Five secrets for quitting smoking in 2014

Washington D.C, Feb 27: New research shows that adults who have low fruit and vegetable intake are more likely to be diagnosed with an anxiety disorder.

"For those who consumed less than 3 sources of fruits and vegetables daily, there was at least at 24% higher odds of anxiety disorder diagnosis," says the lead author of the Canadian Longitudinal Study, Karen Davison, who is a health science faculty member, nutrition informatics lab director at Kwantlen Polytechnic University, (KPU) and North American Primary Care Research Group Fellow.

"This may also partly explain the findings associated with body composition measures. As levels of total body fat increased beyond 36%, the likelihood of anxiety disorder was increased by more than 70%," states co-author Jose Mora-Almanza, a Mitacs Globalink intern who worked with the study at KPU.

"Increased body fat may be linked to greater inflammation. Emerging research suggests that some anxiety disorders can be linked to inflammation," says Davison.

In addition to diet and body composition measures, the prevalence of anxiety disorders also differed by gender, marital status, income, immigrant status and several health issues.

An important limitation of the study was that the assessment of anxiety disorders was mostly based upon self-reporting of a medical diagnosis.

"It is estimated that 10% of the global population will suffer from anxiety disorders which are a leading cause of disability," says Karen Davison

"Our findings suggest that comprehensive approaches that target health behaviours, including diet, as well as social factors, such as economic status, may help to minimize the burden of anxiety disorders among middle-aged and older adults, including immigrants," she concluded.

New Delhi, Jun 24: Expanding the testing criterion for coronavirus, the Indian Council of Medical Research has said it should be made widely available to all symptomatic individuals across the country.

"Since test, track and treat' is the only way to prevent spread of infection and save lives, it is imperative that testing should be made widely available to all symptomatic individuals in every part of the country and contact tracing mechanisms for containment of infection are further strengthened," it said in an advisory on 'Newer Additional Strategies for COVID-19 Testing' on Tuesday.

In its revised testing strategy for COVID-19 issued on May 18, the Indian Council of Medical Research (ICMR) had advised testing for all symptomatic Influenza-like illness (ILI) among returnees and migrants within seven days of illness.

All hospitalised patients who develop ILI symptoms, symptomatic individuals living within hotspots or containment zones and healthcare and frontline workers involved in containment and mitigation of coronavirus were also advised testing.

The apex health research body has also advised authorities to enable all government and private hospitals, offices and public sector units to perform antibody-based COVID-19 testing for surveillance to help allay fears and anxiety of healthcare workers and office employees.

The earlier advisories on rapid antibody testing advisories had focused on areas reporting clusters (containment zones), large migration gatherings/evacuees centers and testing of symptomatic ILI individuals at facility level.

Besides, the ICMR on Tuesday also recommended deployment of rapid antigen detection tests for COVID-19 in combination with RT-PCR tests in all containment zones, all central and state government medical colleges and government hospitals, all private hospitals approved by the National Accreditation Board for Hospitals and Healthcare (NABH), all NABL-accredited and ICMR approved private labs, for COVID-19 testing.

All hospitals, laboratories and state governments intending to perform the point-of-care antigen tests need to register with ICMR to obtain the login credentials for data entry.

"ICMR advises all state governments, public and private institutions concerned to take required steps to scale up testing for COVID-19 by deploying combination of various tests as advised," the advisory added.

New York, Feb 26:  A new wearable sensor that works in conjunction with artificial intelligence (AI) technology could help doctors remotely detect critical changes in heart failure patients days before a health crisis occurs, says a study.

The researchers said the system could eventually help avert up to one in three heart failure readmissions in the weeks following initial discharge from the hospital and help patients sustain a better quality of life.

"This study shows that we can accurately predict the likelihood of hospitalisation for heart failure deterioration well before doctors and patients know that something is wrong," says the study's lead author Josef Stehlik from University of Utah in the US.

"Being able to readily detect changes in the heart sufficiently early will allow physicians to initiate prompt interventions that could prevent rehospitalisation and stave off worsening heart failure," Stehlik added.

According to the researchers, even if patients survive, they have poor functional capacity, poor exercise tolerance and low quality of life after hospitalisations.

"This patch, this new diagnostic tool, could potentially help us prevent hospitalizations and decline in patient status," Stehlik said.

For the findings, published in the journal Circulation: Heart Failure, the researchers followed 100 heart failure patients, average age 68, who were diagnosed and treated at four veterans administration (VA) hospitals in Utah, Texas, California, and Florida.

After discharge, participants wore an adhesive sensor patch on their chests 24 hours a day for up to three months.

The sensor monitored continuous electrocardiogram (ECG) and motion of each subject.

This information was transmitted from the sensor via Bluetooth to a smartphone and then passed on to an analytics platform, developed by PhysIQ, on a secure server, which derived heart rate, heart rhythm, respiratory rate, walking, sleep, body posture and other normal activities.

Using artificial intelligence, the analytics established a normal baseline for each patient. When the data deviated from normal, the platform generated an indication that the patient's heart failure was getting worse.

Overall, the system accurately predicted the impending need for hospitalization more than 80 per cent of the time.

On average, this prediction occurred 10.4 days before a readmission took place (median 6.5 days), the study said.