{"id":"W4312964792","doi":"10.2196/38603","title":"Personalization of Mobile Apps for Health Behavior Change: Protocol for a Cross-sectional Study","year":2022,"lang":"en","type":"article","venue":"JMIR Research Protocols","topic":"Mobile Health and mHealth Applications","field":"Health Professions","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Personalization; Logistic regression; Psychological intervention; Scale (ratio); Computer science; Applied psychology; Cross-sectional study; Data collection; Psychology; Ordered logit; Protocol (science); Medicine; World Wide Web; Statistics; Machine learning; Mathematics; Geography","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":[0.02465777,0.002319794,0.003340275,0.002163752,0.002864694,0.001534792,0.001321409,0.003250501,0.04151618],"category_scores_gemma":[0.02309986,0.001676969,0.002674543,0.002127439,0.001247144,0.001363322,0.00143007,0.00362616,0.009278606],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002547235,"about_ca_system_score_gemma":0.01060041,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003146915,"about_ca_topic_score_gemma":0.004002724,"domain_scores_codex":[0.9911897,0.004406426,0.001849365,0.0007947607,0.0009912193,0.0007685826],"domain_scores_gemma":[0.9805459,0.005600299,0.002886111,0.002806031,0.007060653,0.001100954],"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":[0.167914,0.08264265,0.02780306,0.06840724,0.00243738,0.001967984,0.007843679,0.006636013,0.01321807,0.01118821,0.1316342,0.4783075],"study_design_scores_gemma":[0.2001087,0.1226759,0.1729328,0.03618896,0.002525893,0.0009028944,0.00743179,0.007186598,0.01177935,0.01241362,0.424972,0.0008813575],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"protocol","genre_gemma":"protocol","genre_scores_codex":[0.004925447,0.0003197626,0.003269935,0.0001822701,0.0001663491,0.9869199,0.002633038,0.00009078734,0.001492471],"genre_scores_gemma":[0.002237967,0.0000914122,0.002366257,0.00009951433,0.00001310383,0.9946948,0.0001845424,0.000004203301,0.0003082365],"genre_candidate":"protocol","genre_consensus":"protocol","teacher_disagreement_score":0.04151618,"threshold_uncertainty_score":0.1388855,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.675461946218924,"score_gpt":0.7439841738083922,"score_spread":0.06852222758946824,"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."}}