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. Author manuscript; available in PMC: 2019 Nov 1.
Published in final edited form as: Schizophr Res. 2018 Jun 13;201:400–405. doi: 10.1016/j.schres.2018.06.011

What prevents youth at clinical high risk for psychosis from engaging in physical activity? An examination of the barriers to physical activity

Raeana E Newberry a, Derek J Dean a,b,*, Madison D Sayyah a, Vijay A Mittal c,d,e,f,g
PMCID: PMC6252130  NIHMSID: NIHMS976009  PMID: 29907494

Abstract

Background

Exercise has increasingly been proposed as a healthful intervention prior to and after the onset of psychosis. There is some evidence to suggest that youth at clinical high risk (CHR) for psychosis are less physically active and report more barriers to engaging in exercise; however, there has been relatively limited empirical work documenting this phenomenon, and to date, relationships between physical activity, barriers, and clinical phenomenology have been unclear.

Methods

CHR (N = 51) and healthy control (N = 37) participants completed a structured clinical interview assessing attenuated psychotic symptoms and substance use, and an exercise survey that assessed current exercise practices, perceived physical fitness, and barriers related to engaging in exercise.

Results

CHR youth engaged in less physical activity, exhibited lower perception of fitness, and endorsed more barriers related to motivation for exercise. The CHR group showed significant negative correlations where lower perceptions of fitness were associated with increased negative, disorganized, and general symptoms. Decreased frequency of activity was related to more barriers of motivation. Interestingly, greater symptomatology in the CHR group was associated with more barriers of self-perception and motivation for engaging in exercise. However, findings suggested a nuanced relationship in this area; for example, increased physical activity was associated with increased substance use.

Conclusions

The results of the current study support the notion that sedentary behavior is common in CHR youth, and more broadly, provide an impetus to target motivation through supervised exercise and fitness tracking to promote the health and well-being of CHR individuals.

Keywords: clinical high risk, psychosis, exercise, physical fitness, barriers

1. Introduction

There has been growing attention to the physical health and well-being of people with serious mental illness. Specifically, several large, global meta-analytic studies suggest that people with psychosis are at increased risk for mortality due to health problems including type 2 diabetes, cardiovascular disease, and metabolic syndrome (Correll et al., 2017; Vancampfort et al., 2016, 2015). These health problems are associated with more time spent in sedentary activities, unhealthy lifestyle factors, and lower engagement in vigorous exercise (Carney et al., 2015; Stubbs et al., 2016b, 2016a; Vancampfort et al., 2017). Furthermore, there is evidence that more sedentary behavior and poor physical healthy is present prior to the onset of psychosis (Deighton and Addington, 2015; Mittal et al., 2013). Understanding physical activity and exercise habits in youth at clinical high risk (CHR) for psychosis is critical for prescribing better interventions that may decrease the risk of serious cardiometabolic problems later on.

The CHR period is a critical window for early intervention and prevention of more serious illness. Notably, a recent meta-analysis has noted that engaging in exercise may prevent more serious mental illness (Schuch et al., 2018). There is also evidence that exercise interventions may benefit cognitive functioning and neurodevelopment in CHR (Dean et al., 2017) and in psychosis populations (Kimhy et al., 2015; Malchow et al., 2015; Nuechterlein et al., 2016; Oertel-Knöchel et al., 2014; Oest et al., 2010; Sommer and Kahn, 2015; Vinogradov et al., 2009). Promoting exercise in the CHR population may be an important adjunct or standalone treatment option for prevention and perhaps improving risk factors such as impaired cognitive functioning in the development of psychosis.

Self-report studies indicate that CHR individuals endorse more barriers to engaging in exercise than healthy peers (Deighton and Addington, 2015). In this context, several domains of barriers have been proposed to explain why CHR individuals engage in less exercise, including access and availability, self-confidence in engaging in exercise, and motivation (Carney et al., 2017a; Deighton and Addington, 2015). However, to date there have been few empirical reports characterizing exercise behaviors or barriers in CHR youth. This is particularly relevant as patterns of physical activity engagement and reported barriers to exercise are heterogeneous in normative samples of adolescents and young adults (Deflandre et al., 2004).

In addition, our understanding of the clinical correlates of physical activity remains unclear. Among the small number of published papers evaluating activity in CHR youth, investigators have observed no relationship between physical activity and symptomatology (Deighton and Addington, 2015) and, conversely, that lower physical activity is linked to more severe symptoms (Koivukangas et al., 2010). Furthermore, substance use has been identified as a possible risk factor for increased symptomatology and may also be a prominent risk factor for poor cardiometabolic health in CHR youth (Carney et al., 2016b). Recent longitudinal work in a large CHR sample suggests that alcohol, cannabis, and tobacco are the most widely used substances and may be associated with a more severe course of symptoms (Addington et al., 2014; Auther et al., 2015, 2012; Buchy et al., 2015). However, the relationship between physical activity and substance use in CHR individuals remains unclear (Deighton and Addington, 2015).

Before we can define intervention points for CHR youth, it is necessary to have a clear understanding of what specific factors contribute to sedentary behavior in this population. The current study sought to replicate findings of decreased activity and increased barriers in CHR youth (Deighton and Addington, 2015), characterizing these phenomena in a large, multi-site independent sample, and in addition, aimed to shed light on the correlates of physical activity, barriers, symptoms, and substance use.

2. Materials and methods

2.1. Participants

CHR (N = 51) and healthy control (N = 37) participants between ages 12-24 (mean age = 19.90, SD = 2.30) completed the survey. CHR participants were recruited in Boulder, Colorado (N = 31) and Chicago, Illinois (N = 20). The healthy controls were recruited from Boulder, Colorado. Exclusion criteria consisted of head injury, neurological disorders, and the presence of an Axis I psychotic disorder. A psychotic disorder in a 1st degree relative was an exclusion criterion for controls. The protocol and informed consent procedures were approved by the University Institutional Review Board.

2.2. Clinical Interviews

The Structured Interview for Prodromal Syndromes (SIPS) (Miller et al., 1999) was administered to diagnose a CHR syndrome and rule out CHR symptoms in healthy controls. A total sum score for positive, negative, disorganized, and general symptom domains were used as an indicator of the respective dimensions of symptomatology. The Structured Clinical Interview for Axis-I DSM-IV Disorders (SCID) (First et al., 2002) was administered to rule out a psychosis diagnoses. Advanced doctoral student interviewers were trained over a 2-month period, and inter-rater reliabilities exceeded the minimum criterion of Kappa ≥ 0.80.

Substance use frequency for alcohol, cannabis, and tobacco was assessed using the Alcohol and Drug Use Scale (Drake et al., 1996). Clinical interviewers asked participants to rate the frequency of use for each substance in the last month from 1 (no use) to 5 (almost daily).

2.3 Physical activity

Participants completed a self-report survey designed by Deighton and Addington (2015) to assess physical activity in CHR individuals. The questionnaire is comprised of questions from several sources including the International Physical Activity Questionnaire: Short (Bergier et al., 2012), the World Health Organization Quality of Life questionnaire (Lucas-Carrasco, 2012), Motivations for Physical Activities Measure-Revised (Kane et al., 2012) and the Physical Self-Description Questionnaire (Simons et al., 2012). A total sum score for number of current fitness activities was recorded. From the list of current fitness activities, participants reported number of current fitness activities in terms of either group or individual activities. Group activities included team sports and group fitness classes. Individual activities included indoor/outdoor sports and strength/flexibility training. Participants rated frequency of engagement in activities that result in sweating or rapid heartrate in a typical week from 0 (rarely or never) to 3 (five or more times). Time spent exercising was measured from 0 (less than 30 minutes) to 3 (more than 60 minutes). Intensity of exercise was reported for how much exercise results in sweating or rapid heartrate from 0 (never while I am exercising) to 3 (always, every time I exercise). Participants indicated their current perceived fitness from 0 (poor) to 3 (excellent). A total of 20 barriers to physical activity were ranked as never, sometimes, or usually a barrier to exercise (scored 0-2). In order to examine hypothesized domains of barriers that might impact physical activity, barrier items were grouped into access and availability of exercise programs (e.g. lack of time, lack of transportation), self-perception related to engaging in physical activity (e.g., lack of skills/ability to do a certain type of exercise, failure to achieve exercise goals in the past), and motivation (e.g. lack of energy, lack of motivation).

2.4 Statistical analysis

Using a similar strategy to Deighton and Addington (2015), t-tests, chi-square tests, and Mann-Whitney U tests were conducted for demographic, physical activity, barriers, and substance use frequency between the CHR and healthy control groups. In order to rule out an effect of seasonality on the participant’s responses on the survey, the season that the participants completed the exercise questionnaire was recorded based on meteorological classification. Differences among seasons within the entire sample and CHR group alone were conducted using Chi-square and Kruskal-Wallace tests for measures of physical activity and barriers. In an effort to reduce the number of comparisons and potential for Type I error, correlations were only evaluated in CHR group; this approach is also in line with the goal to utilize the current descriptive research approach to inform future treatment efforts.

3. Results

3.1 Demographics between groups

There were no differences between CHR and healthy controls in age, gender, years of education, parent education or season in which participants completed the exercise survey. As expected, the CHR group was rated significantly higher on positive, negative, disorganized, and general symptoms compared to healthy controls. The CHR groups from Boulder and Chicago did not differ in regards to positive, negative, disorganized or general symptoms or activity variables (all p values > 0.15). There was no difference between CHR locations in regards to the season in which they completed the exercise survey. See Table 1.

Table 1.

Demographic characteristics of the sample. NS indicates not significant.

Healthy Control CHR Statistic p ≤ CHR Boulder CHR Chicago Statistic p ≤
Age
 Mean (SD) 19.86 (2.63) 19.92 (2.05) t(86) = 0.11 NS 19.61 (1.78) 20.40 (2.37) t(49) = 1.35 NS
Gender
 Male 15 29 17 12
 Female 22 22 14 8
 Total 37 51 χ2(1, N = 88) = 2.29 31 20 χ2(1, N = 51) = 0.13 NS
Education (years)
 Mean (SD) 13.68 (2.44) 13.56 (1.63) t(58.75) = 0.25 NS 13.38 (1.52) 13.83 (1.80) t(49) = 0.93 NS
Parent Education
 Mean (SD) 16.05 (2.60) 15.71 (2.75) t(86) = 0.58 NS 16.05 (2.52) 15.20 (3.07) t(49) = 1.08 NS
Season Completed Survey
 Spring 7 14 9 5
 Summer 11 9 7 2
 Autumn 12 10 6 4
 Winter 7 18 χ2(3, N = 88) = 5.47 NS 9 9 χ2(3, N = 51) = 2.04 NS
Symptoms
 Positive: Mean (SD) 0.30 (0.66) 11.63 (4.74) t(52.66) = 16.84 0.001 11.16 (4.91) 12.35 (4.49) t(49) = 0.87 NS
 Negative: Mean (SD) 0.24 (0.55) 9.02 (6.96) t(50.85) = 8.97 0.001 10.06 (7.92) 7.40 (4.88) t(49) = 1.49 NS
 Disorganized: Mean (SD) 0.27 (0.56) 4.86 (3.47) t(53.56) = 9.29 0.001 5.29 (3.88) 4.2 (2.67) t (49) = 1.10 NS
 General: Mean (SD) 0.59 (0.98) 7.06 (3.94) t(58.39) = 11.25 0.001 7.39 (4.09) 6.55 (3.73) t(49) = 0.74 NS
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There were no significant group differences in terms of frequency of alcohol use between CHR and healthy controls, U = 982.5, p = 0.8. The CHR group reported more frequent use of cannabis U = 1172.5, p = 0.03 and tobacco U = 1252, p = 0.001 than healthy controls. There were no significant differences between CHR participants from Boulder and Chicago in regards to alcohol, cannabis or tobacco frequency (all p values > 0.5).

3.2 CHR and healthy control group differences

Results indicated that the CHR group participated in significantly fewer physical activities U = 496.5, p ≤ 0.001 than healthy controls. On average, both CHR and healthy controls engaged in fewer group related activities (33% of sample) than individual activities (74% of sample). The CHR participants reported engaging in fewer group activities at a trend level χ2(1, N = 88) = 2.99, p = 0.08 and significantly fewer individual activities χ2(1, N = 88) = 5.76, p = 0.02 than healthy controls. There was a trend level group difference for frequency of moderate or vigorous activity, U = 724, p = 0.07 and the CHR group spends significantly less time exercising than healthy controls U = 684, p = 0.02 when they do engage in physical activity. The groups did not differ on reported level of exercise intensity. The CHR group reported significantly lower perceived fitness, U = 678, p ≤ 0.05 than healthy controls. There were no significant differences between CHR participants from Boulder and Chicago in terms of physical activity measures (all p values > 0.2). There were no significant differences among seasons for measures of physical activity in the study sample or within the CHR group alone (all p-values > 0.1).

There were no group differences in barriers of access or availability U = 1024, p = 0.5 or self-perception U = 984, p = 0.7. Notably, CHR individuals endorsed more barriers related to motivation compared to healthy controls, U = 1332, p < 0.001. There were significant group differences between CHR participants from Boulder and Chicago in regards to barriers of access and availability U = 205.5, p = 0.04, where CHR participants from Boulder (M = 4.03, SD = 2.43) reported fewer barriers of access and availability compared to CHR participants from Chicago (M = 5.35, SD = 2.87). There were no significant differences between CHR samples for barriers of self-perception or barriers of motivation. There were no significant differences among seasons for barrier domains in the sample and within the CHR group alone (all p-values > 0.5).

3.3 Correlation results

The total number of current activities was positively correlated with perceptions of fitness rs = 0.39, p = 0.005 and alcohol frequency rs = 0.3, p = 0.03. Activity frequency was positively correlated with perceptions of fitness rs = 0.6, p < 0.001 and tobacco frequency rs = 0.29, p = 0.04. Activity frequency was negatively correlated with barriers of motivation rs = −0.38, p = 0.006. Time spent exercising was positively correlated with intensity of exercise. Lower perceptions of fitness were correlated with more severe negative symptoms rs = −0.36, p = 0.008, disorganized symptoms rs = −0.35, p = 0.01, general symptoms rs = −0.42, p = 0.002, increased barriers of motivation rs = −0.68, p < 0.001, and increased barriers of self-perception rs = −0.35, p = 0.02. Positive symptoms were not correlated with any of the physical activity measures. Disorganized symptoms were positively correlated with barriers of self-perception rs = 0.47, p < 0.001 and barriers of motivation rs = 0.35, p = 0.01. General symptoms were also positively correlated with barriers of self-perception rs = 0.30, p = 0.03 and barriers of motivation rs = 0.28, p = 0.04. See Figure 2.

Figure 2.

Figure 2

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A correlation table showing the associations between measures of physical activity, barriers, symptoms, and substance use in the CHR group alone. Cells in red indicate a significant positive correlation while cells in blue indicate significant negative correlations, p < 0.05. The depth of the colors indicate the strength of the relationship, as illustrated by the vertical color key on the right-hand side of the graph.

4. Discussion

The current study replicated previous findings showing that CHR participants are less physically active and perceive more barriers to engaging in activity (Deighton and Addington, 2015). The replication of group differences in physical activity is largely consistent with previous qualitative studies examining physical activity in CHR populations (Carney et al., 2017a). While previous studies have noted that CHR individuals perceive more barriers to physical activity across multiple domains, the current study found that the CHR group identifies significantly more barriers related to motivation. The associations with perceived physical fitness, barriers related to self-perception and motivation, and symptoms point towards psychological processes that may partially explain why CHR individuals engage in less physical activity. Importantly, these results argue for continued motivational interviewing, goal setting, and fitness tracking within the context of individualized indoor/outdoor exercise and strength/weight lifting as potential targets of intervention for improving the health and well-being of CHR individuals.

The CHR participants endorsed similar amounts of external barriers to engaging in physical activity as the healthy controls, such as access to activities. It is important to note that the CHR individuals were from two different sites. While similar in most other physical activity domains, the difference in barriers of access and availability between the two CHR sites suggest that it is important to consider the environment when helping CHR youth develop exercise routines. The group differences in barriers to motivation between CHR and healthy controls suggests that psychological processes, such as motivation and perceptions of fitness, may be more prominent in this CHR sample and associated with negative, disorganized and general symptoms. These associations are consistent with the current understanding that health related lifestyle factors (i.e., physical and sedentary behavior, substance use) is associated with mental health domains such as negative symptoms, self-esteem and self-efficacy in patients with schizophrenia (Vancampfort et al., 2010).

Addressing mental health domains related to physical exercise is important for improving the well-being of CHR participants. The current study showed that negative, disorganized, and general symptoms were associated with more barriers of motivation and self-perceived competency engaging in exercise. This is relevant considering that recent meta-analyses in patients with schizophrenia highlight reduction of stress and improving fitness as primary motivational factors for engaging in exercise (Firth et al., 2016). Developments in intervention planning for CHR youth suggest that motivation, opportunity, and capability are necessary for behavioral change and effectual implementation of exercise as treatment prior to the onset of psychosis (Carney et al., 2016a; Yung and Firth, 2017). Overall, the results of the current study suggests that motivation and perceived fitness may be important targets for promoting exercise. Future interventions could target these domains by including motivational interviewing and goal-setting prior to and throughout physical activity interventions to maintain engagement. Supervised exercise may be helpful also for maintaining motivation and improving competency around best exercise practices (Dean et al., 2017; Firth et al., 2016; Vancampfort et al., 2018). Along with targeting motivation related factors, physical fitness tracking and identification of advancement in personal fitness goals may also be helpful for CHR individuals. Such tracking may allow CHR individuals to reflect on their fitness and identify objective markers of improvement towards their goals (Ridgers et al., 2016; Schaefer et al., 2016).

The finding of similar patterns of alcohol frequency and greater cannabis and tobacco frequency in the CHR is consistent with past work in this population (Buchy et al., 2015). It is interesting that alcohol frequency was associated with a greater number of current activities. Alcohol use in college-aged students is positively associated with more physical activity, which is partially explained by more involvement in social activities that follow physical activity behaviors (Leasure et al., 2015). Although exercise engagement overall was lower in the CHR group, the positive association found in the CHR group may be pointing towards age-consistent behavior in some of the participants. While drinking in adolescence poses several legal and health related problems for CHR individuals (Carney et al., 2017), the association with physical activity and alcohol use in this population provides an impetus for a more careful examination of engagement in activities and social relationships in CHR individuals (Robustelli et al., 2017). It is also notable that tobacco use was associated with more frequent activities in the CHR group. Past research looking at the relationship between tobacco and physical activity has noted mixed findings in regard to smoking and smokeless forms of tobacco (Terry-Mcelrath et al., 2011). We did not assess for the method of delivery for tobacco, although future work may wish to take this under consideration when discussing tobacco use. Overall, associations between physical activity and substance use require further investigation and longitudinal studies to better examine the impact on substances when considering interventions for promoting exercise behavior in CHR youth.

The present study benefits from having a large multi-site CHR sample, but longitudinal investigation of exercise behavior as psychosis develops is needed. The data in this sample were taken from participant self-report and are largely consistent with past research using the same survey (Deighton and Addington, 2015). The current survey provides information on physical activity in the context of moderate to vigorous exercise, (i.e., activity that produces a sweat and increased heartrate). While moderate to vigorous physical activity produces better cardiometabolic health outcomes (Ekelund et al., 2012), it would also be informative to gather information about the frequency, time spent, and intensity of exercise at each level of exertion (i.e., sedentary, light, moderate, vigorous) so that clinicians could provide more targeted recommendations for increasing physical activity in this population. Self-report surveys are limited by important factors including participant memory and response bias, and have been shown to be less reliable than objective measures, such as actigraphy, in SMI populations (Firth et al., 2017; Soundy et al., 2014). However, there are benefits to using self-report as it does not require expensive equipment, is easily administered and scored, and provides a good starting point for assessing physical activity. Multimethod analysis of activity, such as using self-report with actigraphy would be helpful for measuring physical activity in this population in future research.

The results of this study highlight domains that clinicians could consider when prescribing exercise to CHR participants. First, physical activity could be promoted using motivational interviewing and restructuring thoughts related to motivation. Second, providing supervised exercise opportunities or promoting objective measures of fitness through obtainable goals or fitness trackers could help promote more activity engagement in CHR populations.

Figure 1.

Figure 1

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A bar graph illustrating group differences in physical activity and barriers. Error bars indicate the standard error of the mean.

Acknowledgments

This work was supported by National Institutes of Health Grants R01MH094650 and R21/R33MH103231 to V.A.M.

None.

Footnotes

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Contributors

All authors developed the study concept and contributed to the study design. V.A.M. obtained funding for the study. Testing, data collection as well as data analysis and interpretation were performed by R.E.N, D.J.D, and M.D.S under the supervision of V.A.M. R.E.N and D.J.D drafted the paper; M.D.S and V.A.M provided the critical revisions. All authors approved the final version of the paper for submission.

Conflict of Interest

Dr. Mittal is a consultant with Takeda Pharmaceuticals. No other authors have conflicts to disclose.

References

  1. Addington J, Case N, Saleem MM, Auther AM, Cornblatt BA, Cadenhead KS. Substance use in clinical high risk for psychosis: A review of the literature. Early Interv Psychiatry. 2014 doi: 10.1111/eip.12100. https://doi.org/10.1111/eip.12100 [DOI] [PMC free article] [PubMed]
  2. Auther AM, Cadenhead KS, Carrión RE, Addington J, Bearden CE, Cannon TD, Mcglashan TH, Perkins DO, Seidman L, Tsuang M, Walker EF, Woods SW, Cornblatt BA. Alcohol confounds relationship between cannabis misuse and psychosis conversion in a high-risk sample. Acta Psychiatr Scand. 2015;132:60–68. doi: 10.1111/acps.12382. https://doi.org/10.1111/acps.12382 [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Auther AM, McLaughlin D, Carrión RE, Nagachandran P, Correll CU, Cornblatt BA. Prospective study of cannabis use in adolescents at clinical high risk for psychosis: Impact on conversion to psychosis and functional outcome. Psychol Med. 2012;42:2485–2497. doi: 10.1017/S0033291712000803. https://doi.org/10.1017/S0033291712000803 [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Bergier J, Kapka-Skrzypczak L, Biliński P, Paprzycki P, Wojtyła A. Physical activity of Polish adolescents and young adults according to IPAQ: a population based study. Ann Agric Environ Med. 2012;19:109–15. [PubMed] [Google Scholar]
  5. Buchy L, Cadenhead KS, Cannon TD, Cornblatt BA, Mcglashan TH, Perkins DO. Substance use in individuals at clinical high risk of psychosis 2275-2284. 2015 doi: 10.1017/S0033291715000227. https://doi.org/10.1017/S0033291715000227 [DOI] [PMC free article] [PubMed]
  6. Carney R, Bradshaw T, Yung AR. Physical health promotion for young people at ultra-high risk for psychosis: An application of the COM-B model and behaviour-change wheel 536-545. 2016a doi: 10.1111/inm.12243. https://doi.org/10.1111/inm.12243 [DOI] [PMC free article] [PubMed]
  7. Carney R, Bradshaw T, Yung AR. Monitoring of physical health in services for young people at ultra-high risk of psychosis. Early Interv Psychiatry. 2015:1–7. doi: 10.1111/eip.12288. https://doi.org/10.1111/eip.12288 [DOI] [PMC free article] [PubMed]
  8. Carney R, Cotter J, Bradshaw T, Firth J, Yung AR. Cardiometabolic risk factors in young people at ultra-high risk for psychosis: A systematic review and meta-analysis. Schizophr Res. 2016b;170:290–300. doi: 10.1016/j.schres.2016.01.010. https://doi.org/10.1016/j.schres.2016.01.010 [DOI] [PubMed] [Google Scholar]
  9. Carney R, Cotter J, Bradshaw T, Yung AR. Examining the physical health and lifestyle of young people at ultra-high risk for psychosis: A qualitative study involving service users, parents and clinicians. Psychiatry Res. 2017a;255:87–93. doi: 10.1016/j.psychres.2017.05.023. https://doi.org/10.1016/j.psychres.2017.05.023 [DOI] [PubMed] [Google Scholar]
  10. Carney R, Yung AR, Amminger GP, Bradshaw T, Glozier N, Hermens DF, Hickie IB, Killackey E, McGorry P, Pantelis C, Wood SJ, Purcell R. Substance use in youth at risk for psychosis. Schizophr Res. 2017b;181:23–29. doi: 10.1016/j.schres.2016.08.026. https://doi.org/10.1016/j.schres.2016.08.026 [DOI] [PubMed] [Google Scholar]
  11. Correll CU, Solmi M, Veronese N, Bortolato B, Rosson S, Santonastaso P, Thapa-Chhetri N, Fornaro M, Gallicchio D, Collantoni E, Pigato G, Favaro A, Monaco F, Kohler C, Vancampfort D, Ward PB, Gaughran F, Carvalho AF, Stubbs B. Prevalence, incidence and mortality from cardiovascular disease in patients with pooled and specific severe mental illness: a large-scale meta-analysis of 3,211,768 patients and 113,383,368 controls. World Psychiatry. 2017;16:163–180. doi: 10.1002/wps.20420. https://doi.org/10.1002/wps.20420 [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Dean DJ, Bryan AD, Newberry R, Gupta T, Carol E, Mittal VA. A supervised exercise intervention for you at risk for psychosis: an open-label pilot study. J Clin Psychiatry. 2017;77:22–27. doi: 10.4088/JCP.16m11365. https://doi.org/dx.doi.org/10.4088/JCP.14m09658 [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Deflandre A, Antonini PR, Lorant J. Perceived benefits and barriers to physical activity among children, adolescents and adults. Int J Sport Psychol. 2004;35:23–36. [Google Scholar]
  14. Deighton S, Addington J. Exercise practices in individuals at clinical high risk of developing psychosis. Early Interv Psychiatry. 2015;9:284–291. doi: 10.1111/eip.12107. https://doi.org/10.1111/eip.12107 [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Drake RE, Mueser KT, McHugo GJ, Sederer L. Clinician rating scales: alcohol use scale (AUS), drug use scale (DUS), and substance abuse treatment scale (SATS). Outcomes Assess. Clin Pract Balt Williams Wilkins. 1996:113–116. [Google Scholar]
  16. Ekelund U, Luan J, Sherar LB, Esliger DW, Griew P, Cooper A. Moderate to vigorous physical activity and sedentary time and cardiometabolic risk factors in children and adolescents. JAMA - J Am Med Assoc. 2012;307:704–712. doi: 10.1001/jama.2012.156. https://doi.org/10.1001/jama.2012.156 [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Firth J, Rosenbaum S, Stubbs B, Gorczynski P, Yung AR, Vancampfort D. Motivating factors and barriers towards exercise in severe mental illness: a systematic review and meta-analysis. Psychol Med. 2016;46:2869–2881. doi: 10.1017/S0033291716001732. https://doi.org/10.1017/S0033291716001732 [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Firth J, Stubbs B, Vancampfort D, Schuch FB, Rosenbaum S, Ward PB, Firth JA, Sarris J, Yung AR. The Validity and Value of Self-reported Physical Activity and Accelerometry in People With Schizophrenia: A Population-Scale Study of the UK Biobank. Schizophr Bull. 2017:4–11. doi: 10.1093/schbul/sbx149. https://doi.org/10.1093/schbul/sbx149 [DOI] [PMC free article] [PubMed]
  19. Kane I, Lee H, Sereika S, Brar J. Feasibility of pedometers for adults with schizophrenia: Pilot study. J Psychiatr Ment Health Nurs. 2012;19:8–14. doi: 10.1111/j.1365-2850.2011.01747.x. https://doi.org/10.1111/j.1365-2850.2011.01747.x [DOI] [PubMed] [Google Scholar]
  20. Kimhy D, Vakhrusheva J, Bartels MN, Armstrong HF, Ballon JS, Khan S, Chang RW, Hansen MC, Ayanruoh L, Lister A, Castrén E, Smith EE, Sloan RP. The impact of aerobic exercise on brain-derived neurotrophic factor and neurocognition in individuals with schizophrenia: A single-blind, randomized clinical trial. Schizophr Bull. 2015;41:859–868. doi: 10.1093/schbul/sbv022. https://doi.org/10.1093/schbul/sbv022 [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Koivukangas J, Tammelin T, Kaakinen M, Maki P, Moilanen I, Taanila A, Veijola J. Physical activity and fitness in adolescents at risk for psychosis within the Northern Finland 1986 Birth Cohort. Schizophr Res. 2010;116:152–158. doi: 10.1016/j.schres.2009.10.022. https://doi.org/10.1016/j.schres.2009.10.022 [DOI] [PubMed] [Google Scholar]
  22. Leasure JL, Neighbors C, Henderson CE, Young CM, Janiri L, Leasure JL. Considerable Evidence Indicates that. 2015;6:1–13. https://doi.org/10.3389/fpsyt.2015.00156 [Google Scholar]
  23. Lucas-Carrasco R. The WHO quality of life (WHOQOL) questionnaire: Spanish development and validation studies. Qual Life Res. 2012;21:161–165. doi: 10.1007/s11136-011-9926-3. https://doi.org/10.1007/s11136-011-9926-3 [DOI] [PubMed] [Google Scholar]
  24. Malchow B, Keller K, Hasan A, Dörfler S, Schneider-Axmann T, Hillmer-Vogel U, Honer WG, Schulze TG, Niklas A, Wobrock T, Schmitt A, Falkai P. Effects of Endurance Training Combined with Cognitive Remediation on Everyday Functioning, Symptoms, and Cognition in Multiepisode Schizophrenia Patients. Schizophr Bull. 2015;41:847–858. doi: 10.1093/schbul/sbv020. https://doi.org/10.1093/schbul/sbv020 [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Mittal VA, Gupta T, Orr JM, Pelletier-Baldelli A, Dean DJ, Lunsford-Avery JR, Smith AK, Robustelli BL, Leopold DR, Millman ZB. Physical activity level and medial temporal health in youth at ultra high-risk for psychosis. J Abnorm Psychol. 2013;122:1101–1110. doi: 10.1037/a0034085. https://doi.org/10.1037/a0034085 [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Nuechterlein KH, Ventura J, McEwen SC, Gretchen-Doorly D, Vinogradov S, Subotnik KL. Enhancing Cognitive Training Through Aerobic Exercise After a First Schizophrenia Episode: Theoretical Conception and Pilot Study. Schizophr Bull. 2016;42:S44–S52. doi: 10.1093/schbul/sbw007. https://doi.org/10.1093/schbul/sbw007 [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Oertel-Knöchel V, Mehler P, Thiel C, Steinbrecher K, Malchow B, Tesky V, Ademmer K, Prvulovic D, Banzer W, Zopf Y, Schmitt A, Hänsel F. Effects of aerobic exercise on cognitive performance and individual psychopathology in depressive and schizophrenia patients. Eur Arch Psychiatry Clin Neurosci. 2014;264:589–604. doi: 10.1007/s00406-014-0485-9. https://doi.org/10.1007/s00406-014-0485-9 [DOI] [PubMed] [Google Scholar]
  28. Oest M, Meyer T, Backens M, Schneider-axmann T. Hippocampal Plasticity in Response to Exercise in Schizophrenia. Arch Gen Psychiatry. 2010;67:133–143. doi: 10.1001/archgenpsychiatry.2009.193. https://doi.org/10.1001/archgenpsychiatry.2009.193 [DOI] [PubMed] [Google Scholar]
  29. Ridgers ND, McNarry MA, Mackintosh KA. Feasibility and Effectiveness of Using Wearable Activity Trackers in Youth: A Systematic Review. JMIR mHealth uHealth. 2016;4:e129. doi: 10.2196/mhealth.6540. https://doi.org/10.2196/mhealth.6540 [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Robustelli BL, Newberry RE, Whisman MA, Mittal VA. Social relationships in young adults at ultra high risk for psychosis. Psychiatry Res. 2017;247:345–351. doi: 10.1016/j.psychres.2016.12.008. https://doi.org/10.1016/j.psychres.2016.12.008 [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Schaefer SE, Ching CC, Breen H, German JB. Wearing, Thinking, and Moving: Testing the Feasibility of Fitness Tracking with Urban Youth. Am J Heal Educ. 2016;47:8–16. https://doi.org/10.1080/19325037.2015.1111174 [Google Scholar]
  32. Schuch FB, Vancampfort D, Firth J, Rosenbaum S, Ward PB, Silva ES, Hallgren M, Ponce De Leon A, Dunn AL, Deslandes AC, Fleck MP, Carvalho AF, Stubbs B. Physical Activity and Incident Depression: A Meta-Analysis of Prospective Cohort Studies. Am J Psychiatry appi ajp. 2018;2018:1. doi: 10.1176/appi.ajp.2018.17111194. https://doi.org/10.1176/appi.ajp.2018.17111194 [DOI] [PubMed] [Google Scholar]
  33. Simons J, Capio CM, Adriaenssens P, Delbroek H, Vandenbussche I. Self-concept and physical self-concept in psychiatric children and adolescents. Res Dev Disabil. 2012;33:874–881. doi: 10.1016/j.ridd.2011.12.012. https://doi.org/10.1016/J.RIDD.2011.12.012 [DOI] [PubMed] [Google Scholar]
  34. Sommer IE, Kahn RS. The Magic of Movement; The Potential of Exercise to Improve Cognition. Schizophr Bull. 2015;41:776–778. doi: 10.1093/schbul/sbv031. https://doi.org/10.1093/schbul/sbv031 [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Soundy A, Roskell C, Stubbs B, Vancampfort D. Selection, Use and Psychometric Properties of Physical Activity Measures to Assess Individuals with Severe Mental Illness: A Narrative Synthesis. Arch Psychiatr Nurs. 2014 doi: 10.1016/j.apnu.2013.12.002. https://doi.org/10.1016/j.apnu.2013.12.002 [DOI] [PubMed]
  36. Stubbs B, Firth J, Berry A, Schuch FB, Rosenbaum S, Gaughran F, Veronesse N, Williams J, Craig T, Yung AR, Vancampfort D. How much physical activity do people with schizophrenia engage in? A systematic review, comparative meta-analysis and meta-regression. Schizophr Res. 2016a doi: 10.1016/j.schres.2016.05.017. https://doi.org/10.1016/j.schres.2016.05.017 [DOI] [PubMed]
  37. Stubbs B, Williams J, Gaughran F, Craig T. How sedentary are people with psychosis? A systematic review and meta-analysis. Schizophr Res. 2016b doi: 10.1016/j.schres.2016.01.034. https://doi.org/10.1016/j.schres.2016.01.034 [DOI] [PubMed]
  38. Terry-Mcelrath YM, O’Malley PM, Johnston LD. Exercise and substance use among american youth, 1991-2009. Am J Prev Med. 2011;40:530–540. doi: 10.1016/j.amepre.2010.12.021. https://doi.org/10.1016/j.amepre.2010.12.021 [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Vancampfort D, Correll CU, Galling B, Probst M, De Hert M, Ward PB, Rosenbaum S, Gaughran F, Lally J, Stubbs B. Diabetes mellitus in people with schizophrenia, bipolar disorder and major depressive disorder: a systematic review and large scale meta-analysis. World Psychiatry. 2016;15:166–74. doi: 10.1002/wps.20309. https://doi.org/10.1002/wps.20309 [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Vancampfort D, Firth J, Schuch FB, Rosenbaum S, Mugisha J, Hallgren M, Probst M, Ward PB, Gaughran F, De Hert M, Carvalho AF, Stubbs B. 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. 2017;16:308–315. doi: 10.1002/wps.20458. https://doi.org/10.1002/wps.20458 [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Vancampfort D, Gorczynski P, De Hert M, Probst M, Naisiga A, Basangwa D, Mugisha J. Exercise self-efficacy correlates in people with psychosis. Psychiatry Res. 2018;262:359–362. doi: 10.1016/j.psychres.2017.08.095. https://doi.org/10.1016/j.psychres.2017.08.095 [DOI] [PubMed] [Google Scholar]
  42. Vancampfort D, Knapen J, Probst M, van Winkel R, Deckx S, Maurissen K, Peuskens J, De Hert M. Considering a frame of reference for physical activity research related to the cardiometabolic risk profile in schizophrenia. Psychiatry Res. 2010;177:271–279. doi: 10.1016/j.psychres.2010.03.011. https://doi.org/10.1016/j.psychres.2010.03.011 [DOI] [PubMed] [Google Scholar]
  43. Vancampfort D, Stubbs B, Mitchell AJ, De Hert M, Wampers M, Ward PB, Rosenbaum S, Correll CU. Risk of metabolic syndrome and its components in people with schizophrenia and related psychotic disorders, bipolar disorder and major depressive disorder: a systematic review and meta-analysis. World Psychiatry. 2015;14:339–47. doi: 10.1002/wps.20252. https://doi.org/10.1002/wps.20252 [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Vinogradov S, Fisher M, Holland C, Shelly W, Wolkowitz O, Mellon SH. Is Serum Brain-Derived Neurotrophic Factor a Biomarker for Cognitive Enhancement in Schizophrenia? Biol Psychiatry. 2009;66:549–553. doi: 10.1016/j.biopsych.2009.02.017. https://doi.org/10.1016/j.biopsych.2009.02.017 [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Yung AR, Firth J. How should physical exercise be used in schizophrenia treatment? Expert Rev Neurother. 2017;17:213–214. doi: 10.1080/14737175.2017.1275571. https://doi.org/10.1080/14737175.2017.1275571 [DOI] [PubMed] [Google Scholar]

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