We found a high PIU prevalence among Japanese young adults. The factors that predicted PIU were: female sex, older age, poor sleep quality, ADHD tendencies, depression, and anxiety.

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We investigated sleep quality and melatonin in 12 adults who wore blue-light shield or control eyewear 2 hours before sleep while using a self-luminous portable device, and assessed visual quality for the two eyewear types. Overnight melatonin secretion was significantly higher after using the blue-light shield (P < 0.05) than with the control eyewear. Sleep efficacy and sleep latency were significantly superior for wearers of the blue-light shield (P < 0.05 for both), and this group reported greater sleepiness during portable device use compared to those using the control eyewear. Participants rated the blue-light shield as providing acceptable visual quality.

We aimed to develop a machine learning algorithm to screen for depression and assess severity based on data from wearable devices. Methods: We used a wearable device that calculates steps, energy expenditure, body movement, sleep time, heart rate, skin temperature, and ultraviolet light exposure. Depressed patients and healthy volunteers wore the device continuously for the study period. The modalities were compared hourly between patients and healthy volunteers. XGBoost was used to build machine learning models and 10-fold cross-validation was applied for the validation. Results: Forty-five depressed patients and 41 healthy controls participated, creating a combined 5,250 days' worth of data. Heart rate, steps, and sleep were significantly different between patients and healthy volunteers in some comparisons. Similar differences were also observed longitudinally when patients' symptoms improved. Based on seven days' data, the model identified symptomatic patients with 0.76 accuracy and predicted Hamilton Depression Rating Scale-17 scores with a 0.61 correlation coefficient. Skin temperature, sleep time-related features, and the correlation of those modalities were the most significant features in machine learning. Limitations: The small number of subjects who participated in this study may have weakened the statistical significance of the study. There are differences in the demographic data among groups although we performed a correction for multiple comparisons. Validation in independent datasets was not performed, although 10-fold cross validation with the internal data was conducted. Conclusion:The results indicated that utilizing wearable devices and machine learning may be useful in identifying depression as well as assessing severity.

There are studies reporting the negative impact of smartphone utilization on sleep. It is considered that reduction of melatonin secretion under the blue light exposure from smart-phone displays is one of the causes. The viewing distance may cause sleep disturbance, because the viewing distance determines the screen illuminance and/or asthenopia. However, to date, there has been no study closely investigating the impact of viewing distance on sleep; therefore, we sought to determine the relationship between smartphone viewing distance and subjective sleep status. Twenty-three nursing students (mean age ± standard deviation of 19.7±3.1 years) participated in the study. Subjective sleep status was assessed using the Pittsburgh Sleep Quality Index, morningness–eveningness questionnaire, and the Epworth sleepiness scale. We used the distance between the head and the hand while holding a smartphone to measure the viewing distance while using smartphones in sitting and lying positions. The distance was calculated using the three-dimensional coordinates obtained by a noncontact motion-sensing device. The viewing distance of smartphones in the sitting position ranged from 13.3 to 32.9 cm among participants. In the lying position, it ranged from 9.9 to 21.3cm. The viewing distance was longer in the sitting position than in the lying position (mean ± standard deviation: 20.3±4.7 vs 16.4±2.7, respectively, P<0.01). We found that the short viewing distance in the lying position had a positive correlation to a poorer sleep state (R2=0.27, P<0.05), lower sleep efficiency (R2=0.35, P<0.05), and longer sleep latency (R2=0.38, P<0.05). Moreover, smartphone viewing distances in lying position correlated negatively with subjective sleep status. Therefore, when recommending ideal smartphone use in lying position, one should take into account the viewing distances.