{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005475794,0.0002591777,0.000329433,0.0001487407,0.0009208931,0.0002883665,0.0001103871,0.000103249,0.00005875971],"category_scores_gemma":[0.00002885008,0.0002096529,0.00006103911,0.0003391182,0.00006944798,0.0004662957,0.00006446115,0.0003973222,0.00001331386],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001134677,"about_ca_system_score_gemma":0.0002640109,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004990537,"about_ca_topic_score_gemma":0.000727412,"domain_scores_codex":[0.99732,0.0001339328,0.0006460815,0.0007830809,0.0003402585,0.0007766361],"domain_scores_gemma":[0.9985647,0.0001697615,0.000271391,0.0002607787,0.0001996431,0.0005337804],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.003494466,0.001041959,0.9586454,0.0008333923,0.0001176624,0.00003077542,0.0004657982,0.0005371396,5.616831e-7,0.0001673095,0.00003038868,0.03463516],"study_design_scores_gemma":[0.01103012,0.009906844,0.9417603,0.001017286,0.00009350866,0.0005690823,0.005778733,0.02722205,0.000007278434,0.0001705691,0.00183196,0.0006122675],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9792436,0.00211678,0.01359843,0.0002675763,0.0002581662,0.001372725,0.0001261582,0.0001494342,0.002867113],"genre_scores_gemma":[0.9954998,0.00001273415,0.001582281,0.0002865244,0.0001006287,0.0004442986,0.00015721,0.0000636332,0.001852903],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03402289,"threshold_uncertainty_score":0.8549398,"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."}}