Loneliness and Travel: The Impact of COVID-19 Lockdown on Mental Health and Touristic Travel Pattern

This two-part study had two aims: a) to define psychological markers for the future development of depression symptoms following the lockdown caused by the COVID-19 outbreak; b) to examine the impact of the COVID-19 mobility restrictions and vaccinations on people’s behavioral intentions to travel and their actual travel patterns. This study was conducted at four-time points, during and after general lockdowns in Israel, enabling examination of immediate as well as short-term influences of the lockdown on subjective well-being and travel intentions.

We found that subjective loneliness, rather than objective isolation, was a strong predictor for symptoms of depression five weeks after the first lockdown when controlling for depression levels during the lockdown. Younger age and health stress also predicted higher non-clinical levels of depression and emotional distress. Regarding touristic travel patterns, attitudes towards tourism were significant predictors of domestic and international travel intentions and actual domestic travel. Of the psychosocial factors examined (including intolerance of uncertainty, economic stress, and health risk factors) only depression levels were negatively correlated with domestic tourism.

Considering the global rise in mental health problems due to the COVID-19 outbreak, our results shed light on some of the predictive factors that contribute to the development of depression symptoms. At a global level, focusing on psychological factors rather than only objective measures is important for the efforts of identifying individuals at risk of developing depression, and for promoting new prevention strategies.

Can we train our pain?

My study comprehensively compares two training methods for pain reduction, aiming to change either attention to pain or perceptions about pain. I investigate the mechanisms underlying pain sensitivity reduction induced by these trainings, by employing multidimensional assessment including emotional, cognitive, and physiological measures. I will also use machine learning methods to evaluate whether individual characteristics can predict the beneficiaries of one specific training.

Learn More: https://dsrc.haifa.ac.il/index.php/component/content/article/101-research-blog/307-let-it-hurt-can-we-train-our-pain?Itemid=437

Cognitive Training Using Machine Learning

Cognitive training comprises a class of relatively new therapeutic interventions that target mechanisms implicated across different mental health conditions. Cognitive training programs are designed to enhance emotional functioning, either directly by cultivating different strategies for emotional processing and/or responses or indirectly by bolstering cognitive control processes in a non-emotional context, which in turn should improve emotional functioning.

Although many recent studies indicate that cognitive training shows merit, others fail to demonstrate its efficacy. These inconsistent findings may at least partly result from differences in individuals’ ability to benefit from cognitive training in general, and from specific training types in particular. Consistent with the move toward personalized medicine, we propose using machine learning approaches to help optimize cognitive training gains. More specifically, machine learning algorithms are incorporated to identify which individuals will be most responsive to cognitive training in general and to discern which methods may be a better fit for certain individuals.

Relevant Literature:

Shani, R., Tal, S., Zilcha-Mano, S., & Okon-Singer, H. (2019). Can machine learning approaches lead toward personalized cognitive training?. Frontiers in behavioral neuroscience, 64.

Shani, R., Tal, S., Derakshan, N., Cohen, N., Enock, P. M., McNally, R. J., ... & Okon-Singer, H. (2021). Personalized cognitive training: Protocol for individual-level meta-analysis implementing machine learning methods. Journal of psychiatric research, 138, 342-348.