; F, Daneau C, Plourde C, Girard S, Romain AJ. Characteristics and impact of physical activity interventions during substance use disorder treatment excluding tobacco: A systematic review. PloS One. 2023;18(4):e0283861.\u003c/li\u003e\n\u003cli\u003eHallgren M, Vancampfort D, Giesen ES, Lundin A, Stubbs B. Exercise as treatment for alcohol use disorders: systematic review and meta-analysis. Br J Sports Med. 2017 Jul;51(14):1058\u0026ndash;64.\u003c/li\u003e\n\u003cli\u003eAlessi SM, Rash CJ, Pescatello LS. Reinforcing exercise to improve drug abuse treatment outcomes: A randomized controlled study in a substance use disorder outpatient treatment setting. Psychol Addict Behav J Soc Psychol Addict Behav. 2020 Feb;34(1):52\u0026ndash;64.\u003c/li\u003e\n\u003cli\u003eShreffler J, Genova G, Huecker M. Physical activity and exercise interventions for individuals with opioid use disorder: a scoping review. J Addict Dis. 2022;40(4):452\u0026ndash;62. \u003c/li\u003e\n\u003cli\u003eOnerup A, Arvidsson D, Blomqvist \u0026Aring;, Daxberg EL, Jiveg\u0026aring;rd L, Jonsdottir IH, et al. Physical activity on prescription in accordance with the Swedish model increases physical activity: a systematic review. Br J Sports Med. 20181109th ed. 2019 Mar;53(6):383\u0026ndash;8.\u003c/li\u003e\n\u003cli\u003eMino E, Hanson CL, Naber I, Weissenfels A, McHale S, Saftig J, et al. A systematic review and narrative synthesis of physical activity referral schemes\u0026rsquo; components. Int J Behav Nutr Phys Act. 2023 Nov 27;20(1):140.\u003c/li\u003e\n\u003cli\u003eNational Institute for Health and Care Excellency. Recommendations. In: Physical activity: exercise referral schemes. NICE. 2024. https://www.nice.org.uk/guidance/ph54/resources/physical-activity-exercise-referral-schemes-pdf-1996418406085. Accessed 10 August 2024.\u003c/li\u003e\n\u003cli\u003eAnger \u0026Aring;, Wallerblad A, Kaaman L, Broman R, Holmberg J, Lundgren T, et al. Introducing Braining-physical exercise as adjunctive therapy in psychiatric care: a retrospective cohort study of a new method. BMC Psychiatry. 2023 Aug 7;23(1):566.\u003c/li\u003e\n\u003cli\u003eEldridge SM, Chan CL, Campbell MJ, Bond CM, Hopewell S, Thabane L, et al. CONSORT 2010 statement: extension to randomised pilot and feasibility trials. BMJ. 2016 Oct 24;355:i5239.\u003c/li\u003e\n\u003cli\u003eLancaster GA, Thabane L. Guidelines for reporting non-randomised pilot and feasibility studies. Pilot Feasibility Stud. 2019 Oct 6;5(1):114.\u003c/li\u003e\n\u003cli\u003eAnger \u0026Aring;, Sundelin S, Lithman M, Martinsson L. Brainingboxen [Internet]. 2023. Available from: https://www.braining.nu/brainingboxen. Accessed 18 Jun 2023.\u003c/li\u003e\n\u003cli\u003eBorg GA. Psychophysical bases of perceived exertion. Med Sci Sports Exerc. 1982;14(5):377\u0026ndash;81.\u003c/li\u003e\n\u003cli\u003eFixsen, D. L., Naoom, S. F., Blase, K. A., Friedman, R. M., Wallace, F. Implementation Reserch: A synthesis of the Literature. Tampa, FL: University of South Florida, Louis de la Parte Florida Mental Health Institute, The National Implementation Research Network (FMHI Publication #231); 2005.\u003c/li\u003e\n\u003cli\u003eB\u0026ouml;rjesson M. Riskbed\u0026ouml;mning vid fysisk aktivitet. In: Professional Associations for Physical Activity. Dohrn IM, Jansson E, B\u0026ouml;rjesson M, Hagstr\u0026ouml;mer M, eds. Physical Activity in the Prevention and Treatment of Disease, FYSS 2021. (in Swedish: Fysisk aktivitet i sjukdomsprevention och sjukdomsbehandling, FYSS 2021). Stockholm: L\u0026auml;kartidningen f\u0026ouml;rlag AB; 2021. p. 180-187.\u003c/li\u003e\n\u003cli\u003ePelliccia A, Sharma S, Gati S, B\u0026auml;ck M, B\u0026ouml;rjesson M, Caselli S, et al. 2020 ESC Guidelines on sports cardiology and exercise in patients with cardiovascular disease. Eur Heart J. 2021 Jan 1;42(1):17\u0026ndash;96.\u003c/li\u003e\n\u003cli\u003eMiller WR, Rollnick S. Motivational interviewing: Helping people change, 3rd edition. New York, NY, US: Guilford Press; 2013. xii, 482 p.\u003c/li\u003e\n\u003cli\u003eRosenberger ME, Haskell WL, Albinali F, Mota S, Nawyn J, Intille S. Estimating activity and sedentary behavior from an accelerometer on the hip or wrist. Med Sci Sports Exerc. 2013 May;45(5):964\u0026ndash;75.\u003c/li\u003e\n\u003cli\u003eDettori JR. Loss to follow-up. Evid-Based Spine-Care J. 2011 Feb;2(1):7\u0026ndash;10.\u003c/li\u003e\n\u003cli\u003eAttkisson CC, Zwick R. The client satisfaction questionnaire. Psychometric properties and correlations with service utilization and psychotherapy outcome. Eval Program Plann. 1982;5(3):233\u0026ndash;7.\u003c/li\u003e\n\u003cli\u003eThompson-Hollands J, Bentley KH, Gallagher MW, Boswell JF, Barlow DH. Credibility and outcome expectancy in the unified protocol: Relationship to outcomes. J Exp Psychopathol. 2014;5(1):72\u0026ndash;82.\u003c/li\u003e\n\u003cli\u003eDevilly GJ, Borkovec TD. Psychometric properties of the credibility/expectancy questionnaire. J Behav Ther Exp Psychiatry. 2000 Jun;31(2):73\u0026ndash;86.\u003c/li\u003e\n\u003cli\u003eRozental A, Kottorp A, Forsstr\u0026ouml;m D, M\u0026aring;nsson K, Boettcher J, Andersson G, et al. The Negative Effects Questionnaire: psychometric properties of an instrument for assessing negative effects in psychological treatments. Behav Cogn Psychother. 2019 Sep;47(5):559\u0026ndash;72.\u003c/li\u003e\n\u003cli\u003eArvidsson D, Fridolfsson J, B\u0026ouml;rjesson M. Measurement of physical activity in clinical practice using accelerometers. J Intern Med. 2019 Aug;286(2):137\u0026ndash;53.\u003c/li\u003e\n\u003cli\u003eTroiano RP, Berrigan D, Dodd KW, M\u0026acirc;sse LC, Tilert T, McDowell M. Physical activity in the United States measured by accelerometer. Med Sci Sports Exerc. 2008 Jan;40(1):181\u0026ndash;8.\u003c/li\u003e\n\u003cli\u003eHart TL, Swartz AM, Cashin SE, Strath SJ. How many days of monitoring predict physical activity and sedentary behaviour in older adults? Int J Behav Nutr Phys Act. 2011 Jun 16;8:62.\u003c/li\u003e\n\u003cli\u003eMatthews CE, Chen KY, Freedson PS, Buchowski MS, Beech BM, Pate RR, et al. Amount of time spent in sedentary behaviors in the United States, 2003-2004. Am J Epidemiol. 2008 Apr 1;167(7):875\u0026ndash;81.\u003c/li\u003e\n\u003cli\u003eFreedson PS, Melanson E, Sirard J. Calibration of the Computer Science and Applications, Inc. accelerometer. Med Sci Sports Exerc. 1998 May;30(5):777\u0026ndash;81.\u003c/li\u003e\n\u003cli\u003eEkelund U, Sepp H, Brage S, Becker W, Jakes R, Hennings M, et al. Criterion-related validity of the last 7-day, short form of the International Physical Activity Questionnaire in Swedish adults. Public Health Nutr. 2006 Apr;9(2):258\u0026ndash;65.\u003c/li\u003e\n\u003cli\u003eSimon CB, McCabe CJ, Matson TE, Oliver M, Bradley KA, Hallgren KA. High test-retest reliability of the Alcohol Use Disorders Identification Test-Consumption (AUDIT-C) questionnaire completed by primary care patients in routine care. Alcohol Clin Exp Res. 2024 Feb;48(2):302\u0026ndash;8.\u003c/li\u003e\n\u003cli\u003eHildebrand M. The Psychometric Properties of the Drug Use Disorders Identification Test (DUDIT): A Review of Recent Research. J Subst Abuse Treat. 2015 Jun;53:52\u0026ndash;9.\u003c/li\u003e\n\u003cli\u003eLevis B, Benedetti A, Thombs BD, Collaboration DepresSD (DEPRESSD). Accuracy of Patient Health Questionnaire-9 (PHQ-9) for screening to detect major depression: individual participant data meta-analysis. BMJ. 20190409th ed. 2019 Apr;365:l1476.\u003c/li\u003e\n\u003cli\u003eKroenke K, Spitzer RL, Williams JB. The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med. 2001 Sep;16(9):606\u0026ndash;13.\u003c/li\u003e\n\u003cli\u003eToussaint A, H\u0026uuml;sing P, Gumz A, Wingenfeld K, H\u0026auml;rter M, Schramm E, et al. Sensitivity to change and minimal clinically important difference of the 7-item Generalized Anxiety Disorder Questionnaire (GAD-7). J Affect Disord. 20200115th ed. 2020 Mar 15;265:395\u0026ndash;401.\u003c/li\u003e\n\u003cli\u003eMorin CM, Belleville G, B\u0026eacute;langer L, Ivers H. The Insomnia Severity Index: psychometric indicators to detect insomnia cases and evaluate treatment response. Sleep. 20110501st ed. 2011 May 1;34(5):601\u0026ndash;8.\u003c/li\u003e\n\u003cli\u003eLindner P, Frykheden O, Forsstr\u0026ouml;m D, Andersson E, Lj\u0026oacute;tsson B, Hedman E, et al. The Brunnsviken Brief Quality of Life Scale (BBQ): Development and Psychometric Evaluation. Cogn Behav Ther. 2016 Apr;45(3):182\u0026ndash;95.\u003c/li\u003e\n\u003cli\u003eUstun TB, Kostanjesek N, Chatterji S, Rehm J, World Health Organization. Measuring health and disability : manual for WHO Disability Assessment Schedule (WHODAS 2.0) / edited by T.B. \u0026Uuml;st\u0026uuml;n, N. Kostanjsek, S. Chatterji, J.Rehm. 2010;88.\u003c/li\u003e\n\u003cli\u003ePayakachat N, Ali MM, Tilford JM. Can The EQ-5D Detect Meaningful Change? A Systematic Review. Pharmacoeconomics. 2015 Nov;33(11):1137\u0026ndash;54. \u003c/li\u003e\n\u003cli\u003eBouwmans C, De Jong K, Timman R, Zijlstra-Vlasveld M, Van der Feltz-Cornelis C, Tan Swan S, et al. Feasibility, reliability and validity of a questionnaire on healthcare consumption and productivity loss in patients with a psychiatric disorder (TiC-P). BMC Health Serv Res. 2013 Jun 15;13:217.\u003c/li\u003e\n\u003cli\u003eMa WY, Yang CY, Shih SR, Hsieh HJ, Hung CS, Chiu FC, et al. Measurement of Waist Circumference: midabdominal or iliac crest? Diabetes Care. 2013 Jun;36(6):1660\u0026ndash;6.\u003c/li\u003e\n\u003cli\u003eThe jamovi project (2024). jamovi (Version 2.5) [Microsoft]. Retrieved from https://www.jamovi.org.\u003c/li\u003e\n\u003cli\u003eThe National Collaborating Centre for Mental Health. NHS Talking Therapies for anxiety and depression Manual. June 2018, updated March 2024. Available from: https://www.england.nhs.uk/wp-content/uploads/2018/06/NHS-talking-therapies-manual-v7-1.pdf. Accessed 16 Jul 2024.\u003c/li\u003e\n\u003cli\u003eWhelton PK, Carey RM, Aronow WS, Casey DE, Collins KJ, Dennison HC, et al. 2017 ACC/AHA/AAPA/ABC/ACPM/AGS/APhA/ASH/ASPC/NMA/PCNA Guideline for the Prevention, Detection, Evaluation, and Management of High Blood Pressure in Adults. J Am Coll Cardiol. 2018 May 15;71(19):e127\u0026ndash;248.\u003c/li\u003e\n\u003cli\u003eGlobal BMI Mortality Collaboration, Di Angelantonio E, Bhupathiraju S, Wormser D, Gao P, Kaptoge S, et al. Body-mass index and all-cause mortality: individual-participant-data meta-analysis of 239 prospective studies in four continents. Lancet Lond Engl. 2016 Aug 20;388(10046):776\u0026ndash;86.\u003c/li\u003e\n\u003cli\u003eLean MEJ, Han TS, Morrison CE. Waist circumference as a measure for indicating need for weight management. BMJ. 1995 Jul 15;311(6998):158\u0026ndash;61.\u003c/li\u003e\n\u003cli\u003eGunillasdotter V, Andr\u0026eacute;asson S, Jirwe M, Ekblom \u0026Ouml;, Hallgren M. Effects of exercise in non-treatment seeking adults with alcohol use disorder: A three-armed randomized controlled trial (FitForChange). Drug Alcohol Depend. 2022 Mar 1;232:109266.\u003c/li\u003e\n\u003cli\u003eRawson RA, Chudzynski J, Mooney L, Gonzales R, Ang A, Dickerson D, et al. Impact of an exercise intervention on methamphetamine use outcomes post-residential treatment care. Drug Alcohol Depend. 2015 Nov 1;156:21\u0026ndash;8.\u003c/li\u003e\n\u003cli\u003eTrivedi MH, Greer TL, Rethorst CD, Carmody T, Grannemann BD, Walker R, et al. Randomized Trial Comparing Exercise to Health Education for Stimulant Use Disorder: Results from STimulant Reduction Intervention using Dosed Exercise (CTN-0037; STRIDE). J Clin Psychiatry. 2017;78(8):1075\u0026ndash;82.\u003c/li\u003e\n\u003cli\u003eHenriksson M, Wall A, Nyberg J, Adiels M, Lundin K, Bergh Y, et al. Effects of exercise on symptoms of anxiety in primary care patients: A randomized controlled trial. J Affect Disord. 2022 Jan 15;297:26\u0026ndash;34.\u003c/li\u003e\n\u003cli\u003eHelgad\u0026oacute;ttir B, Forsell Y, Ekblom \u0026Ouml;. Physical Activity Patterns of People Affected by Depressive and Anxiety Disorders as Measured by Accelerometers: A Cross-Sectional Study. PLoS ONE. 2015 Jan 13;10(1):e0115894.\u003c/li\u003e\n\u003cli\u003eSchuch F, Vancampfort D, Firth J, Rosenbaum S, Ward P, Reichert T, et al. Physical activity and sedentary behavior in people with major depressive disorder: A systematic review and meta-analysis. J Affect Disord. 20161129th ed. 2017 Mar;210:139\u0026ndash;50.\u003c/li\u003e\n\u003cli\u003eVancampfort D, Firth J, Schuch FB, Rosenbaum S, Mugisha J, Hallgren M, et al. Sedentary behavior and physical activity levels in people with schizophrenia, bipolar disorder and major depressive disorder: a global systematic review and meta-analysis. World Psychiatry Off J World Psychiatr Assoc WPA. 2017 Oct;16(3):308\u0026ndash;15.\u003c/li\u003e\n\u003cli\u003eTudor-Locke C, Craig CL, Brown WJ, Clemes SA, De Cocker K, Giles-Corti B, et al. How many steps/day are enough? for adults. Int J Behav Nutr Phys Act. 2011 Jul 28;8(1):79.\u003c/li\u003e\n\u003cli\u003eSmits JAJ, Berry AC, Rosenfield D, Powers MB, Behar E, Otto MW. Reducing anxiety sensitivity with exercise. Depress Anxiety. 2008;25(8):689\u0026ndash;99.\u003c/li\u003e\n\u003cli\u003eAndersen TE, Ravn SL, Armfield N, Maujean A, Requena SS, Sterling M. Trauma-focused cognitive behavioural therapy and exercise for chronic whiplash with comorbid posttraumatic stress disorder: a randomised controlled trial. PAIN. 2021 Apr;162(4):1221.\u003c/li\u003e\n\u003cli\u003eNiemeijer A, Lund H, Stafne SN, Ipsen T, Goldschmidt CL, J\u0026oslash;rgensen CT, et al. Adverse events of exercise therapy in randomised controlled trials: a systematic review and meta-analysis. Br J Sports Med. 2020 Sep;54(18):1073\u0026ndash;80.\u003c/li\u003e\n\u003cli\u003eSobell LC, Sobell MB. Timeline Follow-Back. In: Litten RZ, Allen JP, editors. Measuring Alcohol Consumption: Psychosocial and Biochemical Methods [Internet]. Totowa, NJ: Humana Press; 1992. p. 41\u0026ndash;72. Available from: https://doi.org/10.1007/978-1-4612-0357-5_3. Accessed 16 Jul 2024.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"alcohol use disorder, anxiety, depression, exercise, feasibility, metabolic disorder, physical activity, psychiatry, substance use disorder","lastPublishedDoi":"10.21203/rs.3.rs-7409582/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7409582/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eIndividuals with substance use disorders (SUD) have poor metabolic and cardiovascular health as well as high prevalence of symptoms of depression and anxiety. Regular physical activity can markedly improve these and other somatic and mental health conditions. The present study aimed to evaluate the feasibility and preliminary effects of an aerobic physical activity intervention termed Braining, in psychiatric care specialized in SUD.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eFor this uncontrolled open trial with pre-, post- and 12-month follow-up measures, 22 patients undergoing treatment for SUD and comorbid psychiatric disorders, participated in a 12-week physical activity intervention. Feasibility was assessed by adherence to exercise sessions and measurement procedures. Acceptability, credibility and negative treatment effects were measured through self-report questionnaires and reported adverse events. Physical activity was measured via accelerometry (Actigraph GT3x) and self-reporting. Psychiatric symptoms including substance use, functional status, and quality of life, were measured with self-assessments. Somatic health was evaluated through physical examinations and metabolic blood markers. Preliminary effects of psychiatric symptoms were analyzed via linear mixed models, and effects on metabolic outcomes were analyzed via t-tests.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eThe mean number of exercise sessions attended was 9 (SD\u0026thinsp;=\u0026thinsp;7.7). Adherence to Actigraph measurements was 73% at baseline, 46% at mid-intervention, and 32% post-intervention. Completion of self-assessments was 100% at baseline, 86% at post-intervention, and 77% at the 12-month follow-up. The mean score for the Client Satisfaction Questionnaire (CSQ-8) was 27 (SD\u0026thinsp;=\u0026thinsp;3.9). No serious adverse events were reported. Significant positive changes were detected in 6 out of 8 self-assessment scales regarding psychiatric symptoms and quality of life, with effect sizes ranging from 0.28\u0026ndash;0.77. Mean systolic blood pressure 131 (SD\u0026thinsp;=\u0026thinsp;2.6) and mean diastolic blood pressure 83.5 (SD\u0026thinsp;=\u0026thinsp;2.2), was significantly reduced at post-measurement (diastolic) and at follow-up (systolic).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eThe participants were satisfied with the Braining intervention and participated in exercise sessions and measurements, although adherence to the Actigraph measurements was low. With specified adjustments, the method was deemed feasible for future studies.\u003c/p\u003e","manuscriptTitle":"Braining, Structured Physical Activity in Specialized Psychiatry for Patients with Substance Use Disorders - A Feasibility Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-09 13:22:55","doi":"10.21203/rs.3.rs-7409582/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e38cf210-6f75-4360-a12e-689bdaa2852f","owner":[],"postedDate":"September 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-27T14:41:34+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-09 13:22:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7409582","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7409582","identity":"rs-7409582","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}