Virtually in synch: a pilot study on affective dimensions of dancing with Parkinson’s during COVID-19

ABSTRACT

Loss of social supports and community programs due to lockdowns and other measures associated with COVID-19 has been linked to concerns over mental health and feelings of isolation. These challenges can be particularly acute for the elderly and people living with chronic or pervasive health conditions. Dance for PD, a program specifically developed for people living with Parkinson’s Disease, formerly offered in hundreds of locations around the globe, either halted or shifted to a virtual format. Our study investigates the transition of these dance-based programs to an online environment, with the aim of determining the extent to which a virtual format provides affective support or other benefits. Given the increased incidence of mental health problems and social isolation associated with COVID-19, this investigation aims to contribute to the development of better supports for vulnerable populations while helping us understand the specific contributions of dance-based programs in a virtual environment.

KEYWORDS:

• Parkinson’s disease
• music
• dance
• zoom
• mood

Acknowledgments

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Correction Statement

This article has been corrected with minor changes. These changes do not impact the academic content of the article.

Additional information

Notes on contributors

Katayoun Ghanai

Katayoun Ghanai, a classical pianist, a certified RCM piano teacher and PhD candidate in Musicology at York University in Canada, completing the Graduate Diploma Program in Neuroscience. Her background includes statistics (MA Economics) and medicine (Associate Anesthesiology). Her research concentrates on integrating diverse and ancient knowledge pool and neuroscience to better understand the positive effects of music. Working with an interdisciplinary research group under Dr. Joseph DeSouza of York University’s Faculty of Health, Katayoun’s research concentrates on the concept of healing through music. This investigation is currently focused on the analysis of the effects of dancing and singing on the elderly and patients diagnosed with Parkinson’s or Alzheimer’s.

Rebecca E. Barnstaple

Rebecca E. Barnstaple is a Postdoctoral Fellow at York University engaged in research on the neurobiological effects of dance in health and disease. A graduate of the National Centre for Dance Therapy at Les Grands Ballets Canadiens in Montreal (2015), she provides dance-therapy based programs for people with chronic pain, Parkinson’s, and Alzheimer’s, and is a core instructor for IMPROVment (http://www.improvment.us). Rebecca is a member of the steering committee of Dance Movement Therapy Ontario, the Research and Practice committee of the American Dance Therapy association, and the Groupe d’intérêt scientifique of the NCDT.

Joseph FX DeSouza

Joseph FX DeSouza, PhD is an Associate Professor in the Department of Psychology & Graduate programs in Biology, Interdisciplinary Studies and Neuroscience Graduate Diploma Program at York University’s Centre for Vision Research. He received his PhD in Neuroscience at the University of Western Ontario in 2001, followed by postdoctoral training at the Robarts Research Institute and the Centre for Vision Research. Since 2006, his lab (http://www.joeLAB.com) has focused on how multisensory signals are attended and/or suppressed depending on behavioural contexts, and how decisions are made through improvised or trained motor movements (eye, hand, body and/or dance). His lab’s translational research with people with Parkinson’s Disease, depression, chronic pain and Problem Gamblers aims to show how and where brain networks change as a function of neurorehabilitation. His lifelong pursuit is to ease the burden of disease and identify associated neuroimaging biomarkers through computational modelling.