2021-01-012024-05-18https://scholars.lib.ntu.edu.tw/handle/123456789/701438"躁鬱症與重鬱症屬於情緒疾患，是一常見且嚴重的精神疾病類別，常伴隨反覆發作的特性而致慢性之病程，影響病人日常生活功能甚鉅，為全球疾病負擔之前幾名，也成為二十一世紀重要公共衛生議題之一。情緒疾患的特性為患者在單一的鬱症，或是躁症與鬱症兩類極端情緒間反覆發作，然至今尚未能夠以客觀的測量數據或生物標記來協助精準的診斷，或是預測疾病病程中之重要變項或預後，如躁症或鬱症發作類型間的轉換、治療反應等，亦無有效的監測系統能預報躁症或鬱症的復發，至於病患治療後功能性的評估也常闕如。因此建構一客觀的評量工具系統性平台，不但可以為早期診斷立下基礎，更可以作為治療方案的依據，順勢而上，亦可扮演復發監控的預後觀察重要角色，再者，評量工具的數據亦可作為研究情緒疾患背後生物性調控機轉的重要量化資訊。 此計畫的目的為大量的收集各個躁症和鬱症患者與健康控制組的詳細表型數據，包含藉由診斷式的訪談與主觀性自評問卷、主動且客觀式的數據收集(如配戴裝置Actiwatch數據)、被動式的數據收集(如手機內建感測數據)等，取得客觀與主觀的大量臨床表徵數據，並進行前瞻式追蹤研究，了解病人在疾病病程中詳細表型數據的波動。利用以上方法收集的數據，透過整合性的分析架構，將各層面的表徵以及數據整併為一預測平台，並利用一群獨立的新病患樣本進行平台的預測效度評估，以預估病患的復發風險與發作類型症狀、治療效果和治療後的功能性表現等。我們期望此研究的結果能夠加速設立情緒疾患預後的即時監控，以及追蹤治療效果的基礎，在未來能夠在臨床上幫助醫護專業人員，提供病患更即時又適切的幫助。" Mood disorders, specifically bipolar disorder (BPD) and major depressive disorder (MDD) belong to a category of severe psychiatric disorder that causes high disease burden worldwide, for which patients experience disruptive mood swings and impairment of daily function. A special characteristic in mood disorders is its episodic feature in nature. However, there are no objective measurements or biomarkers that can be easily implemented for diagnosing mood disorders nor to predict the relapse or recurrence of manic or depressive episodes and functional outcomes in patients. To have better assessment tools would form important foundation for assisting diagnosis, advising treatment options, monitoring patients’ outcomes, as well as investigating the underlying biological mechanisms of mood disorders. The aim of this proposal is to extensively collect phenotypic data of a variety of dimensions for patients with BPD and MDD as well as healthy controls using approaches of diagnostic interview and self-report, active (e.g. wearable devise) and passive (e.g. mobile sensors) data collection. Additionally, data collection is for patients at different disease status, including acute episode, inter-episode, and remission state. We aim to develop an integrative analytic framework to incorporate different dimensions of phenotypic and digital data collection for feature identification, and building up optimal prediction models for validation of outcomes prediction in an independent sample, including patients’ recurrence, episodic switches, treatment response, and functional outcomes. We anticipate our results to serve as the foundation for designing more timely intervention by monitoring patients’ disease course, and treatment regimen for improving patients’ functional outcomes and care delivering in the near future.急性發作功能性預後數位表型活動力節律episodefunctional outcomesdigital phenotypingactivityrhythm