Exercise therapy is a common supportive strategy in curative cancer treatment with strong evidence regarding its positive effects on, for example, cancer-related fatigue, health- related quality of life, and physical function. In the field of advanced cancer patients, knowledge about exercise as a useful supportive strategy is missing. The aim of this systematic review was to evaluate the feasibility and safety of exercise interventions as well as its effects on lowering the symptom burden. We included randomized controlled trials and nonrandomized controlled trials with advanced cancer patients receiving any type of exercise intervention. After an extensive literature search (in accordance to PRIMSA guidelines) in PubMed, Cochrane Library, and SPORTDiscus, 14 studies including 940 participants with different cancer entities were eligible. The results indicated the safety of exercise. In total, 493 participants received exercise interventions, with nine adverse events and no severe adverse events. The median recruitment rate was 68.33%, and adherence to exercise intervention was 86%. Further research with a high-quality and larger sample size is needed to clarify the potential of exercise with advanced cancer patients. Different advanced cancer entities have distinguished symptoms, and future research should construct entities-specific trial populations to figure out the best supportive exercise interventions.

Purpose Physical activity (PA) is recommended to improve advanced cancer patients’ (ACP) physical functioning, fatigue, and quality of life. Yet, little is known about ACPs’ attitude towards PA and its influence on fatigue and depressiveness over a longer period. This prospective, non-interventional cohort study examined ACPs’ fatigue, depression, motivation, and barriers towards PA before and after 12 months of treatment among ACP Methods Outpatients with incurable cancer receiving treatment at a German Comprehensive Cancer Center reporting moderate/severe weakness/tiredness during self-assessment via MIDOS II were enrolled. Fatigue (FACT-F), depression (PHQ-8), cancer-related parameters, self-assessed PA behavior, motivation for and barriers against PA were evaluated (T0). Follow-up data was acquired after 12 months (T1) using the same questionnaire. Results At follow-up, fatigue (p=0.017) and depressiveness (p=0.015) had increased in clinical relevant extent. Physically active ACP did not show significant progress of FACT-F (p=0.836) or PHQ-8 (p=0.799). Patient-reported barriers towards PA remained stable. Logistic regression analyses identified motivation as a positive predictor for PA at both time points (T0, β=2.152, p=0.017; T1, β =2.264, p=0.009). Clinically relevant depression was a negative predictor for PA at T0 and T1 (T0, β=−3.187, p=0.044; T1, β=−3.521, p=0.041). Conclusion Our findings emphasize the importance of psychological conditions in physical activity behavior of ACP. Since psychological conditions seem to worsen over time, early integration of treatment is necessary. By combining therapy approaches of cognitive behavioral therapy and exercise in interdisciplinary care programs, the two treatment options might reinforce each other and sustainably improve ACPs’ fatigue, physical functioning, and QoL. Trial registration German Register of Clinical Trials, DRKS00012514, registration date: 30.05.2017

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Zusammenfassung HintergrundDie wechselnde Symptomlast ist eine große Hürde in der Sporttherapie von onkologischen Palliativpatienten. Die täglich variierende Symptomstärke erschwert die Einstellung einer optimalen Trainingsbelastung und stellt neben der Motivation eine große Barriere für die Teilnahme an bewegungstherapeutischen Interventionen dar. Ein durch Künstliche Intelligenz (KI) gesteuertes Training könnte helfen, die Trainingseinheiten individuell anzupassen und die Autonomie von Palliativpatienten zu erhalten. Methoden Fünf Patienten mit fortgeschrittener unheilbarer Krebsdiagnose haben im Rahmen der Routineversorgung eine supervidierte Bewegungstherapie absolviert. Dabei wurde ein Elektrokardiogramm über einen Polar H10 Brustgurt aufgezeichnet und daraus kardiale und respiratorische Vitalparameter extrahiert. Eine Klassifikation in drei Intensitätsstufen über KI erfolgte anhand von neuronalen Netzen. Ergebnisse Das KI-gesteuerte Training hat eine sehr hohe Klassifikationsgüte (F1-Score: 0,95±0,05) durch die Vereinigung von respiratorischen und kardialen Vitalparametern. Diese Kombination erzielt genauere Klassifikationsergebnisse als die einzelnen Datensätze für kardiale Parameter (0,93±0,06) und respiratorische Parameter (0,72±0,06). Die Berücksichtigung einer Baselinemessung hat eine positive Wirkung auf die Klassifikationsgenauigkeit. Diskussion Diese Studie stellt die erste Untersuchung zum Einsatz von KI zur Klassifizierung von trainingswissenschaftlichen Inhalten bei onkologischen Palliativpatienten dar. Diese vulnerable Patientengruppe kann von einer objektiven Erfassung des Belastungsniveaus anhand von Parametern des kardiovaskulären Systems profitieren. Mit nur fünf Patienten wird die Aussagekraft dieser explorativen Studie über Kreuzvalidierung hergestellt. Zukünftig sollen weitere Parameter wie ein subjektives Empfinden, Alter, Größe und Geschlecht die Klassifikation weiter verbessern. In einem integrierten System ist eine individuelle Trainingssteuerung in Echtzeit möglich.