The current paper introduced the two joint studies by the study group of social medicine and health economist. The study group collected digital data consisting of individual activity records such as location or mobility information (lifelog data) from 89 patients who were on maintenance therapy for depression for a year, using a smartphone application and a wearable device. 　 Before these joint studies, no prior studies have established a method that could identify the warning signs of its recurrence. In addition, to prevent relapse of patients with major depression, it is necessary to examine the predictors influencing false negatives and identify the early signs of recurrence. 　The first study by a panel vector autoregressive model indicated that long sleep time was an important risk factor for the recurrence of depression. Long sleep predicted the recurrence of depression after 3 weeks. Recurrent depression was defined by the Kessler Psychological Distress Scale and the Patient Health Questionnaire-9 scores. In contrast, considering the small number of relapsed cases and the overdispersion generated by excess zero, the second study aimed to investigate “sitting idly” as a predictor influencing falsenegatives using a zero-inflated negative binomial model because false negatives which can lead to delays in diagnosis and treatment must be avoided. Estimates of the population-averaged parameters indicated that daily hours of sitting idly increased the chances of recurrent depression two or four weeks later. The change in exposure to daily UV light from non-exposure reduced recurrent depression. 　Before the commencement of the joint study, it is indispensable to explain the significance of the economic study clearly to medical researchers. In the academic society where both of medical researchers and economists belong to, it is important to arrange the opportunity to understand the difference in each idea.

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We estimated the number of labor force exiters unemployed due to family care and their lost income. We defined labor force exiters as those who were out of work 2 years after they exited. Among the 99.1 thousand annual care leavers, 35.7 % restarted work and the remaining 64.3 % exited the labor market. Three-fourths of the exiters were non-regular workers. Among the care leavers previously in regular employment and those who returned to the labor market, 45.6 % of them restarted work as regular workers and the remainder restarted work as non-regular workers. Among the exiters, the ratio of care leavers to all exiters was 3.6 %. About 10 % of women exiters of 40-50 years of age were care leavers. Hence, the loss of middle-aged employment due to care leaves is significant. 　The wages of care leavers’ previous jobs is indispensable for calculating the loss of income. However, our data did not contain the necessary information. Hence, as a proxy variable, we used the wages of workers who provide care 6 days a week since their attributes are similar to those of care leavers. Additionally, in the loss of income calculation, we considered the income accrued from restarting work. 　The total loss of income during one year after care leave was 182.1 billion yen. The 270 billion yen loss of income suggested by the Ministry of Economy, Trade and Industry (2018) was larger than ours by 1.48 times. Approximately 70 % of the difference of the loss was due to usage of the wage of general workers, whose attributes were different from those of care leavers as a proxy variable for the wage of care leavers’ previous jobs. The remaining 30 % occurred because the calculation did not account for the income accrued from restarting work. Income loss can be due to the decrease in the employment ratio (77.1 %) or due to the wage gap between the previous and current jobs (22.9 %). Regular workers sustained three-fourths of the income loss due to the wage gap, approximately 70 % of which was due to restarting work as low wage non-regular workers.

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