{"id":"W2914522586","doi":"10.2196/10019","title":"Mobilizing mHealth Data Collection in Older Adults: Challenges and Opportunities","year":2019,"lang":"en","type":"article","venue":"JMIR Aging","topic":"Mobile Health and mHealth Applications","field":"Health Professions","cited_by":58,"is_retracted":false,"has_abstract":true,"ca_institutions":"Fraser Institute; Simon Fraser University","funders":"","keywords":"mHealth; Leverage (statistics); Wearable technology; Digital health; Wearable computer; Population ageing; Population; Digital divide; Emerging technologies; Data collection; Quality of life (healthcare); Health care; Internet privacy; Gerontology; Business; Psychology; Information and Communications Technology; Computer science; Medicine; Political science; Sociology; Environmental health; Nursing; World Wide Web","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.1199845,0.0007792376,0.001193346,0.002983562,0.00490813,0.0109294,0.005126007,0.01250466,0.00302589],"category_scores_gemma":[0.2821324,0.0006263679,0.002086785,0.003147881,0.01151307,0.01705564,0.007876894,0.01139081,0.001288276],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005110749,"about_ca_system_score_gemma":0.02298906,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00795412,"about_ca_topic_score_gemma":0.01623076,"domain_scores_codex":[0.8558672,0.1108285,0.01248099,0.004837706,0.01398252,0.002003047],"domain_scores_gemma":[0.5874373,0.3530479,0.01230355,0.008930274,0.033313,0.004968015],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"qualitative","study_design_scores_codex":[0.0002157614,0.0001151867,0.006780024,0.0164211,0.0002837946,0.001110934,0.05745823,0.0006837626,0.0009434979,0.1268166,0.3086313,0.4805399],"study_design_scores_gemma":[0.0000515703,0.0001848386,0.003598385,0.03717117,0.0001502783,0.001224848,0.03272817,0.001626216,0.0007395169,0.08690021,0.8353893,0.000235448],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.002470838,0.05103272,0.01409721,0.9104098,0.01685535,0.0002467409,0.0001250528,0.00006258234,0.004699714],"genre_scores_gemma":[0.09189029,0.2037411,0.05879265,0.5412266,0.09671914,0.002182393,0.0002934088,0.0002249491,0.004929484],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1199845,"threshold_uncertainty_score":0.6345463,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1651792766826527,"score_gpt":0.4469562369076515,"score_spread":0.2817769602249989,"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."}}