Published papers

INTRODUCTION: The COVID-19 pandemic has had a profound impact on the mental health of people around the world. Anxiety related to infection, stress and stigma caused by the forced changes in daily life have reportedly increased the incidence and symptoms of depression, anxiety disorder and obsessive-compulsive disorder. Under such circumstances, telepsychiatry is gaining importance and attracting a great deal of attention. However, few large pragmatic clinical trials on the use of telepsychiatry targeting multiple psychiatric disorders have been conducted to date. METHODS: The targeted study cohort will consist of adults (>18 years) who meet the DSM-5 diagnostic criteria for either (1) depressive disorders, (2) anxiety disorders, or (3) obsessive-compulsive and related disorders. Patients will be assigned in a 1:1 ratio to either a "telepsychiatry group" (at least 50% of treatments to be conducted using telemedicine, with at least one face-to-face treatment [FTF] within six months) or an "FTF group" (all treatments to be conducted FTF, with no telemedicine). Both groups will receive the usual treatment covered by public medical insurance. The study will utilize a master protocol design in that there will be primary and secondary outcomes for the entire group regardless of diagnosis, as well as the outcomes for each individual disorder group. DISCUSSION: This study will be a non-inferiority trial to test that the treatment effect of telepsychiatry is not inferior to that of FTF alone. This study will provide useful insights into the effect of the COVID-19 pandemic on the practice of psychiatry. TRIAL REGISTRATION: jRCT1030210037, Japan Registry of Clinical Trials (jRCT).

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].

BACKGROUND: The COVID-19 pandemic has transformed healthcare significantly and telepsychiatry is now the primary means of treatment in some countries. AIMS: To compare the efficacy of telepsychiatry and face-to-face treatment. METHOD: A comprehensive meta-analysis comparing telepsychiatry with face-to-face treatment for psychiatric disorders. The primary outcome was the mean change in the standard symptom scale scores used for each psychiatric disorder. Secondary outcomes included all meta-analysable outcomes, such as all-cause discontinuation and safety/tolerability. RESULTS: We identified 32 studies (n = 3592 participants) across 11 mental illnesses. Disease-specific analyses showed that telepsychiatry was superior to face-to-face treatment regarding symptom improvement for depressive disorders (k = 6 studies, n = 561; standardised mean difference s.m.d. = -0.325, 95% CI -0.640 to -0.011, P = 0.043), whereas face-to-face treatment was superior to telepsychiatry for eating disorder (k = 1, n = 128; s.m.d. = 0.368, 95% CI 0.018-0.717, P = 0.039). No significant difference was seen between telepsychiatry and face-to-face treatment when all the studies/diagnoses were combined (k = 26, n = 2290; P = 0.248). Telepsychiatry had significantly fewer all-cause discontinuations than face-to-face treatment for mild cognitive impairment (k = 1, n = 61; risk ratio RR = 0.552, 95% CI 0.312-0.975, P = 0.040), whereas the opposite was seen for substance misuse (k = 1, n = 85; RR = 37.41, 95% CI 2.356-594.1, P = 0.010). No significant difference regarding all-cause discontinuation was seen between telepsychiatry and face-to-face treatment when all the studies/diagnoses were combined (k = 27, n = 3341; P = 0.564). CONCLUSIONS: Telepsychiatry achieved a symptom improvement effect for various psychiatric disorders similar to that of face-to-face treatment. However, some superiorities/inferiorities were seen across a few specific psychiatric disorders, suggesting that its efficacy may vary according to disease type.

Aim Telepsychiatry is increasingly integral to psychiatric practice. However, few reviews have examined the concordance between assessments conducted in telepsychiatric and face‐to‐face settings. This systematic review and meta‐analysis evaluated the agreement between telepsychiatric and face‐to‐face settings in the diagnosis and symptom assessment of various psychiatric disorders. Methods A literature search was conducted using MEDLINE/PubMed, Cochrane Library, Scopus, EMBASE, CINAHL, and PsycINFO. Studies evaluating the concordance between telepsychiatric and face‐to‐face settings in the diagnosis and symptom assessment of various psychiatric disorders were included and analyzed. Results Of the 6875 studies in the initial search, 22 studies that met the inclusion criteria were included in the analyses. The diagnostic concordance for 16 psychiatric disorders was “almost perfect” between the two settings (N = 16, n = 848, Cohen's κ = 0.824, confidence interval [CI] = 0.466 to 0.950, P < 0.001). Additionally, the concordance for the symptom rating scales between the two settings ranged from “substantial” in the Brief Psychiatric Rating Scale (N = 1, n = 533, Cohen's κ = 0.789, CI = 0.699 to 0.855, P < 0.001) to “almost perfect” in the Autism Diagnostic Observation Schedule (N = 1, n = 92, intraclass correlation coefficients = 0.943, CI = 0.798 to 0.985, P < 0.001). Conclusions Telepsychiatry is highly concordant with face‐to‐face settings regarding psychiatric diagnoses and symptom assessment. In contrast, the present results also suggest that the suitability of telepsychiatry varies across disease types, specific symptoms, and assessment modalities. Although the present results must be interpreted with caution owing to the small number of studies for each assessment and disease, our findings suggest that telepsychiatry may have greater utility in psychiatric diagnostic assessment settings.

Online Ahead of Print:December 16, 2021