Archana Podury1,2, Sophia M Raefsky3, Lucy Dodakian3, Liam McCafferty2, Vu Le3, Alison McKenzie3,4, Jill See3, Robert J Zhou3, Thalia Nguyen3, Benjamin Vanderschelden3, Gene Wong3, Laila Nazarzai3, Jutta Heckhausen3,5, Steven C Cramer6,7, Amar Dhand1,2,8. 1. Harvard Medical School, Boston, MA, United States. 2. Department of Neurology, Brigham and Women's Hospital, Boston, MA, United States. 3. Department of Neurology, University of California, Irvine, Irvine, CA, United States. 4. Department of Physical Therapy, Chapman University, Orange, CA, United States. 5. Department of Psychological Science, University of California, Irvine, Irvine, CA, United States. 6. Department of Neurology, University of California, Los Angeles, Los Angeles, CA, United States. 7. California Rehabilitation Institute, Los Angeles, CA, United States. 8. Network Science Institute, Northeastern University, Boston, MA, United States.
Abstract
Objective: Telerehabilitation (TR) is now, in the context of COVID-19, more clinically relevant than ever as a major source of outpatient care. The social network of a patient is a critical yet understudied factor in the success of TR that may influence both engagement in therapy programs and post-stroke outcomes. We designed a 12-week home-based TR program for stroke patients and evaluated which social factors might be related to motor gains and reduced depressive symptoms. Methods: Stroke patients (n = 13) with arm motor deficits underwent supervised home-based TR for 12 weeks with routine assessments of motor function and mood. At the 6-week midpoint, we mapped each patient's personal social network and evaluated relationships between social network metrics and functional improvements from TR. Finally, we compared social networks of TR patients with a historical cohort of 176 stroke patients who did not receive any TR to identify social network differences. Results: Both network size and network density were related to walk time improvement (p = 0.025; p = 0.003). Social network density was related to arm motor gains (p = 0.003). Social network size was related to reduced depressive symptoms (p = 0.015). TR patient networks were larger (p = 0.012) and less dense (p = 0.046) than historical stroke control networks. Conclusions: Social network structure is positively related to improvement in motor status and mood from TR. TR patients had larger and more open social networks than stroke patients who did not receive TR. Understanding how social networks intersect with TR outcomes is crucial to maximize effects of virtual rehabilitation.
Objective: Telerehabilitation (TR) is now, in the context of COVID-19, more clinically relevant than ever as a major source of outpatient care. The social network of a patient is a critical yet understudied factor in the success of TR that may influence both engagement in therapy programs and post-stroke outcomes. We designed a 12-week home-based TR program for strokepatients and evaluated which social factors might be related to motor gains and reduced depressive symptoms. Methods:Strokepatients (n = 13) with arm motor deficits underwent supervised home-based TR for 12 weeks with routine assessments of motor function and mood. At the 6-week midpoint, we mapped each patient's personal social network and evaluated relationships between social network metrics and functional improvements from TR. Finally, we compared social networks of TRpatients with a historical cohort of 176 strokepatients who did not receive any TR to identify social network differences. Results: Both network size and network density were related to walk time improvement (p = 0.025; p = 0.003). Social network density was related to arm motor gains (p = 0.003). Social network size was related to reduced depressive symptoms (p = 0.015). TRpatient networks were larger (p = 0.012) and less dense (p = 0.046) than historical stroke control networks. Conclusions: Social network structure is positively related to improvement in motor status and mood from TR. TRpatients had larger and more open social networks than strokepatients who did not receive TR. Understanding how social networks intersect with TR outcomes is crucial to maximize effects of virtual rehabilitation.
Authors: Stephanie E Chiuve; Kathryn M Rexrode; Donna Spiegelman; Giancarlo Logroscino; JoAnn E Manson; Eric B Rimm Journal: Circulation Date: 2008-08-12 Impact factor: 29.690
Authors: Amar Dhand; Catherine E Lang; Douglas A Luke; Angela Kim; Karen Li; Liam McCafferty; Yi Mu; Bernard Rosner; Steven K Feske; Jin-Moo Lee Journal: Neurorehabil Neural Repair Date: 2019-09-15 Impact factor: 3.919
Authors: Jacob John Capin; Sarah E Jolley; Mary Morrow; Meghan Connors; Kristine Hare; Samantha MaWhinney; Amy Nordon-Craft; Michelle Rauzi; Sheryl Flynn; Jennifer E Stevens-Lapsley; Kristine M Erlandson Journal: BMJ Open Date: 2022-07-26 Impact factor: 3.006