{"id":"W2800486045","doi":"10.2196/10042","title":"Applying Natural Language Processing to Understand Motivational Profiles for Maintaining Physical Activity After a Mobile App and Accelerometer-Based Intervention: The mPED Randomized Controlled Trial","year":2018,"lang":"en","type":"article","venue":"JMIR mhealth and uhealth","topic":"Physical Activity and Health","field":"Medicine","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Nursing Research; National Heart, Lung, and Blood Institute; American Heart Association","keywords":"Intervention (counseling); mHealth; Psychological intervention; Descriptive statistics; Randomized controlled trial; Physical activity; Accelerometer; Psychology; eHealth; Mobile phone; Cluster randomised controlled trial; Physical therapy; Medicine; Computer science; Health care; Statistics; Nursing","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":[0.00480075,0.001908601,0.003586245,0.0008618117,0.0007745853,0.001557284,0.001074289,0.002595428,0.00783712],"category_scores_gemma":[0.009651676,0.0007566589,0.003023668,0.0006585048,0.001229553,0.001709523,0.000968391,0.003147638,0.0007493911],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009753932,"about_ca_system_score_gemma":0.001834081,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001810701,"about_ca_topic_score_gemma":0.001919843,"domain_scores_codex":[0.9964417,0.002117318,0.000417761,0.0004999053,0.0002593661,0.0002640323],"domain_scores_gemma":[0.997453,0.001485896,0.0004422318,0.0001770071,0.0001874849,0.0002542741],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"randomized_trial","study_design_gemma":"randomized_trial","study_design_scores_codex":[0.9619127,0.01254775,0.0004586344,0.002388428,0.001889816,0.00003510598,0.0001129811,0.000194411,0.0006227275,0.0001559671,0.000486975,0.01919442],"study_design_scores_gemma":[0.9562821,0.03903554,0.001538728,0.0001537319,0.001420203,0.00001026027,0.00004294184,0.000535299,0.0002090796,0.0002588371,0.0004964106,0.00001692627],"study_design_candidate":"randomized_trial","study_design_consensus":"randomized_trial","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8941038,0.008167868,0.006596132,0.002109594,0.002843911,0.07892063,0.003018242,0.0005828812,0.003656999],"genre_scores_gemma":[0.8267704,0.003547301,0.01707436,0.001867907,0.001278831,0.1436455,0.00120648,0.00005277913,0.004556496],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00783712,"threshold_uncertainty_score":0.02621776,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05751161565160162,"score_gpt":0.4179154296410133,"score_spread":0.3604038139894117,"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."}}