Scroll to top
Tag: stress

Technology Fueled America’s Youth Mental Health Crisis, But It Can Help End It

Article Excerpt: Sian Leah Beilock is a cognitive scientist who is the new president of Dartmouth College, the first woman to hold that position since the school was founded in 1769. An expert in, among other things, the effect of stress on academic performance, she is starting her tenure by putting health and wellness at the center of her leadership agenda with a focus on the country’s youth mental health crisis…Substance abuse, which is both helping drive the mental health crisis and is drastically undertreated with nearly 90 percent of sufferers going without treatment, offers an example of the power of technology to provide clinical care in underserved areas or in cases in which stigmatization prevents people from seeking the help they need. Lisa Marsch and her team at the Dartmouth Center for Technology and Behavioral Health (CBTH) created and validated the first Food and Drug Administration-cleared digital therapeutic for the treatment of opioid addiction, which provides cognitive behavioral therapy interventions though the user’s digital device and has since helped roughly double rates of abstinence by lowering the threshold for access to treatment.

Full Article:

Article Source: The Washington Post


The Impact of Wearable Technology on Mental Health and Wellness

Article Excerpt: The impact of wearable technology on mental health and wellness is a topic that has been gaining increasing attention in recent years. As the use of wearable devices such as smartwatches, fitness trackers, and even smart clothing becomes more widespread, researchers and mental health professionals are exploring the potential benefits and drawbacks of these devices on our mental well-being.

Full Article:

Article Source: CityLife


Can Machine Learning, Wearable Tech Help Treat Mental Health?

Article Excerpt: New research from the Icahn School of Medicine at Mount Sinai in New York indicated that using Apple Watch data, such as heart rate variability and resting heart rate, could assist in training machine learning models to determine patient well-being and resilience. According to the Centers for Disease Control and Prevention (CDC), over 20 percent of US adults have a mental illness. The CDC also noted that mental health diagnoses are some of the most common health conditions in the US. This latest study showed that wearable devices could help support patients with mental health diagnoses by collecting assistive data.

Full Article:

Article Source: Health IT Analytics


Professor Campbell wins the 2022 ACM UbiComp 10-year Impact Award

Article Excerpt: In 2012 Hong Lu, a PhD student in Computer Science at Dartmouth co-advised by Professors Andrew Campbell and Tanzeem Choudhury, developed StressSense, an innovative smartphone app to detect stress from human voice. StressSense won the 2022 ACM UbiComp10-year impact award. Given annually, this award recognizes papers with sustained and significant impact over at least a decade… This is the third time Professor Campbell has received a 10 year impact award for his research in mobile sensing. In 2019, the CenceMe app received the ACM SIGMOBILE Test of Time Award for “inspiring a huge body of research and commercial endeavors that has continued to increase the breadth and depth of mobile sensing”. In 2018, his work was recognized for “pioneering machine learning across mobile phones and servers” with the ACM SenSys Test of Time Award.

Full Article:

Article Source: Dartmouth Computer Sciences News


The Efficacy of “Foundations,” a Digital Mental Health App to Improve Mental Well-being During COVID-19: Proof-of-Principle Randomized Controlled Trial

Catuara-Solarz S, Skorulski B, Estella-Aguerri I, Avella-Garcia C, Shepherd S, Stott E, Hemmings N, Ruiz de Villa A, Schulze L, Dix S. The Efficacy of “Foundations,” a Digital Mental Health App to Improve Mental Well-being During COVID-19: Proof-of-Principle Randomized Controlled Trial. JMIR Mhealth Uhealth 2022;10(7):e30976 DOI: 10.2196/30976

This study aimed to evaluate the efficacy of a mobile app, “Foundations”, to reduce self-reported symptoms of anxiety and stress in a randomized control trial during the COVID-19 pandemic in the United Kingdom. Adults (N=136) with mild to severe anxiety and moderate to high levels of perceived stress were randomly assigned to four weeks of the Foundations app or a waitlist control. The Foundations app includes cognitive behavioral therapy interventions and psychoeducation aimed at reducing stress and promoting mental well-being. Activities consist of reading articles, journaling, meditation, and relaxation. Resilience, anxiety, well-being, and sleep were assessed at baseline, weeks 2 and 4. Perceived stress was assessed weekly. The intervention group (n=62) showed significant improvement in anxiety (p=0.04), resilience (p<0.001), sleep (p=0.01), and mental well-being (p=0.02) compared to the control group (n=74). This improvement was observed within 2 weeks of the intervention and sustained at week 4. There was no significant difference in perceived stress between the intervention and control groups (p=0.20). Overall, this study provides a proof of principle that the Foundations app may improve mental well-being, anxiety, resilience, and sleep. Future research should evaluate the long-term effects of the Foundations app and the scalability and cost-effectiveness of the intervention. The passive nature of the control group in this study does not rule out placebo effects in the digital intervention group and future research would benefit from an active control condition.


Predicting the Next-Day Perceived and Physiological Stress of Pregnant Women by Using Machine Learning and Explainability: Algorithm Development and Validation

Ng A, Wei B, Jain J, Ward E, Tandon S, Moskowitz J, Krogh-Jespersen S, Wakschlag L, Alshurafa N. Predicting the Next-Day Perceived and Physiological Stress of Pregnant Women by Using Machine Learning and Explainability: Algorithm Development and Validation. JMIR Mhealth Uhealth 2022;10(8):e33850. DOI: 10.2196/33850

This study aimed to develop and evaluate a machine learning model to predict next-day physiological and prenatal stress by collecting sensor heart rate data and ecological momentary assessment (EMA) questionnaires. This study applied an explainability model for the prediction results. A total of 16 adult pregnant women from an obstetrics and gynecology clinic were enrolled in the study. Participants received a 12-week cognitive behavioral therapy intervention and wore a mobile electrocardiography (heart rate) sensor for 12 weeks. Participants completed EMAs for perceived stress on their mobile phones 5 times a day for 12 weeks. In total, about 4000 hours of data were collected and participants completed 2800 EMAs. Researchers used these data to train and evaluate 6 different machine learning models to select the best performing model for predicting next-day physiological and perceived stress. The random forest classifier performed the best for both physiological and perceived stress, with an average F1 score (a commonly used evaluation metric) of 81.9% and 72.5%, respectively. Two features significantly predicted both physiological and perceived stress: feeling unable to overcome difficulties and participants’ number of children. Results demonstrated that a machine learning model can predict next-day physiological and perceived stress among pregnant women. Future studies should validate the model with a larger sample size.


On a Mission for Mental Health

Article Excerpt: (Andrew) Campbell, the Albert Bradley 1915 Third Century Professor (at Dartmouth College), thinks about his brother every day in his research on computer science and mental health. Ed suffered his first depressive episode as a freshman at Durham University in the early 1980s. He battled bipolar disorder his entire adult life and died by suicide in 2009, at age 48. “The story about how I got involved in student health all goes back to my brother,” Campbell says. Ed’s family was blindsided by his death. After that, computer science was no longer just an academic interest to his older brother. For Campbell, who earned a PhD at Lancaster University in 1996 and came to Dartmouth in 2006, it became a tool to help those with mental health issues.

Full Article:

Article Source: Dartmouth News


Walking or Biking to Work Could Make You More Productive

Article Excerpt: For 14 years, Kerry Mellin commuted 40 miles to her job as a motion picture costumer at Nickelodeon Studios in Burbank, Calif. The trip from her home in Simi Valley took her east via Route 118, then south onto Interstate 5. Three turns later, she was there. On a good day, the drive took 75 minutes. “On bad traffic days, it was easily two hours,” she says. “The road rage was real. I felt trapped in my lane, and my sciatica was killing me.” No productivity guru preaches the benefits of morning anger and back pain. But exactly how an odyssey such as Mellin’s affects the workday hasn’t been fully understood. New research from Dartmouth helps quantify the cost of commuting on performance. “Your commute predicts your day,” says Andrew Campbell, lead researcher of the study and a professor of computer science.

Full Article:

Article Source: Bloomberg


Pandemic Exposed Mental Health Divide Among College Students, Study Says

Article Excerpt: The coronavirus pandemic has revealed a deep divide among college students: Young people with the most amount of concern about the virus tended to struggle more than others with anxiety, depression and low self-esteem, according to researchers at Dartmouth College. “The pandemic has put students on a literal mental health roller coaster, mostly heading downward,” Andrew Campbell, a researcher and computer science professor, said in a news release. Using smartphone data, he and other researchers have been able to track the highs and lows many students experienced over the past two years — from rushing off campus at the start of the pandemic, to feelings of isolation while taking classes online, to returning to campus and having new social interactions.

Full Article:

Article Source: The Washington Post