Mobile Health Crowdsensing (MHCS) Intervention on Chronic Disease Awareness: Protocol for a Systematic Review
Notice bibliographique
Résumé
Background Mobile health crowdsensing (MHCS) involves the use of mobile communication technologies to promote health by supporting health care practices (eg, health data collection, delivery of health care information, or patient observation and provision of care). MHCS technologies (eg, smartphones) have sensory capabilities, such as GPS, voice, light, and camera, to collect, analyze, and share user-centered data (explicit and implicit). The current literature indicates no scientific study related to MHCS interventions for chronic diseases. The proposed systematic review will examine the impact of MHCS interventions on chronic disease awareness. Objective The objectives of this study are to identify and describe various MHCS intervention strategies applied to chronic disease awareness. Methods Literature from various databases, such as MEDLINE, Embase, PsycINFO, CINAHL, and Cochrane Central Register of Controlled Trials, will be examined. Trial registers, reports, grey literature, and unpublished academic theses will also be included. All mobile technologies, such as cell phones, personal digital assistants, and tablets that have short message service, multimedia message service, video, and audio capabilities, will be included. MHCS will be the primary intervention strategy. The search strategy will include keywords such as mHealth, crowdsensing, and awareness among other medical subject heading terms. Articles published from January 1, 1945, to December 31, 2019, will be eligible for inclusion. The authors will independently screen and select studies, extract data, and assess the risk of bias, with discrepancies resolved by an independent party not involved in the study. The authors will assess statistical heterogeneity by examining the types of participants, interventions, study designs, and outcomes in each study, and pool studies judged to be statistically homogeneous. In the assessment of heterogeneity, a sensitivity analysis will be considered to explore statistical heterogeneity. Statistical heterogeneity will be investigated using the chi-square test of homogeneity on Cochrane Q test, and quantified using the I2 statistic. Results The preliminary search query found 1 paper. Further literature search commenced in mid-March 2021 and is to be concluded in April 2021. The proposed systematic review protocol has been registered in PROSPERO (The International Prospective Register of Systematic Reviews; no. CRD42020161435). Furthermore, the use of search data extraction and capturing in Review Manager version 5.3 (Cochrane) commenced in January 2021 and ended in February 2021. Further literature search will begin in mid-March 2021 and will be concluded in April 2021. The final stages will include analyses and writing, which are anticipated to start and be completed in May 2021. Conclusions The knowledge derived from this study will inform health care stakeholders—including researchers, policy makers, investors, health professionals, technologists, and engineers—of the impact of MHCS interventions on chronic disease awareness. International Registered Report Identifier (IRRID) PRR1-10.2196/24589
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,055 | 0,060 |
| Méta-épidémiologie (sens strict) | 0,006 | 0,005 |
| Méta-épidémiologie (sens large) | 0,019 | 0,016 |
| Bibliométrie | 0,011 | 0,010 |
| Études des sciences et des technologies | 0,004 | 0,004 |
| Communication savante | 0,007 | 0,008 |
| Science ouverte | 0,005 | 0,005 |
| Intégrité de la recherche | 0,007 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,082 | 0,009 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».