Hostname: page-component-5d59c44645-klj7v Total loading time: 0 Render date: 2024-02-22T11:14:42.798Z Has data issue: false hasContentIssue false

How to improve the physical health of people with severe mental illness? A multicentric randomized controlled trial on the efficacy of a lifestyle group intervention

Published online by Cambridge University Press:  23 November 2021

Mario Luciano*
Affiliation:
Department of Psychiatry, University of Campania “L. Vanvitelli”, Naples, Italy
Gaia Sampogna
Affiliation:
Department of Psychiatry, University of Campania “L. Vanvitelli”, Naples, Italy
Mario Amore
Affiliation:
Section of Psychiatry, Department of Neuroscience, Ophthalmology, Genetics and Infant-Maternal Science, University of Genoa, Genoa, Italy
Ileana Andriola
Affiliation:
Department of Basic Medical Science, Neuroscience and Sense Organs, University of Bari “Aldo Moro”, Bari, Italy
Pietro Calcagno
Affiliation:
Section of Psychiatry, Department of Neuroscience, Ophthalmology, Genetics and Infant-Maternal Science, University of Genoa, Genoa, Italy
Claudia Carmassi
Affiliation:
Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
Valeria Del Vecchio
Affiliation:
Department of Psychiatry, University of Campania “L. Vanvitelli”, Naples, Italy
Liliana Dell’Osso
Affiliation:
Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
Giorgio Di Lorenzo
Affiliation:
Department of System Medicine, University of Rome Tor Vergata, Rome, Italy
Barbara Gelao
Affiliation:
Department of Basic Medical Science, Neuroscience and Sense Organs, University of Bari “Aldo Moro”, Bari, Italy
Vincenzo Giallonardo
Affiliation:
Department of Psychiatry, University of Campania “L. Vanvitelli”, Naples, Italy
Alessandro Rossi
Affiliation:
Section of Psychiatry, Department of Biotechnological and Applied Clinical Sciences, University of L’Aquila, L’Aquila, Italy
Rodolfo Rossi
Affiliation:
Section of Psychiatry, Department of Biotechnological and Applied Clinical Sciences, University of L’Aquila, L’Aquila, Italy
Alberto Siracusano
Affiliation:
Department of System Medicine, University of Rome Tor Vergata, Rome, Italy
Andrea Fiorillo
Affiliation:
Department of Psychiatry, University of Campania “L. Vanvitelli”, Naples, Italy
*
*Corresponding Author: Luciano Mario, E-mail: mario.luciano@unicampania.it

Abstract

Background

People with severe mental illnesses (SMI) have a mortality rate two times higher compared to the general population, with a decade of years of life lost. In this randomized controlled trial (RCT), we assessed in a sample of people with bipolar disorder, major depressive disorder, and schizophrenia spectrum disorder, the efficacy of an innovative psychosocial group intervention compared to a brief psychoeducational group intervention on patients’ body mass index (BMI), body weight, waist circumference, Framingham and HOMA-IR indexes.

Methods

This is a multicentric RCT with blinded outcome assessments carried out in six Italian university centers. After recruitment patients were randomized to receive a 6-month psychosocial intervention to improve patients’ physical health or a brief psychoeducational intervention. All recruited patients were assessed with standardized assessment instruments at baseline and after 6 months. Anthropometric parameters and blood samples have also been collected.

Results

Four-hundred and two patients with a diagnosis of bipolar disorder (43.3%), schizophrenia or other psychotic disorder (29.9%), or major depression (26.9%) were randomly allocated to the experimental (N = 206) or the control group (N = 195). After 6 months, patients from the experimental group reported a significant reduction in BMI (odds ratio [OR]: 1.93, 95% confidence intervals [CI]: 1.31–2.84; p < 0.001), body weight (OR = 4.78, 95% CI: 0.80–28.27, p < 0.05), and waist circumference (OR = 5.43, 95% CI: 1.45–20.30, p < 0.05). Participants with impaired cognitive and psychosocial functioning had a worse response to the intervention.

Conclusions

The experimental group intervention was effective in improving the physical health in SMI patients. Further studies are needed to evaluate the feasibility of this intervention in real-world settings.

Type
Research Article
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
© The Author(s), 2021. Published by Cambridge University Press on behalf of the European Psychiatric Association

Introduction

People with severe mental illnesses (SMI), such as schizophrenia, bipolar disorder, and major depression, are more likely to suffer from obesity, diabetes, dyslipidemias, and metabolic syndrome with an increase of cardiovascular risk [Reference van Os, Guloksuz, Vijn, Hafkenscheid and Delespaul1Reference Bartoli, Carrà, Crocamo, Carretta and Clerici7] and a reduced life expectancy of up to 25 years compared with the general population, representing a major public health concern [Reference Plana-Ripoll, Musliner, Dalsgaard, Momen, Weye and Christensen813] and a priority for health agencies and national governments [Reference Fiorillo, Luciano, Del Vecchio, Sampogna, Obradors-Tarragó and Maj14].

Several factors can contribute to the poor physical health in this patient population. First, they frequently adopt unhealthy lifestyle behaviors, characterized by lack of physical activity, unhealthy diet (rich in carbohydrate and fat), heavy smoking, and use of alcohol or illicit substances [Reference Kuppili and Nebhinani15Reference Di Forti19]. Second, several psychotropic medications have metabolic side effects, further increasing the cardiometabolic risk [Reference Solmi, Fornaro, Ostinelli, Zangani, Croatto and Monaco20Reference Ebdrup and Knop26]. Third, patients with SMI rarely access screening procedures for physical illnesses due to patients’ lack of motivation and stigma from other physicians [Reference Hassan, Heinkel, Burton, Blackburn, McCloud and Ross27Reference Lawrence, Holman, Jablensky and Hobbs30]. Finally, several illness-related factors, including cognitive impairment, reduced psychosocial functioning, social isolation, and self-stigma [Reference Thornicroft, Bakolis, Evans-Lacko, Gronholm, Henderson and Kohrt31Reference Valtorta, Kanaan, Gilbody and Hanratty34], can reduce patients’ autonomy to make decisions about their physical health condition. In fact, SMI patients tend to perceive stigmatizing attitudes from their healthcare providers, and thus are reluctant to seek medical help [Reference Thornicroft5]. Internalized stigma causes social withdrawal which may lead to further reduce check-up visits for physical health [Reference Mazzi, Baccari, Mungai, Ciambellini, Brescancin and Starace35], and a consequent increase of incidence of cardiovascular diseases [Reference Valtorta, Kanaan, Gilbody and Hanratty34] and excess mortality [Reference Thornicroft, Bakolis, Evans-Lacko, Gronholm, Henderson and Kohrt31,Reference Holt-Lunstad, Smith, Baker, Harris and Stephenson36].

The increase in life expectancy of the general population, due to better living conditions and advanced medical care, may increase the prevalence of physical comorbidities, particularly noncommunicable diseases (such as cancer, diabetes, and cardiovascular illness). Despite this, the management of physical comorbidities in SMI patients is still a neglected and understudied area for healthcare professionals, in particular due to the over-fragmentation of medical disciplines [Reference Fiorillo, Luciano, Pompili and Sartorius4].

The improvement of patients’ lifestyle behaviors can be associated with a significant improvement in quality of life, with a reduction in morbidity and mortality rate in the long term [Reference Luciano, Sampogna, Del Vecchio, Giallonardo, Palummo and Andriola37,Reference Bauer, Gálvez, Hamilton, Balanzá-Martínez, Zunta-Soares and Soares38]. In the past decades, several supportive interventions, including nutritional and motivational components, have been developed with the aim to modify patients’ lifestyle behaviors [Reference Speyer, Jakobsen, Westergaard, Nørgaard, Jørgensen and Pisinger39]. However, evidence to support the efficacy of lifestyle interventions remains scarce [Reference Naslund, Whiteman, McHugo, Aschbrenner and Marsch3,Reference Speyer, Jakobsen, Westergaard, Nørgaard, Jørgensen and Pisinger39,Reference Marcos-Delgado, Hernández-Segura, Fernández-Villa, Molina and Martín40]. In fact, while some studies have found that patients receiving a lifestyle intervention report a significant weight loss and a reduction in cardiovascular risk factors [Reference Verhaeghe, Clays, Vereecken, De Maeseneer, Maes and Van Heeringen41Reference McEwen43], other studies failed to demonstrate the benefits of such interventions [Reference Speyer, Christian Brix Nørgaard, Birk, Karlsen, Storch Jakobsen and Pedersen44]. This discrepancy in findings can partly be attributed to methodological limitations, with many studies carried out on small sample sizes [Reference Plana-Ripoll, Musliner, Dalsgaard, Momen, Weye and Christensen8], or lacking adequate control groups [Reference De Rosa, Sampogna, Luciano, Del Vecchio, Pocai and Borriello10], or with participants who were not overweight or obese.

Other studies have focused on the difficulties in implementing lifestyle interventions in routine clinical care, which are mainly due to the involvement of multidisciplinary teams, the long duration of the intervention, the structured model of the programs, and the high costs of the intervention in the real word practice [Reference Verhaeghe, Clays, Vereecken, De Maeseneer, Maes and Van Heeringen41]. Despite this, psychosocial interventions for promoting healthy lifestyle behaviors among people with SMI are effective and very well received by patients [Reference Cabassa, Ezell and Lewis-Fernández45,Reference Alvarez-Jiménez, Hetrick, González-Blanch, Gleeson and McGorry46].

We carried out a multicentric study, coordinated by the Department of Psychiatry of the University of Campania “L. Vanvitelli” and carried out in six university sites, with the aim to assess the efficacy of a new psychosocial group intervention (the LIFESTYLE intervention) in a sample of patients with SMI in the real world. The main innovations of our intervention include: (a) the use of elements derived from classical psychoeducation, motivational interview, and cognitive-behavioral therapy; (b) the adoption of a comprehensive approach focusing on all aspects of unhealthy lifestyles, and not addressing only unhealthy diet; and (c) the provision of the intervention to mixed diagnostic groups of patients.

The primary aim of the study is to evaluate the efficacy of the LIFESTYLE intervention in terms of reduction of body mass index (BMI) at 6 months of follow-up, compared to a brief psychoeducational group intervention (control intervention). The secondary aims include the reduction of the Framingham and HOMA-IR (homeostasis model assessment of insulin resistance) indexes, waist circumference, and of comorbidity and severity indexes at the cumulative illness rating scale (CIRS). Moreover, we aimed to explore the impact of several illness-related variables, such as cognition and psychosocial functioning, on the efficacy of the intervention. We hypothesize that at 6 months patients receiving the experimental intervention will observe a reduction of at least one point of BMI and that patients with a worse psychosocial and cognitive functioning will benefit less from the experimental intervention.

Methods

The LIFESTYLE trial is a multicentric, randomized controlled trial (RCT) with blinded outcome assessments, carried out in the outpatient units of the University of Campania “Luigi Vanvitelli” in Naples, University of Bari, University of Genova, University of L’Aquila, University of Pisa, and University of Rome-Tor Vergata, and funded by the Italian Ministry of Education, Universities and Research within the framework of the “Progetti di Rilevante Interesse Nazionale (PRIN).” Each center was expected to recruit 70 patients, 35 per each arm, with a total sample size of 420 participants.

Eligible patients were identified by their clinicians and referred to the LIFESTYLE research staff by phone, email, or in-person meeting. After the informed consent, all patients were randomly allocated to the two arms by the coordinating center. The randomization procedure was stratified according to center, age, gender, and educational level with a 1:1 ratio.

To be included in the study patients had to fulfill the following inclusion criteria: (a) age between 18 and 65 years; (b) diagnosis of schizophrenia, schizoaffective disorder, delusional disorder, other psychotic disorders, major depressive disorder, or bipolar disorder according to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) and confirmed by the Structured Clinical Interview for DSM-5 (SCID-5) [47]; (c) ability to provide written informed consent; (d) BMI ≥ 25; (e) in charge to the local mental health unit for at least 3 months before the inclusion in the study. Patients were excluded in case of: (a) inability to perform moderate physical activity (i.e., walking at least 150 min per week, or 75 min of vigorous activity twice a week, according to the guidelines of the Italian Ministry of Health); (b) pregnancy or breast-feeding; (c) intellectual disability or severe cognitive impairment; and (d) worsening of clinical status or hospital admission in the previous 3 months. All recruited patients provided written informed consent to participate in the study.

Researchers and statisticians involved in assessments of patients were blinded to patient’s allocation. This study was conducted in accordance with globally accepted standards of good practice, in agreement with the Declaration of Helsinki and with local regulations. The study protocol was formally approved by the Ethics Committee of the Coordinating Center on January 24, 2017 (approval number: prot. 64).

Interventions

Arm I: Lifestyle psychosocial group intervention

The theoretical background of the experimental intervention includes techniques derived from classic psychoeducation [Reference Falloon48,Reference Fiorillo, Del Vecchio, Luciano, Sampogna, De Rosa and Malangone49], motivational interview [50Reference Miller and Rose52], and cognitive-behavioral therapy [Reference Hofmann, Asnaani, Vonk, Sawyer and Fang53]. The intervention was developed following the guidelines on the management of physical health in people with mental disorders produced by the World Health Organization [54,55], the European Association for the Study of Diabetes [Reference Rydén, Grant, Anker, Berne, Cosentino and Danchin56], the European Society of Cardiology [Reference Piepoli, Hoes, Agewall, Albus, Brotons and Catapano57], and the European Psychiatric Association [Reference De Hert, Dekker, Wood, Kahl, Holt and Möller58].

The full methodology adopted to develop the intervention has been reported in detail elsewhere [Reference Sampogna, Fiorillo, Luciano, Del Vecchio, Steardo and Pocai59]. The intervention lasted 6 months and was administered to groups of 5–10 patients every 7–10 days. The following topics were covered during the sessions: (a) healthy diet; (b) physical activity; (c) smoking habits; (d) medication adherence; (e) risky behaviors; and (f) promotion of circadian rhythms. Each module included the following components: (a) education on the risks and benefits of each lifestyle behavior; (b) provision of practical strategies to reduce unhealthy behaviors; and (c) for each participant, identification of personal life goals, motivation to change, and problem-solving strategies. During sessions, working groups and active interaction among participants were developed to stimulate discussion. At the end of each meeting, a 20-min session of moderate physical activity was scheduled.

Arm II: Brief psychoeducational group intervention

The brief psychoeducational group intervention consisted of five weekly sessions administered to groups of 5–10 patients on: (a) healthy lifestyle; (b) early detection of clinical relapses; (c) effects of pharmacological treatment and management of side effects; (d) stress management techniques; and (e) problem-solving techniques. Manuals were developed for both interventions in order to ensure treatment fidelity among the centers.

Training of mental health professionals

Three mental health professionals (at least one being a psychiatrist) per each center participated to a 5-day training course on the two interventions. All mental health professionals received regular phone and e-mail supervisions during the whole study period. Moreover, a site visit was organized by the coordinating center to guarantee an in vivo supervision.

Assessment times and instruments

A further 2-day meeting was organized for the use of assessment instruments, according to study protocol.

Researchers participating to the study were different from those running the interventions and were blinded to patient allocation.

Patients were assessed at baseline (T0) and 6 months after randomization (T1). Patients’ diagnosis was confirmed through the SCID-5.

Patients’ psychiatric symptoms and psychosocial functioning were assessed by: (a) the Brief Psychiatric Rating Scale (BPRS) [Reference Lukoff, Nuechterlein and Ventura60], a semi-structured 24-item interview on psychopathological status. Items are grouped in four subscales: positive symptoms, negative symptoms, depressive-anxiety symptoms, and manic/hostility symptoms; (b) the Personal and Social Performance Scale (PSP) [Reference Morosini, Magliano, Brambilla, Ugolini and Pioli61], a 100-point single-item rating scale, subdivided into 10 equal intervals; and (c) the Measurement and Treatment Research to Improve Cognition in Schizophrenia (MATRICS) Consensus Cognitive Battery (MCCB)—brief version, which includes the MATRICS Consensus Trail Making Test—Part A, Brief Assessment of Cognition in Schizophrenia: Symbol Coding, Category Fluency-Animal Naming [Reference Kern, Nuechterlein, Green, Baade, Fenton and Gold62,Reference Nuechterlein, Green, Kern, Baade, Barch and Cohen63].

Patients’ physical health was assessed with the following instruments: (a) the CIRS [Reference Linn, Linn and Gurel64], a 14-item questionnaire exploring presence and severity of physical comorbidities; (b) an anthropometric schedule with information on weight, height, BMI, waist circumference, blood pressure, resting heart rate, high-density lipoprotein, low-density lipoprotein, overall cholesterol levels, blood glucose, triglycerides, and blood insulin; (c) the homeostasis model assessment of insulin resistance (HOMA-IR), calculated as follows: fasting insulin (mg/dL) × fasting glucose (mmol/L)/405 [Reference Freeman and Pennings65]; (d) the Framingham risk score (FRS) for the evaluation of cardiovascular risk [Reference Andersson, Nayor, Tsao, Levy and Vasan66].

The inter-rater reliability of researchers was tested through the Cohen’s Kappa coefficient, which was satisfactory for both the PSP (K value = 0.918) and the BPRS (K value ranging from 0.835 to 0.972). A 100% agreement rate was found for the SCID-5 diagnoses.

Statistical analyses

Statistical analyses were conducted according to the “Intention To Treat” principle. Missing data were handled using the Last Observation Carried Forward. Descriptive statistics (frequency table, means and standard deviation) were calculated for both experimental and control groups at baseline and at the end of the intervention. Differences in sociodemographic and clinical characteristics among the two groups at baseline and at the end of the intervention were tested using χ 2 or t-test for independent samples, as appropriate. The impact of the interventions on physical health related domains was explored by the Student’s t-test for paired sample in each group.

Generalized estimating equation (GEE) models were used for evaluating the impact of the experimental intervention on the primary outcome (i.e., reduction of BMI at the end of the intervention). GEE models allow estimation of population-averaged models in repeated-measures data. Control vs. intervention interaction terms assessed changes between groups over time; Wald tests determined whether joint effects of time-by-group equaled zero. Age and center were included as time-invariant covariates; time-varying covariates included medications, cognitive functioning, age, gender, and type of mental disorder diagnosis. We used GEE models with a normal distribution and identity link. We report covariate-adjusted results using robust estimates of standard errors. All models were adjusted for diagnosis, pharmacological treatments, duration of illness, and educational level. Pharmacological treatments and psychiatric diagnoses have been included in the regression models as dummy variables (i.e., mood stabilizers, tricyclic antidepressants, new-generation antidepressants, first- and second-generation antipsychotics, depressive disorder, bipolar disorders, psychosis).

The Statistical Package for Social Sciences (SPSS), version 17.0 (SPSS Inc., Chicago), and STATA, version 15 (StataCorp LLC, College Station, TX), were used for statistical analyses. For all analyses, the level of statistical significance was set at p < 0.05.

Results

A total of 401 patients agreed to participate in the study and were randomly allocated to receive the experimental or the control intervention (206 from the experimental group and 195 from the control group). Two hundred and twenty-four patients (112 in the experimental and 112 in the control group) did not complete the intervention due to: practical difficulties in reaching the study site (27%), not anymore in charge to the local mental health center (25%), exacerbation of psychiatric symptoms (20%), and lack of interest (18%). Therefore, the final sample consisted of 177 patients (94 in the experimental and 83 in the control group).

Sociodemographic and clinical characteristics of the two samples

Of the 402 recruited patients, 43.3% had a diagnosis of bipolar disorder, 29.9% of psychosis, and 26.9% of major depression. Patients were mainly female (57%), with an average age of 45.6 ± 11.8 years and educational level of 11.7 ± 2.9 years. Of them 28.6% were married (Table 1). All patients were treated with at least one psychotropic drug: 35% of the sample was treated with one pharmacological agent, 39% with two, 21% was taking three different drugs and up to 5% was treated with four different psychotropic drugs.

Table 1. Sociodemographic and clinical characteristics of the sample (N = 402).

Abbreviations: B-MCCB: Brief MATRICS Consensus Cognitive Battery; BPRS: Brief Psychiatric Rating Scale; PSPS: Personal and Social Performance Scale; SD, standard deviation.

Patients had a mean body weight of 91.4 kg (±17.4), with a BMI of 32.5 (±5.5) and a waist circumference of 109.3 cm (±14.2). The systolic and diastolic blood pressure were 125.6 (±13.53) and 80.7 (±9.0), respectively. The mean score at the CIRS severity index was 0.5 (±4.6) and the mean score at the CIRS comorbidity index was 0.3 (±1.4). The mean FRS was 9.8 (±4.5) and the mean HOMA-IR index was 4.9 (±11.6) (Table 2).

Table 2. Comparisons of health-related domains in the two groups.

Abbreviations: BMI, body mass index; CIRS, Cumulative Illness Rating Scale; DS, Standard Deviation; HOMA-IR, homeostatic model assessment of insulin resistance; SD, standard deviation.

* p < 0.05.

** p < 0.001.

No statistically significant differences were found in sociodemographic and clinical characteristics as well as in anthropometric and metabolic parameters between experimental and control groups.

Efficacy of the experimental intervention

At the univariate analyses, patients from the experimental group reported a significant reduction in BMI and body weight (32.2 ± 5.2 at T0 vs. 30.6 ± 4.5 at T1 and 91.8 ± 17.2 vs. 87.6 ± 15.3, p < 0.01, respectively). Moreover, at the end of the intervention, patients from the experimental group reported a mean reduction in waist circumference of 2.75 cm (p < 0.05) (Table 2).

The GEE model showed a significant effect of the experimental intervention on BMI. In particular, at the end of the intervention, patients receiving the experimental intervention had a BMI reduction of almost two points (odds ratio [OR]: 1.93, 95% confidence intervals [CI]: 1.31–2.84; p < 0.001). Other factors positively impacting the reduction of BMI were female gender (OR: 0.32, 95% CI: 0.12–0.84), a better cognitive functioning (B-MCCB symbol coding, OR: 0.99, 95% CI: 0.94–1.03, p < 0.001; B-MCCB animal naming, OR: 0.94, 95% CI: 0.85–1.03, p < 0.001; B-MCCB trial making test A, OR: 0.99, 95% CI: 0.90–1.02, p < 0.001), and a better psychosocial functioning (PSP total score: OR: 0.73, 95% CI: 0.98–1.02, p < 0.05) (Table 3).

Table 3. Generalized estimating equation (GEE) models.

GEE have been adjusted for diagnosis, pharmacological treatments, duration of illness, and years of education.

Abbreviations: B-MCCB, Brief MATRICS Consensus Cognitive Battery; BMI, body mass index; CI, confidence intervals; CIRS, Cumulative Illness Rating Scale; FRS, Framingham risk score; HOMA-IR, homeostatic model assessment of insulin resistance; PSP, Personal and Social Performance.

* p < 0.01.

** p < 0.01.

*** p < 0.001.

The GEE model also showed a significant effect of the experimental intervention on body weight and waist circumference. In fact, people receiving the experimental intervention had a 4.78 times increase in the probability to significantly reduce their body weight (OR = 4.78, 95% CI: 0.80–28.27, p < 0.05) and a 5.43 times increase in the probability to significantly reduce waist circumference (OR = 5.43, 95% CI: 1.45–20.30, p < 0.05). Moreover, several factors including male gender (OR = 5.02, 95% CI: 5.32–6.12, p < 0.001), high cognitive functioning (B-MCCB symbol coding, OR: 0.95, 95% CI: 0.80–1.13, p < 0.01; B-MCCB Trial making test A, OR: 0.96, 95% CI: 0.76–1.03, p < 0.01), and better psychosocial functioning (PSP total score, OR: 0.88; 95% CI: 0.75–1.4) had a positive impact on the reduction of body weight. Similarly, male gender (OR = 5.80, 95% CI: 3.94–6.01, p < 0.05), age (OR: 1.22, 95% CI: 1.13–1.32, p < 0.001), a better psychosocial functioning (PSP total score, OR: 0.99, 95% CI: 0.98–0.99, p < 0.01), and a better cognitive functioning (B-MCCB animal naming, OR: 0.98, 95% CI: 0.97–0.98, p < 0.001) had a significant impact on the efficacy of the intervention.

At the GEE models, the experimental intervention did not have a significant impact on the probability of reducing the FRS, the HOMA-IR index, and the CIRS severity indexes (Table 3). In all GEE models, the effect of the intervention was controlled for several confounding variables, including diagnosis, pharmacological treatments, duration of illness, and years of education.

Discussion

The LIFESTYLE trial represents the first multicenter study carried out in Italy aiming to assess the impact of a psychosocial intervention targeting lifestyle behaviors in people with SMI in real-world settings. Effective interventions for improving physical health in people with severe mental disorders are highly needed in order to reduce the premature mortality in this vulnerable group of people [11].

The main aim of the study was to evaluate the efficacy of the LIFESTYLE intervention, compared to a brief educational program, in reducing BMI of overweight patients with SMI. Our findings confirmed our hypothesis, with a significant reduction in BMI, body weight, and waist circumference in patients receiving the experimental intervention. Our results also support the recent finding that people with SMI can achieve healthy lifestyle through the provision of behavioral programs [Reference Nieste, Franssen, Spaas, Bruckers, Savelberg and Eijnde67].

We selected the BMI as primary outcome since it is a reliable and easy to assess anthropometric parameter that can provide stable and useful information compared to body weight changes [Reference Sampogna, Fiorillo, Luciano, Del Vecchio, Steardo and Pocai59]. In the present trial, treated patients reported a significant reduction in BMI of almost two points, confirming the efficacy of the intervention. When comparing our results with those derived from other non-pharmacological interventions for lifestyle behaviors, we found higher weight losses in the ACHIEVE [Reference Daumit, Dalcin, Dickerson, Miller, Evins and Cather42] and in the STRIDE [Reference Yarborough, Leo, Stumbo, Perrin and Green68] trials, of 3.2 and 4.4 kg, respectively. On the contrary, other RCTs, such as the STEPWISE [Reference Gossage-Worrall, Hind, Barnard-Kelly, Shiers, Etherington and Swaby69] and CHANGE trials [Reference Speyer, Christian Brix Nørgaard, Birk, Karlsen, Storch Jakobsen and Pedersen44,Reference Speyer, Nørgaard, Hjorthøj, Madsen, Drivsholm and Pisinger70], did not find any effect of the intervention on weight losses [Reference Reynolds71]. For this reason, we believe that a reduction of BMI may be a more reliable outcome measure, which should be adopted to evaluate the efficacy of lifestyle interventions [Reference Firth, Teasdale, Allott, Siskind, Marx and Cotter16].

Another significant finding of our study is the efficacy of the experimental intervention on waist circumference. Differently from BMI and body weight, waist circumference is a specific proxy measure of abdominal obesity, which is highly correlated with the lifetime risk of cardiovascular disorders [Reference Ross, Neeland, Yamashita, Shai, Seidell and Magni72].

We had a quite high drop-out rate both in the experimental and in the control group. However, this finding is in line with previous trials on behavioral interventions, where attrition rates of up to 40% were reported, even with brief interventions [Reference Speyer, Christian Brix Nørgaard, Birk, Karlsen, Storch Jakobsen and Pedersen44,Reference Ross, Neeland, Yamashita, Shai, Seidell and Magni73]. Moreover, this finding is also similar to that reported with other psychosocial interventions for people with SMI [Reference Speyer, Christian Brix Nørgaard, Birk, Karlsen, Storch Jakobsen and Pedersen44,Reference Speyer, Nørgaard, Hjorthøj, Madsen, Drivsholm and Pisinger70,Reference Ross, Neeland, Yamashita, Shai, Seidell and Magni73]. Several strategies could improve the rate of participants who complete the psychoeducational intervention; in particular, such electronic reminders (e.g., phone calls, emails, instant messages), availability of dedicated staff members and of rooms/spaces to run the intervention, and more time for professionals to run the intervention [Reference Fiorillo, Del Vecchio, Luciano, Sampogna, De Rosa and Malangone49,Reference Masa-Font, Fernández-San-Martín, Martín López, Alba Muñoz, Oller Canet and Martín Royo74,Reference Del Vecchio, Luciano, Malangone, Giacco, De Rosa and Sampogna75]. Future implementation strategies should include web-based components with the integration of smartphone apps and wearable devices, for increasing real-time interactions with participants [Reference Fiorillo, Del Vecchio, Luciano, Sampogna, Sbordone and Catapano76].

The efficacy of the experimental intervention was influenced by several clinical domains, such as poor cognitive performance and impaired psychosocial functioning, but not by others, such as the levels of psychiatric symptoms and the number of hospitalizations. It is likely that patients with SMI presenting with a worse performance on recall, verbal, and working memory, have a high BMI and a significantly high risk for cardiovascular diseases [Reference Luciano, Sampogna, Del Vecchio, Giallonardo, Palummo and Andriola37, Reference Foguet-Boreu, Guàrdia Sancho, Santos Lopez, Roura Poch, Palmarola Ginesta and Puig-Ribera77Reference de Raykeer R, Hoertel, Blanco, Lavaud, Kaladjian and Blumenstock79] due to cognitive deficits in recalling the medical appointments and in taking medications properly. However, only a few studies have assessed the impact of cognitive performance on the efficacy of behavioral intervention focused on weight reduction, and this should be further explored in subsequent studies. Moreover, patients with cognitive deficits may be less motivated to participate and to be actively involved in interventions for the promotion of their physical health [Reference de Raykeer R, Hoertel, Blanco, Lavaud, Kaladjian and Blumenstock80,Reference Galderisi, Rucci, Mucci, Rossi, Rocca and Bertolino81]. Therefore, cognitive deficits may represent the mechanisms underlying poor physical health in patients with SMI, independently from the main psychiatric diagnosis and symptoms.

The relationship between physical health and patients’ social functioning has been explored only in a few studies. Patients with reduced social functioning have a higher risk of developing physical illnesses due to poor skills in help seeking [Reference Sahakian and Savulich82] and to low levels of physical and daily activities [Reference Harvey and Strassnig83]. Patients with poor psychosocial functioning have a reduced autonomy in performing daily tasks, in engaging in complex behavioral changes, and in participating in social activities, highlighting that an improvement of psychosocial functioning may result in an improvement of physical health.

In this article, we have not evaluated the differences between the three diagnostic groups in response to the psychosocial intervention; however, the multivariable models have been adjusted according to patients’ diagnoses, and therefore we can assume that there is not a diagnosis effect, but this finding needs confirmation [Reference Luciano, Sampogna, Del Vecchio, Giallonardo, Palummo and Andriola37], supporting the hypothesis of a transdiagnostic approach to mental healthcare [Reference Reininghaus, Böhnke, Chavez-Baldini, Gibbons, Ivleva and Clementz84Reference McGorry and Nelson86].

The main strengths of the LIFESTYLE intervention, compared to already available interventions for people with SMI, include the following: (a) the integration of psychoeducational, motivational, and cognitive-behavioral techniques, with the motivational component resulting as one of the most important strategies to support behavioral changes and promote weight loss [Reference Zygmunt, Olfson, Boyer and Mechanic87Reference Priebe and Miglietta89]; (b) a comprehensive approach addressing almost all components of healthy living, such as healthy diet, reduction of sedentary behaviors, promotion of physical exercise, retaining from tobacco smoking and risky behaviors, improving sleep hygiene and promoting the regularization of circadian rhythms, and increasing medication adherence; (c) the provision of the intervention in a group format for patients with different diagnoses, which allowed us to compare patients’ experiences and lifestyles not directly linked with the mental disorder; and (d) the inclusion of the recommendations on the management of physical health provided by the World Health Organization, the European Association for Study of Diabetes, the European Society of Cardiology, and the European Psychiatric Association.

Among the study limitations, we must acknowledge the high drop-out rate, which reduces the magnitude of our results. However, the final sample size can be considered satisfactory if compared with already available studies, also because there were no differences at baseline between completers and drop-outs. Furthermore, we did not assess the time of exposure to psychotropic agents, but only the class of pharmacological agents prescribed during the study. However, the multivariable models were controlled for the impact of pharmacological treatments and only stable patients already in charge to the local mental health center for at least 3 months have been included in the study in order to overcome the possible bias due to the effect of medications. Finally, the efficacy of the intervention has been tested only at 6-month follow-up; however, according to the study protocol [Reference Sampogna, Fiorillo, Luciano, Del Vecchio, Steardo and Pocai59] we aim to assess the long-term efficacy of the intervention at 12 and 24 months of follow-up. Another possible limitation is the adoption of the BPRS to assess the severity of clinical symptoms. We are aware that this tool may have not captured some disease-specific clinical features. However, the BPRS is a well-known instrument frequently used in ordinary psychiatric settings, and it can be used by mental health professionals with different background and following a relatively short training. Another possible limitation is the recruitment of patients with a BMI≥25, which may have reduced the generalizability of our findings. We recruited overweight or obese patients in order to select those at higher risk of developing comorbidities or with compromised lifestyle behaviors already present at the beginning of the intervention. However, next steps would be to provide the intervention regardless the BMI, in order to test its efficacy as a preventive intervention. Finally, it must be acknowledged that all patients were receiving a pharmacological treatment, which can have metabolic side-effect profiles, leading to weight gain, increase in BMI and waist circumference, and alterations in lipid and glucose profiles. However, in order to overcome the possible bias due to the effect of medications, only patients in a stable phase and on a stable medication regimen for at least 3 months have been included in the study. Moreover, we have also controlled the multivariable models for the effect of pharmacological treatments. In our real-world sample 26% of patients received three or more psychotropic drugs, which can be defined as “polypragmasy,” that is the desire to enhance the efficiency of treatment and to help the patient to recover leading to the use of a large number of medications. Such data should be carefully considered in order to improve the good clinical practice in everyday real-world mental health services. Clinicians should be aware that the prescription of polypharmacotherapy can have a negative impact on the patient’s physical health, with a reduction of tolerability of each specific prescribed medication and with the risk of further reduce the efficacy of psychosocial interventions.

Our findings confirm that nonpharmacological interventions improve the physical health of overweight patients with SMI in routine clinical care. In fact, in order to improve the implementation of our approach on a large scale, we decided not to include dieticians, physical trainers, or other professionals not directly present in mental health centers. Moreover, the approach can be provided to the majority of patients with SMI, since its efficacy has been independent from main diagnosis of patients.

These interventions should be better tailored on the unmet needs of patients [Reference Priebe and Miglietta90], as well as on the improvement of cognitive deficits and social functioning. New strategies to provide the interventions [Reference Barber and Solomonov91], including the use of new technologies and online sessions [Reference Linardon, Cuijpers, Carlbring, Messer and Fuller-Tyszkiewicz92Reference Herrman94], could further improve the acceptability and the feasibility of the intervention in real-world settings.

Author Contribution

Conceptualization: L.D.; Data curation: M.L., G.S., I.A., P.C., C.C., V.D.V., L.D., G.D.L., B.G., and V.G.; Investigation: M.L., G.S., I.A., P.C., C.C., V.D.V., G.D.L., B.G., V.G., and R.R.; Methodology: G.S., C.C., V.D.V., L.D., G.D.L., B.G., and V.G.; Formal analysis: G.S.; Funding acquisition: L.D.; Supervision: G.D.L. and A.R.; Writing-original draft: M.L.; Writing-review & editing: M.L., I.A., P.C., C.C., V.D.V., L.D., G.D.L., B.G., V.G., A.R., and R.R.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon request.

Acknowledgments

This work was supported by the Italian Ministry of Education, Universities and Research within the framework of the “Progetti di Rilevante Interesse Nazionale (PRIN)—year 2015” (Grant Number: 2015C7374S).

Conflicts of Interest

The authors have no conflicts of interest to declare.

Footnotes

LIFESTYLE Working Group: Carmen Ciampi, Lisa Giannelli, Carmela Palummo, Francesca Zinno (Department of Psychiatry, University of Campania “L. Vanvitelli”, Naples); Vitalba Calia, Piergiuseppe Di Palo, Marco Papalino (Department of Basic Medical Science, Neuroscience and Sense Organs, University of Bari “Aldo Moro”); Martino Belvederi Murri, Simone Cattedra, Domenico Zampogna (Section of Psychiatry, Department of Neuroscience, Ophthalmology, Genetics and Infant-Maternal Science, University of Genoa, Genoa, Italy); Luca Bonanni (Section of Psychiatry, Department of Biotechnological and Applied Clinical Sciences, University of L’Aquila); Filippo Maria Barberi, Annalisa Cordone, Virginia Pedrinelli (Department of Clinical and Experimental Medicine, University of Pisa); Emanuela Bianciardi, Cinzia Niolu (Department of System Medicine, University of Rome Tor Vergata).

References

van Os, J, Guloksuz, S, Vijn, TW, Hafkenscheid, A, Delespaul, P. The evidence-based group-level symptom-reduction model as the organizing principle for mental health care: time for change? World Psychiatry. 2019;18:8896.CrossRefGoogle Scholar
Walburg, FS, van Meijel, B, van Tulder, MW, Adriaanse, MC. Cost-effectiveness of a lifestyle intervention for people with a serious mental illness (SMILE): design of a pragmatic cluster-randomised controlled trial. BMC Psychiatry. 2019;19:151.CrossRefGoogle ScholarPubMed
Naslund, JA, Whiteman, KL, McHugo, GJ, Aschbrenner, KA, Marsch, LA, et al.Lifestyle interventions for weight loss among overweight and obese adults with serious mental illness: a systematic review and meta-analysis. Gen Hosp Psychiatry. 2017;47:83102.CrossRefGoogle ScholarPubMed
Fiorillo, A, Luciano, M, Pompili, M, Sartorius, N. Editorial: reducing the mortality gap in people with severe mental disorders: the role of lifestyle psychosocial interventions. Front Psychiatry. 2019;20(10):434.CrossRefGoogle Scholar
Thornicroft, G. Physical health disparities and mental illness: the scandal of premature mortality. Br J Psychiatry. 2011;199:441–2.CrossRefGoogle ScholarPubMed
Carrà, G, Bartoli, F, Carretta, D, Crocamo, C, Bozzetti, A, Clerici, M, et al. The prevalence of metabolic syndrome in people with severe mental illness: a mediation analysis. Soc Psychiatry Psychiatr Epidemiol. 2014;49:1739–46.CrossRefGoogle ScholarPubMed
Bartoli, F, Carrà, G, Crocamo, C, Carretta, D, Clerici, M. Bipolar disorder, schizophrenia, and metabolic syndrome. Am J Psychiatry. 2013;170:927–8.CrossRefGoogle ScholarPubMed
Plana-Ripoll, O, Musliner, KL, Dalsgaard, S, Momen, NC, Weye, N, Christensen, MK et al. Nature and prevalence of combinations of mental disorders and their association with excess mortality in a population-based cohort study. World Psychiatry. 2020;19:339–49.CrossRefGoogle Scholar
Taipale, H, Tanskanen, A, Mehtälä, J, Vattulainen, P, Correll, CU, Tiihonen, J. 20-year follow-up study of physical morbidity and mortality in relationship to antipsychotic treatment in a nationwide cohort of 62,250 patients with schizophrenia (FIN20). World Psychiatry. 2020;19:61–8.CrossRefGoogle Scholar
De Rosa, C, Sampogna, G, Luciano, M, Del Vecchio, V, Pocai, B, Borriello, G, et al. Improving physical health of patients with severe mental disorders: a critical review of lifestyle psychosocial interventions. Expert Rev Neurother. 2017;17:667–81.CrossRefGoogle ScholarPubMed
World Health Organization (WHO). Management of physical health conditions in adults with severe mental disorders. WHO Guidelines.Click here to enter text. https://www.who.int/mental_health/evidence/guidelines_physical_health_and_severe_mental_disorders/en/; 2018.Google Scholar
Hoang, U, Goldacre, MJ, Stewart, R. Avoidable mortality in people with schizophrenia or bipolar disorder in England. Acta Psychiatr Scand. 2003;127:195201.CrossRefGoogle Scholar
GBD 2016 DALYs and HALE Collaborators. Global, regional, and national disability-adjusted life-years (DALYs) for 333 diseases and injuries and healthy life expectancy (HALE) for 195 countries and territories, 1990–2016: a systematic analysis for the Global Burden of Disease Study. The Lancet. 2016;390:1260–344.Google Scholar
Fiorillo, A, Luciano, M, Del Vecchio, V, Sampogna, G, Obradors-Tarragó, C, Maj, M et al. Priorities for mental health research in Europe: a survey among national stakeholders’ associations within the ROAMER project. World Psychiatry. 2013;12:165–70.CrossRefGoogle ScholarPubMed
Kuppili, PP, Nebhinani, N. Role of integrated and multidisciplinary approach in combating metabolic syndrome in patients with severe mental illness. Indian J Psychol Med.2019;41:466–71.CrossRefGoogle ScholarPubMed
Firth, J, Teasdale, SB, Allott, K, Siskind, D, Marx, W, Cotter, J et al. The efficacy and safety of nutrient supplements in the treatment of mental disorders: a meta-review of meta-analyses of randomized controlled trials. World Psychiatry. 2019;18:308–24.CrossRefGoogle ScholarPubMed
Volkow, ND, Torrens, M, Poznyak, V, Sáenz, E, Busse, A, Kashino, W, et al.Managing dual disorders: a statement by the Informal Scientific Network, UN Commission on Narcotic Drugs. World Psychiatry. 2020;19:396–7.CrossRefGoogle ScholarPubMed
Drake, RE, Xie, H, McHugo, GJ. A 16-year follow-up of patients with serious mental illness and co-occurring substance use disorder. World Psychiatry. 2020;19:397–8.CrossRefGoogle ScholarPubMed
Di Forti, M. To legalize or not to legalize cannabis, that is the question! World Psychiatry. 2020;19:188–9.CrossRefGoogle ScholarPubMed
Solmi, M, Fornaro, M, Ostinelli, EG, Zangani, C, Croatto, G, Monaco, F et al. Safety of 80 antidepressants, antipsychotics, anti-attention-deficit/hyperactivity medications and mood stabilizers in children and adolescents with psychiatric disorders: a large scale systematic meta-review of 78 adverse effects. World Psychiatry 2020;19:214–32.CrossRefGoogle ScholarPubMed
Vancampfort, D, Firth, J, Correll, CU, Solmi, M, Siskind, D, De Hert, M et al. The impact of pharmacological and non-pharmacological interventions to improve physical health outcomes in people with schizophrenia: a meta-review of meta-analyses of randomized controlled trials. World Psychiatry. 2019;18:5366.CrossRefGoogle ScholarPubMed
Rognoni, C, Bertolani, A, Jommi, C. Second-generation antipsychotic drugs for patients with schizophrenia: systematic literature review and meta-analysis of metabolic and cardiovascular side effects. Clin Drug Investig. 2021;41:303–19.CrossRefGoogle ScholarPubMed
Correll, CU, Sikich, L, Reeves, G, Johnson, J, Keeton, C, Spanos, M et al. Metformin add-on vs. antipsychotic switch vs. continued antipsychotic treatment plus healthy lifestyle education in overweight or obese youth with severe mental illness: results from the IMPACT trial. World Psychiatry. 2020;19:6980.CrossRefGoogle ScholarPubMed
Schneider, M, Pauwels, P, Toto, S, Bleich, S, Grohmann, R, Heinze, M et al. Severe weight gain as an adverse drug reaction of psychotropics: data from the AMSP project between 2001 and 2016. Eur Neuropsychopharmacol. 2020;36:6071.CrossRefGoogle ScholarPubMed
Kishimoto, T, Hagi, K, Nitta, M, Kane, JM, Correll, CU. Long-term effectiveness of oral second-generation antipsychotics in patients with schizophrenia and related disorders: a systematic review and meta-analysis of direct head-to-head comparisons. World Psychiatry. 2019;18(2):208–24.CrossRefGoogle ScholarPubMed
Ebdrup, BH, Knop, FK. Weight gain on antipsychotics—a perfect storm of complex pathophysiology and psychopharmacology. Acta Psychiatr Scand. 2021;144:521–3.CrossRefGoogle Scholar
Hassan, S, Heinkel, S, Burton, A, Blackburn, R, McCloud, T, Ross, J et al. A qualitative study exploring the barriers and facilitators of implementing a cardiovascular disease risk reducing intervention for people with severe mental illness into primary care contexts across England: the ‘PRIMROSE’ trial. BMC Health Serv Res. 2020;20:753.CrossRefGoogle Scholar
Collins, PY. What is global mental health? World Psychiatry. 2020;19:265–6.CrossRefGoogle ScholarPubMed
Unutzer, J, Carlo, AD, Collins, PY. Leveraging collaborative care to improve access to mental health care on a global scale. World Psychiatry. 2020;19:36–7.CrossRefGoogle ScholarPubMed
Lawrence, D., Holman, C.D.J., Jablensky, A.V., Hobbs, M.S. 2003. Death rate from ischemic heart disease in Western Australian psychiatric patients 1980–1998. Br J Psychiatry. 182, 31–6.CrossRefGoogle ScholarPubMed
Thornicroft, G, Bakolis, I, Evans-Lacko, S, Gronholm, PC, Henderson, C, Kohrt, BAet al.Key lessons learned from the INDIGO global network on mental health related stigma and discrimination. World Psychiatry. 2019;18:229–30.CrossRefGoogle ScholarPubMed
Menon, V. Brain networks and cognitive impairment in psychiatric disorders. World Psychiatry. 2020;19(3):309–10.CrossRefGoogle ScholarPubMed
Maj, M, Stein, DJ, Parker, G, Zimmerman, M, Fava, GADe Hert, M, et al.The clinical characterization of the adult patient with depression aimed at personalization of management. World Psychiatry. 2020;19(3):269–93.CrossRefGoogle ScholarPubMed
Valtorta, N.K, Kanaan, M., Gilbody, S., Hanratty, B. Loneliness, social isolation and risk of cardiovascular disease in the English Longitudinal Study of Ageing. Eur J Prev Cardiol. 2018;25:1387–96.CrossRefGoogle ScholarPubMed
Mazzi, F, Baccari, F, Mungai, F, Ciambellini, M, Brescancin, L, Starace, F. Effectiveness of a social inclusion program in people with non-affective psychosis. BMC Psychiatry. 2018;18:179.CrossRefGoogle ScholarPubMed
Holt-Lunstad, J, Smith, TB, Baker, M, Harris, T, Stephenson, D. Loneliness and social isolation as risk factors for mortality: a meta-analytic review. Perspect Psychol Sci. 2015;10:227–37.CrossRefGoogle ScholarPubMed
Luciano, M, Sampogna, G, Del Vecchio, V, Giallonardo, V, Palummo, C, Andriola, Iet al.The impact of clinical and social factors on the physical health of people with severe mental illness: results from an Italian multicentre study. Psychiatry Res. 2021;303:114073.CrossRefGoogle ScholarPubMed
Bauer, IE, Gálvez, JF, Hamilton, JE, Balanzá-Martínez, V, Zunta-Soares, GB, Soares, JC, et al. Lifestyle interventions targeting dietary habits and exercise in bipolar disorder: a systematic review. J Psychiatr Res. 2016;74:17.CrossRefGoogle ScholarPubMed
Speyer, H, Jakobsen, AS, Westergaard, C, Nørgaard, HCB, Jørgensen, KB, Pisinger, Cet al.Lifestyle interventions for weight management in people with serious mental illness: a systematic review with meta-analysis, trial sequential analysis, and meta-regression analysis exploring the mediators and moderators of treatment effects. Psychother Psychosom. 2019;88:350–62.CrossRefGoogle ScholarPubMed
Marcos-Delgado, A, Hernández-Segura, N, Fernández-Villa, T, Molina, AJ, Martín, V. The effect of lifestyle intervention on health-related quality of life in adults with metabolic syndrome: a meta-analysis. Int J Environ Res Public Health. 2021;18:887.CrossRefGoogle ScholarPubMed
Verhaeghe, N, Clays, E, Vereecken, C, De Maeseneer, J, Maes, L, Van Heeringen, Cet al.Health promotion in individuals with mental disorders: a cluster preference randomized controlled trial. BMC Public Health. 2013;13:657.CrossRefGoogle ScholarPubMed
Daumit, GL, Dalcin, AT, Dickerson, FB, Miller, ER, Evins, AE, Cather, Cet al.Effect of a comprehensive cardiovascular risk reduction intervention in persons with serious mental illness: a randomized clinical trial. JAMA Netw Open. 2020;3(6):e207247.CrossRefGoogle ScholarPubMed
McEwen, BS. The untapped power of allostasis promoted by healthy lifestyles. World Psychiatry. 2020;19:57–8.CrossRefGoogle ScholarPubMed
Speyer, H, Christian Brix Nørgaard, H, Birk, M, Karlsen, M, Storch Jakobsen, A, Pedersen, K, et al.The CHANGE trial: no superiority of lifestyle coaching plus care coordination plus treatment as usual compared to treatment as usual alone in reducing risk of cardiovascular disease in adults with schizophrenia spectrum disorders and abdominal obesity. World Psychiatry. 2016;15:155–65.CrossRefGoogle ScholarPubMed
Cabassa, LJ, Ezell, JM, Lewis-Fernández, R. Lifestyle interventions for adults with serious mental illness: a systematic literature review. Psychiatr Serv. 2010;61(8):774–82.CrossRefGoogle ScholarPubMed
Alvarez-Jiménez, M, Hetrick, SE, González-Blanch, C, Gleeson, JF, McGorry, PD. Non-pharmacological management of antipsychotic-induced weight gain: systematic review and meta-analysis of randomised controlled trials. Br J Psychiatry. 2008;193:101–7.CrossRefGoogle ScholarPubMed
American Psychiatric Association. Structured Clinical Interview for DSM-5 (SCID-5). American Psychiatric Association Publishing; 2013.Google Scholar
Falloon, IRH. Family management of schizophrenia: a controlled study of clinical, social, family and economic benefits. Baltimore, MD: John Hopkins University Press; 1985.Google Scholar
Fiorillo, A, Del Vecchio, V, Luciano, M, Sampogna, G, De Rosa, C, Malangone, C, et al.Efficacy of psychoeducational family intervention for bipolar I disorder: A controlled, multicentric, real-world study. J Affect Disord. 2015;172:291–9.CrossRefGoogle ScholarPubMed
National Institute of Clinical Excellence. Behaviour change: general approaches: NICE; 2007.Google Scholar
Rogers, RW. A protection motivation theory of fear appeals and attitude change. J Psychol. 1975;91:93114. doi:10.1080/00223980.1975.9915803CrossRefGoogle Scholar
Miller, WR, Rose, GR. Toward a theory of motivational interviewing. Am Psychol. 2009;64:527–37. doi:10.1037/a0016830CrossRefGoogle Scholar
Hofmann, SG, Asnaani, A, Vonk, IJ, Sawyer, AT, Fang, A. The efficacy of cognitive behavioral therapy: a review of meta-analyses. Cognit Ther Res. 2012;36:427–40. doi: 10.1007/s10608-012-9476-1CrossRefGoogle ScholarPubMed
World Health Organization. Global Recommendations on Physical Activity for Health. 2010.Google Scholar
World Health Organization. Global Action Plan for the Prevention and Control of NCDs 2013–2020. 2013.Google Scholar
Rydén, L, Grant, PJ, Anker, SD, Berne, C, Cosentino, F, Danchin, N, et al.ESC guidelines on diabetes, pre-diabetes, and cardiovascular diseases developed in collaboration with the EASD: the Task Force on diabetes, prediabetes, and cardiovascular diseases of the European Society of Cardiology (ESC) and developed in collaboration with the European Association for the Study of Diabetes (EASD). Eur Heart J. 2013;34:3035–87. doi:10.1093/eurheartj/eht108Google Scholar
Piepoli, MF, Hoes, AW, Agewall, S, Albus, C, Brotons, C, Catapano, AL, et al.2016 European guidelines on cardiovascular disease prevention in clinical practice: The Sixth Joint Task Force of the European Society of Cardiology and Other Societies on Cardiovascular Disease Prevention in Clinical Practice (constituted by representatives of 10 societies and by invited experts) developed with the special contribution of the European Association for Cardiovascular Prevention & Rehabilitation (EACPR). Atherosclerosis. 2016:252:207–74. doi: 10.1093/eurheartj/ehw106CrossRefGoogle Scholar
De Hert, M, Dekker, JM, Wood, D, Kahl, KG, Holt, RI, Möller, HJ. Cardiovascular disease and diabetes in people with severe mental illness position statement from the European Psychiatric Association (EPA), supported by the European Association for the Study of Diabetes (EASD) and the European Society of Cardiology (ESC). Eur Psychiatry. 2009;24:412–24. doi:10.1016/j.eurpsy.2009.01.005CrossRefGoogle Scholar
Sampogna, G, Fiorillo, A, Luciano, M, Del Vecchio, V, Steardo, L Jr, Pocai, B, et al. A randomized controlled trial on the efficacy of a psychosocial behavioral intervention to improve the lifestyle of patients with severe mental disorders: study protocol. Front Psychiatry. 2018;9:235.CrossRefGoogle ScholarPubMed
Lukoff, D, Nuechterlein, KH, Ventura, J. Appendix A: manual for expanded Brief Psychiatric Rating Scale (BPRS). Schizophr Bull. 1986;12:594602.Google Scholar
Morosini, PL, Magliano, L, Brambilla, L, Ugolini, S, Pioli, R. Development, reliability and acceptability of a new version of the DSM-IV Social and Occupational Functioning Assessment Scale (SOFAS) to assess routine social functioning. Acta Psychiatr Scand. 2000;101:323–9.CrossRefGoogle ScholarPubMed
Kern, RS, Nuechterlein, KH, Green, MF, Baade, LE, Fenton, WS, Gold, JM, et al. The MATRICS consensus cognitive battery, part 2: co-norming and standardization. Am J Psychiatry. 2008;165:214–20.CrossRefGoogle ScholarPubMed
Nuechterlein, KH, Green, MF, Kern, RS, Baade, LE, Barch, DM, Cohen, JD, et al.The MATRICS Consensus Cognitive Battery, part 1: test selection, reliability, and validity. Am J Psychiatry. 2008;165:203–13.CrossRefGoogle ScholarPubMed
Linn, BS, Linn, MW, Gurel, L. Cumulative Illness Rating Scale. J Am Geriatr Soc. 1968;16:622–6.CrossRefGoogle ScholarPubMed
Freeman, AM, Pennings, N. Insulin resistance. In: StatPearls [Internet]. Treasure Island, FL: StatPearls Publishing; 2021Google Scholar
Andersson, C, Nayor, M, Tsao, CW, Levy, D, Vasan, RS. Framingham Heart Study: JACC Focus Seminar, 1/8. J Am Coll Cardiol. 2021;77:2680–92.CrossRefGoogle ScholarPubMed
Nieste, I, Franssen, WMA, Spaas, J, Bruckers, L, Savelberg, HHCM, Eijnde, BO. Lifestyle interventions to reduce sedentary behaviour in clinical populations: a systematic review and meta-analysis of different strategies and effects on cardiometabolic health. Prev Med. 2021;148:106593.CrossRefGoogle ScholarPubMed
Yarborough, BJ, Leo, MC, Stumbo, S, Perrin, NA, Green, CA. STRIDE: a randomized trial of a lifestyle intervention to promote weight loss among individuals taking antipsychotic medications. BMC Psychiatry. 2013;13:238.CrossRefGoogle ScholarPubMed
Gossage-Worrall, R, Hind, D, Barnard-Kelly, KD, Shiers, D, Etherington, A, Swaby, L, et al.STructured lifestyle education for people WIth SchizophrEnia (STEPWISE): mixed methods process evaluation of a group-based lifestyle education programme to support weight loss in people with schizophrenia. BMC Psychiatry. 2019;19:358.CrossRefGoogle ScholarPubMed
Speyer, H, Nørgaard, HCB, Hjorthøj, C, Madsen, TA, Drivsholm, S, Pisinger, C, et al.Protocol for CHANGE: a randomized clinical trial assessing lifestyle coaching plus care coordination versus care coordination alone versus treatment as usual to reduce risks of cardiovascular disease in adults with schizophrenia and abdominal obesity. BMC Psychiatry. 2015;15:119CrossRefGoogle ScholarPubMed
Reynolds, CF. Optimizing personalized management of depression: the importance of real-world contexts and the need for a new convergence paradigm in mental health. World Psychiatry. 2020;19(3):266–68.CrossRefGoogle ScholarPubMed
Ross, R, Neeland, IJ, Yamashita, S, Shai, I, Seidell, J, Magni, P, et al. Waist circumference as a vital sign in clinical practice: a consensus statement from the IAS and ICCR Working Group on Visceral Obesity. Nat Rev Endocrinol. 2020;16:177–89.CrossRefGoogle Scholar
Masa-Font, R, Fernández-San-Martín, MI, Martín López, LM, Alba Muñoz, AM, Oller Canet, S, Martín Royo, J, et al. The effectiveness of a program of physical activity and diet to modify cardiovascular risk factors in patients with severe mental illness after 3-month follow-up: CAPiCOR randomized clinical trial. Eur Psychiatry. 2015;30:1028–36.CrossRefGoogle ScholarPubMed
Del Vecchio, V, Luciano, M, Malangone, C, Giacco, D, De Rosa, C, Sampogna, G, et al. Implementing family psychoeducational intervention for bipolar I disorder in 11 Italian Mental Health Centres. Ital J Psychopathol. 2011;17:277–82.Google Scholar
Fiorillo, A, Del Vecchio, V, Luciano, M, Sampogna, G, Sbordone, D, Catapano, F, et al. Feasibility of a psychoeducational family intervention for people with bipolar I disorder and their relatives: results from an Italian real-world multicentre study. J Affect Disord. 2016;190:657–62.CrossRefGoogle ScholarPubMed
Andersson, G, Titov, N, Dear, BF, Rozental, A, Carlbring, P. Internet-delivered psychological treatments: from innovation to implementation. World Psychiatry. 2019;18:20–8.CrossRefGoogle Scholar
Foguet-Boreu, Q, Guàrdia Sancho, A, Santos Lopez, JM, Roura Poch, P, Palmarola Ginesta, J, Puig-Ribera, AM, et al. Association between cognitive impairment and cardiovascular burden in patients with severe mental disorder. Cogn Neuropsychiatry. 2020;25:13.CrossRefGoogle ScholarPubMed
Green, MF, Horan, WP, Lee, J. Nonsocial and social cognition in schizophrenia: current evidence and future directions. World Psychiatry. 2019;18:146–61.CrossRefGoogle ScholarPubMed
de Raykeer R, Pascal, Hoertel, N, Blanco, C, Lavaud, P, Kaladjian, A, Blumenstock, Y, et al. Effects of depression and cognitive impairment on quality of life in older adults with schizophrenia spectrum disorder: results from a multicenter study. J Affect Disord. 2019;256:164–75.CrossRefGoogle Scholar
Galderisi, S, Rucci, P, Mucci, A, Rossi, A, Rocca, P, Bertolino, A, et al. The interplay among psychopathology, personal resources, context-related factors and real-life functioning in schizophrenia: stability in relationships after 4 years and differences in network structure between recovered and non-recovered patients. World Psychiatry. 2020;19(1):8191.CrossRefGoogle ScholarPubMed
Sahakian, BJ, Savulich, G. Innovative methods for improving cognition, motivation and wellbeing in schizophrenia. World Psychiatry. 2019;18:168–70.CrossRefGoogle Scholar
Harvey, PD, Strassnig, MT. Cognition and disability in schizophrenia: cognition-related skills deficits and decision-making challenges add to morbidity. World Psychiatry. 2019;18(2):165–67.CrossRefGoogle ScholarPubMed
Falkai, P, Schmitt, A. The need to develop personalized interventions to improve cognition in schizophrenia. World Psychiatry. 2019;18:170.CrossRefGoogle ScholarPubMed
Reininghaus, U, Böhnke, JR, Chavez-Baldini, U, Gibbons, R, Ivleva, E, Clementz, BA, et al. Transdiagnostic dimensions of psychosis in the Bipolar-Schizophrenia Network on Intermediate Phenotypes (B-SNIP). World Psychiatry. 2019;18:6776.CrossRefGoogle Scholar
Fusar-Poli, P, Solmi, M, Brondino, N, Davies, C, Chae, C, Politi, P, et al. Transdiagnostic psychiatry: a systematic review. World Psychiatry. 2019;18:192207.CrossRefGoogle ScholarPubMed
McGorry, PD, Nelson, B. Transdiagnostic psychiatry: premature closure on a crucial pathway to clinical utility for psychiatric diagnosis. World Psychiatry. 2019;18:359–60.CrossRefGoogle ScholarPubMed
Zygmunt, A, Olfson, M, Boyer, CA, Mechanic, D. Interventions to improve medication adherence in schizophrenia. Am J Psychiatry. 2002;159:1653–64.CrossRefGoogle Scholar
Rubak, S, Sandbaek, A, Lauritzen, T, Christensen, B. Motivational interviewing: a systematic review and meta-analysis. Br J Gen Pract. 2005;55:305–12.Google ScholarPubMed
Priebe, S, Miglietta, E. Assessment and determinants of patient satisfaction with mental health care. World Psychiatry. 2019;18(1):3031.CrossRefGoogle ScholarPubMed
Barber, JP, Solomonov, N. Toward a personalized approach to psychotherapy outcome and the study of therapeutic change. World Psychiatry. 2019;18:291–2.CrossRefGoogle Scholar
Dixon, LB, Patel, SR. The application of implementation sciences to community mental health. World Psychiatry. 2020;19:173–4.CrossRefGoogle ScholarPubMed
Linardon, J, Cuijpers, P, Carlbring, P, Messer, M, Fuller-Tyszkiewicz, M. The efficacy of app-supported smartphone interventions for mental health problems: a meta-analysis of randomized controlled trials. World Psychiatry. 2019;18:325–36.CrossRefGoogle ScholarPubMed
Hickie, IB. The role of new technologies in monitoring the evolution of psychopathology and providing measurement-based care in young people. World Psychiatry. 2020;19:38–9.CrossRefGoogle ScholarPubMed
Herrman, H. The practice of psychiatry in health care and sustainable development: progress on the WPA Action Plan 2017–2020. World Psychiatry. 2020;19:256–7.CrossRefGoogle ScholarPubMed
Figure 0

Table 1. Sociodemographic and clinical characteristics of the sample (N = 402).

Figure 1

Table 2. Comparisons of health-related domains in the two groups.

Figure 2

Table 3. Generalized estimating equation (GEE) models.

Submit a response

Comments

No Comments have been published for this article.