» Articles » PMID: 34875978

Characteristics of Smartphone-based Dietary Assessment Tools: a Systematic Review

Overview
Date 2021 Dec 8
PMID 34875978
Citations 9
Authors
Affiliations
Soon will be listed here.
Abstract

Smartphones have become popular in assessing eating behaviour in real-life and real-time. This systematic review provides a comprehensive overview of smartphone-based dietary assessment tools, focusing on how dietary data is assessed and its completeness ensured. Seven databases from behavioural, social and computer science were searched in March 2020. All observational, experimental or intervention studies and study protocols using a smartphone-based assessment tool for dietary intake were included if they reported data collected by adults and were published in English. Out of 21,722 records initially screened, 117 publications using 129 tools were included. Five core assessment features were identified: photo-based assessment (48.8% of tools), assessed serving/ portion sizes (48.8%), free-text descriptions of food intake (42.6%), food databases (30.2%), and classification systems (27.9%). On average, a tool used two features. The majority of studies did not implement any features to improve completeness of the records. This review provides a comprehensive overview and framework of smartphone-based dietary assessment tools to help researchers identify suitable assessment tools for their studies. Future research needs to address the potential impact of specific dietary assessment methods on data quality and participants' willingness to record their behaviour to ultimately improve the quality of smartphone-based dietary assessment for health research.

Citing Articles

NutriDiary, a Smartphone-Based Dietary Record App: Description and Usability Evaluation.

Klasen L, Koch S, Benz M, Conrad J, Alexy U, Blaszkiewicz K JMIR Hum Factors. 2025; 12:e62776.

PMID: 39930984 PMC: 11833184. DOI: 10.2196/62776.


Nutrition: A non-negligible factor in the pathogenesis and treatment of Alzheimer's disease.

Wen B, Han X, Gong J, Wang P, Sun W, Xu C Alzheimers Dement. 2025; 21(2):e14547.

PMID: 39868840 PMC: 11863745. DOI: 10.1002/alz.14547.


Opportunities to design better computer vison-assisted food diaries to support individuals and experts in dietary assessment: An observation and interview study with nutrition experts.

Chung C, Chiang P, Tan C, Wu C, Schmidt H, Kotarski A PLOS Digit Health. 2024; 3(11):e0000665.

PMID: 39602480 PMC: 11602110. DOI: 10.1371/journal.pdig.0000665.


Development and User Experience Evaluation of an Experience Sampling-Based Dietary Assessment Method.

Verbeke J, Matthys C Curr Dev Nutr. 2024; 8(11):104479.

PMID: 39582947 PMC: 11585766. DOI: 10.1016/j.cdnut.2024.104479.


Nutrient dataset development via FAO/INFOODS approach for infant nutritional survey in rural Matiari, Pakistan.

Soomro S, Jamil Z, Memon N, Ahmed S, Umrani F, Choudhri I J Food Compost Anal. 2024; 133:106471.

PMID: 39221176 PMC: 11287758. DOI: 10.1016/j.jfca.2024.106471.