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Randomized Controlled Trial
. 2024 Sep;274(6):1265-1276.
doi: 10.1007/s00406-024-01789-w. Epub 2024 Mar 29.

Predictors of adherence to exercise interventions in people with schizophrenia

Rebecca Schwaiger  1   2 , Isabel Maurus  1   2 , Moritz Lembeck  1   2 , Irina Papazova  3   2 , David Greska  1   2 , Susanne Muenz  1   2 , Eliska Sykorova  4   2 , Cristina E Thieme  4   2 , Bob O Vogel  5   2 , Sebastian Mohnke  5   2 , Charlotte Huppertz  6 , Astrid Roeh  3   2 , Katriona Keller-Varady  7 , Berend Malchow  8 , Henrik Walter  5   2 , Bernd Wolfarth  9   2 , Wolfgang Wölwer  10 , Karsten Henkel  6 , Dusan Hirjak  4   2 , Andrea Schmitt  1   11   2 , Alkomiet Hasan  3   2 , Andreas Meyer-Lindenberg  4   2 , Peter Falkai  1   12   2 , Lukas Roell  13   14
Affiliations
Randomized Controlled Trial

Predictors of adherence to exercise interventions in people with schizophrenia

Rebecca Schwaiger et al. Eur Arch Psychiatry Clin Neurosci. 2024 Sep.

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.

Keywords: Adherence; Exercise; Machine learning; Randomized-controlled trial; Schizophrenia.

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Conflict of interest statement

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.

Figures

Fig. 1
Fig. 1
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
Fig. 2
Fig. 2
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
Fig. 3
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
See this image and copyright information in PMC

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