Source [[Article - Technology and Mental Health. State of the Art for Assessment and Treatment]] Currently there are still inconsistencies due to - Sensor data doesn't match across studies - Different sensors are used - Time frames used to calculate associations differ across studies - Different study methods and analyses. "However, there are numerous inconsistencies regarding the predictive value of specific metrics and measures for classifying individual disorders or symptom states, in- cluding geolocation, accelerometry, ambient speech, and ambulatory psychophysiology (113–116). For example, clinical data on sleep did not match sensor report in one study (94), and results are not comparable across studies because of differences in sensors utilized, in the clinical targets, in time frames for calculating associations across assessment modalities (e.g., daily or monthly), and in the populations studied. There are also fundamental differ- ences across studies in methods and analyses, such as controlling for multiple comparisons when examining correlational data." - 94 - Staples P, Torous J, Barnett I, et al: A comparison of passive and active estimates of sleep in a cohort with schizophrenia. NPJ Schizophrenia 2017; 3:37 - 113 - Fraccaro P, Beukenhorst A, Sperrin M, et al: Digital biomarkers from geolocation data in bipolar disorder and schizophrenia: a systematic review. J Am Med Inform Assoc 2019; 26:1412–1420 114. - 114 Or F, Torous J, Onnela JP: High potential but limited evidence: using voice data from smartphones to monitor and diagnose mood disorders. Psychiatr Rehabil J 2017; 40:320–324 - 115 Raugh IM, Chapman HC, Bartolomeo LA, et al: A comprehensive review of psychophysiological applications for ecological mo- mentary assessment in psychiatric populations. Psychol Assess 2019; 31:304–317 - 116 Tazawa Y, Wada M, Mitsukura Y, et al: Actigraphy for evaluation of mood disorders: a systematic review and meta-analysis. J Affect Disord 2019; 253:257–269