{"id":"W3010400771","doi":"10.25300/misq/2020/15108","title":"IT-Enabled Self-Monitoring for Chronic Disease Self-Management: An Interdisciplinary Review","year":2020,"lang":"en","type":"article","venue":"MIS Quarterly","topic":"Mobile Health and mHealth Applications","field":"Health Professions","cited_by":108,"is_retracted":false,"has_abstract":true,"ca_institutions":"HEC Montréal","funders":"","keywords":"Wearable computer; Wearable technology; Chronic disease; Computer science; Disease monitoring; Disease; Medicine; Risk analysis (engineering); Engineering; Knowledge management; Embedded system; Intensive care medicine; Pathology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006502826,0.000280947,0.0004661012,0.00007004399,0.001548925,0.0000176231,0.0004505975,0.0001221554,0.000310708],"category_scores_gemma":[0.00002693099,0.000269731,0.000140606,0.0003531876,0.00001869716,0.0002507981,0.00007721342,0.0004693877,0.0008516065],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003843037,"about_ca_system_score_gemma":0.0006506501,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001340471,"about_ca_topic_score_gemma":0.00002923247,"domain_scores_codex":[0.9966503,0.0003436521,0.001046431,0.0007073086,0.0002461488,0.00100617],"domain_scores_gemma":[0.9968985,0.0002102687,0.0003337668,0.0007462529,0.0001731259,0.001638076],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005742486,0.000972921,0.003647718,0.2277353,0.0002063955,0.00003921245,0.03361706,0.000005395624,0.00006707315,0.005543349,0.5014244,0.2261669],"study_design_scores_gemma":[0.002026228,0.001076035,0.002931186,0.003077759,0.0002692627,6.926271e-7,0.002246336,0.001366945,0.000001311439,0.0004837499,0.986186,0.0003345139],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.03254573,0.1100125,0.02622178,0.6235355,0.007928348,0.1377743,0.001036645,0.01020976,0.05073544],"genre_scores_gemma":[0.5750296,0.03552567,0.02677267,0.1084922,0.01637168,0.232388,0.001427204,0.0005716647,0.0034214],"genre_candidate":"commentary","genre_consensus":null,"teacher_disagreement_score":0.5424838,"threshold_uncertainty_score":0.9999755,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04715266818277238,"score_gpt":0.4429695301397256,"score_spread":0.3958168619569532,"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."}}