logo
More than 9 hours of sleep? Science says your memory may suffer

More than 9 hours of sleep? Science says your memory may suffer

Time of India17-05-2025

If you've ever felt proud of clocking in over nine hours of sleep, thinking it's the ultimate health hack, recent research suggests you should reconsider. A study from the University of Texas Health Science Center reveals that
excessive sleep
, specifically more than nine hours per night, may be linked to poorer cognitive performance, especially in individuals experiencing symptoms of depression.
The study analyzed data from nearly 2,000 dementia-free adults aged 27 to 85, focusing on sleep duration and cognitive function.
Dementia
is a term for several diseases that affect memory, thinking, and the ability to perform daily activities.
Also Read:
War of the Worlds? AI is growing a mind of its own, soon it will make decisions for you
Continue to video
5
5
Next
Stay
Playback speed
1x Normal
Back
0.25x
0.5x
1x Normal
1.5x
2x
5
5
/
Skip
Ads by
Sponsored Links
Sponsored Links
Promoted Links
Promoted Links
You May Like
Top Packaging Trends In 2024 - Take A Look
Packaging Machines | Search Ads
Search Now
Undo
The findings indicated that participants who slept longer than nine hours exhibited decreased memory, visuospatial abilities, and executive functions. These effects were more pronounced in individuals with depressive symptoms, regardless of whether they were on antidepressant medication.
Live Events
Vanessa Young, a clinical research project manager at the Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, stated that sleep could be a modifiable risk factor for cognitive decline in individuals with depression.
This research suggests that those with mental health conditions should be more serious about their sleep; they might need personalized sleep recommendations. While sleep is essential for brain health, both insufficient and excessive sleep can have detrimental effects. The Global Council on Brain Health recommends 7 to 8 hours of nightly sleep for adults to preserve cognitive function.
Also Read:
300 years after alchemy failed, CERN scientists finally turn lead into gold
It's crucial to pay attention to your sleep patterns and consult healthcare professionals if you experience persistent changes in sleep duration or quality, especially if accompanied by depressive symptoms. People who work shifts might be more vulnerable, as their sleeping cycle is often disrupted by work. Balancing sleep duration could be a key factor in maintaining cognitive health and overall well-being.

Orange background

Try Our AI Features

Explore what Daily8 AI can do for you:

Comments

No comments yet...

Related Articles

Hey Siri, Am I Okay? : AI tools are being trained to detect suicidal signals.
Hey Siri, Am I Okay? : AI tools are being trained to detect suicidal signals.

Time of India

time5 hours ago

  • Time of India

Hey Siri, Am I Okay? : AI tools are being trained to detect suicidal signals.

Live Events Suicidal risk identification on SNS: The prompts fed to AI do not remain confined to tasks related to needing help in everyday activities, such as asking Alexa to play the family's favourite song, asking Siri on a random Tuesday to set a reminder, or asking Google Assistant to search the song based on humming. But what if users, in an especially low moment, were to ask, 'Am I okay?' Or maybe other such prompts that insinuate the user's want to harm themselves, whether through means of self-harm or and suicide attempts remain alarmingly prevalent, requiring more effective strategies to identify and support individuals at high risk. Current methods of suicide risk assessment largely rely on direct questioning, which can be limited by subjectivity and inconsistent interpretation. Simply put, their accuracy and predictive value remain limited, regardless of the large variety of scales that can be used to assess the risk; predictability remains unimproved over the past 50 intelligence and machine learning offer new ways to improve risk detection, but their accuracy depends heavily on access to large datasets that can help identify patient profiles and key risk factors. As outlined in a clinical review, AI tools can help identify patterns in the dataset, generate risk algorithms, and determine the effect of risk and protective factors on suicide. The use of AI reassures healthcare professionals with an improved accuracy rate, especially when combined with their skills and expertise, even when diagnostic accuracy could never reach 100%.According to Burke et al. , there are three main goals of machine learning studies in suicide: the first is improving the accuracy of risk prediction, the second is identifying important predictors and the interaction between them, and the last one is to model subgroups of patients. At an individual level, AI could allow for better identification of individuals in crisis and appropriate intervention, while at a population level, the algorithm could find groups at risk and individuals at risk of suicide attempts within these groups. Social media platforms have become both the cause and solution for the mental health crisis. While they are often criticized for contributing to the mental health crisis, these platforms also provide a rich source of real-time data to AI, enabling it to identify individuals portraying signs of suicidal intent. This is achieved by analyzing users' posts, comments, and behavioral patterns, allowing AI tools to detect linguistic cues, such as expressions of hopelessness or other emotional signals that may indicate psychological distress. For instance, Meta employs AI algorithms to scan user content and identify signs of distress, allowing the company to reach out and offer support or even connect users with crisis helplines. Studies such as those by the Black Dog Institute also demonstrate how AI's natural language processing can flag at-risk individuals earlier than traditional methods, enabling timely are also companies such as Samurai Labs and Sentinet that have developed AI-driven systems that monitor social media content and flag posts that insinuate suicidal ideation. For example, Samurai Labs 'One Life' project scans online conversations to detect signs that indicate high suicide risk. Upon detecting these indicators, the platform then leads the user to support resources or emergency assistance. In the same manner, Sentient's algorithms analyze thousands of posts on a daily basis, triggering alerts when users express some form of emotional distress, allowing for timely AI isn't a replacement for human empathy or professional mental health care, it offers a promising advancement in suicide prevention. By identifying warning signs at a much faster and more precise rate than human diagnosis and enabling early interventions, AI tools can serve as valuable allies in this fight against suicide.

Curing the incurable: How is AI revolutionising treatment for ALS, Alzheimer's and Dementia?
Curing the incurable: How is AI revolutionising treatment for ALS, Alzheimer's and Dementia?

Time of India

time5 hours ago

  • Time of India

Curing the incurable: How is AI revolutionising treatment for ALS, Alzheimer's and Dementia?

ALS Detection: Predicting chances of developing Alzheimer's years ahead: Live Events Advancements in Dementia Research with AI: Enhanced patient care with AI-enabled technology: AI ushers in new hope in the fight against neurodegenerative diseases such as amyotrophic lateral sclerosis (ALS), Alzheimer's, and various forms of dementia. Through the predictive capabilities of machine and deep learning (two subsets of AI), healthcare professionals today are able to predict the onset of these ailments years prior. Enabling early detection uncovers hidden risk factors and allows personalised care, thereby transforming the landscape of diagnosis and treatment. The traditional method of diagnosing ALS stands to be an unnecessarily long procedure, inculcating unwanted spinal surgeries, carpal tunnel releases, and immunotherapies; redundant tests and labs; merely to exclude every other possible condition that could align with the symptoms the patient shows. As a result, this procedure could take up to a year or even more. Before this procedure procures information that could successfully diagnose the patient, the damage stands to be made, making it too late for any treatment to garner AI and its ability to analyse vast amounts of data enable identifying early signs of the disease. For example, the uses AI to observe speech patterns, motor abilities, and the individual's manner of walking. The AI notices subtle changes portrayed by the individual that the human eye tends to have trouble noticing until made explicitly evident. Integrating AI in the diagnosis of ALS makes it faster, giving patients more time to treat themselves with an increased amount of hope than with traditional also enables diagnosis of patients who do not have any mentions of ALS in their medical records but could have undiagnosed ALS by recognising patterns in their medical histories. This reduces time and allows faster intervention, leading to better patient outcomes. Scientists at UCSF have developed a way to predict Alzheimer's disease up to seven years before the symptoms start to appear via having machine learning analyse patient records. The machine learning analyses the patient's electronic health records and also considers factors such as high cholesterol, osteoporosis (especially in women), depression, and vitamin D deficiency, thereby fostering a success rate of nearly 72%.This early prediction model allows patients to try various treatment plans, provides them the liberty to hope for improvement, and also lets them make lifestyle modifications that could procure delays or mitigations in the progression of Alzheimer' is an amalgamation of various diseases caused by various underlying disorders like Alzheimer's, vascular dementia frontotemporal dementia , and more. Each kind of dementia varies with the different ancestral backgrounds, environmental exposures, and genetics. With this kind of complexity, it is inevitable that there will occur a large data set. Thereby, making the inculcation of AI into dementia-related studies essential, as emphasised by the NIH's Center for Alzheimer's and Related Dementias (CARD). AI's capacity to analyse complex and large datasets uncovers subtle patterns in data, aiding in early detection and diagnosis of various dementia types and understanding their AI is exceptional in identifying previously unknown genetic risk factors, enhancing healthcare professionals' understanding of the ailment's biology, and paving the way for personalised from being entangled in vital research-related roles and predictive diagnosis-related roles in healthcare, AI is also equally entangled in caregiving. AI-powered robots are being explored by researchers to support dementia patients in their daily lives, promoting independence and reducing familial burden. These robots are being studied/built with the aim to respect the autonomy and dignity of patients while providing practical help with tasks such as medication reminders and emergency alerts. Innovations such as these are bound to be an improving agent for the patients' quality of life and offer reassurance to the existence within this field shall potentially revolutionise the approaches taken towards the ailments deemed incurable, offering hope for earlier diagnoses, personalised treatments, and improved patient outcomes. As studies advance and technology evolves, AI is equipped to become an invulnerable warrior in this ongoing battle against ALS, Alzheimer's, and dementia of other forms ( source ).

VC company HealthKois plans $400 million fund for healthcare play
VC company HealthKois plans $400 million fund for healthcare play

Time of India

time15 hours ago

  • Time of India

VC company HealthKois plans $400 million fund for healthcare play

HealthKois , a Delhi-based growth stage venture capital fund, plans to roll out a $400-million fund to invest in India's healthcare sector. The fund intends to target companies in AI-led healthtech , medtech, biopharma, healthcare delivery and climate health , its managing partner Charles Janssen told ET in an exclusive interaction. "We have started raising this fund from LPs (limited partners). Our LPs are split between Asia, Europe, and the US," Janssen said. HealthKois is aiming at a first close at the end of this year or early 2026. The investments will be deployed over the next four years, with ticket size of each of the investments likely in the range of $7 million to $25 million. "We are looking at a $300 million fund with a green shoe option of another $100 million," said Ajay Mahipal, partner at HealthKois. "We are looking at 13-16 portfolio companies in the HealthKois fund," he said. by Taboola by Taboola Sponsored Links Sponsored Links Promoted Links Promoted Links You May Like The Top 25 Most Beautiful Women In The World Car Novels Undo HealthKois is a successor fund to HealthQuad I and II, which are invested in GoApptiv, and Cureskin, among others. It plans to deploy 50% of its investment towards healthtech, and about 10-15% each across biopharma, medtech, healthcare delivery and climate health. "The idea is to back early growth, Indian healthcare companies that use tech or innovative business models to address critical challenges in the healthcare system and solve for affordability, accessibility, and quality of care," Mahipal said. Discover the stories of your interest Blockchain 5 Stories Cyber-safety 7 Stories Fintech 9 Stories E-comm 9 Stories ML 8 Stories Edtech 6 Stories

DOWNLOAD THE APP

Get Started Now: Download the App

Ready to dive into the world of global news and events? Download our app today from your preferred app store and start exploring.
app-storeplay-store