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Chronic pain is currently diagnosed using verbal self-reports, which present a challenge for patients with cognitive or physiological disorders. Prior work has explored machine learning prediction of pain from clinical data, which requires active user involvement and does not capture their behavior in natural settings. Passive objective assessment is desirable. Circadian Rhythms, including sleep-wake cycles, are biological processes that reoccur every 24 h and can be derived from physiological data such as heart rate, activity, and sleep, gathered using widely-owned smart wearables. This study investigated the feasibility of using machine learning and rest-activity circadian rhythm features to predict patients’ pain, including pain intensity, its interference with the patient’s life (dysregulation), and their difficulty in performing physical functions using passively gathered actigraphy data. We found that chronic pain predictions were more accurate using rest-activity rhythm features than sleep or activity features. Of all the rhythmic features, Intradaily Variability (IV) was the most predictive feature, with elevated values in pain associated with disturbed sleep. PainRhythms provides preliminary evidence that rest-activity rhythms can effectively detect subjects with chronic pain. In future work, we aim to gather more data and confirm our preliminary findings on a large, class-balanced and diverse dataset.

I have a good experience in in-vivo animal modeling and immunohistochemical techniques, designing in-vivo experimental paradigms that include behavioral testing, as well as standardizing and developing psychological assessment scales. Furthermore, I have a good experience in developing programs & curricula and was the scientific team leader for many projects for UAE government organizations. I am a licensed psychotherapist (CBT & mindfulness) and active in community service including conducting workshops and offering consultation to different government organization. Other social activities include psychoeducation & psychological support for PTSD victims. My research focuses on neuroprotective agents and neurodegenerative disorders.

Dr Gwladys Temkou Ngoupaye’s research interests include but not limited to understand the cognitive component of affective disorders and how it can be a prodromal stage to neurodegenerative disorders, as well as scaling epilepsy associated comorbidities. She has developed an animal model of depression and associated cognition that can be used in drugs discovery. The model features the phenotype of human depression and was able to induce cognitive in a shorter period (14 days) that what usually seen (21 to 28 days). She further displayed the molecular pathway altered in the model. She also modified the kindle epilepsy model by giving a challenging PTZ administration dose prior to sub effective dose rather than after, and later scale it in anxiety and depression comorbidities occurrence. She is also interested in investigating the role of novel alternative therapeutics like natural extracts and studying their function as either protective or therapeutic agents in affective disorders and epilepsy.

Passionate about understanding the role of early life stress adulthood.