{"id":"W3183239414","doi":"10.2196/24872","title":"Digital Biomarkers for Depression Screening With Wearable Devices: Cross-sectional Study With Machine Learning Modeling","year":2021,"lang":"en","type":"article","venue":"JMIR mhealth and uhealth","topic":"Digital Mental Health Interventions","field":"Psychology","cited_by":145,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Medicine; Patient Health Questionnaire; Depression (economics); Activity tracker; Wearable computer; Cross-sectional study; Confounding; Major depressive disorder; Physical therapy; Clinical psychology; Psychiatry; Anxiety; Mood; Physical activity; Internal medicine; Computer science; Depressive symptoms","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005119756,0.0005867869,0.0004775781,0.0006967275,0.0007079027,0.0008757781,0.000653505,0.00094335,0.001662465],"category_scores_gemma":[0.009249533,0.000809651,0.001217883,0.000976168,0.000306279,0.0009302903,0.0007362198,0.001408724,0.0006857842],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002224259,"about_ca_system_score_gemma":0.0003476615,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002880286,"about_ca_topic_score_gemma":0.002307293,"domain_scores_codex":[0.9973907,0.001380207,0.0001802651,0.0005727409,0.0002982581,0.0001778736],"domain_scores_gemma":[0.9944946,0.001986284,0.001145221,0.001259687,0.0007575328,0.0003566093],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000258883,0.0005353976,0.9962314,0.0000146526,0.0002378783,0.00003587243,0.0001197641,0.0001437327,0.0001389776,0.00003223087,0.0001767485,0.002074617],"study_design_scores_gemma":[0.00005704387,0.00126103,0.9913303,0.00002741239,0.0002851864,0.00026168,0.0003719745,0.005593627,0.0001518921,0.0001481061,0.00049845,0.00001332409],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9961154,0.0002635198,0.002712181,0.0000884641,0.00002123718,0.00008979433,0.0003523836,0.000009404206,0.0003474548],"genre_scores_gemma":[0.9971463,0.000122106,0.001743888,0.00008057911,0.000023626,0.0001103599,0.0004742252,0.00000762524,0.0002912714],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005119756,"threshold_uncertainty_score":0.02707618,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08715813988345181,"score_gpt":0.4356516746981173,"score_spread":0.3484935348146655,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}