{"id":"W3002261952","doi":"10.2196/16072","title":"Low-Burden Mobile Monitoring, Intervention, and Real-Time Analysis Using the Wear-IT Framework: Example and Usability Study","year":2020,"lang":"en","type":"article","venue":"JMIR Formative Research","topic":"Digital Mental Health Interventions","field":"Psychology","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"University of Pennsylvania; National Institute of Biomedical Imaging and Bioengineering; National Institute on Alcohol Abuse and Alcoholism; Office of Behavioral and Social Sciences Research; Social Science Research Institute, Pennsylvania State University; Pennsylvania State University","keywords":"mHealth; Usability; Computer science; Psychological intervention; Wearable computer; Wearable technology; Cloud computing; Leverage (statistics); Everyday life; Mobile device; Human–computer interaction; Data science; Artificial intelligence; World Wide Web; Psychology; Embedded system","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":[0.009846671,0.001278608,0.0009066763,0.001103473,0.0006658698,0.001313629,0.001416804,0.001215489,0.002219105],"category_scores_gemma":[0.01718284,0.0004060788,0.0009900224,0.00055692,0.0007093212,0.001325175,0.001263633,0.0007047172,0.0004649241],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005740788,"about_ca_system_score_gemma":0.0006226814,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001853403,"about_ca_topic_score_gemma":0.003894333,"domain_scores_codex":[0.9942687,0.003966636,0.000403102,0.0004193492,0.0006438059,0.0002984306],"domain_scores_gemma":[0.9802908,0.01467766,0.0004780335,0.001453949,0.002547082,0.0005524907],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.007596636,0.01814465,0.04311853,0.01923574,0.001373827,0.005531078,0.05384131,0.01808827,0.1092799,0.005253126,0.02508121,0.6934558],"study_design_scores_gemma":[0.008215416,0.1254505,0.245049,0.005788137,0.0032208,0.01107397,0.05787506,0.2385582,0.1477938,0.00707137,0.1482791,0.001624644],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9033185,0.0009157741,0.08467992,0.0006871572,0.0001262484,0.004090338,0.0005727197,0.001968656,0.003640777],"genre_scores_gemma":[0.8233864,0.0007459995,0.1697809,0.0004273079,0.00007028732,0.002490778,0.0005533738,0.0003044119,0.002240505],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009846671,"threshold_uncertainty_score":0.05207479,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1480526418697039,"score_gpt":0.521705054120188,"score_spread":0.3736524122504841,"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."}}