Measurement Properties of Smartphone Approaches to Assess Diet, Alcohol Use, and Tobacco Use: Systematic Review
Notice bibliographique
Résumé
BACKGROUND: Poor diet, alcohol use, and tobacco smoking have been identified as strong determinants of chronic diseases, such as cardiovascular disease, diabetes, and cancer. Smartphones have the potential to provide a real-time, pervasive, unobtrusive, and cost-effective way to measure these health behaviors and deliver instant feedback to users. Despite this, the validity of using smartphones to measure these behaviors is largely unknown. OBJECTIVE: The aim of our review is to identify existing smartphone-based approaches to measure these health behaviors and critically appraise the quality of their measurement properties. METHODS: We conducted a systematic search of the Ovid MEDLINE, Embase (Elsevier), Cochrane Library (Wiley), PsycINFO (EBSCOhost), CINAHL (EBSCOHost), Web of Science (Clarivate), SPORTDiscus (EBSCOhost), and IEEE Xplore Digital Library databases in March 2020. Articles that were written in English; reported measuring diet, alcohol use, or tobacco use via a smartphone; and reported on at least one measurement property (eg, validity, reliability, and responsiveness) were eligible. The methodological quality of the included studies was assessed using the Consensus-Based Standards for the Selection of Health Measurement Instruments Risk of Bias checklist. Outcomes were summarized in a narrative synthesis. This systematic review was registered with PROSPERO, identifier CRD42019122242. RESULTS: Of 12,261 records, 72 studies describing the measurement properties of smartphone-based approaches to measure diet (48/72, 67%), alcohol use (16/72, 22%), and tobacco use (8/72, 11%) were identified and included in this review. Across the health behaviors, 18 different measurement techniques were used in smartphones. The measurement properties most commonly examined were construct validity, measurement error, and criterion validity. The results varied by behavior and measurement approach, and the methodological quality of the studies varied widely. Most studies investigating the measurement of diet and alcohol received very good or adequate methodological quality ratings, that is, 73% (35/48) and 69% (11/16), respectively, whereas only 13% (1/8) investigating the measurement of tobacco use received a very good or adequate rating. CONCLUSIONS: This review is the first to provide evidence regarding the different types of smartphone-based approaches currently used to measure key behavioral risk factors for chronic diseases (diet, alcohol use, and tobacco use) and the quality of their measurement properties. A total of 19 measurement techniques were identified, most of which assessed dietary behaviors (48/72, 67%). Some evidence exists to support the reliability and validity of using smartphones to assess these behaviors; however, the results varied by behavior and measurement approach. The methodological quality of the included studies also varied. Overall, more high-quality studies validating smartphone-based approaches against criterion measures are needed. Further research investigating the use of smartphones to assess alcohol and tobacco use and objective measurement approaches is also needed. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-https://doi.org/10.1186/s13643-020-01375-w.
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 | 0,033 | 0,150 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,011 | 0,014 |
| Bibliométrie | 0,015 | 0,015 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,005 | 0,004 |
| Science ouverte | 0,003 | 0,003 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
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 ».