Abstract
Exercise interventions are nowadays considered as effective add-on treatments in people with schizophrenia but are usually associated with high dropout rates. Therefore, the present study investigated potential predictors of adherence from a large multicenter study, encompassing two types of exercise training, conducted over a 6-month period with individuals with schizophrenia. First, we examined the role of multiple participants’ characteristics, including levels of functioning, symptom severity, cognitive performance, quality of life, and physical fitness. Second, we used K-means clustering to identify clinical subgroups of participants that potentially exhibited superior adherence. Last, we explored if adherence could be predicted on the individual level using Random Forest, Logistic Regression, and Ridge Regression. We found that individuals with higher levels of functioning at baseline were more likely to adhere to the exercise interventions, while other factors such as symptom severity, cognitive performance, quality of life or physical fitness seemed to be less influential. Accordingly, the high-functioning group with low symptoms exhibited a greater likelihood of adhering to the interventions compared to the severely ill group. Despite incorporating various algorithms, it was not possible to predict adherence at the individual level. These findings add to the understanding of the factors that influence adherence to exercise interventions. They underscore the predictive importance of daily life functioning while indicating a lack of association between symptom severity and adherence. Future research should focus on developing targeted strategies to improve adherence, particularly for people with schizophrenia who suffer from impairments in daily functioning.
Clinical trials registration The study of this manuscript which the manuscript is based was registered in the International Clinical Trials Database, ClinicalTrials.gov (NCT number: NCT03466112, https://clinicaltrials.gov/ct2/show/NCT03466112?term=NCT03466112&draw=2&rank=1) and in the German Clinical Trials Register (DRKS-ID: DRKS00009804.
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Introduction
Given the remarkable reduction in life expectancy of 10–20 years in people with schizophrenia [1,2,3], understanding the underlying factors contributing to their increased risk of premature death has become a critical area of research. Individuals with schizophrenia exhibit an elevated likelihood to develop diabetes [4], metabolic syndrome [5, 6], and cardiovascular diseases [3, 7]. While medication use [8] and genetic factors [9] contribute to this increased risk, lifestyle habits, including poor diet, smoking, and low physical activity levels, also exert a substantial influence [10, 11]. Therefore, addressing these factors and developing effective interventions is crucial for improving the overall health outcomes and reducing premature mortality among individuals with schizophrenia.
Integrating exercise interventions into the lives of individuals with schizophrenia holds great potential for positive outcomes. A scientometric analysis underscoring the importance of physical activity revealed a substantial body of evidence that has systematically shaped a significant research trend regarding the advantages of engaging in physical activity for preventing and treating severe mental disorders [12]. Exercise interventions reveal beneficial effects on overall cognitive performance [13,14,15,16], positive and negative symptoms [14, 17,18,19,20,21,22], depressive symptoms [14], levels of functioning [14, 19, 20], and quality of life [14, 17,18,19] among people with schizophrenia. Moreover, exercise interventions lead to improvements in several physical domains, including cardiovascular fitness [20, 23, 24], reduction in BMI [18, 19], and a tendency to reduce triglyceride levels [18].
In brief, exercise interventions for people with schizophrenia have a wide range of beneficial effects, covering both physical and mental domains.
However, despite their proven benefits, the issue of adherence and dropout rates is a barrier in implementing and sustaining interventions in people with schizophrenia. In particular, exercise interventions for individuals with schizophrenia are characterized by high dropout rates, spanning a range of approximately 30–80% [20, 25]. Importantly, participants cannot maximize the benefit of interventions unless they maintain adherence: for instance, substantial improvements in physical fitness, psychiatric symptoms, and overall functioning have been shown to be particularly present in individuals who successfully completed more than 50% of exercise sessions [26]. Beyond being important for clinical ameliorations of the individual patient, the dropout in clinical interventions contributes to an increased risk of re-hospitalization, which in turn increases the strain on public resources [27]. Moreover, dropout from studies introduces a strong risk for biased results, as the existing evidence relies heavily on participants who have successfully completed the intervention, potentially limiting the generalizability and validity of the findings [20].
With the evidence supporting the effectiveness of exercise interventions for individuals with schizophrenia [13,14,15,16,17,18,19,20,21,22,23,24] and the recognized difficulties in maintaining adherence to such interventions [20, 25], there is a need to identify potential predictors of adherence.
In investigations centered on exercise interventions for major depressive disorder, it has been observed that greater symptom severity [28, 29] and lower global functioning and quality of life are indicative of higher probabilities of dropout [30]. In older people, adherence to exercise interventions was positively associated with both physical ability [31] and body mass index (BMI) [32]. Further investigation into predictors of dropout from exercise interventions for people diagnosed with Parkinson’s disease indicated that the higher the cognitive functioning, the less likely was the dropout [33]. In sum, these studies highlight predictors of adherence in exercise interventions across diverse populations, including symptom severity, medication dosage, global functioning, quality of life, physical ability, BMI, and cognition.
The current study aims to enhance the understanding of potential predictors of adherence to exercise interventions in people with schizophrenia, based on the comprehensive data from a large multicenter randomized controlled clinical trial [34]. First, the influence of clinical baseline characteristics on adherence is explored, hypothesizing that higher levels of functioning, lower symptom severity, improved quality of life, lower BMI, and superior physical and cognitive scores are associated with better adherence to the exercise programs. Second, we aim to identify clinical subgroups of patients that differ in adherence. Lastly, we investigate whether adherence can be predicted on the individual level based on a combination of these various clinical characteristics.
Methods
Study design
The current investigation is based on data from the Enhancing Schizophrenia Prevention and Recovery through Innovative Treatments (ESPRIT) C3 study [34]. The ESPIRT C3 study is a multicenter randomized controlled trial that assessed the effects of aerobic exercise on various health outcomes in people with schizophrenia. A total of 180 participants were enrolled and randomly assigned to either aerobic endurance training (AET) or flexibility, strengthening, and balance training (FSBT). Participants in the AET group cycled on bicycle ergometers at a moderate exercise intensity level, determined through a lactate threshold test conducted prior to the intervention. Participants in the FSBT group participated in a series of exercises that addressed stretching, mobility, stability, balance, and relaxation techniques. Both groups underwent supervised exercise sessions up to three times per week, with each session lasting 40–50 min. The intervention spanned 26 weeks in total. Participants had the option to cancel training sessions without facing exclusion. After the intervention phase, there was a 26-week follow-up phase. The study was conducted at five hospitals in Germany, namely Ludwig-Maximilians-Universität Munich, Zentralinstitut für Seelische Gesundheit Mannheim, Charité Berlin, Haus der Universität Dusseldorf, and Rheinisch-Westfälische Technische Hochschule Aachen University. Further details on the study protocol, and the specific criteria for inclusion and exclusion can be found in the corresponding publication [35].
Outcome measurements
Two outcome variables were employed to evaluate adherence. The first variable was binary and indicated whether participants dropped out during the intervention phase or not (completion of visit 6). The second variable was continuous and represented the number of trainings completed by each participant.
Baseline characteristics
Baseline characteristics of the participants were assessed prior to the intervention onset and encompassed clinical symptom ratings, functional ratings, quality of life rating, neurocognitive ratings, and physical fitness ratings. For detailed information, see Table 1.
Statistical analysis
The data analysis for the current study was conducted in Python version 3.10.11. All statistical analyses were performed with a significance threshold of p = 0.05. To ensure data quality, a criterion of including features with less than 20% missing values was applied. For handling the remaining missing values, the K-Nearest Neighbors (KNN) imputation method [48] was employed (refer to supplementary material S1 for more details).
The neurocognitive ratings were combined to create a total cognition score (refer to supplementary materials S2).
To investigate the associations between baseline characteristics, such as clinical symptoms, functioning, quality of life, cognitive performance, and physical fitness (see Table 1), multiple Logistic Regression analyses were conducted for the outcome completion of visit 6, and multiple linear regression analyses were performed for the outcome number of trainings (refer to supplementary materials S3). The analysis incorporated several covariables, including age, gender, site, CPZ, intervention group, and years of education.
Next, principal component analysis (PCA) in combination with K-means clustering [49] was used to identify clinical subgroups. Once the subgroups were identified, pairwise Fisher’s tests were employed to determine if there were differences in adherence, measured by the outcome completion of visit 6. Furthermore, pairwise Mann–Whitney U tests were used to explore if the subgroups differed in adherence, as measured by the outcome number of trainings (refer to supplementary materials S4).
Third, the aim was to predict adherence at an individual level. Therefore, Logistic Regression models and Random Forest (RF) [50] classification models were used to predict the outcome completion of visit 6. In addition, Ridge Regression [51] and RF [50] Regression models were employed to predict the outcome number of trainings (refer to supplementary materials S5).
Results
Study participants
The study included a group of 180 participants with schizophrenia, comprising 103 men and 77 women with an age from 18 to 65 years. This group consisted of both inpatients and outpatients. Detailed information about the characteristics of the participants is available in Table 2.
Of the total participants, 74 (41.11%) successfully completed visit 6, while 106 (58.89%) did not. Furthermore, 16 participants (8.89%) were randomized, but did not undertake any training sessions. Then 73 (40.56%) subjects completed between 1 and 15 training sessions, 31 (17.22%) completed between 16 and 30 training sessions, 34 (18.89%) completed between 31 and 45 training sessions, 19 (10.56%) completed between 46 and 60 training sessions, and 7 (3.89%) completed between 61 and 75 training sessions (refer to supplementary materials S6).
Association between baseline characteristics and adherence
Investigations into the potential impact of baseline characteristics on the outcome variable number of training sessions revealed a significant association with the level of functioning. Among the three functioning scores analyzed, the FROGS score exhibited a statistically significant association (β = 0.436, CI = [0.145, 0.728], p = 0.004, pFDR = 0.029) with the number of training sessions. Participants with a ten-point higher score on the FROGS scale attended approximately four more training sessions on average. In addition, the SOFAS score showed a discernible trend suggesting a potential association with the number of training sessions, although it did not reach statistical significance after FDR correction (β = 0.246, CI = [0.043, 0.448], p = 0.034, pFDR = 0.070). The visualized results of the linear regression, depicting the relationship between the outcome variable number of trainings and the independent variables FROGS, SOFAS, and GAF, can be found in Fig. 1a.
Association between functioning scores and number of trainings or status of visit 6. FROGS, Functional Remission of General Schizophrenia; SOFAS, Social and Occupational Functioning Assessment Scale; GAF, Global Assessment of Functioning scale. a These plots show the associations between baseline assessments of FROGS, SOFAS, or GAF on the x-axis and the number of trainings completed on the y-axis. Each dot in these plots represents an individual participant, the straight line represents the linear regression line fitted to the data, and the shaded area indicates the confidence interval. b These plots show the associations between FROGS, SOFAS, or GAF on the x-axis and the number of trainings completed on the y-axis. Each dot in these plots represents an individual participant
However, other baseline characteristics, including cognition score, fitness ratings, and symptom severity, did not exhibit any significant association with the outcome variable (refer to Supplementary Material S7).
Furthermore, when investigating the association between baseline characteristics and the likelihood to complete visit 6, only the FROGS score (β = 0.470, CI = [0.132, 0.808], p = 0.006, pFDR = 0.052, OR = 1600) showed a trend after FDR correction. On average, each one-unit increase in the FROGS score was linked to a 1.6-fold increase in the odds of completing visit 6. However, the remaining functioning ratings, cognition score, fitness rating, and symptom severity ratings did not demonstrate any significant association with the completion of visit 6 (refer to Supplementary Material S7). The visualized results of the Logistic Regression with the outcome variable completion of visit 6 and the independent variables FROGS, SOFAS, and GAF can also be found in Fig. 1b.
Adherence disparities among clinical clusters
To explore potential clinical patterns that might contribute to increased adherence, unsupervised clustering to identify clinical subgroups of participants was conducted.
Five distinct clusters were identified, each characterized by unique participant profiles. The initial cluster was characterized by a pattern primarily marked by negative symptoms, younger participants, and a higher Childhood Trauma Score (CTS) in comparison to the remaining clusters. In the second cluster, participants displayed elevated functional levels, lower symptom severity, and the highest quality of life compared to the other groups. The third cluster comprised older participants, who received the highest medication dosage, exhibited a higher BMI, and engaged in more physical activity than other groups. Within the fourth cluster, participants demonstrated overall higher symptom severity, lower functioning, and fewer social contacts than other clusters. The fifth cluster encompassed participants with increased depressive symptoms, coupled with a high level of cognitive performance. Figure 2 visualizes these clusters. For more details, refer to the Supplementary Material S8.
Complex radar chart of the clusters, CPZ, chlorpromazine equivalents; IPAQ, International Physical Activity Questionnaire; BMI, body mass index; FROGS, Functional Remission of General Schizophrenia; PANSS, Positive and Negative Syndrome Scale; CDSS, Calgary Depression Scale for Schizophrenia. a Radar chart of subgroup with pronounced negative symptoms and pronounced Childhood Trauma Score. b Radar chart of high-functioning and low-symptom severity subgroup. c Radar chart of subgroup with pronounced positive symptoms, older participants, high CPZ, and high IPAQ. d Radar chart of subgroup with high symptom severity and low functioning. e Radar chart of subgroup with pronounced depressive symptoms and low quality of life
We identified a trend indicating that participants of cluster 2 attend more trainings than participants of cluster 4 (p = 0.025, pFDR = 0.249, effect size (Cohen’s d) = 0.289). In addition, there was a trend showing that participants in cluster 2 were more likely to complete visit 6 than participants in cluster 4 (p = 0.043, pFDR = 0.432, OR = 2.713, effect size (Cramer’s V) = 0.052) (Fig. 3).
a Each cluster is represented by a boxplot indicating the number of completed trainings. Dots on the plot represent individual participants within the respective cluster. b Bar plots are provided for each cluster, illustrating the percentage of participants who completed visit V6 (lower section) and those who did not complete visit 6 (upper section). The absolute number of participants is also displayed within the bars
Individual prediction of adherence
The results of all trained models are shown in Table 3. Neither of the trained machine learning models could predict accurately, indicating that predictions at the individual level were challenging given the sample size of 180 patients. Further details on the ML analysis are provided in S9.
Discussion
The present study investigated the potential of clinical baseline characteristics as predictors of adherence to exercise interventions in individuals with schizophrenia. Our findings revealed that participants with higher levels of daily life functioning at baseline demonstrated better adherence, whereas symptom severity, cognitive performance, quality of life, and physical conditions did not play an important role. Analysis of clinical subgroups revealed that participants characterized by high-functioning and low-symptom severity demonstrated better adherence compared to another subgroup, which comprised individuals with low functioning and high symptom severity.
Our results suggest that mainly levels of functioning in daily life are crucial regarding adherence to exercise interventions in people with schizophrenia. A previous meta-analysis [29] investigated clinical predictors such as age, gender, disorder duration, and symptom severity, but could not find any significant associations with dropout. The current study confirms this finding and additionally identifies levels of functioning to be essential regarding adherence to exercise. Functioning directly relates to an individual’s ability to carry out daily activities and engage in social, occupational, and personal roles successfully. When a person’s functioning is compromised, they may encounter challenges in planning and executing, managing their time efficiently. For example, people with low functioning could have problems to plan their exercise schedule and to organize their way to the gym. In contrast, patients with higher symptom severity but moderate impairments in functioning may still have the capacity and social support to participate in exercise interventions.
The link between functioning and adherence to exercise interventions in individuals with schizophrenia underscores the need to support those patients with lower functioning levels in maintaining their commitment. Such support could involve various behavioral interventions, like reminders through text messages or regular telephone calls. These interventions have shown significant improvements in medication adherence [52]. Another approach to consider is a token economy system with points or financial incentives. Prior research demonstrated the effectiveness of offering financial incentives in enhancing adherence to antipsychotic depot medication among individuals diagnosed with psychotic disorders [53]. Based on our practical experience, it is advisable to establish specific, measurable, and attainable individual objectives. Special attention to goal setting and alignment for individuals with schizophrenia and lower functional levels could increase adherence to exercise programs.
In addition to the examination of single baseline characteristics such as functioning, we identified five clinical clusters of patients with schizophrenia in our sample. These clusters included a resilient functioning group, a severe symptom group, a negative symptom burden group, a depressive symptom burden group, and an active and positive symptom burden group. A previous study identified three clinical subgroups of the participating individuals with schizophrenia; a group with high negative symptoms, a distress subgroup characterized by depressive symptoms and anxiety, along with elevated positive symptoms, and a subgroup with low symptoms and high functioning [54]. And a further study, which detected psychosis subgroups, identified five subgroups termed affective psychosis, suicidal psychosis, depressive psychosis, high-functioning psychosis, and severe psychosis [55]. The subgroups identified in the current work share several similarities with the subgroups found in these studies. In both, the present study and the earlier research, clinical subgroups based on the severity of specific symptoms, such as negative symptoms, depressive symptoms, and positive symptoms, were obtained. In addition, the concept of high-functioning subgroups is evident in both the current study and the earlier research.
When investigating which subgroup demonstrated better adherence to the exercise intervention, a notable trend emerged, indicating that the high-functioning group exhibited higher levels of exercise engagement and were more likely to complete the intervention compared to the severely ill group. These findings supported the idea that the level of functioning plays a crucial role in adherence to exercise interventions. As outlined above, the benefits of higher functioning, such as enhanced planning abilities and adherence to training appointments, can lead to the observed association. Surprisingly, the high-functioning and low-symptom group did not exhibit a distinct advantage in adherence compared to the groups with pronounced negative symptoms and pronounced CTS or pronounced positive symptoms. In these three subgroups, the level of functioning was very similar. The finding suggests that if the level of functioning is sufficiently high and exclusively negative, positive, or depressive symptoms are present, it did not seem to hinder adherence to the exercise intervention.
Attempts to utilize supervised machine learning models for generating individual predictions based on a combination of baseline characteristics resulted in suboptimal outcomes. The performance of these models in terms of classification was only marginally better than chance. Moreover, the results of the regression analysis indicated that the models’ performance was inferior to a simple prediction based on the mean of the outcome variable. These findings suggest overfitting, wherein the models perform well on the training dataset but poorly on the test dataset.
This phenomenon indicates a limitation of the current study. The limited size of the dataset is a challenge when applying machine learning techniques robustly [56]. The potential consequences of overfitting are reflected in poor generalization to the test data, ultimately contributing to the unsatisfactory results observed in the study. Despite having a relatively large dataset with a considerable number of participants, it is important to acknowledge that its size was not sufficient to run complex machine learning algorithms. A larger dataset would be necessary to ensure more reliable results and increase the generalizability of the findings. Furthermore, it is noteworthy that other potential predictors could influence adherence to exercise interventions. These include not only the intensity and duration of the intervention, motivation, and the expertise of the professionals administering the exercise program [29], but also factors like satisfaction with the training, preferences for specific exercises, and the perceived subjective benefits of the intervention. Another potential determinant influencing adherence to exercise interventions may be the patient’s status as either an inpatient or outpatient, as indicated by a recent meta-analysis highlighting the stronger effects of exercise interventions in outpatients compared to inpatients [57]. Interestingly, in our sample, symptom severity did not play a significant role in determining adherence. Therefore, it can be assumed that the distinction between inpatient and outpatient status may not be a crucial factor affecting adherence. A further limitation of the present study is the impact of the COVID-19 pandemic, as some participants may have been unable to attend training sessions due to infection or related limitations. This external factor introduces a potential bias in the adherence and completion rates observed in the study.
In conclusion, the present study revealed a positive association between higher levels of functioning and adherence to exercise interventions among individuals with schizophrenia. Enhancing adherence to exercise interventions is crucial, as these interventions offer multiple benefits in schizophrenia. Future research should focus on strategies to improve adherence, particularly for individuals with schizophrenia who have lower levels of functioning. Possible approaches may involve sending session reminders and considering the implementation of a token economy. Exploring and implementing such strategies may help to improve adherence rates and maximize the effectiveness of exercise interventions for individuals with schizophrenia.
Data availability
All analysis scripts and documentation sheets can be made available upon request.
References
Walker ER, McGee RE, Druss BG (2015) Mortality in mental disorders and global disease burden implications: a systematic review and meta-analysis. JAMA Psychiat 72(4):334–341. https://doi.org/10.1001/jamapsychiatry.2014.2502
Lawrence D, Hancock KJ, Kisely S (2013) The gap in life expectancy from preventable physical illness in psychiatric patients in Western Australia: retrospective analysis of population based registers. BMJ (Clin Res Ed) 346:f2539. https://doi.org/10.1136/bmj.f2539
Crump C, Winkleby MA, Sundquist K, Sundquist J (2013) Comorbidities and mortality in persons with schizophrenia: a Swedish national cohort study. Am J Psychiatry 170(3):324–333. https://doi.org/10.1176/appi.ajp.2012.12050599
Vancampfort D, Correll CU, Galling B, Probst M, de Hert M, Ward PB, Rosenbaum S, Gaughran F, Lally J, Stubbs B (2016) Diabetes mellitus in people with schizophrenia, bipolar disorder and major depressive disorder: a systematic review and large scale meta-analysis. World Psychiatry 15(2):166–174. https://doi.org/10.1002/wps.20309
Vancampfort D, Wampers M, Mitchell AJ, Correll CU, de Herdt A, Probst M, de Hert M (2013) A meta-analysis of cardio-metabolic abnormalities in drug naïve, first-episode and multi-episode patients with schizophrenia versus general population controls. World Psychiatry 12(3):240–250. https://doi.org/10.1002/wps.20069
Vancampfort D, Stubbs B, Mitchell AJ, de Hert M, Wampers M, Ward PB, Rosenbaum S, Correll CU (2015) 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 14(3):339–347. https://doi.org/10.1002/wps.20252
Fan Z, Wu Y, Shen J, Ji T, Zhan R (2013) Schizophrenia and the risk of cardiovascular diseases: a meta-analysis of thirteen cohort studies. J Psychiatr Res 47(11):1549–1556. https://doi.org/10.1016/j.jpsychires.2013.07.011
Correll CU, Detraux J, de Lepeleire J, de Hert M (2015) Effects of antipsychotics, antidepressants and mood stabilizers on risk for physical diseases in people with schizophrenia, depression and bipolar disorder. World Psychiatry 14(2):119–136. https://doi.org/10.1002/wps.20204
Andreassen OA, Djurovic S, Thompson WK, Schork AJ, Kendler KS, O’Donovan MC, Rujescu D, Werge T, van de Bunt M, Morris AP, McCarthy MI, Roddey JC, McEvoy LK, Desikan RS, Dale AM (2013) Improved detection of common variants associated with schizophrenia by leveraging pleiotropy with cardiovascular-disease risk factors. Am J Hum Genet 92(2):197–209. https://doi.org/10.1016/j.ajhg.2013.01.001
Firth J, Siddiqi N, Koyanagi A, Siskind D, Rosenbaum S, Galletly C, Allan S, Caneo C, Carney R, Carvalho AF, Chatterton ML, Correll CU, Curtis J, Gaughran F, Heald A, Hoare E, Jackson SE, Kisely S, Lovell K, Maj M, McGorry PD, Mihalopoulos C, Myles H, O’Donoghue B, Pillinger T, Sarris J, Schuch FB, Shiers D, Smith L, Solmi M, Suetani S, Taylor J, Teasdale SB, Thornicroft G, Torous J, Usherwood T, Vancampfort D, Veronese N, Ward PB, Yung AR, Killackey E, Stubbs B (2019) The lancet psychiatry commission: a blueprint for protecting physical health in people with mental illness. Lancet Psychiatry 6(8):675–712. https://doi.org/10.1016/S2215-0366(19)30132-4
Vancampfort D, Probst M, Scheewe T, de Herdt A, Sweers K, Knapen J, van Winkel R, de Hert M (2013) Relationships between physical fitness, physical activity, smoking and metabolic and mental health parameters in people with schizophrenia. Psychiatry Res 207(1–2):25–32. https://doi.org/10.1016/j.psychres.2012.09.026
Sabe M, Chen C, Sentissi O, Deenik J, Vancampfort D, Firth J, Smith L, Stubbs B, Rosenbaum S, Schuch FB, Solmi M (2022) Thirty years of research on physical activity, mental health, and wellbeing: a scientometric analysis of hotspots and trends. Front Public Health 10:943435. https://doi.org/10.3389/fpubh.2022.943435
Firth J, Stubbs B, Rosenbaum S, Vancampfort D, Malchow B, Schuch F, Elliott R, Nuechterlein KH, Yung AR (2017) Aerobic exercise improves cognitive functioning in people with schizophrenia: a systematic review and meta-analysis. Schizophr Bull 43(3):546–556. https://doi.org/10.1093/schbul/sbw115
Dauwan M, Begemann MJH, Heringa SM, Sommer IE (2016) Exercise improves clinical symptoms, quality of life, global functioning, and depression in schizophrenia: a systematic review and meta-analysis. Schizophr Bull 42(3):588–599. https://doi.org/10.1093/schbul/sbv164
Shimada T, Ito S, Makabe A, Yamanushi A, Takenaka A, Kawano K, Kobayashi M (2022) Aerobic exercise and cognitive functioning in schizophrenia: an updated systematic review and meta-analysis. Psychiatry Res 314:114656. https://doi.org/10.1016/j.psychres.2022.114656
Xu Y, Cai Z, Fang C, Zheng J, Shan J, Yang Y (2022) Impact of aerobic exercise on cognitive function in patients with schizophrenia during daily care: a meta-analysis. Psychiatry Res 312:114560. https://doi.org/10.1016/j.psychres.2022.114560
Ashdown-Franks G, Firth J, Carney R, Carvalho AF, Hallgren M, Koyanagi A, Rosenbaum S, Schuch FB, Smith L, Solmi M, Vancampfort D, Stubbs B (2020) Exercise as medicine for mental and substance use disorders: a meta-review of the benefits for neuropsychiatric and cognitive outcomes. Sports Med (Auckland, N.Z.) 50(1):151–170. https://doi.org/10.1007/s40279-019-01187-6
Vera-Garcia E, Mayoral-Cleries F, Vancampfort D, Stubbs B, Cuesta-Vargas AI (2015) A systematic review of the benefits of physical therapy within a multidisciplinary care approach for people with schizophrenia: an update. Psychiatry Res 229(3):828–839. https://doi.org/10.1016/j.psychres.2015.07.083
Fernández-Abascal B, Suárez-Pinilla P, Cobo-Corrales C, Crespo-Facorro B, Suárez-Pinilla M (2021) In- and outpatient lifestyle interventions on diet and exercise and their effect on physical and psychological health: a systematic review and meta-analysis of randomised controlled trials in patients with schizophrenia spectrum disorders and first episode of psychosis. Neurosci Biobehav Rev 125:535–568. https://doi.org/10.1016/j.neubiorev.2021.01.005
Firth J, Cotter J, Elliott R, French P, Yung AR (2015) A systematic review and meta-analysis of exercise interventions in schizophrenia patients. Psychol Med 45(7):1343–1361. https://doi.org/10.1017/S0033291714003110
Sabe M, Kaiser S, Sentissi O (2020) Physical exercise for negative symptoms of schizophrenia: systematic review of randomized controlled trials and meta-analysis. Gen Hosp Psychiatry 62:13–20. https://doi.org/10.1016/j.genhosppsych.2019.11.002
Vogel JS, van der Gaag M, Slofstra C, Knegtering H, Bruins J, Castelein S (2019) The effect of mind-body and aerobic exercise on negative symptoms in schizophrenia: a meta-analysis. Psychiatry Res 279:295–305. https://doi.org/10.1016/j.psychres.2019.03.012
Curcic D, Stojmenovic T, Djukic-Dejanovic S, Dikic N, Vesic-Vukasinovic M, Radivojevic N, Andjelkovic M, Borovcanin M, Djokic G (2017) Positive impact of prescribed physical activity on symptoms of schizophrenia: randomized clinical trial. Psychiatr Danub 29(4):459–465. https://doi.org/10.24869/psyd.2017.459
Vancampfort D, Rosenbaum S, Ward PB, Stubbs B (2015) Exercise improves cardiorespiratory fitness in people with schizophrenia: a systematic review and meta-analysis. Schizophr Res 169(1–3):453–457. https://doi.org/10.1016/j.schres.2015.09.029
Vancampfort D, Rosenbaum S, Schuch FB, Ward PB, Probst M, Stubbs B (2016) Prevalence and predictors of treatment dropout from physical activity interventions in schizophrenia: a meta-analysis. Gen Hosp Psychiatry 39:15–23. https://doi.org/10.1016/j.genhosppsych.2015.11.008
Scheewe TW, Backx FJG, Takken T, Jörg F, van Strater ACP, Kroes AG, Kahn RS, Cahn W (2013) Exercise therapy improves mental and physical health in schizophrenia: a randomised controlled trial. Acta Psychiatr Scand 127(6):464–473. https://doi.org/10.1111/acps.12029
Markowitz M, Karve S, Panish J, Candrilli SD, Alphs L (2013) Antipsychotic adherence patterns and health care utilization and costs among patients discharged after a schizophrenia-related hospitalization. BMC Psychiatry 13:246. https://doi.org/10.1186/1471-244X-13-246
Kruisdijk F, Hopman-Rock M, Beekman ATF, Hendriksen IJM (2020) Personality traits as predictors of exercise treatment adherence in major depressive disorder: lessons from a randomised clinical trial. Int J Psychiatry Clin Pract 24(4):380–386. https://doi.org/10.1080/13651501.2020.1787452
Stubbs B, Vancampfort D, Rosenbaum S, Ward PB, Richards J, Soundy A, Veronese N, Solmi M, Schuch FB (2016) Dropout from exercise randomized controlled trials among people with depression: a meta-analysis and meta regression. J Affect Disord 190:457–466. https://doi.org/10.1016/j.jad.2015.10.019
Monteiro FC, Schuch FB, Deslandes AC, Mosqueiro BP, Caldieraro MA, Fleck MPdA (2021) Factors associated with adherence to sports and exercise among outpatients with major depressive disorder. Trends Psychiatry Psychother 43(2):108–115. https://doi.org/10.47626/2237-6089-2019-0109
Coley N, Coniasse-Brioude D, Igier V, Fournier T, Poulain J-P, Andrieu S (2021) Disparities in the participation and adherence of older adults in lifestyle-based multidomain dementia prevention and the motivational role of perceived disease risk and intervention benefits: an observational ancillary study to a randomised controlled trial. Alzheimer’s Res Ther 13(1):157. https://doi.org/10.1186/s13195-021-00904-6
Picorelli AMA, Pereira LSM, Pereira DS, Felício D, Sherrington C (2014) Adherence to exercise programs for older people is influenced by program characteristics and personal factors: a systematic review. J Physiother 60(3):151–156. https://doi.org/10.1016/j.jphys.2014.06.012
Allen NE, Song J, Paul SS, Sherrington C, Murray SM, O’Rourke SD, Lord SR, Fung VSC, Close JCT, Howard K, Canning CG (2015) Predictors of adherence to a falls prevention exercise program for people with Parkinson’s disease. Movem Disord Clin Pract 2(4):395–401. https://doi.org/10.1002/mdc3.12208
ClinicalTrials.gov (2016) Identifier: NCT03466112. https://clinicaltrials.gov/ct2/show/NCT03466112
Maurus I, Hasan A, Schmitt A, Roeh A, Keeser D, Malchow B, Schneider-Axmann T, Hellmich M, Schmied S, Lembeck M, Keller-Varady K, Papazova I, Hirjak D, Topor CE, Walter H, Mohnke S, Vogel BO, Wölwer W, Schneider F, Henkel K, Meyer-Lindenberg A, Falkai P (2021) Aerobic endurance training to improve cognition and enhance recovery in schizophrenia: design and methodology of a multicenter randomized controlled trial. Eur Arch Psychiatry Clin Neurosci 271(2):315–324. https://doi.org/10.1007/s00406-020-01175-2
Kay SR, Fiszbein A, Opler LA (1987) The positive and negative syndrome scale (PANSS) for schizophrenia. Schizophr Bull 13(2):261–276. https://doi.org/10.1093/schbul/13.2.261
Addington D, Addington J, Schissel B (1990) A depression rating scale for schizophrenics. Schizophr Res 3(4):247–251. https://doi.org/10.1016/0920-9964(90)90005-R
Llorca P-M, Lançon C, Lancrenon S, Bayle F-J, Caci H, Rouillon F, Gorwood P (2009) The “Functional Remission of General Schizophrenia” (FROGS) scale: development and validation of a new questionnaire. Schizophr Res 113(2–3):218–225. https://doi.org/10.1016/j.schres.2009.04.029
Endicott J, Spitzer RL, Fleiss JL, Cohen J (1976) The global assessment scale. A procedure for measuring overall severity of psychiatric disturbance. Arch General Psychiatry 33(6):766–771. https://doi.org/10.1001/archpsyc.1976.01770060086012
Morosini PL, Magliano L, Brambilla L, Ugolini S, Pioli R (2000) 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 101(4):323–329
The Whoqol Group (1998) The World Health Organization Quality of Life Assessment (WHOQOL): development and general psychometric properties. Social Sci Med 46(12):1569–1585. https://doi.org/10.1016/s0277-9536(98)00009-4
Vakil E, Blachstein H (1993) Rey auditory-verbal learning test: Structure analysis. J Clin Psychol 49(6):883–890. https://doi.org/10.1002/1097-4679(199311)49:6%3c883:AID-JCLP2270490616%3e3.0.CO;2-6
Tewes U (1991) HAWIE-R. Hamburg-Wechsler-Intelligenztest für Erwachsene, Revision 1991; Handbuch und Testanweisung, 1. Aufl. Huber-Psychologie-Tests. Huber, Bern, Stuttgart, Toronto
Hurford IM, Marder SR, Keefe RSE, Reise SP, Bilder RM (2011) A brief cognitive assessment tool for schizophrenia: construction of a tool for clinicians. Schizophr Bull 37(3):538–545. https://doi.org/10.1093/schbul/sbp095
Ekman P, Friesen WV (1974) Detecting deception from the body or face. J Pers Soc Psychol 29(3):288–298. https://doi.org/10.1037/h0036006
Reed JC (1997) Reed HBC The Halstead—Reitan neuropsychological battery. pp 93–129
Maddison R, Ni Mhurchu C, Jiang Y, Vander Hoorn S, Rodgers A, Lawes CM, Rush E (2007) International Physical Activity Questionnaire (IPAQ) and New Zealand Physical Activity Questionnaire (NZPAQ): a doubly labelled water validation. The Int J Behav Nutr Phys Act 4:62. https://doi.org/10.1186/1479-5868-4-62
Jonsson P, Wohlin C (2004) An evaluation of k-nearest neighbour imputation using likert data. pp 108–118
Ding C, He X (2004) K-means clustering via principal component analysis. p 29
Breiman L (2001) Random forests. Mach Learn 45(1):5–32. https://doi.org/10.1023/A:1010933404324
Hoerl AE, Kennard RW (1970) Ridge regression: applications to nonorthogonal problems. Technometrics 12(1):69–82. https://doi.org/10.1080/00401706.1970.10488635
Loots E, Goossens E, Vanwesemael T, Morrens M, van Rompaey B, Dilles T (2021) Interventions to improve medication adherence in patients with schizophrenia or bipolar disorders: a systematic review and meta-analysis. Int J Environ Res Public Health. https://doi.org/10.3390/ijerph181910213
Noordraven EL, Wierdsma AI, Blanken P, Bloemendaal AFT, Staring ABP, Mulder CL (2017) Financial incentives for improving adherence to maintenance treatment in patients with psychotic disorders (money for medication): a multicentre, open-label, randomised controlled trial. Lancet Psychiatry 4(3):199–207. https://doi.org/10.1016/S2215-0366(17)30045-7
Dickinson D, Pratt DN, Giangrande EJ, Grunnagle M, Orel J, Weinberger DR, Callicott JH, Berman KF (2018) Attacking heterogeneity in schizophrenia by deriving clinical subgroups from widely available symptom data. Schizophr Bull 44(1):101–113. https://doi.org/10.1093/schbul/sbx039
Dwyer DB, Kalman JL, Budde M, Kambeitz J, Ruef A, Antonucci LA, Kambeitz-Ilankovic L, Hasan A, Kondofersky I, Anderson-Schmidt H, Gade K, Reich-Erkelenz D, Adorjan K, Senner F, Schaupp S, Andlauer TFM, Comes AL, Schulte EC, Klöhn-Saghatolislam F, Gryaznova A, Hake M, Bartholdi K, Flatau-Nagel L, Reitt M, Quast S, Stegmaier S, Meyers M, Emons B, Haußleiter IS, Juckel G, Nieratschker V, Dannlowski U, Yoshida T, Schmauß M, Zimmermann J, Reimer J, Wiltfang J, Reininghaus E, Anghelescu I-G, Arolt V, Baune BT, Konrad C, Thiel A, Fallgatter AJ, Figge C, von Hagen M, Koller M, Lang FU, Wigand ME, Becker T, Jäger M, Dietrich DE, Scherk H, Spitzer C, Folkerts H, Witt SH, Degenhardt F, Forstner AJ, Rietschel M, Nöthen MM, Mueller N, Papiol S, Heilbronner U, Falkai P, Schulze TG, Koutsouleris N (2020) An investigation of psychosis subgroups with prognostic validation and exploration of genetic underpinnings: the PsyCourse study. JAMA Psychiat 77(5):523–533. https://doi.org/10.1001/jamapsychiatry.2019.4910
Ying X (2019) An overview of overfitting and its solutions. J Phys Conf Ser 1168:22022. https://doi.org/10.1088/1742-6596/1168/2/022022
Gallardo-Gómez D, Noetel M, Álvarez-Barbosa F, Alfonso-Rosa RM, Ramos-Munell J, Del Pozo CB, Del Pozo-Cruz J (2023) Exercise to treat psychopathology and other clinical outcomes in schizophrenia: a systematic review and meta-analysis. Eur Psychiatry J Assoc Eur Psychiatr 66(1):e40. https://doi.org/10.1192/j.eurpsy.2023.24
Acknowledgements
The work was supported by the German Federal Ministry of Education and Research (BMBF) through the research network on psychiatric diseases ESPRIT (Enhancing Schizophrenia Prevention and Recovery through Innovative Treatments; coordinator, Andreas Meyer-Lindenberg; grant number, 01EE1407E) to AML, PF, AH, and AS. Furthermore, the study was supported by the Else Kröner-Fresenius Foundation with the Research College “Translational Psychiatry” to PF, AS, and IM (Residency/PhD track of the International Max Planck Research School for Translational Psychiatry [IMPRS-TP]). The “Studienstiftung des Deutschen Volkes” provided a PhD scholarship to LR. The Medical Faculty of the Ludwig-Maximilians-University provided a PhD scholarship to RS. The authors thank the Clinical Trials Centre Cologne (CTC Cologne) and the Institute for Medical Statistics and Computational Biology of the Medical Faculty of the University of Cologne for developing the database and the secure web-based randomization system and performing data management and monitoring.
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AS was an honorary speaker for TAD Pharma and Roche and a member of Roche advisory boards. AH is an editor of the German (DGPPN) schizophrenia treatment guidelines and first author of the WFSBP schizophrenia treatment guidelines; he has been on the advisory boards of and has received speaker fees from Janssen-Cilag, Lundbeck, Recordati, Rovi, and Otsuka. PF is a co-editor of the German (DGPPN) schizophrenia treatment guidelines and a co-author of the WFSBP schizophrenia treatment guidelines; he is on the advisory boards and receives speaker fees from Janssen, Lundbeck, Otsuka, Servier, and Richter. AML has disclosed receiving consultant fees and speaker fees from multiple organizations and institutions: Boehringer Ingelheim, Elsevier, Brainsway, Lundbeck Int. Neuroscience Foundation, Lundbeck A/S, Sumitomo Dainippon Pharma Co., Academic Medical Center of the University of Amsterdam, Synapsis Foundation-Alzheimer Research Switzerland, IBS Center for Synaptic Brain Dysfunction, Blueprint Partnership, University of Cambridge, Dt. Zentrum für Neurodegenerative Erkrankungen, Zürich University, Brain Mind Institute, L.E.K. Consulting, ICARE Schizophrenia, Science Advances, Foundation FondaMental, v Behring Röntgen Stiftung, The Wolfson Foundation, and Sage Therapeutics; in addition, he has received speaker fees from Lundbeck International Foundation, Paul Martini-Stiftung, Lilly Deutschland, Atheneum, Fama Public Relations, Institut d'investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Janssen-Cilag, Hertie Stiftung, Bodelschwingh-Klinik, Pfizer, Atheneum, University of Freiburg, Schizophrenia Academy, Hong Kong Society of Biological Psychiatry, Fama Public Relations, Spanish Society of Psychiatry, Italian Society of Biological Psychiatry, Reunions I Ciencia S.L. and Brain Center Rudolf Magnus UMC Utrecht. In addition, AML has received grants and awards, including the Prix Roger de Spoelberch grant and the CINP Lilly Neuroscience Clinical Research Award 2016. RS, IM, ML, IP, DG, SM, ES, CET, BOV, SM, CH, AR, KKV, BM, HW, BW, WW, KH, DH and LR report no conflicts of interest.
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Schwaiger, R., Maurus, I., Lembeck, M. et al. Predictors of adherence to exercise interventions in people with schizophrenia. Eur Arch Psychiatry Clin Neurosci (2024). https://doi.org/10.1007/s00406-024-01789-w
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DOI: https://doi.org/10.1007/s00406-024-01789-w