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Automatic Self Transcending Meditation (ASTM) for Patients With Severe Age Related Macular Degeneration (AMD)

London Health Sciences Centre Research Institute OR Lawson Research Institute of St. Joseph's · 2018
ClinicalTrials.gov · Datasets · License: Public Domain · 2018
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age-related-macular-degenerationna
Age-related macular degeneration (AMD) is a condition affecting 20 to 25 million people worldwide. Symptoms of AMD includes difficulty in reading, recognizing faces and completing house work which may result in increased disability which in turn increase symptoms of depression and anxiety. Depression and anxiety could also be worsened by social isolation caused by AMD. Further, AMD causes high levels of emotional distress and reduced quality of life (QoL). Automatic Self Transcending Meditation (ASTM) - a standardized category of meditation - may help reduce stress, depression, anxiety, and may enhance QoL. Automatic Self Transcending Meditation (ASTM) is a class of meditation that helps quiet the mind and induces physiological and mental relaxation whilst the eyes are shut. It utilizes a specific sound value (mantra) to draw attention inward and permit the mind to experience a restful but alert state of consciousness. In the proposed research, the effects of ASTM on health related quality of Life (HRQoL), depression and anxiety in low-vision AMD patients will be studied. A single-center, single-blind longitudinal randomized controlled trial (RCT) will be conducted in London, ON. Patients with AMD (n = 140, 70 in each arm) will be randomized to ASTM plus treatment as usual (TAU) or TAU alone (control) arm. Data on routinely measured ophthalmic clinical variables, HRQoL, depression, and anxiety will be collected from both the arms. Statistical analysis will be conducted using STATA 15.0 to evaluate the effects of ASTM plus TAU compared to TAU alone on HRQoL, depression, and anxiety. Further, for each group - ASTM plus TAU and TAU alone - the investigators develop an association between HRQoL, depression, and anxiety with routinely measured clinical variables using mathematical models.
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