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Political events and mood among young physicians: a prospective cohort study.

ABSTRACT: OBJECTIVE:To study the effects of recent political events on mood among young physicians. DESIGN:Prospective cohort study. SETTING:United States medical centres. PARTICIPANTS:2345 medical interns provided longitudinal mood data as part of the Intern Health Study between 2016 and 2018. MAIN OUTCOME MEASURES:Mean mood score during the week following influential political and non-political events as compared with mean mood during the preceding four week control period. RESULTS:We identified nine political events and eight non-political events for analysis. With the start of internship duties in July, the mean decline in mood for interns was -0.30 (95% confidence interval -0.33 to -0.27, t=-17.45, P<0.001). The decline in mood was of similar magnitude following the 2016 presidential election (mean mood change -0.32, 95% confidence interval -0.45 to -0.19, t=-4.73, P<0.001) and subsequent inauguration (mean mood change -0.25, 95% confidence interval -0.37 to -0.12, t=-3.93, P<0.001). Further, compared with men, women reported greater mood declines after both the 2016 election (mean gender difference 0.31, 95% confidence interval 0.05 to 0.58, t=2.33, P=0.02) and the inauguration (mean gender difference 0.25, 95% confidence interval 0.01 to 0.49, t=2.05, P=0.04). Overall, there were statistically significant changes in mood following 66.7% (6/9) of political events assessed. In contrast, none of the non-political events included in the analysis were statistically significantly associated with a change in mood. CONCLUSIONS:Macro level factors such as politics may be correlated with the mood of young doctors. This finding signals the need for further evaluation of the consequences of increasing entanglement between politics and medicine moving forward for young physicians and their patients.

SUBMITTER: Frank E

PROVIDER: S-EPMC7190044 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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Political events and mood among young physicians: a prospective cohort study.

Frank Elena E Nallamothu Brahmajee K BK Zhao Zhuo Z Sen Srijan S

BMJ (Clinical research ed.) 20191209

<h4>Objective</h4>To study the effects of recent political events on mood among young physicians.<h4>Design</h4>Prospective cohort study.<h4>Setting</h4>United States medical centres.<h4>Participants</h4>2345 medical interns provided longitudinal mood data as part of the Intern Health Study between 2016 and 2018.<h4>Main outcome measures</h4>Mean mood score during the week following influential political and non-political events as compared with mean mood during the preceding four week control p ...[more]

PMID: 31818913

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Project description:BackgroundVirtually, all organisms on Earth have their own circadian rhythm, and humans are no exception. Circadian rhythms are associated with various human states, especially mood disorders, and disturbance of the circadian rhythm is known to be very closely related. Attempts have also been made to derive clinical implications associated with mood disorders using the vast amounts of digital log that is acquired by digital technologies develop and using computational analysis techniques.ObjectiveThis study was conducted to evaluate the mood state or episode, activity, sleep, light exposure, and heart rate during a period of about 2 years by acquiring various digital log data through wearable devices and smartphone apps as well as conventional clinical assessments. We investigated a mood prediction algorithm developed with machine learning using passive data phenotypes based on circadian rhythms.MethodsWe performed a prospective observational cohort study on 55 patients with mood disorders (major depressive disorder [MDD] and bipolar disorder type 1 [BD I] and 2 [BD II]) for 2 years. A smartphone app for self-recording daily mood scores and detecting light exposure (using the installed sensor) were provided. From daily worn activity trackers, digital log data of activity, sleep, and heart rate were collected. Passive digital phenotypes were processed into 130 features based on circadian rhythms, and a mood prediction algorithm was developed by random forest.ResultsThe mood state prediction accuracies for the next 3 days in all patients, MDD patients, BD I patients, and BD II patients were 65%, 65%, 64%, and 65% with 0.7, 0.69, 0.67, and 0.67 area under the curve (AUC) values, respectively. The accuracies of all patients for no episode (NE), depressive episode (DE), manic episode (ME), and hypomanic episode (HME) were 85.3%, 87%, 94%, and 91.2% with 0.87, 0.87, 0.958, and 0.912 AUC values, respectively. The prediction accuracy in BD II patients was distinctively balanced as high showing 82.6%, 74.4%, and 87.5% of accuracy (with generally good sensitivity and specificity) with 0.919, 0.868, and 0.949 AUC values for NE, DE, and HME, respectively.ConclusionsOn the basis of the theoretical basis of chronobiology, this study proposed a good model for future research by developing a mood prediction algorithm using machine learning by processing and reclassifying digital log data. In addition to academic value, it is expected that this study will be of practical help to improve the prognosis of patients with mood disorders by making it possible to apply actual clinical application owing to the rapid expansion of digital technology.

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Political events and mood among young physicians: a prospective cohort study.

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Political events and mood among young physicians: a prospective cohort study.

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