The Kunming CalFit study: Modeling dietary behavioral patterns using smartphone data

Seto, Edmund; Hua, Jenna; Wu, Lemuel; Bestick, Aaron; Shia, Victor; Eom, Sue; Han, Jay J.; Wang, May D.; Li, Yan

doi:10.1109/embc.2014.6945210

Cited by 11 publications

(16 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Other Smartphone apps have also incorporated these types of measures but they have rarely been evaluated [53]. These assessments require further examination with alternative approaches such as direct observation [54] or other technology such as wearable cameras [7]. The assessment and agreement of EEI to an objective measure of MEE is the first step in substantiating that this method is a suitable and reliable method for collecting dietary intake, both nutritional and contextual.…”

Section: Discussionmentioning

confidence: 99%

Evaluation of a smartphone food diary application using objectively measured energy expenditure

Pendergast

Ridgers

Worsley

et al. 2017

Int J Behav Nutr Phys Act

153

View full text Add to dashboard Cite

BackgroundDietary assessment methods are limited in their ability to adequately measure food and beverage consumption. Smartphone applications may provide a novel method of dietary assessment to capture real-time food intake and the contextual factors surrounding eating occasions. The aim of this study is to evaluate the capability of a Smartphone meal diary app (“FoodNow”) to measure food intake using a validated objective method for assessing energy expenditure among young adults.MethodsParticipants (18–30 years) used FoodNow over four non-consecutive days recording all eating occasions through a combination of written text, and/or optional images and voice recordings. A series of contextual questions were also completed. Participants wore the validated SenseWear Armband (BodyMedia Inc, USA) during the same period to measure free-living energy expenditure. Intra-class correlation coefficients (ICC) estimated the reliability of FoodNow to measure estimated energy intake compared to measured energy expenditure.ResultsNinety participants (71 female, 19 male; mean age = 24.9 ± 4.1 years) were recruited to use the FoodNow app to record their eating occasions. Thirteen were excluded as they did not meet minimum requirements for number of reporting days (n = 3) or SenseWear Armband wear time (5 days of 11 h), while 21 participants were excluded after being identified as mis-reporters (Huang method). Among the remaining sample (n = 56), reliability between estimated energy intake and measured energy expenditure was high (ICC, 95% CI: 0.75, 0.61–0.84).ConclusionsFoodNow is a suitable method for capturing estimated energy intake data from young adults. Despite wide levels of agreement at the individual level (−3709 kJ to 2056 kJ), at the group level, FoodNow appears to have potential as a dietary assessment tool. This new dietary assessment method will offer an alternative and novel method of dietary assessment which is capable of collecting both estimated energy intake and contextual factors surrounding eating occasions. Information collected may be used to inform future public health messages or research interventions.

show abstract

Section: Discussionmentioning

confidence: 99%

Evaluation of a smartphone food diary application using objectively measured energy expenditure

Pendergast

Ridgers

Worsley

et al. 2017

Int J Behav Nutr Phys Act

153

View full text Add to dashboard Cite

show abstract

“…In our systematic review, much less restrictive with the concept of EMA, the main built-in sensor used for collecting PA behavior with smartphones was the accelerometer (27 of 39 studies using mHealth). Only 3 studies used GPS (35,89,90) and only 1 study used a cardiac chest band for recording heart rate to collect objective data related to PA (37). Only 3 of these studies used the combination of 2 sensors (37,89,90).…”

Section: Discussionmentioning

confidence: 99%

“…Only 3 studies used GPS (35,89,90) and only 1 study used a cardiac chest band for recording heart rate to collect objective data related to PA (37). Only 3 of these studies used the combination of 2 sensors (37,89,90). In short, it is clear that the use of mHealth is increasing, but Manuscript to be reviewed there is still a long way to make the most of the capabilities of this technology for applying EMA to PA behavior.…”

Section: Discussionmentioning

confidence: 99%

Peer Review #2 of "mHealth technology for ecological momentary assessment in physical activity research: a systematic review (v0.1)"

2020

View full text Add to dashboard Cite

To systematically review the publications on ecological momentary assessment (EMA) relating to physical activity (PA) behavior in order to classify the methodologies and to identify the main mHealth technology-based tools and procedures that have been applied during the first ten years since the emergence of smartphones. As a result of this review we want to ask if there is enough evidence to propose the use of the term "mEMA" (mobile-based EMA).

show abstract

“…Only 3 studies used GPS (35,89,90) and only 1 study used a cardiac chest band for recording heart rate to collect objective data related to PA (Brannon et al, 2016). Only 3 of these studies used the combination of 2 sensors (Brannon et al, 2016;Seto et al, 2014Seto et al, , 2016. In short, it is clear that the use of mHealth is increasing, but there is still a long way to make the most of the capabilities of this technology for applying EMA to PA behavior.…”

Section: Ema Mhealth and Memamentioning

confidence: 99%

mHealth technology for ecological momentary assessment in physical activity research: a systematic review

Zapata-Lamana

Lalanza

Losilla

et al. 2020

PeerJ

View full text Add to dashboard Cite

Objective To systematically review the publications on ecological momentary assessment (EMA) relating to physical activity (PA) behavior in order to classify the methodologies, and to identify the main mHealth technology-based tools and procedures that have been applied during the first 10 years since the emergence of smartphones. As a result of this review, we want to ask if there is enough evidence to propose the use of the term “mEMA” (mobile-based EMA). Design A systematic review according to PRISMA Statement (PROSPERO registration: CRD42018088136). Method Four databases (PsycINFO, CINALH, Medline and Web of Science Core Collection) were searched electronically from 2008 to February 2018. Results A total of 76 studies from 297 potential articles on the use of EMA and PA were included in this review. It was found that 71% of studies specifically used “EMA” for assessing PA behaviors but the rest used other terminology that also adjusted to the inclusion criteria. Just over half (51.3%) of studies (39) used mHealth technology, mainly smartphones, for collecting EMA data. The majority (79.5%) of these studies (31 out of 39) were published during the last 4 years. On the other hand, 58.8% of studies that only used paper-and-pencil were published during the first 3 years of the 10-year period analyzed. An accelerometer was the main built-in sensor used for collecting PA behavior by means of mHealth (69%). Most of the studies were carried out on young-adult samples, with only three studies in older adults. Women were included in 60% of studies, and healthy people in 82%. The studies lasted between 1 and 7 days in 57.9%, and between three and seven assessments per day were carried out in 37%. The most popular topics evaluated together with PA were psychological state and social and environmental context. Conclusions We have classified the EMA methodologies used for assessing PA behaviors. A total of 71% of studies used the term “EMA” and 51.3% used mHealth technology. Accelerometers have been the main built-in sensor used for collecting PA. The change of trend in the use of tools for EMA in PA coincides with the technological advances of the last decade due to the emergence of smartphones and mHealth technology. There is enough evidence to use the term mEMA when mHealth technology is being used for monitoring real-time lifestyle behaviors in natural situations. We define mEMA as the use of mobile computing and communication technologies for the EMA of health and lifestyle behaviors. It is clear that the use of mHealth is increasing, but there is still a lot to be gained from taking advantage of all the capabilities of this technology in order to apply EMA to PA behavior. Thus, mEMA methodology can help in the monitoring of healthy lifestyles under both subjective and objective perspectives. The tendency for future research should be the automatic recognition of the PA of the user without interrupting their behavior. The ecological information could be completed with voice messages, image captures or brief text selections on the touch screen made in real time, all managed through smartphone apps. This methodology could be extended when EMA combined with mHealth are used to evaluate other lifestyle behaviors.

show abstract

The Kunming CalFit study: Modeling dietary behavioral patterns using smartphone data

Cited by 11 publications

References 7 publications

Evaluation of a smartphone food diary application using objectively measured energy expenditure

Evaluation of a smartphone food diary application using objectively measured energy expenditure

Peer Review #2 of "mHealth technology for ecological momentary assessment in physical activity research: a systematic review (v0.1)"

mHealth technology for ecological momentary assessment in physical activity research: a systematic review

Contact Info

Product

Resources

About