# Articles related - Utility of Consumer-Grade Wearable Devices for Inferring Physical and Mental Health Outcomes in Severe Mental Illness: Systematic Review - "Overall, 23 studies were included that reported data from 12 distinct studies, mostly using smartphones and centered on relapse prevention. Only 1 study explicitly aimed to address physical health outcomes among people with SMI. In total, data were included from over 500 participants with SMI, predominantly from high-income countries. Most commonly, papers presented physical activity data (n=18), followed by sleep and circadian rhythm data (n=14) and heart rate data (n=6). The use of smartwatches to support data collection were reported by 8 papers; the rest used only smartphones. There was some evidence that lower levels of activity, higher heart rates, and later and irregular sleep onset times were associated with psychiatric diagnoses or poorer symptoms. However, heterogeneity in devices, measures, sampling and statistical approaches complicated interpretation." - https://pubmed.ncbi.nlm.nih.gov/39773905/ - Classifying and clustering mood disorder patients using smartphone data from a feasibility study - https://www.nature.com/articles/s41746-023-00977-7 # Challenges - inconsistencies in accuracy. What is the definition of constructs not agreed about? - Source [[Article - Technology and Mental Health. State of the Art for Assessment and Treatment]] - "Relapse of psychotic disorders has been predicted by geolocation mobility metrics and text/call behavior (90)." ^86d66f - 90 - Barnett I, Onnela JP: Inferring mobility measures from GPS traces with missing data. Biostatistics 2020; 21:e98–e112 [[DP - Physical Activities]] - [[DP - Geolocation]] [[DP - Text Messages]] [[DP - Phone calls]] - [[Digital Phenotype Signals associated with Anxiety]] - [[Digital Phenotype Signals associated with Depression]] - [[Digital Phenotype Signals associated with Bipolar Disorder]] [[DP - Missing Data]] [[Sociability]] # [[Negative Symptoms]] - "Negative symptoms of schizophrenia measured via EMA or clinical ratings have been predicted by geolocation-based mobility metrics, voice activity, and actigraphy-based metrics of gesture and activity level (99, 107–110)." - 99 - Adler DA, Ben-Zeev D, Tseng VWS, et al: Predicting early warning signs of psychotic relapse from passive sensing data: an approach using encoder-decoder neural networks. JMIR Mhealth Uhealth 2020; 8:e19962 - 107 - Parrish EM, Depp CA, Moore RC, et al: Emotional determinants of life-space through GPS and ecological momentary assessment in schizophrenia: what gets people out of the house? Schizophr Res 2020; 224:67–73 - 108 - Depp CA, Bashem J, Moore RC, et al: GPS mobility as a digital biomarker of negative symptoms in schizophrenia: a case control study. NPJ Digit Med 2019; 2:108 - 109 Umbricht D, Cheng WY, Lipsmeier F, et al: Deep learning–based human activity recognition for continuous activity and gesture monitoring for schizophrenia patients with negative symptoms. Front Psychiatry 2020; 11:574375 - 110 - Wee ZY, Yong SWL, Chew QH, et al: Actigraphy studies and clinical and biobehavioural correlates in schizophrenia: a sys- tematic review. J Neural Transm (Vienna) 2019; 126:531–558 [[DP - Speech]] [[DP - Accelerometry]] - "Raugh et al. (111) found that the combination of geolocation and EMA surveys was a stronger predictor of clinically rated negative symptoms in schizophrenia than either measure alone." - 111- Raugh IM, James SH, Gonzalez CM, et al: Geolocation as a digital phenotyping measure of negative symptoms and functional out- come. Schizophr Bull 2020; 46:1596–1607