This trial is evaluating whether Automated digital reminders will improve 1 primary outcome and 3 secondary outcomes in patients with Aura. Measurement will happen over the course of Month 14.
This trial requires 600 total participants across 2 different treatment groups
This trial involves 2 different treatments. Automated Digital Reminders is the primary treatment being studied. Participants will all receive the same treatment. There is no placebo group. The treatments being tested are in Phase 2 and have already been tested with other people.
The aura is a normal physiological condition, not a pathology. No drug or therapy can cure or treat an aura. Rather, patient attention to the aura helps with its understanding.
There is no single cause of Auras, but multiple causes may be involved in many cases. As well as vascular and peripheral involvement, a number of central nervous system causes may be involved and they may be more common in women.
The clinical and radiological findings of patients with aura were similar to those of patients who were not experiencing aura. This preliminary report may help clinicians in their diagnosis decisions.
A high number of individuals experience aura. If all individuals who have experienced aura were diagnosed and treated as early as possible, the risk of developing psychosis could be reduced by 12.9% over 10 years.
An aura as defined here is a nonvisual sign preceding a transient visual impairment and may be a sign of a migraine or other forms of epilepsy. It is often associated with the aura phase of migraine and may precede the development of a headache and the headache may be partially or totally suppressed by anti-migraine therapy.
The primary treatment option for aura is beta-blockers or magnesium. There is not a single treatment of choice for most patients but general practitioners typically tend to use either beta-blockers or magnesium for most patients. Beta-blockers may be effective for most patients. Larger and prospective studies are needed to investigate these two potential options.
Side effects of the automated reminders can be minimized by careful consideration of the design and content of the messages as well as the timing and location of delivery. These will lead to better acceptance by patients.
Because the number of cases in the US is increasing, it's imperative to find new treatments for these rare but complex conditions. It is also important to continue to conduct basic and clinical research in the US as well as at medical centers and institutions in other parts of the world where these rare conditions arise.
[Treatment of aura by a psychiatrist is the least preferred and least acceptable option by respondents]. [Outcome measures], in particular measures of cognition and behaviour, and a definition of what constitutes clinically significant change that can be used in the setting of an RCT could help to improve response rates.[Auras are common, can be frightening, and can be misunderstood (cf, 'delayed reactions' in epilepsy) therefore, they are an area where RCTs can be introduced. RCTs in the field of mental healthcare would be a valuable addition to the body of research.
Few clinicians thought someone was getting aura at 55 years of age.\n- Optimal clinical trials use<a id="http://www.nbcnews.com/magazine/video/get-a-pro-vault-disease-targeting-nbc-nightly-14762438\">How to find clinical trials: Get a pro-vault Disease-targeting Trial</a>.\n- Clinicians need unbiased medical tests that are as clinically useful as what [NINDS|NIH and the patient needs it.
There have been a number of reported clinical trials in the literature examining the use of automated digital reminders to improve adherence to a prescribed therapy.
There is still tremendous interest in automated digital reminders (ADR) to support prescribing practices and improve patients' engagement and medication adherence. The latest developments in the ADR area are based upon two principles: one that is based on human factors principles and one which is based on health informatics principles. Currently, the majority of ADRs are based on either machine learning or an application of rule-based learning. Our research, however, has shown that the use of machine learning principles can help providers and clinical decision support systems (CDSS) providers maximize their capability of detecting errors in their respective environments.