Measurement Properties of Smartphone Approaches to Assess Diet, Alcohol Use, and Tobacco Use: Systematic Review.

alcohol app diet measurement mobile phone smartphone smoking

Journal

JMIR mHealth and uHealth
ISSN: 2291-5222
Titre abrégé: JMIR Mhealth Uhealth
Pays: Canada
ID NLM: 101624439

Informations de publication

Date de publication:
17 02 2022
Historique:
received: 21 01 2021
accepted: 16 09 2021
revised: 23 06 2021
entrez: 17 2 2022
pubmed: 18 2 2022
medline: 17 3 2022
Statut: epublish

Résumé

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. 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. 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. 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. 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. RR2-https://doi.org/10.1186/s13643-020-01375-w.

Sections du 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.

Identifiants

pubmed: 35175212
pii: v10i2e27337
doi: 10.2196/27337
pmc: PMC8895282
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't Review Systematic Review

Langues

eng

Sous-ensembles de citation

IM

Pagination

e27337

Informations de copyright

©Louise Thornton, Bridie Osman, Katrina Champion, Olivia Green, Annie B Wescott, Lauren A Gardner, Courtney Stewart, Rachel Visontay, Jesse Whife, Belinda Parmenter, Louise Birrell, Zachary Bryant, Cath Chapman, David Lubans, Tim Slade, John Torous, Maree Teesson, Pepijn Van de Ven. Originally published in JMIR mHealth and uHealth (https://mhealth.jmir.org), 17.02.2022.

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Auteurs

Louise Thornton (L)

The Matilda Centre for Research in Mental Health and Substance Use, The University of Sydney, Sydney, Australia.
School of Medicine and Public Health, The University of Newcastle, Newcastle, Australia.
School of Public Health and Community Medicine, University of New South Wales, Kensington, Australia.

Bridie Osman (B)

The Matilda Centre for Research in Mental Health and Substance Use, The University of Sydney, Sydney, Australia.

Katrina Champion (K)

The Matilda Centre for Research in Mental Health and Substance Use, The University of Sydney, Sydney, Australia.

Olivia Green (O)

The Matilda Centre for Research in Mental Health and Substance Use, The University of Sydney, Sydney, Australia.

Annie B Wescott (AB)

Galter Health Sciences Library & Learning Center, Northwestern University, Feinberg School of Medicine, Chicago, IL, United States.

Lauren A Gardner (LA)

The Matilda Centre for Research in Mental Health and Substance Use, The University of Sydney, Sydney, Australia.

Courtney Stewart (C)

National Drug Research Institute, Curtin University, Perth, Australia.

Rachel Visontay (R)

The Matilda Centre for Research in Mental Health and Substance Use, The University of Sydney, Sydney, Australia.

Jesse Whife (J)

National Drug Research Institute, Curtin University, Perth, Australia.

Belinda Parmenter (B)

School of Health Sciences, The University of New South Wales, Sydney, Australia.

Louise Birrell (L)

The Matilda Centre for Research in Mental Health and Substance Use, The University of Sydney, Sydney, Australia.

Zachary Bryant (Z)

The Matilda Centre for Research in Mental Health and Substance Use, The University of Sydney, Sydney, Australia.

Cath Chapman (C)

The Matilda Centre for Research in Mental Health and Substance Use, The University of Sydney, Sydney, Australia.

David Lubans (D)

Priority Research Centre for Physical Activity and Nutrition, The University of Newcastle, Newcastle, Australia.

Tim Slade (T)

The Matilda Centre for Research in Mental Health and Substance Use, The University of Sydney, Sydney, Australia.

John Torous (J)

Beth Israel Deaconness Medical Centre, Harvard Medical School, Boston, MA, United States.

Maree Teesson (M)

The Matilda Centre for Research in Mental Health and Substance Use, The University of Sydney, Sydney, Australia.

Pepijn Van de Ven (P)

Health Research Institute, University of Limerick, Limerick, Ireland.

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