Measuring Physical Activity and Sedentary Behavior in Youth with Type 2 Diabetes

Rockette‐Wagner, Bonny; Storti, Kristi L.; Edelstein, Sharon L.; Delahanty, Linda M.; Galvin, Bryan; Jackson, Alexandra; Kriska, Andrea M.

doi:10.1089/chi.2015.0151

Cited by 12 publications

(11 citation statements)

References 38 publications

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“…For example, the built environment [ 34 ], peer relationships [ 35 ], and the day of the week [ 36 ] have all been shown to influence the levels of sedentary behavior in children. Other retrospective self-report tools for assessing sedentary behavior, such as the outdoor playtime recall questionnaire, are unable to provide such details surrounding sedentary behaviors and have demonstrated weak correlations with the accelerometer-measured ST [ 37 , 38 ]. Therefore, EMA may be more effective at capturing factors relevant to ST than other self-report measures previously utilized by investigators.…”

Section: Discussionmentioning

confidence: 99%

“…When sports or exercise was reported in the past 2 h via EMA, significantly more minutes of MVPA were recorded by accelerometers during this time frame, and this was consistent with previous findings in adults [ 41 ]. A recent study comparing retrospective self-reports of PA and accelerometer-measured PA in youth found no relationship between the two [ 37 ]. Moreover, participants have been shown to overestimate the amounts of PA that they engaged in by an average of 596 minutes per week when utilizing retrospective questionnaires [ 17 ], highlighting the need for more effective self-report methods, particularly in children.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Association Between Self-Reported and Objective Activity Levels by Demographic Factors: Ecological Momentary Assessment Study in Children

Zink¹,

Belcher²,

Dzubur³

et al. 2018

JMIR Mhealth Uhealth

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BackgroundTo address the limitations of the retrospective self-reports of activity, such as its susceptibility to recall bias, researchers have shifted toward collecting real-time activity data on mobile devices via ecological momentary assessment (EMA). Although EMA is becoming increasingly common, it is not known how EMA self-reports of physical activity and sedentary behaviors relate to the objective measures of activity or whether there are factors that may influence the strength of association between these two measures. Understanding the relationship between EMA and accelerometry can optimize future instrument selection in studies assessing activity and health outcomes.ObjectiveThe aim of this study was to examine the associations between EMA-reported sports or exercise using the accelerometer-measured moderate-to-vigorous physical activity (MVPA) and EMA-reported TV, videos, or video games with the accelerometer-measured sedentary time (ST) in children during matched 2-h windows and test potential moderators.MethodsChildren (N=192; mean age 9.6 years; 94/192, 49.0% male; 104/192, 54.2% Hispanic; and 73/192, 38.0% overweight or obese) wore an accelerometer and completed up to 7 EMA prompts per day for 8 days during nonschool time, reporting on past 2-h sports or exercise and TV, videos, or video games. Multilevel models were used to assess the relationship between the accelerometer-measured ST and EMA-reported TV, videos, or video games. Given the zero-inflated distribution of MVPA, 2-part models were used assess the relationship between the accelerometer-measured MVPA and EMA-reported sports or exercise.ResultsEMA-reported TV, videos, or video games were associated with a greater accelerometer-measured ST (beta=7.3, 95% CI 5.5 to 9.0, P<.001). This relationship was stronger in boys (beta=9.9, 95% CI 7.2 to 12.6, P<.001) than that in girls (beta=4.9, 95% CI 2.6 to 7.2, P≤.001). EMA-reported sports or exercise was associated with a greater accelerometer-measured MVPA (zero portion P<.001; positive portion P<.001). This relationship was stronger on weekends, in older children, and in non-Hispanic children (zero portion all P values<.001; positive portion all P values<.001).ConclusionsEMA reports highly relate to accelerometer measures. However, the differences in the strength of association depending on various demographic characteristics suggest that future research should use both EMA and accelerometers to measure activity to collect complementary activity data.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Association Between Self-Reported and Objective Activity Levels by Demographic Factors: Ecological Momentary Assessment Study in Children

Zink¹,

Belcher²,

Dzubur³

et al. 2018

JMIR Mhealth Uhealth

View full text Add to dashboard Cite

show abstract

“…For example, the built environment [27], peer relationships [28], and day of week [29] have all been shown to influence levels of sedentary behavior in children. Other retrospective self-report tools for assessing sedentary behavior, such as the Outdoor Playtime Recall Questionnaire, are unable to provide such details surrounding sedentary behaviors and have demonstrated weak correlations with accelerometer-measured ST [30,31]. Therefore, EMA may be more effective at capturing factors relevant to ST than other self-report measures previously utilized by investigators.…”

Section: Discussionmentioning

confidence: 99%

“…When sports/exercise were reported in the past two hours via EMA, significantly more minutes of MVPA were recorded by accelerometers during this time frame. A recent study comparing paper self-reports of PA and accelerometer-measured PA in youth found no relationship between the two [30]. Moreover, participants have been shown to overestimate the amounts of PA that they engaged in when utilizing questionnaires [14], highlighting the need for a more effective self-report methods, particularly in children.…”

Section: Discussionmentioning

confidence: 99%

Association Between Self-Reported and Objective Activity Levels by Demographic Factors: Ecological Momentary Assessment Study in Children (Preprint)

Zink¹,

Belcher²,

Dzubur³

et al. 2017

Preprint

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“…Researchers utilize various subjective and objective techniques as well as PA dimensions (FITT) to characterize and interpret PA. There are numerous studies using thresholds to derive PA intensity (or PA level , i.e., categorized PA intensity) to quantify human PA (Clemente et al, 2016; Vanroy et al, 2016; Krüger et al, 2017; Laakkonen et al, 2017; McCarthy et al, 2017; Rockette-Wagner et al, 2017; Vanderloo and Tucker, 2017). However, it is controversial whether defined thresholds for assessing PA levels can be applied to people from different age groups.…”

Section: Introductionmentioning

confidence: 99%

The Key Factors in Physical Activity Type Detection Using Real-Life Data: A Systematic Review

et al. 2019

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Background: Physical activity (PA) is paramount for human health and well-being. However, there is a lack of information regarding the types of PA and the way they can exert an influence on functional and mental health as well as quality of life. Studies have measured and classified PA type in controlled conditions, but only provided limited insight into the validity of classifiers under real-life conditions. The advantage of utilizing the type dimension and the significance of real-life study designs for PA monitoring brought us to conduct a systematic literature review on PA type detection (PATD) under real-life conditions focused on three main criteria: methods for detecting PA types, using accelerometer data collected by portable devices, and real-life settings. Method: The search of the databases, Web of Science, Scopus, PsycINFO, and PubMed, identified 1,170 publications. After screening of titles, abstracts and full texts using the above selection criteria, 21 publications were included in this review. Results: This review is organized according to the three key elements constituting the PATD process using real-life datasets, including data collection, preprocessing, and PATD methods. Recommendations regarding these key elements are proposed, particularly regarding two important PA classes, i.e., posture and motion activities. Existing studies generally reported high to near-perfect classification accuracies. However, the data collection protocols and performance reporting schemes used varied significantly between studies, hindering a transparent performance comparison across methods. Conclusion: Generally, considerably less studies focused on PA types, compared to other measures of PA assessment, such as PA intensity, and even less focused on real-life settings. To reliably differentiate the basic postures and motion activities in real life, two 3D accelerometers (thigh and hip) sampling at 20 Hz were found to provide the minimal sensor configuration. Decision trees are the most common classifier used in practical applications with real-life data. Despite the significant progress made over the past year in assessing PA in real-life settings, it remains difficult, if not impossible, to compare the performance of the various proposed methods. Thus, there is an urgent need for labeled, fully documented, and openly available reference datasets including a common evaluation framework.

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Measuring Physical Activity and Sedentary Behavior in Youth with Type 2 Diabetes

Cited by 12 publications

References 38 publications

Association Between Self-Reported and Objective Activity Levels by Demographic Factors: Ecological Momentary Assessment Study in Children

Association Between Self-Reported and Objective Activity Levels by Demographic Factors: Ecological Momentary Assessment Study in Children

Association Between Self-Reported and Objective Activity Levels by Demographic Factors: Ecological Momentary Assessment Study in Children (Preprint)

The Key Factors in Physical Activity Type Detection Using Real-Life Data: A Systematic Review

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