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Systematic Review of Accelerometer Responsiveness to Change for Measuring Physical Activity, Sedentary Behavior, or Sleep

Kimberly A. Clevenger and Alexander H.K. Montoye

Measurement of 24-hr movement behaviors is important for assessing adherence to guidelines, participation trends over time, group differences, and whether health-promoting interventions are successful. For a measurement tool to be useful, it must be valid, reliable, and able to detect change, the latter being a measurement property called responsiveness, sensitivity to change, or longitudinal validity. We systematically reviewed literature on the responsiveness of accelerometers to detect change in 24-hr movement behaviors. Databases (PubMed, Scopus, and EBSCOHost) were searched for peer-reviewed papers published in English between 1998 and 2023. Quality/risk of bias was assessed using a customized tool. This study is registered at https://osf.io/qrn8a. Twenty-six papers met the inclusion/exclusion criteria with an overall sample of 1,939 participants. Narrative synthesis was used. Most studies focused on adults (n = 21), and almost half (n = 12) included individuals with specific medical conditions. Studies primarily took place in free-living settings (n = 21) and used research-grade accelerometers (n = 24) worn on the hip (n = 18), thigh (n = 7), or wrist (n = 9). Outcomes included physical activity (n = 19), sedentary time/behavior (n = 12), or sleep (n = 2) and were calculated using proprietary formulas (e.g., Fitbit algorithm), cut points, and/or count-based methods. Most studies calculated responsiveness by comparing before versus after an intervention (n = 16). Six studies included a criterion measure to confirm that changes occurred. Limited research is available on the responsiveness of accelerometers for detecting change in 24-hr movement behaviors, particularly in youth populations, for sleep outcomes, and for commercial and thigh- or wrist-worn devices. Lack of a criterion measure precludes conclusions about the responsiveness even in more frequently studied outcomes/populations.

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Does Wearing a Portable Metabolic Unit Affect Youth’s Physical Activity or Enjoyment During Physically Active Games or Video Games?

Kimberly A. Clevenger, Karin A. Pfeiffer, and Cheryl A. Howe

Portable metabolic units (PMUs) are used to assess energy expenditure, with the assumption that physical activity level and enjoyment are unaffected due to the light weight and small size. Purpose: To assess differences in physical activity level and enjoyment while wearing and not wearing a PMU. Method: Youth (8–17 y; N = 73) played children’s games or active video games while wearing and not wearing a PMU (crossover design). Participants wore an accelerometer and heart rate monitor and responded to questions about enjoyment on a facial affective scale. A repeated-measures analysis of variance was used to determine if accelerometer measures, heart rate, or enjoyment differed between conditions overall and by sex and weight status. Results: Steps per minute were lower while wearing the PMU than not wearing the PMU (40 vs 44, P = .03). There was an interaction between PMU condition and weight status for enjoyment (P = .01), with overweight participants reporting less enjoyment when wearing the PMU compared with not wearing the PMU (72 vs 75 out of 100). Heart rate, vector magnitude, and counts per minute were not different. Conclusion: There may be psychosocial effects of wearing the PMU, specifically in overweight participants. Activity level was minimally affected, but the practical significance for research is still unknown.

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Association of Recess Provision With Elementary School-Aged Children’s Physical Activity, Adiposity, and Cardiorespiratory and Muscular Fitness

Kimberly A. Clevenger, Melitta A. McNarry, Kelly A. Mackintosh, and David Berrigan

Purpose: To identify associations between amount of school recess provision and children’s physical activity (PA), weight status, adiposity, cardiorespiratory endurance, muscular strength, and muscular endurance. Method: Data from 6- to 11-year-old participants (n = 499) in the 2012 National Youth Fitness Survey were analyzed. Parents/guardians reported children’s PA levels and recess provision, categorized as no/minimal (9.0%), low (26.1%), medium (46.0%), or high (18.9%). Children wore a wrist-worn accelerometer for 7 days and completed anthropometric measurements. Fitness was assessed using grip strength and treadmill, pull-up, and plank tests. Cross-sectional linear and logistic regression compared outcomes across levels of recess provision adjusting for the survey’s complex sampling design. Results: Children with high provision of recess were 2.31 times more likely to meet PA guidelines according to parent report than those with no/minimal recess. Accelerometer-measured PA followed a more U-shaped pattern, wherein PA was higher in children with high, compared to low, recess provision but comparable to those with no/minimal recess provision. There were no associations with weight status, adiposity, or fitness. Conclusion: Current recess recommendations (20 min·d−1) may be insufficient as 30 minutes per day of recess was associated with a 2-fold greater likelihood of achieving recommended PA levels. Additional research on recess quantity and quality is needed.

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Physical Activity Interventions During Childhood and Adolescence: A Narrative Umbrella Review Addressing Characteristics, Conclusions, and Gaps in Knowledge

Karin A. Pfeiffer, Katherine L. McKee, Cailyn A. Van Camp, and Kimberly A. Clevenger

Given the multifaceted nature of physical activity behavior in children and adolescents, researchers have conducted myriad intervention studies designed to increase physical activity across many populations, study designs, contexts, and settings. This narrative review overviews the characteristics, conclusions, and research gaps/future directions indicated in prior reviews of interventions to promote physical activity in youth and identifies potential knowledge gaps. Seven databases were searched for articles published between January 2012 and September 2022. A predetermined list of characteristics of included reviews was extracted. Reviews (n = 68) concluded that interventions were generally effective. Little attention was paid to implementation, theoretical framework was only addressed in about half of reviews, and only a quarter specifically examined individuals from underrepresented groups. Family, community, and policy work are needed, and overarching reviews such as this study should occasionally occur given the high number of reviews focusing on specific populations or settings.

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A Systematic Review of Child and Adolescent Physical Activity by Schoolyard Location

Kimberly A. Clevenger, Michael J. Wierenga, Cheryl A. Howe, and Karin A. Pfeiffer

The authors conducted a systematic review of children’s and adolescent’s physical activity by schoolyard location. PubMed and Web of Science were searched and articles were selected that included 3- to 17-year-olds and specifically examined and reported physical activity by schoolyard location. The primary outcomes of interest were the percentage of total time or observation intervals spent in each location and percentage of time or observation intervals in each location being sedentary or participating in moderate to vigorous physical activity. Included studies (N = 24) focused on preschoolers (n = 6), children (n = 11), adolescents (n = 2), or children and adolescents (n = 5) and primarily used direct observation (n = 17). Fields, fixed equipment, and blacktop were all important locations for physical activity participation, but there were differences by age group and sex. More research is needed that uses consistent methodology and accounts for other factors such as time of year, provided equipment, and differences in schoolyard designs.

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Measurement of Children’s Real-Time Physical Activity Enjoyment Using a New Visual Analog Scale

Cheryl A. Howe, Kimberly A. Clevenger, Danielle McElhiney, Camille Mihalic, and Moira A. Ragan

Background: This study validated the How(e) Happy Scale (HHS) for measuring children’s real-time physical activity (PA) enjoyment across PA type, intensity, sex, and weight status and compared state versus trait enjoyment. Methods: Children’s (N = 31; 9.7 [1.7] y) PA intensity was measured during sport, play, and locomotive PA. Following each activity, children rated their perceived state (HHS) of enjoyment across 4 constructs (social engagement). Questionnaires measured trait PA enjoyment prior to play. Rasch Rating Scale analysis assessed model-data fit and probability distribution of HHS responses. Analyses of variance compared state versus trait PA enjoyment across main effects, and correlations assessed relationships between measured PA intensity versus state and trait PA enjoyment. Results: Trait PA enjoyment was neither different across sex and weight status nor correlated with PA intensity (r = −.16 to .22). By contrast, HHS responses differed across sex, weight status, and PA type and intensity and correlated with PA type (r = −.56 to −.28) and intensity (r = −.29 to −.32). HHS responses were ordered along the probability curve and showed good infit (0.76–1.22) and outfit (0.71–1.28) statistics and good person (r = .62) and item (r = .88) reliability. Conclusion: HHS is valid for detecting differences in real-time enjoyment across PA type and intensity in all children.

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Using Video Direct Observation to Assess Children’s Physical Activity During Recess

Cheryl A. Howe, Kimberly A. Clevenger, Brian Plow, Steve Porter, and Gaurav Sinha

Purpose : Traditional direct observation cannot provide continuous, individual-level physical activity (PA) data throughout recess. This study piloted video direct observation to characterize children’s recess PA overall and by sex and weight status. Methods: Children (N = 23; 11 boys; 6 overweight; third to fifth grade) were recorded during 2 recess periods, coding for PA duration, intensity, location, and type. Duration of PA type and intensity across sex and weight status overall and between/within locations were assessed using 1- and 2-way analysis of variances. Results: The field elicited more sedentary behavior (39% of time) and light PA (17%) and less moderate to vigorous PA (41%) compared with the fixed equipment (13%, 7%, and 71%, respectively) or the court (21%, 7%, and 68%, respectively). Boys engaged in significantly more vigorous-intensity activity on the court (35%) than girls (14%), whereas girls engaged in more moderate to vigorous PA on the fixed equipment (77% vs 61%) and field (46% vs 35%) than boys (all Ps > .05). PA type also differed by sex and weight status. Conclusion: Video direct observation was capable of detecting and characterizing children’s entire recess PA while providing valuable context to the behavior. The authors confirmed previous findings that PA intensity was not uniform by schoolyard location and further differences exist by sex and weight status.

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Comparison of Six Accelerometer Metrics for Assessing the Temporal Patterns of Children’s Free-Play Physical Activity

Katherine L. McKee, Karin A. Pfeiffer, Amber L. Pearson, and Kimberly A. Clevenger

Accelerometers are frequently used to measure physical activity in children, but lack of uniformity in data processing methods, such as the metric used to summarize accelerometer data, limits comparability between studies. The objective was to compare six accelerometer metrics (raw: mean amplitude deviation, Euclidean norm minus one, activity index, monitor-independent movement summary units; count: vertical axis, vector magnitude) for characterizing the intensity and temporal patterns of first and second graders’ (n = 88; age = 7.8 ± 0.7 years) recess physical activity. At a 5-s epoch level, Pearson’s correlations (r) between metrics ranged from .66 to .98. When each epoch was classified into one of four intensity levels based on quartiles, agreement between metrics as indicated by weighted kappa ranged from .81 to .96. When collapsed to time spent in each intensity level, metrics were strongly correlated (r = .76–.99) and most often statistically equivalent for estimating time spent in Quartile 3 or 4. Children were ranked from least to most active, and agreement between metrics was strong (Spearman’s correlation ≥ .87). Temporal patterns were characterized using five fragmentation indices calculated using each of the six metrics, which were fair-to-strongly correlated (r = .53–.99), with the strongest associations for number of high-intensity activity bouts (r ≥ .89). Most fragmentation indices were not statistically equivalent between metrics. While metrics captured similar trends in activity intensity and temporal patterns, caution is warranted when making comparisons of point estimates derived from different metrics. However, all metrics were able to similarly capture higher intensity activity (i.e., Quartile 3 or 4), the most common outcome of interest in intervention studies.

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Association of Recess Provision With Accelerometer-Measured Physical Activity and Sedentary Time in a Representative Sample of 6- to 11-Year-Old Children in the United States

Kimberly A. Clevenger, Katherine L. McKee, Melitta A. McNarry, Kelly A. Mackintosh, and David Berrigan

Purpose: To assess the association between the amount of recess provision and children’s accelerometer-measured physical activity (PA) levels. Methods: Parents/guardians of 6- to 11-year-olds (n = 451) in the 2012 National Youth Fitness Survey reported recess provision, categorized as low (10–15 min; 31.9%), medium (16–30 min; 48.0%), or high (>30 min; 20.1%). Children wore a wrist-worn accelerometer for 7 days to estimate time spent sedentary, in light PA, and in moderate to vigorous PA using 2 different cut points for either activity counts or raw acceleration. Outcomes were compared between levels of recess provision while adjusting for covariates and the survey’s multistage, probability sampling design. Results: Children with high recess provision spent less time sedentary, irrespective of type of day (week vs weekend) and engaged in more light or moderate to vigorous PA on weekdays than those with low recess provision. The magnitude and statistical significance of effects differed based on the cut points used to classify PA (eg, 4.7 vs 11.9 additional min·d−1 of moderate to vigorous PA). Conclusions: Providing children with >30 minutes of daily recess, which exceeds current recommendations of ≥20 minutes, is associated with more favorable PA levels and not just on school days. Identifying the optimal method for analyzing wrist-worn accelerometer data could clarify the magnitude of this effect.

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Understanding Physical Behaviors During Periods of Accelerometer Wear and Nonwear in College Students

Alexander H.K. Montoye, Kimberly A. Clevenger, Benjamin D. Boudreaux, and Michael D. Schmidt

Accelerometers are increasingly used to measure 24-hr movement behaviors but are sometimes removed intermittently (e.g., for sleep or bathing), resulting in missing data. This study compared physical behaviors between times a hip-placed accelerometer was worn versus not worn in a college student sample. Participants (n = 115) wore a hip-placed ActiGraph during waking times and a thigh-placed activPAL continuously for at least 7 days (mean ± SD 7.5 ± 1.1 days). Thirteen nonwear algorithms determined ActiGraph nonwear; days included in the analysis had to have at least 1 min where the ActiGraph classified nonwear while participant was classified as awake by the activPAL. activPAL data for steps, time in sedentary behaviors (SB), light-intensity physical activity (LPA), and moderate- to vigorous-intensity physical activity (MVPA) from ActiGraph wear times were then compared with activPAL data from ActiGraph nonwear times. Participants took more steps (10.2–11.8 steps/min) and had higher proportions of MVPA (5.0%–5.9%) during ActiGraph wear time than nonwear time (3.1–8.0 steps/min, 0.8%–1.3% in MVPA). Effects were variable for SB (62.6%–66.9% of wear, 45.5%–76.2% of nonwear) and LPA (28.2%–31.5% of wear, 23.0%–53.2% of nonwear) depending on nonwear algorithm. Rescaling to a 12-hr day reduced SB and LPA error but increased MVPA error. Requiring minimum wear time (e.g., 600 min/day) reduced error but resulted in 10%–22% of days removed as invalid. In conclusion, missing data had minimal effect on MVPA but resulted in underestimation of SB and LPA. Strategies like scaling SB and LPA, but not MVPA, may improve physical behavior estimates from incomplete accelerometer data.