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Objective: To identify the COVID-19 impact on well-being/mental health, coping strategies and risk factors in adolescent worldwide., Method: Anonymous online multi-national/language survey in the general population (representative/weighted non-representative samples, 14-17years), measuring change in well-being (WHO-5/range=0-100) and psychopathology (validated composite P-score/range=0-100), WHO-5 <50 and <29, pre- versus during COVID-19 pandemic (26/04/2020-26/06/2022). Coping strategies, nine a-priori defined individual/cumulative risk factors were measured. χ 2 , penalized cubic splines, linear regression, and correlation analyses were conducted., Results: Analyzing 8,115 of 8,762 initiated surveys (representative=75.1%), the pre-pandemic WHO-5 and P-score remained stable during the study (excluding relevant recall bias/drift), but worsened intra-pandemic by 5.55±17.13 (standard deviation) and 6.74±16.06 points, respectively (effect size d=0.27 and d=0.28). The proportion of adolescents with WHO-5 scores suggesting depression screening (<50) and major depression (<29) increased from 9% to 17% and 2% to 6%. WHO-5 worsened (descending magnitude, with cumulative effect) in adolescents with a mental or physical disorder, female gender, and with school closure. Results were similar for P-score, with the exception of school closure (not significant) and living in a low-income country, as well as not living in a large city (significant). Changes were significantly but minimally related to COVID-19 deaths/restrictions, returning to near-pre-pandemic values after >2 years. The three most subjectively effective coping strategies were internet use, exercise/walking, and social contacts., Conclusion: Overall, well-being/mental health worsened (small effect sizes) during early stages of COVID-19, especially in vulnerable subpopulations. Identified at-risk groups, association with pandemic-related measures, and coping strategies can inform individual behaviours and global public health strategies., (Copyright © 2024. Published by Elsevier Inc.)

There is no multi-country/multi-language study testing a-priori multivariable associations between non-modifiable/modifiable factors and validated wellbeing/multidimensional mental health outcomes before/during the COVID-19 pandemic. Moreover, studies during COVID-19 pandemic generally do not report on representative/weighted non-probability samples. The Collaborative Outcomes study on Health and Functioning during Infection Times (COH-FIT) is a multi-country/multi-language survey conducting multivariable/LASSO-regularized regression models and network analyses to identify modifiable/non-modifiable factors associated with wellbeing (WHO-5)/composite psychopathology (P-score) change. It enrolled general population-representative/weighted-non-probability samples (26/04/2020-19/06/2022). Participants included 121,066 adults (age=42±15.9 years, females=64 %, representative sample=29 %) WHO-5/P-score worsened (SMD=0.53/SMD=0.74), especially initially during the pandemic. We identified 15 modifiable/nine non-modifiable risk and 13 modifiable/three non-modifiable protective factors for WHO-5, 16 modifiable/11 non-modifiable risk and 10 modifiable/six non-modifiable protective factors for P-score. The 12 shared risk/protective factors with highest centrality (network-analysis) were, for non-modifiable factors, country income, ethnicity, age, gender, education, mental disorder history, COVID-19-related restrictions, urbanicity, physical disorder history, household room numbers and green space, and socioeconomic status. For modifiable factors, we identified medications, learning, internet, pet-ownership, working and religion as coping strategies, plus pre-pandemic levels of stress, fear, TV, social media or reading time, and COVID-19 information. In multivariable models, for WHO-5, additional non-modifiable factors with |B|>1 were income loss, COVID-19 deaths. For modifiable factors we identified pre-pandemic levels of social functioning, hobbies, frustration and loneliness, and social interactions as coping strategy. For P-scores, additional non-modifiable/modifiable factors were income loss, pre-pandemic infection fear, and social interactions as coping strategy. COH-FIT identified vulnerable sub-populations and actionable individual/environmental factors to protect well-being/mental health during crisis times. Results inform public health policies, and clinical practice., Competing Interests: Conflict of interest Conflict of interest statements of all authors are detailed in eTable 12., (Copyright © 2024. Published by Elsevier B.V.)

Abnormalities in MYO5B , which encodes an unconventional myosin Vb, not only cause microvillus inclusion disease but also cholestatic liver disease, including benign recurrent intrahepatic cholestasis (BRIC). However, MYO5B -related cholestasis has not yet been reported in Japan. In this study, we present the case of a female patient in her thirties, who had developed jaundice, without diarrhea, in the first year after birth. The jaundice spontaneously subsided and occasionally recurred. Whole-exome sequencing identified two pathogenic variants in MYO5B : a nonsense mutation (c. G1124A: p. W375X) and a missense mutation (c.C2470T: p.R824C). Therefore, the patient was diagnosed with MYO5B -associated BRIC. This is the first reported case of cholestasis with a defined MYO5B defect in Japan.

A man in his 70s came to our department with jaundice. He was diagnosed with Merkel cell carcinoma in the left eyebrow area 3 years ago and was followed up after tumor resection. Endoscopic retrograde cholangiopancreatography was performed because the computed tomography and abdominal ultrasonography revealed bile duct dilatation. However, the duodenum was too narrow and deformed to reach the ventral papilla. Percutaneous transhepatic cholangiodrainage was performed, and bile cytology revealed no malignant findings; however, it did not sufficiently reduce the jaundice. Subsequently, endoscopic ultrasound-fine needle biopsy was performed on his right adrenal mass, which led to a diagnosis of the rare Merkel cell carcinoma. In summary, we report here of a case in which endoscopic ultrasound-fine needle biopsy led to the diagnosis of Merkel cell carcinoma recurrence.

International studies measuring wellbeing/multidimensional mental health before/ during the COVID-19 pandemic, including representative samples for >2 years, identifying risk groups and coping strategies are lacking. COH-FIT is an online, international, anonymous survey measuring changes in well-being (WHO-5) and a composite psychopathology P-score, and their associations with COVID-19 deaths/restrictions, 12 a-priori defined risk individual/cumulative factors, and coping strategies during COVID-19 pandemic (26/04/2020-26/06/2022) in 30 languages (representative, weighted non-representative, adults). T-test, χ 2 , penalized cubic splines, linear regression, correlation analyses were conducted. Analyzing 121,066/142,364 initiated surveys, WHO-5/P-score worsened intra-pandemic by 11.1±21.1/13.2±17.9 points (effect size d=0.50/0.60) (comparable results in representative/weighted non-probability samples). Persons with WHO-5 scores indicative of depression screening (<50, 13% to 32%) and major depression (<29, 3% to 12%) significantly increased. WHO-5 worsened from those with mental disorders, female sex, COVID-19-related loss, low-income country location, physical disorders, healthcare worker occupations, large city location, COVID-19 infection, unemployment, first-generation immigration, to age=18-29 with a cumulative effect. Similar findings emerged for P-score. Changes were significantly but minimally related to COVID-19 deaths, returning to near-pre-pandemic values after >2 years. The most subjectively effective coping strategies were exercise and walking, internet use, social contacts. Identified risk groups, coping strategies and outcome trajectories can inform global public health strategies., Competing Interests: Declaration of competing interest Conflict of interest statements of all authors are detailed in eTable 8., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Background: Frail older adults require nursing care following hospitalization for acute illnesses. Frailty is reversible, and appropriate nutritional management and rehabilitation during hospitalization are essential. However, optimal nutritional management for patients who are unable to obtain adequate nutrition via oral intake has not been established. We aimed to determine whether peripheral parenteral nutrition (PPN) promotes the recovery of activities of daily living (ADLs) in frail older patients., Methods: This was a retrospective, observational cohort study conducted at the General Medicine Department of Aomori Prefectural Central Hospital in Aomori, Japan. The primary outcome was recovery of the Barthel index (BI) from the beginning of rehabilitation to discharge, and the secondary outcomes were the proportion of patients transferred for rehabilitation and the nutritional status., Results: In total, 342 patients hospitalized during the period of April 2018 to January 2022 were included, of whom 127 (37.1%) received PPN and 215 (62.9%) did not. Contrary to our expectations, recovery of the BI was lower in the PPN group than that in the non-PPN group (12.2 (95% confidence interval (CI): 8.5-16.0) vs. 22.4 (18.8-23.0); p < 0.01). Multivariable analysis revealed PPN as an independent risk factor for poor BI recovery (mean difference = -7.3 (95% CI = -12.7 to -1.9))., Conclusion: Nutritional management through PPN for frail older adults may not improve physical activity. The nutritional management of frail patients with inadequate oral intake remains challenging., Competing Interests: The authors have declared that no competing interests exist., (Copyright © 2023, Soma et al.)

Background: The Collaborative Outcome study on Health and Functioning during Infection Times (COH-FIT; www.coh-fit.com) is an anonymous and global online survey measuring health and functioning during the COVID-19 pandemic. The aim of this study was to test concurrently the validity of COH-FIT items and the internal validity of the co-primary outcome, a composite psychopathology "P-score"., Methods: The COH-FIT survey has been translated into 30 languages (two blind forward-translations, consensus, one independent English back-translation, final harmonization). To measure mental health, 1-4 items ("COH-FIT items") were extracted from validated questionnaires (e.g. Patient Health Questionnaire 9). COH-FIT items measured anxiety, depressive, post-traumatic, obsessive-compulsive, bipolar and psychotic symptoms, as well as stress, sleep and concentration. COH-FIT Items which correlated r ≥ 0.5 with validated companion questionnaires, were initially retained. A P-score factor structure was then identified from these items using exploratory factor analysis (EFA) and confirmatory factor analyses (CFA) on data split into training and validation sets. Consistency of results across languages, gender and age was assessed., Results: From >150,000 adult responses by May 6th, 2022, a subset of 22,456 completed both COH-FIT items and validated questionnaires. Concurrent validity was consistently demonstrated across different languages for COH-FIT items. CFA confirmed EFA results of five first-order factors (anxiety, depression, post-traumatic, psychotic, psychophysiologic symptoms) and revealed a single second-order factor P-score, with high internal reliability (ω = 0.95). Factor structure was consistent across age and sex., Conclusions: COH-FIT is a valid instrument to globally measure mental health during infection times. The P-score is a valid measure of multidimensional mental health., Competing Interests: Conflict of interest Conflict of interest statements of all authors are detailed in Supplementary Table 7., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Therapeutic drug monitoring (TDM) is recommended for voriconazole (VRCZ) to avoid adverse events and maximize antifungal efficacy. Currently, the appropriate dose for patients under the age of 2 years is unknown. Here, we report the case of a 1.5-month-old infant with inborn errors of immunity who was orally administered VRCZ. This patient's plasma concentration decreased significantly from 3.8 µg/mL (day 6) to 0.09 µg/mL (day 21), leading to repeated dose escalations to achieve the target concentration (1.38 µg/mL, day 58). The signal intensity ratio of VRCZ to its main metabolite, N-oxide VRCZ, in LC/MS/MS also decreased from 5.30 (day 6) to 0.57 (day 64). Consequently, we suspected that VRCZ metabolism may be enhanced during infant growth. To our knowledge, this is the first report of remarkable changes in VRCZ pharmacokinetics with metabolic activity enhanced by the growth process. In conclusion, we propose that frequent TDM helped to maintain adequate VRCZ plasma concentration in a infants less than 6 months of age.

Introduction: Few biomarkers can be used clinically to diagnose and assess the severity of depression. However, a decrease in activity and sleep efficiency can be observed in depressed patients, and recent technological developments have made it possible to measure these changes. In addition, physiological changes, such as heart rate variability, can be used to distinguish depressed patients from normal persons; these parameters can be used to improve diagnostic accuracy. The proposed research will explore and construct machine learning models capable of detecting depressive episodes and assessing their severity using data collected from wristband-type wearable devices., Methods and Analysis: Patients with depressive symptoms and healthy subjects will wear a wristband-type wearable device for 7 days; data on triaxial acceleration, pulse rate, skin temperature, and ultraviolet light will be collected. On the seventh day of wearing, the severity of depressive episodes will be assessed using Structured Clinical Interview for DSM-5 (SCID-5), Hamilton Depression Rating Scale (HAMD), and other scales. Data for up to five 7-day periods of device wearing will be collected from each subject. Using wearable device data associated with clinical symptoms as supervisory data, we will explore and build a machine learning model capable of identifying the presence or absence of depressive episodes and predicting the HAMD scores for an unknown data set., Discussion: Our machine learning model could improve the clinical diagnosis and management of depression through the use of a wearable medical device., Clinical Trial Registration: [https://jrct.niph.go.jp/latest-detail/jRCT1031210478], identifier [jRCT1031210478]., Competing Interests: TaK was the president of i2medical LLC and has received consultant fees from Otsuka, Pfizer, and Sumitomo and speaking fee from Banyu, Eli Lilly, Sumitomo, Janssen, Novartis, Otsuka, and Pfizer. TH was a board member of i2medical LLC and had received consulting fees from FRONTEO Inc. YTa, K-cL, and NK were a board member of i2medical LLC. YO, HI, YM, and YTs were employed by Sumitomo Pharma Co., Ltd. GM has received speaking fees from Janssen, Meiji Seika Pharma, Otsuka, and Yoshitomi Yakuhin. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Kishimoto, Kinoshita, Kikuchi, Bun, Kitazawa, Horigome, Tazawa, Takamiya, Hirano, Mimura, Liang, Koga, Ochiai, Ito, Miyamae, Tsujimoto, Sakuma, Kida, Miura, Kawade, Goto and Yoshino.)

Background: Previous studies have attempted to characterize depression using electroencephalography (EEG), but results have been inconsistent. New noise reduction technology allows EEG acquisition during conversation., Methods: We recorded EEG from 40 patients with depression as they engaged in conversation using a single-channel EEG device while conducting real-time noise reduction and compared them to those of 40 healthy subjects. Differences in EEG between patients and controls, as well as differences in patients' depression severity, were examined using the ratio of the power spectrum at each frequency. In addition, the effects of medications were examined in a similar way., Results: In comparing healthy controls and depression patients, significant power spectrum differences were observed at 3 Hz, 4 Hz, and 10 Hz and higher frequencies. In the patient group, differences in the power spectrum were observed between asymptomatic patients and healthy individuals, and between patients of each respective severity level and healthy individuals. In addition, significant differences were observed at multiple frequencies when comparing patients who did and did not take antidepressants, antipsychotics, and/or benzodiazepines. However, the power spectra still remained significantly different between non-medicated patients and healthy individuals., Limitations: The small sample size may have caused Type II error. Patients' demographic characteristics varied. Moreover, most patients were taking various medications, and cannot be compared to the non-medicated control group., Conclusion: A study with a larger sample size should be conducted to gauge reproducibility, but the methods used in this study could be useful in clinical practice as a biomarker of depression., Competing Interests: The authors have declared that no competing interests exist.

Objectives: We developed the Adachi Rehabilitation Programme (ARP), a community rehabilitation program. Under the supervision of professional caregivers, older adults cleaned and planted flowers in the park and they walked and shopped in the community. We examined the effects of ARP on individuals receiving small-group multifunctional at-home care at community facilities., Methods: This was a multi-centre controlled trial at thirteen small multifunctional at-home care facilities in Adachi, Tokyo. The primary outcomes of the study were daily step counts and timed up & go (TUG). Secondary outcomes included gait speed, step length, Barthel Index for Activities of Daily Living, Functional Independence Measure, Mini-Mental State Examination (MMSE) and EuroQOL 5 Dimension., Results: Ninety-six individuals at thirteen small multifunctional at-home care facilities were recruited for participation in December 2017. They were allocated to intervention (38) and control (40) groups. The average daily step count of the control group decreased from 852 to 727, but it increased by approximately 650 steps, from 990 to 1635, for the intervention group. Average TUG decreased from 16.1 s to 14.0 s and MMSE score increased from 15.9 to 16.3 for the intervention group, but a significant interaction was not found. On non-intervention home days, the daily step counts of the intervention group increased significantly from 908 steps to 1485 steps, while those of the control group decreased from 865 steps to 722 steps., Conclusions: ARP may have effectively increased the physical activity of older adults under long-term care by increasing motivation and changing behaviour., Competing Interests: The authors have declared that no competing interests exist.

Given the growing demand for environmentally friendly energy sources, thermoelectric energy conversion has attracted increased interest as a promising CO 2 -free technology. SnSe single crystals have attracted attention as a next generation thermoelectric material due to outstanding thermoelectric properties arising from ultralow thermal conductivity. For practical applications, on the other hand, polycrystalline SnSe should be also focused because the production cost and the flexibility for applications are important factors, which requires the systematic investigation of the stability of thermoelectric performance under a pseudo operating environment. Here, we report that the physical properties of SnSe crystals with nano to submicron scale are drastically modified by atmospheric annealing. We measured the Seebeck effect while changing the annealing time and found that the large positive thermopower, + 757 μV K -1 , was completely suppressed by annealing for only a few minutes and was eventually inverted to be the large negative value, - 427 μV K -1 . This result would further accelerate intensive studies on SnSe nanostructures, especially focusing on the realistic device structures and sealing technologies for energy harvesting applications.

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)., Competing Interests: T. Kishimoto has received consultant fees from Otsuka, Pfizer, and Dainippon Sumitomo, and speaker's honoraria from Banyu, Eli Lilly, Dainippon Sumitomo, Janssen, Novartis, Otsuka, and Pfizer. KF has received speaker's honoraria from Novartis and Otsuka. HT is an employee of FRONTEO. TH received speaker's honoraria from Yoishitomi. T. Kikuchi has received speaker's honoraria from Astellas, Dainippon Sumitomo, Eli Lilly, Janssen, MSD, Otsuka, Yoshitomi Yakuhin, Pfizer, and Takeda. JM has received speaker's honoraria from Eli Lilly, Janssen, Otsuka, MSD, Shionogi, and Pfizer. MM has received speaker's honoraria from Daiichi Sankyo, Dainippon-Sumitomo Pharma, Eisai, Eli Lilly, Fuji Film RI Pharma, Janssen Pharmaceutical, Mochida Pharmaceutical, MSD, Nippon Chemipher, Novartis Pharma, Ono Yakuhin, Otsuka Pharmaceutical, Pfizer, Takeda Yakuhin, Tsumura, and Yoshitomi Yakuhin. Also, he received grants from Daiichi Sankyo, Eisai, Pfizer, Shionogi, Takeda, Tanabe Mitsubishi, and Tsumura. Other authors have no conflict of interest., (© 2020 The Authors.)

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., Competing Interests: Declaration of competing interest Dr. Mimura has received grants and/or speaker's honoraria from Daiichi Sankyo, Dainippon-Sumitomo Pharma, Eisai, Eli Lilly, Fuji Film RI Pharma, Janssen Pharmaceutical, Mochida Pharmaceutical, MSD, Nippon Chemipher, Novartis Pharma, Ono Pharma, Otsuka Pharmaceutical, Pfizer, Takeda Pharma, Tsumura, and Yoshitomi Pharma within the past three years. Dr. Kishimoto has received consultant fees from Dainippon Sumitomo, Novartis, and Otsuka, and speaker's honoraria from Banyu, Eli Lilly, Dainippon Sumitomo, Janssen, Novartis, Otsuka, and Pfizer. He has received grant support from Pfizer Health Research Foundation, Dainippon Sumitomo, Otsuka, and Mochida., (Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.)

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., (© 2020 The Authors. Published by Elsevier Ltd.)

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., (Copyright © 2019. Published by Elsevier B.V.)

Functional plasticity of the sensorimotor cortex occurs following motor practice, as well as after limb amputation. However, the joint effect of limb amputation and intensive, long-term motor practice on cortical plasticity remains unclear. Here, we recorded brain activity during unilateral contraction of the hip, knee, and ankle joint muscles from a long jump Paralympic gold medalist with a unilateral below-knee amputation (Amputee Long Jumper, ALJ). He used the amputated leg with a prosthesis for take-off. Under similar conditions to the ALJ, we also recorded brain activity from healthy long jumpers (HLJ) and non-athletes with a below-knee amputation. During a rhythmic isometric contraction of knee extensor muscles with the take-off/prosthetic leg, the ALJ activated not only the contralateral primary sensorimotor cortex (M1/S1), but also the ipsilateral M1/S1. In addition, this ipsilateral M1/S1 activation was significantly greater than that seen in the HLJ. However, we did not find any significant differences between the ALJ and HLJ in M1/S1 activation during knee muscle contraction in the non-take-off/intact leg, nor during hip muscle contraction on either side. Region of interest analysis revealed that the ALJ exhibited a greater difference in M1/S1 activity and activated areas ipsilateral to the movement side between the take-off/prosthetic and non-take-off/intact legs during knee muscle contraction compared with the other two groups. However, difference in activity in M1/S1 contralateral to the movement side did not differ across groups. These results suggest that a combination of below-knee amputation and intensive, prolonged long jump training using a prosthesis (i.e. fine knee joint control) induced an expansion of the functional representation of the take-off/prosthetic leg in the ipsilateral M1/S1 in a muscle-specific manner. These results provide novel insights into the potential for substantial cortical plasticity with an extensive motor rehabilitation program., (Copyright © 2019 The Authors. Published by Elsevier Inc. All rights reserved.)