Article type
Year
Abstract
Background:
Non-pharmacological treatment options, especially physical interventions, are known to be effective in the management of movement disorders such as Parkinson’s disease improving motor functioning and quality of life. In this broad field, it is still not clear which types of physical intervention (e.g., gait training, Tai Chi, cycling) are most effective in specific patient groups. Network meta-analyses (NMA) have become a popular method to address this question. However, clustering these interventions when conducting NMA can be challenging due to their high complexity, overlapping intervention components and missing information on the specific intervention content.
Objectives:
To present an approach of clustering physical interventions for patients with Parkinson’s disease within a NMA that allows the integration of interventions that are highly diverse with respect to the training modality, environment, use of devices, and further features.
Methods:
We conducted a systematic search for randomized controlled trials (RCTs) of physical interventions for patients with Parkinson’s disease and clustered eligible trials using pre-existing approaches to categorize physical exercise for the elderly. We took an approach that had been developed for falls prevention trials in the elderly (ProFaNE taxonomy, Lamb 2011) as a basis, adapted the original categories and added new categories to integrate eligible interventions that could not be matched clearly to any of the original categories.
Results:
The original ProFaNE taxonomy specified five categories of structured exercise: Gait, balance, functional training; strength/resistance; flexibility; three-dimensional (3D) exercise (e.g. Tai Chi, dance); endurance. We separated the original category 3D into mind-body and dance which we considered distinct interventions, and added water-based training as a third 3D category in order to integrate interventions delivered in an aquatic setting. For the integration of structured physical interventions delivered using a virtual reality (VR) device which was not covered by any of the existing categories, we added the category VR.
Conclusions:
Our adaptation of the pre-existing taxonomy allows to cluster a wide range of physical interventions in NMA. Therefore, a more realistic picture of current non-pharmacological physical interventions can be represented in analyses comparing several treatment approaches. Our operationalization of each cluster may help trial investigators describe their interventions more precisely. The adapted system may be used when synthesizing evidence on physical interventions in other diseases.
Patient or healthcare consumer involvement:
Within the scope of our overall project investigating physical interventions in patients with Parkinson’s disease, we separately conduct focus group discussions with patients and providers of physical interventions to get further insight on the potential and the subjective meaning of physical interventions as a treatment option for Parkinson’s disease.
Non-pharmacological treatment options, especially physical interventions, are known to be effective in the management of movement disorders such as Parkinson’s disease improving motor functioning and quality of life. In this broad field, it is still not clear which types of physical intervention (e.g., gait training, Tai Chi, cycling) are most effective in specific patient groups. Network meta-analyses (NMA) have become a popular method to address this question. However, clustering these interventions when conducting NMA can be challenging due to their high complexity, overlapping intervention components and missing information on the specific intervention content.
Objectives:
To present an approach of clustering physical interventions for patients with Parkinson’s disease within a NMA that allows the integration of interventions that are highly diverse with respect to the training modality, environment, use of devices, and further features.
Methods:
We conducted a systematic search for randomized controlled trials (RCTs) of physical interventions for patients with Parkinson’s disease and clustered eligible trials using pre-existing approaches to categorize physical exercise for the elderly. We took an approach that had been developed for falls prevention trials in the elderly (ProFaNE taxonomy, Lamb 2011) as a basis, adapted the original categories and added new categories to integrate eligible interventions that could not be matched clearly to any of the original categories.
Results:
The original ProFaNE taxonomy specified five categories of structured exercise: Gait, balance, functional training; strength/resistance; flexibility; three-dimensional (3D) exercise (e.g. Tai Chi, dance); endurance. We separated the original category 3D into mind-body and dance which we considered distinct interventions, and added water-based training as a third 3D category in order to integrate interventions delivered in an aquatic setting. For the integration of structured physical interventions delivered using a virtual reality (VR) device which was not covered by any of the existing categories, we added the category VR.
Conclusions:
Our adaptation of the pre-existing taxonomy allows to cluster a wide range of physical interventions in NMA. Therefore, a more realistic picture of current non-pharmacological physical interventions can be represented in analyses comparing several treatment approaches. Our operationalization of each cluster may help trial investigators describe their interventions more precisely. The adapted system may be used when synthesizing evidence on physical interventions in other diseases.
Patient or healthcare consumer involvement:
Within the scope of our overall project investigating physical interventions in patients with Parkinson’s disease, we separately conduct focus group discussions with patients and providers of physical interventions to get further insight on the potential and the subjective meaning of physical interventions as a treatment option for Parkinson’s disease.