Association of work environment with stress and depression among Japanese workers.

BACKGROUND: There is a lack of studies that investigated the effect of a wide range of work environmental factors on stress and depression in Japan. OBJECTIVES: To examine the association of work environment factors with stress and depression among workers in Japan. METHODS: We conducted questionnaire surveys of workers that mainly engage in desk work in Japan. Stress was assessed through the Perceived Stress Scale (PSS), depression through the Patient Health Questionnaire-9 (PHQ-9), and work environment through physical and psychological workplace environment questionnaires. Workers were divided into low and high stress groups based on PSS score (median split), and divided into non-depressed and depressed groups based on their PHQ-9 score (< 5, and ≥5); these groups were then compared with their working environment. In addition, a multiple regression analysis was performed. RESULTS: Responses were obtained from 210 subjects. Multiple regression analysis showed that "Ability to work at one's own pace" and "Ability to apply personal viewpoint to work," etc., had effect on stress, while "Workplace harassment" and "Support from colleagues," etc., had effect on depression. CONCLUSIONS: The results suggest that stress and depression in Japanese workers are related to factors such as job demands, control of work, workplace harassment, and psychological safety.

Subjective well-being and month-long LF/HF ratio among deskworkers.

The importance of workers' well-being has been recognized in recent years. The assessment of well-being has been subjective, and few studies have sought potential biomarkers of well-being to date. This study examined the relationship between well-being and the LF/HF ratio, an index of heart rate variability that reflects sympathetic and parasympathetic nerve activity. Pulse waves were measured using photoplethysmography through a web camera attached to the computer used by each participant. The participants were asked to measure their pulse waves while working for 4 weeks, and well-being was assessed using self-reported measures such as the Satisfaction With Life Scale (SWLS), the Positive and Negative Affect Schedule (PANAS), and the Flourishing Scale (FS). Each of the well-being scores were split into two groups according to the median value, and the LF/HF ratio during work, as well as the number of times an LF/HF ratio threshold was either exceeded or subceeded, were compared between the high and low SWLS, positive emotion, negative emotion, and FS groups. Furthermore, to examine the effects of the LF/HF ratio and demographic characteristics on well-being, a multiple regression analysis was conducted. Data were obtained from 169 participants. The results showed that the low FS group had a higher mean LF/HF ratio during work than the high FS group. No significant differences were seen between the high and low SWLS groups, the high and low positive emotion groups, or the high and low negative emotion groups. The multiple regression analysis showed that the mean LF/HF ratio during work affected the FS and SWLS scores, and the number of times the mean LF/HF ratio exceeded +3 SD had an effect on the positive emotion. No effect of the LF/HF ratio on negative emotions was shown. The LF/HF ratio might be applicable as an objective measure of well-being.

Using speech recognition technology to investigate the association between timing-related speech features and depression severity.

BACKGROUND: There are no reliable and validated objective biomarkers for the assessment of depression severity. We aimed to investigate the association between depression severity and timing-related speech features using speech recognition technology. METHOD: Patients with major depressive disorder (MDD), those with bipolar disorder (BP), and healthy controls (HC) were asked to engage in a non-structured interview with research psychologists. Using automated speech recognition technology, we measured three timing-related speech features: speech rate, pause time, and response time. The severity of depression was assessed using the Hamilton Depression Rating Scale 17-item version (HAMD-17). We conducted the current study to answer the following questions: 1) Are there differences in speech features among MDD, BP, and HC? 2) Do speech features correlate with depression severity? 3) Do changes in speech features correlate with within-subject changes in depression severity? RESULTS: We collected 1058 data sets from 241 individuals for the study (97 MDD, 68 BP, and 76 HC). There were significant differences in speech features among groups; depressed patients showed slower speech rate, longer pause time, and longer response time than HC. All timing-related speech features showed significant associations with HAMD-17 total scores. Longitudinal changes in speech rate correlated with changes in HAMD-17 total scores. CONCLUSIONS: Depressed individuals showed longer response time, longer pause time, and slower speech rate than healthy individuals, all of which were suggestive of psychomotor retardation. Our study suggests that speech features could be used as objective biomarkers for the assessment of depression severity.

The project for objective measures using computational psychiatry technology (PROMPT): Rationale, design, and methodology.

INTRODUCTION: Depressive and neurocognitive disorders are debilitating conditions that account for the leading causes of years lived with disability worldwide. However, there are no biomarkers that are objective or easy-to-obtain in daily clinical practice, which leads to difficulties in assessing treatment response and developing new drugs. New technology allows quantification of features that clinicians perceive as reflective of disorder severity, such as facial expressions, phonic/speech information, body motion, daily activity, and sleep. METHODS: Major depressive disorder, bipolar disorder, and major and minor neurocognitive disorders as well as healthy controls are recruited for the study. A psychiatrist/psychologist conducts conversational 10-min interviews with participants ≤10 times within up to five years of follow-up. Interviews are recorded using RGB and infrared cameras, and an array microphone. As an option, participants are asked to wear wrist-band type devices during the observational period. Various software is used to process the raw video, voice, infrared, and wearable device data. A machine learning approach is used to predict the presence of symptoms, severity, and the improvement/deterioration of symptoms. DISCUSSION: The overall goal of this proposed study, the Project for Objective Measures Using Computational Psychiatry Technology (PROMPT), is to develop objective, noninvasive, and easy-to-use biomarkers for assessing the severity of depressive and neurocognitive disorders in the hopes of guiding decision-making in clinical settings as well as reducing the risk of clinical trial failure. Challenges may include the large variability of samples, which makes it difficult to extract the features that commonly reflect disorder severity. TRIAL REGISTRATION: UMIN000021396, University Hospital Medical Information Network (UMIN).

Speech Quality Feature Analysis for Classification of Depression and Dementia Patients.

Loss of cognitive ability is commonly associated with dementia, a broad category of progressive brain diseases. However, major depressive disorder may also cause temporary deterioration of one's cognition known as pseudodementia. Differentiating a true dementia and pseudodementia is still difficult even for an experienced clinician and extensive and careful examinations must be performed. Although mental disorders such as depression and dementia have been studied, there is still no solution for shorter and undemanding pseudodementia screening. This study inspects the distribution and statistical characteristics from both dementia patient and depression patient, and compared them. It is found that some acoustic features were shared in both dementia and depression, albeit their correlation was reversed. Statistical significance was also found when comparing the features. Additionally, the possibility of utilizing machine learning for automatic pseudodementia screening was explored. The machine learning part includes feature selection using LASSO algorithm and support vector machine (SVM) with linear kernel as the predictive model with age-matched symptomatic depression patient and dementia patient as the database. High accuracy, sensitivity, and specificity was obtained in both training session and testing session. The resulting model was also tested against other datasets that were not included and still performs considerably well. These results imply that dementia and depression might be both detected and differentiated based on acoustic features alone. Automated screening is also possible based on the high accuracy of machine learning results.

Evaluating the severity of depressive symptoms using upper body motion captured by RGB-depth sensors and machine learning in a clinical interview setting: A preliminary study.

BACKGROUND: Mood disorders have long been known to affect motor function. While methods to objectively assess such symptoms have been used in experiments, those same methods have not yet been applied in clinical practice because the methods are time-consuming, labor-intensive, or invasive. METHODS: We videotaped the upper body of each subject using a Red-Green-Blue-Depth (RGB-D) sensor during a clinical interview setting. We then examined the relationship between depressive symptoms and body motion by comparing the head motion of patients with major depressive disorders (MDD) and bipolar disorders (BD) to the motion of healthy controls (HC). Furthermore, we attempted to predict the severity of depressive symptoms by using machine learning. RESULTS: A total of 47 participants (HC, n = 16; MDD, n = 17; BD, n = 14) participated in the study, contributing to 144 data sets. It was found that patients with depression move significantly slower compared to HC in the 5th percentile and 50th percentile of motion speed. In addition, Hamilton Depression Rating Scale (HAMD)-17 scores correlated with 5th percentile, 50th percentile, and mean speed of motion. Moreover, using machine learning, the presence and/or severity of depressive symptoms based on HAMD-17 scores were distinguished by a kappa coefficient of 0.37 to 0.43. LIMITATIONS: Limitations include the small number of subjects, especially the number of severe cases and young people. CONCLUSIONS: The RGB-D sensor captured some differences in upper body motion between depressed patients and controls. If much larger samples are accumulated, machine learning may be useful in identifying objective measures for depression in the future.

Evaluating depression with multimodal wristband-type wearable device: screening and assessing patient severity utilizing machine-learning.

OBJECTIVE: 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.

Survey of the effects of internet usage on the happiness of Japanese university students.

BACKGROUND: Besides research on psychiatric diseases related to problematic Internet use (PIU), a growing number of studies focus on the impact of Internet on subjective well-being (SWB). However, in previous studies on the relationship between PIU and SWB, there is little data for Japanese people specifically, and there is a lack of consideration for differences in perception of happiness due to cultural differences. Therefore, we aimed to clarify how happiness is interdependent on PIU measures, with a focus on how the concept of happiness is interpreted among Japanese people, and specifically among Japanese university students. METHODS: A paper-based survey was conducted with 1258 Japanese university students. Respondents were asked to fill out self-report scales regarding their happiness using the Interdependent Happiness Scale (IHS). The relationship between IHS and Internet use (Japanese version of the Internet addiction test, JIAT), use of social networking services, as well as social function and sleep quality (Pittsburgh Sleep Quality Index, PSQI) were sought using multiple regression analyses. RESULTS: Based on multiple regression analyses, the following factors related positively to IHS: female gender and the number of Twitter followers. Conversely, the following factors related negatively to IHS: poor sleep, high- PIU, and the number of times the subject skipped a whole day of school. CONCLUSIONS: It was shown that there was a significant negative correlation between Japanese youths' happiness and PIU. Since epidemiological research on happiness that reflects the cultural background is still scarce, we believe future studies shall accumulate similar evidence in this regard.

Actigraphy for evaluation of mood disorders: A systematic review and meta-analysis.

BACKGROUND: Actigraphy has enabled consecutive observation of individual health conditions such as sleep or daily activity. This study aimed to examine the usefulness of actigraphy in evaluating depressive and/or bipolar disorder symptoms. METHOD: A systematic review and meta-analysis was conducted. We selected studies that used actigraphy to compare either patients vs. healthy controls, or pre- vs. post-treatment data from the same patient group. Common actigraphy measurements, namely daily activity and sleep-related data, were extracted and synthesized. RESULTS: Thirty-eight studies (n = 3,758) were included in the analysis. Compared with healthy controls, depressive patients were less active (standardized mean difference; SMD=1.27, 95%CI=[0.97, 1.57], P<0.001) and had longer wake after sleep onset (SMD= - 0.729, 95%CI=[- 1.20, - 0.25], p = 0.003). Total sleep time (SMD= - 0.33, 95%CI=[- 0.55, - 0.11], P = 0.004), sleep latency (SMD= - 0.22, 95%CI=[- 0.42, - 0.02], P = 0.032), and wake after sleep onset (SMD= - 0.22, 95%CI=[- 0.39, - 0.04], P = 0.015) were longer in euthymic/remitted patients compared to healthy controls. In pre- and post-treatment comparisons, sleep latency (SMD=- 0.85, 95%CI=[- 1.53, - 0.17], P = 0.015), wake after sleep onset (SMD= - 0.65, 95%CI=[- 1.20, - 0.10], P = 0.022), and sleep efficiency (SMD=0.77, 95%CI=[0.29, 1.24], P = 0.002) showed significant improvement. LIMITATION: The sample sizes for each outcome were small. The type of actigraphy devices and patients' illness severity differed across studies. It is possible that hospitalizations and medication influenced the outcomes. CONCLUSION: We found significant differences between healthy controls and mood disorders patients for some actigraphy-measured modalities. Specific measurement patterns characterizing each mood disorder/status were also found. Additional actigraphy data linked to severity and/or treatment could enhance the clinical utility of actigraphy.

Utilization of Facial Image Analysis Technology for Blink Detection: A Validation Study.

PURPOSE: The assessment of anterior eye diseases and the understanding of psychological functions of blinking can benefit greatly from a validated blinking detection technology. In this work, we proposed an algorithm based on facial recognition built on current video processing technologies to automatically filter and analyze blinking movements. We compared electrooculography (EOG), the gold standard of blinking measurement, with manual video tape recording counting (mVTRc) and our proposed automated video tape recording analysis (aVTRa) in both static and dynamic conditions to validate our aVTRa method. METHODS: We measured blinking in both static condition, where the subject was sitting still with chin fixed on the table, and dynamic condition, where the subject's face was not fixed and natural communication was taking place between the subject and interviewer. We defined concordance of blinks between measurement methods as having less than 50 ms difference between eyes opening and closing. RESULTS: The subjects consisted of seven healthy Japanese volunteers (3 male, four female) without significant eye disease with average age of 31.4±7.2. The concordance of EOG vs. aVTRa, EOG vs. mVTRc, and aVTRa vs. mVTRc (average±SD) were found to be 92.2±10.8%, 85.0±16.5%, and 99.6±1.0% in static conditions and 32.6±31.0%, 28.0±24.2%, and 98.5±2.7% in dynamic conditions, respectively. CONCLUSIONS: In static conditions, we have found a high blink concordance rate between the proposed aVTRa versus EOG, and confirmed the validity of aVTRa in both static and dynamic conditions.

Associations between problematic Internet use and psychiatric symptoms among university students in Japan.

AIM: Research on the adverse effects of Internet use has gained importance recently. However, there is currently insufficient data on Japanese young adults' Internet use, so we conducted a survey targeting Japanese university students to research problematic Internet use (PIU). We also investigated the relationship between PIU and multiple psychiatric symptoms. METHODS: A paper-based survey was conducted at five universities in Japan. Respondents were asked to fill out self-report scales regarding their Internet dependency using the Internet Addiction Test (IAT). Sleep quality, attention-deficit hyperactivity disorder (ADHD) tendency, depression, and anxiety symptom data were also collected based on respective self-reports. RESULTS: There were 1336 responses and 1258 were included in the analysis. The mean IAT score (± SD) was 37.87 ± 12.59; and 38.2% of participants were classified as PIU, and 61.8% as non-PIU. The trend level for young women showed that they were more likely to be classified as PIU than young men (40.6% and 35.2% respectively, P = 0.05). Compared to the non-PIU group, the PIU group used the Internet longer (P < 0.001), had significantly lower sleep quality (P < 0.001), had stronger ADHD tendencies (P < 0.001), had higher Depression scores (P < 0.001), and had higher Trait-Anxiety scores (P < 0.001). Based on multiple logistic regression analyses, the factors that contributed to an increased risk of PIU were: being female (odds ratio [OR] = 1.52), being older (OR = 1.17), having poor sleep quality (OR = 1.52), having ADHD tendencies (OR = 2.70), having depression (OR = 2.24), and having anxiety tendencies (OR = 1.43). CONCLUSION: 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.

The Relationship of Dry Eye Disease with Depression and Anxiety: A Naturalistic Observational Study.

PURPOSE: In recent years, the relationship between dry eye disease (DED) and psychiatric disorders has been gaining attention. The relationship between dry eye symptoms and psychiatric symptoms has been reported in multiple retrospective studies. However, in previous studies there have been limitations to these observations, such as a lack of close examination of either DED or mood symptoms. METHODS: In this study, we evaluated the psychological state and social functionality of DED patients by administering validated psychiatric tests as well as ophthalmologic examinations twice during the course of DED treatment. Forty subjects (61.3 ± 18.1-years old) received the primary psychiatric assessments and 26 received the secondary psychiatric assessments. RESULTS: In a cross-sectional examination, we found patients with depressive and/or anxiety symptoms had higher Dry Eye Related Quality of Life Score (DEQ) scores, whereas the objective symptoms of DED did not differ between groups. We also found a positive relationship between depression/anxiety scores and DED subjective symptoms. On the other hand, in longitudinal examination, we found psychiatric symptoms had no impact on subjective and objective DED symptoms throughout the course of DED symptoms. CONCLUSIONS: We found depression and anxiety were related to the subjective symptoms of DED but not the objective symptoms. TRANSLATIONAL RELEVANCE: It is important to pay attention to psychiatric symptoms in patients with DED and an investigation into appropriate treatment strategies for patients with DED in combination with psychiatric symptoms is needed in the future.

Two Days' Sleep Debt Causes Mood Decline During Resting State Via Diminished Amygdala-Prefrontal Connectivity.

Study objectives: Sleep debt (SD) has been suggested to evoke emotional instability by diminishing the suppression of the amygdala by the medial prefrontal cortex (MPFC). Here, we investigated how short-term SD affects resting-state functional connectivity between the amygdala and MPFC, self-reported mood, and sleep parameters. Methods: Eighteen healthy adult men aged 29 ± 8.24 years participated in a 2-day sleep control session (SC; time in bed [TIB], 9 hours) and 2-day SD session (TIB, 3 hours). On day 2 of each session, resting-state functional magnetic resonance imaging was performed, followed immediately by measuring self-reported mood on the State-Trait Anxiety Inventory-State subscale (STAI-S). Results: STAI-S score was significantly increased, and functional connectivity between the amygdala and MPFC was significantly decreased in SD compared with SC. Significant correlations were observed between reduced rapid eye movement (REM) sleep and reduced left amygdala-MPFC functional connectivity (FCL_amg-MPFC) and between reduced FCL_amg-MPFC and increased STAI-S score in SD compared with SC. Conclusions: These findings suggest that reduced MPFC functional connectivity of amygdala activity is involved in mood deterioration under SD, and that REM sleep reduction is involved in functional changes in the corresponding brain regions. Having adequate REM sleep may be important for mental health maintenance.

Smartphone viewing distance and sleep: an experimental study utilizing motion capture technology.

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.

Protective effect of blue-light shield eyewear for adults against light pollution from self-luminous devices used at night.

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.