Are parent-reported sleep logs essential? A comparison of three approaches to guide open source accelerometry-based nocturnal sleep processing in children.

We examined the comparability of children's nocturnal sleep estimates using accelerometry data, processed with and without a sleep log. In a secondary analysis, we evaluated factors associated with disagreement between processing approaches. Children (n = 722, age 5-12 years) wore a wrist-based accelerometer for 14 days during Autumn 2020, Spring 2021, and/or Summer 2021. Outcomes included sleep period, duration, wake after sleep onset (WASO), and timing (onset, midpoint, waketime). Parents completed surveys including children's nightly bed/wake time. Data were processed with parent-reported bed/wake time (sleep log), the Heuristic algorithm looking at Distribution of Change in Z-Angle (HDCZA) algorithm (no log), and an 8 p.m.-8 a.m. window (generic log) using the R-package 'GGIR' (version 2.6-4). Mean/absolute bias and limits of agreement were calculated and visualised with Bland-Altman plots. Associations between child, home, and survey characteristics and disagreement were examined with tobit regression. Just over half of nights demonstrated no difference in sleep period between sleep log and no log approaches. Among all nights, the sleep log approach produced longer sleep periods (9.3 min; absolute mean bias [AMB] = 28.0 min), shorter duration (1.4 min; AMB = 14.0 min), greater WASO (11.0 min; AMB = 15.4 min), and earlier onset (13.4 min; AMB = 17.4 min), midpoint (8.8 min; AMB = 15.3 min), and waketime (3.9 min; AMB = 14.8 min) than no log. Factors associated with discrepancies included smartphone ownership, bedroom screens, nontraditional parent work schedule, and completion on weekend/summer nights (range = 0.4-10.2 min). The generic log resulted in greater AMB among sleep outcomes. Small mean differences were observed between nights with and without a sleep log. Discrepancies existed on weekends, in summer, and for children with smartphones and screens in the bedroom.

Sleep onset, duration, or regularity: which matters most for child adiposity outcomes?

BACKGROUND/OBJECTIVES: Sleep measures, such as duration and onset timing, are associated with adiposity outcomes among children. Recent research among adults has considered variability in sleep and wake onset times, with the Sleep Regularity Index (SRI) as a comprehensive metric to measure shifts in sleep and wake onset times between days. However, little research has examined regularity and adiposity outcomes among children. This study examined the associations of three sleep measures (i.e., sleep duration, sleep onset time, and SRI) with three measures of adiposity (i.e., body mass index [BMI], waist circumference, and waist-to-height ratio [WHtR]) in a pediatric sample. SUBJECTS/METHODS: Children (ages 4-13 years) who were part of the U.S. Newborn Epigenetic STudy (NEST) participated. Children (N = 144) wore an ActiGraph for 1 week. Sleep measures were estimated from actigraphy data. Weight, height, and waist circumference were measured by trained researchers. BMI and WHtR was calculated with the objectively measured waist and height values. Multiple linear regression models examined associations between child sleep and adiposity outcomes, controlling for race/ethnicity, child sex, age, mothers' BMI and sleep duration. RESULTS: When considering sleep onset timing and duration, along with demographic covariates, sleep onset timing was not significantly associated with any of the three adiposity measures, but a longer duration was significantly associated with a lower BMI Z-score (ß = -0.29, p < 0.001), waist circumference (ß = -0.31, p < 0.001), and WHtR (ß = -0.38, p < 0.001). When considering SRI and duration, duration remained significantly associated with the adiposity measures. The SRI and adiposity associations were in the expected direction, but were non-significant, except the SRI and WHtR association (ß = -0.16, p = 0.077) was marginally non-significant. CONCLUSIONS: Sleep duration was consistently associated with adiposity measures in children 4-13 years of age. Pediatric sleep interventions should focus first on elongating nighttime sleep duration, and examine if this improves child adiposity outcomes.

Pediatric Surgery and School Nurse Attitudes Regarding Children with Obesity.

PURPOSE: Weight bias towards individuals with obesity exists among medical professionals; yet, there is less evidence of the extent to which weight biases exist in different types of nursing professions. This study aimed to describe pediatric surgery and school nurses' weight biases towards children with obesity and examine factors associated with weight biases. DESIGN AND METHODS: Pediatric surgery (n = 108) and school nurses (n = 177) completed a single online survey that assessed their attitudes towards weight regarding children with obesity. Responses were categorized as % agreement (responded "agree" or "strongly agree" to a given statement). Nurses also reported % time spent working with children who had obesity. RESULTS: Almost all nurses (>93%) agreed it was important to treat patients with obesity with compassion and respect. However, many nurses endorsed statements stating that patients with obesity are often non-compliant with treatment recommendations (47%) and can be difficult to deal with (35%). Only about half of school nurses (53%) and surgical nurses (56%) felt professionally prepared to effectively treat patients with obesity. Nurses commonly reported hearing/witnessing other professionals in their field make negative comments (69%) or convey negative stereotypes (55%) about patients with obesity. School nurses who spent a greater % of time working with children with overweight had more positive weight attitudes (p = 0.04). CONCLUSIONS: Negative attitudes towards patients with obesity were relatively prevalent in this population of pediatric surgery and school nurses. PRACTICE IMPLICATIONS: There is a need for more educational opportunities, professional trainings, and policy initiatives to reduce weight bias among nurses.

Ecological momentary assessment of using food to soothe during infancy in the INSIGHT trial.

BACKGROUND: Use of food to soothe infant distress has been linked to greater weight in observational studies. We used ecological momentary assessment to capture detailed patterns of food to soothe and evaluate if a responsive parenting intervention reduced parents' use of food to soothe. METHODS: Primiparous mother-newborn dyads were randomized to a responsive parenting intervention designed for obesity prevention or a safety control group. Responsive parenting curriculum included guidance on using alternative soothing strategies (e.g., swaddling), rather than feeding, as the first response to infant fussiness. After the initial intervention visit 3 weeks after delivery, mothers (n = 157) were surveyed for two 5-8 day bursts at infant ages 3 and 8 weeks. Surveys were sent via text message every 4 h between 10:00 AM-10:00 PM, with 2 surveys sent at 8:00 AM asking about nighttime hours. Infant fusses and feeds were reported for each 4-h interval. Food to soothe was defined as "Fed First" and "Not Fed First" in response to a fussy event. Use of food to soothe was modeled using random-intercept logistic regression. RESULTS: The control group had greater odds of having Fed First, compared to the responsive parenting group at ages 3 and 8 weeks (3 weeks: OR = 1.9; 95% CI = 1.4-2.7; p < 0.01; 8 weeks: OR = 1.4; 95% CI = 1.0-2.1; p = 0.053). More responsive parenting mothers reported using a responsive parenting intervention strategy first, before feeding, than controls at ages 3 and 8 weeks (3 weeks: 58.1% vs. 41.9%; 8 weeks: 57.1% vs. 42.9%, respectively; p < 0.01 for both). At both ages combined, fewer fusses from responsive parenting infants were soothed best by feeding compared to controls (49.5% vs. 61.0%, respectively; p < 0.01). For both study groups combined, parents had greater odds of having Fed First during the nighttime compared to the daytime at both ages (3 weeks: OR = 1.6, 95% CI = 1.4-1.8; p < 0.01; 8 weeks: OR = 2.1; 95% CI = 1.7-2.6; p < 0.01). CONCLUSIONS: INSIGHT's responsive parenting intervention reduced use of food to soothe and increased use of alternative soothing strategies in response to infant fussiness. Education on responsive parenting behaviors around fussing and feeding during early infancy has the potential to improve later self-regulation and weight gain trajectory. TRIAL REGISTRATION: NCT01167270 . Registered July 21, 2010.

Breastfeeding initiation and duration and child health outcomes in the first baby study.

Despite the known benefits of breastmilk, associations between breastfeeding and child overall health outcomes remain unclear. We aimed to understand associations between breastfeeding and health outcomes, including child weight, through age 3. Analysis included women (N = 3006) in the longitudinal, prospective First Baby Study from 2009 to 2014. For this analysis, breastfeeding initiation and duration were measured using self-reported data from the 1-, 6- and 12-month surveys; child illnesses were analyzed from the 6-, 12-, and 24-month interviews; height and weight at age 3 were used to determine overweight/obese (≥85th percentile) and obese (≥95th percentile). Adjusted logistic regressions were utilized to determine significance. Greater duration of breastfeeding was associated with fewer reported acute illnesses at 6 months (p < 0.001) and fewer diarrheal illness/constipation episodes at 6, 12, and 24 months (p = 0.05) in adjusted analyses. Fewer breastfed children, compared to non-breastfed children, were overweight/obese (23.5% vs. 37.8%; p = 0.032) or obese (9.1% vs. 21.6%; p = 0.012) at age 3. Breastfeeding duration was negatively associated with overweight/obese (never breastfed: 37.8%, 0-6 months: 26.9%, >6 months: 20.2%; p = 0.020) and obesity (never breastfed: 21.6%, 0-6 months: 11.0%, >6 months: 7.3%; p = 0.012). Overall, our findings support the hypothesis that duration of breastfeeding is associated with fewer reported acute illnesses at 6 months of age and diarrheal illness and/or constipation episodes at 6, 12, and 24 months. Additionally, results from our study suggest a protective effect of breastfeeding from childhood overweight/obesity, as children who received breastmilk for 6 months or longer had lower odds of overweight/obesity at age 3 years.

Heat Exposure and Hypohydration Exacerbate Physiological Strain During Load Carrying.

Adams, EL, Casa, DJ, Huggins, RA, DeMartini-Nolan, JK, Stearns, RL, Kennedy, RM, Bosworth, MM, DiStefano, LJ, Armstrong, LE, and Maresh, CM. Heat exposure and hypohydration exacerbate physiological strain during load carrying. J Strength Cond Res 33(3): 727-735, 2019-Heat exposure and hypohydration induce physiological and psychological strain during exercise; however, it is unknown if the separate effects of heat exposure and hypohydration are synergistic when co-occurring during loaded exercise. This study compared separate and combined effects of heat exposure and hypohydration on physiological strain, mood state, and visual vigilance during loaded exercise. Twelve men (mean ± SD; age, 20 ± 2 years; body mass, 74.0 ± 8.2 kg; maximal oxygen uptake, 57.0 ± 6.0 ml·kg·min) completed 4 trials under the following conditions: euhydrated temperate (EUT), hypohydrated temperate (HYT), euhydrated hot (EUH), and hypohydrated hot (HYH). Exercise was 90 minutes of treadmill walking (∼50% V[Combining Dot Above]O2max, 5% grade) while carrying a 45-lb rucksack. Profile of Mood States and the Scanning Visual Vigilance Test were completed before and after exercise. The separate effects of heat exposure (EUH) and hypohydration (HYT) on post-exercise rectal temperature (Tre) were similar (38.25 ± 0.63°C vs. 38.22 ± 0.29°C, respectively, p > 0.05), whereas in combination (HYH), post-exercise Tre was far greater (39.32 ± 0.43°C). Increase in Tre per 1% body mass loss (BML) for HYH (vs. EUH) was greater than HYT (vs. EUT) (0.32 vs. 0.04°C, respectively, p = 0.02); heart rate increase per 1% BML for HYH (vs. EUH) was 7 b·min compared with HYT (vs. EUT) at 3 b·min (p = 0.30). Hypohydrated hot induced greater mood disturbance (post-exercise - pre-exercise) (35 ± 21 units) compared with other conditions (EUT = 3 ± 9 units; HYT = 3 ± 16 units; EUH = 16 ± 26 units; p < 0.001). No differences occurred in visual vigilance (p > 0.05). Independently, heat exposure and hypohydration induced similar physiological strain during loaded exercise; when combined, heat exposure with hypohydration, synergistically exacerbated physiological strain and mood disturbance.

INSIGHT responsive parenting intervention reduces infant's screen time and television exposure.

BACKGROUND: Sedentary behaviors, including screen time, in childhood have been associated with an increased risk for overweight. Beginning in infancy, we sought to reduce screen time and television exposure and increase time spent in interactive play as one component of a responsive parenting (RP) intervention designed for obesity prevention. METHODS: The Intervention Nurses Start Infants Growing on Healthy Trajectories (INSIGHT) study is a randomized trial comparing a RP intervention with a safety control intervention. Primiparous mother-newborn dyads (N = 279) were randomized after childbirth. Research nurses delivered intervention content at infant ages 3, 16, 28, and 40 weeks and research center visits at 1 and 2 years. As one component of INSIGHT, developmentally appropriate messages on minimizing screen time, reducing television exposure in the home, and promoting parent-child engagement through interactive play were delivered. Mothers self-reported their infant's screen time at ages 44 weeks, 1, 1.5, 2 and 2.5 years; interactive play was reported at 8 and 20 weeks and 2 years. RESULTS: More RP than control parents reported their infants met the American Academy of Pediatrics' no screen time recommendation at 44 weeks (53.0% vs. 30.2%) and at 1 year on weekdays (42.5% vs. 27.6%) and weekends (45.5% vs. 26.8%), but not after age 1 year. RP mothers and RP children had less daily screen time than controls at each time point (p ≤ 0.01). Fewer RP than control group mothers reported the television was ever on during infant meals (p < 0.05). The frequency of tummy time and floor play did not differ by study group; approximately 95% of infants spent time in restrictive devices (i.e. swing) at 8 and 20 weeks. At 2 years of age, there were no study group differences for time children spent in interactive play. CONCLUSION: From infancy to early childhood, the INSIGHT RP intervention reduced screen time and television exposure, but did not increase the frequency or amount of interactive play. TRIAL REGISTRATION: clinicaltrials.gov NCT01167270 . Registered on 21 July 2010.

From the children's perspective: What are candy, snacks, and meals?

OBJECTIVE: There remains a lack of consensus on what distinguishes candy (i.e. features sugar as a principal ingredient, also called sweets or lollies), snack foods, and foods served at meals; therefore, this study examined characteristics elementary-aged children use to distinguish between these food categories. METHODS: Participants were children aged 5-8 years (N = 41). Children were given 39 cards, each containing an image of a common American food (e.g. ice cream, fruit). Children sorted each card into either a "snack" or "candy" pile followed by a semi-structured one-on-one interview to identify children's perceptions of candy, snack foods, and foods served at meals. Verbatim transcripts were coded using a grounded theory approach to derive major themes. RESULTS: All children classified foods such as crackers and dry cereal as snacks; all children classified foods such as skittles and solid chocolate as candy. There was less agreement for "dessert like foods," such as cookies and ice cream, whereby some children classified these foods as candy and others as snacks. Specifically, more children categorized ice cream and chocolate chip cookies as candy (61% and 63%, respectively), than children who categorized these as snack foods (39% and 36%, respectively). Qualitative interviews revealed 4 overarching themes that distinguished among candy, snack foods, and food served at meals: (1) taste, texture, and type; (2) portion size; (3) perception of health; and (4) time of day. CONCLUSION: Children categorized a variety of foods as both a candy and a snack. Accurate measurement of candy and snack consumption is needed through the use of clear, consistent terminology and comprehensive diet assessment tools. Intervention messaging should clearly distinguish between candy, snack foods, and foods served at meals to improve children's eating behavior.

Effects of heat acclimation on hand cooling efficacy following exercise in the heat.

This study examined the separate and combined effects of heat acclimation and hand cooling on post-exercise cooling rates following bouts of exercise in the heat. Seventeen non-heat acclimated (NHA) males (mean ± SE; age, 23 ± 1 y; mass, 75.30 ± 2.27 kg; maximal oxygen consumption [VO2 max], 54.1 ± 1.3 ml·kg-1·min-1) completed 2 heat stress tests (HST) when NHA, then 10 days of heat acclimation, then 2 HST once heat acclimated (HA) in an environmental chamber (40°C; 40%RH). HSTs were 2 60-min bouts of treadmill exercise (45% VO2 max; 2% grade) each followed by 10 min of hand cooling (C) or no cooling (NC). Heat acclimation sessions were 90-240 min of treadmill or stationary bike exercise (60-80% VO2 max). Repeated measures ANOVA with Fishers LSD post hoc (α < 0.05) identified differences. When NHA, C (0.020 ± 0.003°C·min-1) had a greater cooling rate than NC (0.013 ± 0.003°C·min-1) (mean difference [95%CI]; 0.007°C [0.001,0.013], P = 0.035). Once HA, C (0.021 ± 0.002°C·min-1) was similar to NC (0.025 ± 0.002°C·min-1) (0.004°C [-0.003,0.011], P = 0.216). Hand cooling when HA (0.021 ± 0.002°C·min-1) was similar to when NHA (0.020 ± 0.003°C·min-1) (P = 0.77). In conclusion, when NHA, C provided greater cooling rates than NC. Once HA, C and NC provided similar cooling rates.

Prioritize "Food is Medicine" initiatives in the 2024 Farm Bill for human and planetary health.

The Society of Behavioral Medicine (SBM) supports increased funding for policies in the 2024 Farm Bill that align with a Food is Medicine (FIM) framework and address multiple dimensions of human and planetary health.

The Society of Behavioral Medicine (SBM) supports funding for policies in 2024 Farm Bill that align with a Food is Medicine (FIM) framework and address multiple dimensions of human and planetary health. Recommendations include increasing funding for produce prescription programs, establishing systems to align federal- and state-funded initiatives, and the allocation of funding for financial incentives when sustainable agricultural practices are utilized in government-funded local and regional farm-to-institution programs.

A comparison of perceived barriers to optimal child sleep among families with low and high income.

OBJECTIVE: Families with low-income experience suboptimal sleep compared to families with higher-income. Unique drivers likely contribute to these disparities, along with factors that universally impede sleep patterns, despite income level. To inform intervention tailoring, this mixed-methods study gathered parent's perceptions about child sleep challenges to identify similarities/differences in families with lower-income and higher-income. METHODS: Parents who experienced difficulties with their child (ages 2-4years) sleep were categorized as lower income (n = 15; $30,000 ± 17,845/year) or higher income (n = 15; $142,400 ± 61,373/year). Parents completed a survey and semistructured interview to explore barriers and facilitators for child sleep. Two coders independently evaluated transcripts for lower-income and higher-income groups using inductive analyses. Constant-comparison methods generated themes and characterized similarities/differences by income group. RESULTS: Groups were similar in themes related to diverse bedtime routines, nighttime struggles with child sleep, parent strategies to reduce night wakings, parent effort to provide a sleep-promoting environment, and presence of electronic rules. Groups differed in themes related to factors influencing routine setting (eg, lower income: external factors influencing routines; higher income: personal attributes for structure), parent appraisal of child sleep (eg, higher income: ambivalence; lower income: mostly negative appraisal), nap timing and duration (eg, lower income: longer naps), and strategy utilization and pursuit of resources (eg, higher income: more parents tried various strategies and accessed online/print resources). CONCLUSIONS: Parents experienced many similar barriers to child sleep, with a few distinct differences by income group. These findings can inform future intervention components for all families, as well as customized components to address the unique needs of families across income levels.

Understanding Accelerated Summer Body Mass Index Gain by Tracking Changes in Children's Height, Weight, and Body Mass Index Throughout the Year.

Background: Drivers of summer body mass index (BMI) gain in children remain unclear. The Circadian and Circannual Rhythm Model (CCRM) posits summer BMI gain is biologically driven, while the Structured Days Hypothesis (SDH) proposes it is driven by reduced structure. Objectives: Identify the mechanisms driving children's seasonal BMI gain through the CCRM and SDH. Methods: Children's (N = 147, mean age = 8.2 years) height and weight were measured monthly during the school year, and once in summer (July-August). BMI z-score (zBMI) was calculated using CDC growth charts. Behaviors were measured once per season. Mixed methods regression estimated monthly percent change in children's height (%HΔ), weight (%WΔ), and monthly zBMI for school year vs. summer vacation, seasonally, and during school months with no breaks vs. school months with a break ≥1 week. Results: School year vs. summer vacation analyses showed accelerations in children's %WΔ (Δ = 0.9, Standard Error (SE) = 0.1 vs. Δ = 1.4, SE = 0.1) and zBMI (Δ = -0.01, SE = 0.01 vs. Δ = 0.04, SE = 0.3) during summer vacation, but %HΔ remained relatively constant during summer vacation compared with school (Δ = 0.3, SE = 0.0 vs. Δ = 0.4, SE = 0.1). Seasonal analyses showed summer had the greatest %WΔ (Δ = 1.8, SE = 0.4) and zBMI change (Δ = 0.05, SE = 0.03) while %HΔ was relatively constant across seasons. Compared with school months without a break, months with a break showed higher %WΔ (Δ = 0.7, SE = 0.1 vs. Δ = 1.6, SE = 0.2) and zBMI change (Δ = -0.03, SE = 0.01 vs. Δ = 0.04, SE = 0.01), but %HΔ was constant (Δ = 0.4, SE = 0.0 vs. Δ = 0.3, SE = 0.1). Fluctuations in sleep timing and screen time may explain these changes. Conclusions: Evidence for both the CCRM and SDH was identified but the SDH may more fully explain BMI gain. Interventions targeting consistent sleep and reduced screen time during breaks from school may be warranted no matter the season.

A Device Agnostic Approach to Predict Children's Activity from Consumer Wearable Accelerometer Data: A Proof-of-Concept Study.

INTRODUCTION: This study examined the potential of a device agnostic approach for predicting physical activity from consumer wearable accelerometry compared with a research-grade accelerometry. METHODS: Seventy-five 5- to 12-year-olds (58% male, 63% White) participated in a 60-min protocol. Children wore wrist-placed consumer wearables (Apple Watch Series 7 and Garmin Vivoactive 4) and a research-grade device (ActiGraph GT9X) concurrently with an indirect calorimeter (COSMED K5). Activity intensities (i.e., inactive, light, moderate-to-vigorous physical activity) were estimated via indirect calorimetry (criterion), and the Hildebrand thresholds were applied to the raw accelerometer data from the consumer wearables and research-grade device. Epoch-by-epoch (e.g., weighted sensitivity, specificity) and discrepancy (e.g., mean bias, absolute error) analyses evaluated agreement between accelerometry-derived and criterion estimates. Equivalence testing evaluated the equivalence of estimates produced by the consumer wearables and ActiGraph. RESULTS: Estimates produced by the raw accelerometry data from ActiGraph, Apple, and Garmin produced similar criterion agreement with weighted sensitivity = 68.2% (95% confidence interval (CI), 67.1%-69.3%), 73.0% (95% CI, 71.8%-74.3%), and 66.6% (95% CI, 65.7%-67.5%), respectively, and weighted specificity = 84.4% (95% CI, 83.6%-85.2%), 82.0% (95% CI, 80.6%-83.4%), and 75.3% (95% CI, 74.7%-75.9%), respectively. Apple Watch produced the lowest mean bias (inactive, -4.0 ± 4.5; light activity, 2.1 ± 4.0) and absolute error (inactive, 4.9 ± 3.4; light activity, 3.6 ± 2.7) for inactive and light physical activity minutes. For moderate-to-vigorous physical activity, ActiGraph produced the lowest mean bias (1.0 ± 2.9) and absolute error (2.8 ± 2.4). No ActiGraph and consumer wearable device estimates were statistically significantly equivalent. CONCLUSIONS: Raw accelerometry estimated inactive and light activity from wrist-placed consumer wearables performed similarly to, if not better than, a research-grade device, when compared with indirect calorimetry. This proof-of-concept study highlights the potential of device-agnostic methods for quantifying physical activity intensity via consumer wearables.

Comparison of raw accelerometry data from ActiGraph, Apple Watch, Garmin, and Fitbit using a mechanical shaker table.

The purpose of this study was to evaluate the reliability and validity of the raw accelerometry output from research-grade and consumer wearable devices compared to accelerations produced by a mechanical shaker table. Raw accelerometry data from a total of 40 devices (i.e., n = 10 ActiGraph wGT3X-BT, n = 10 Apple Watch Series 7, n = 10 Garmin Vivoactive 4S, and n = 10 Fitbit Sense) were compared to reference accelerations produced by an orbital shaker table at speeds ranging from 0.6 Hz (4.4 milligravity-mg) to 3.2 Hz (124.7mg). Two-way random effects absolute intraclass correlation coefficients (ICC) tested inter-device reliability. Pearson product moment, Lin's concordance correlation coefficient (CCC), absolute error, mean bias, and equivalence testing were calculated to assess the validity between the raw estimates from the devices and the reference metric. Estimates from Apple, ActiGraph, Garmin, and Fitbit were reliable, with ICCs = 0.99, 0.97, 0.88, and 0.88, respectively. Estimates from ActiGraph, Apple, and Fitbit devices exhibited excellent concordance with the reference CCCs = 0.88, 0.83, and 0.85, respectively, while estimates from Garmin exhibited moderate concordance CCC = 0.59 based on the mean aggregation method. ActiGraph, Apple, and Fitbit produced similar absolute errors = 16.9mg, 21.6mg, and 22.0mg, respectively, while Garmin produced higher absolute error = 32.5mg compared to the reference. ActiGraph produced the lowest mean bias 0.0mg (95%CI = -40.0, 41.0). Equivalence testing revealed raw accelerometry data from all devices were not statistically significantly within the equivalence bounds of the shaker speed. Findings from this study provide evidence that raw accelerometry data from Apple, Garmin, and Fitbit devices can be used to reliably estimate movement; however, no estimates were statistically significantly equivalent to the reference. Future studies could explore device-agnostic and harmonization methods for estimating physical activity using the raw accelerometry signals from the consumer wearables studied herein.

Jerks are Useful: Extracting pulse rate from wrist-placed accelerometry jerk during sleep in children.

STUDY OBJECTIVES: Evaluate wrist-placed accelerometry predicted heartrate compared to electrocardiogram (ECG) heartrate in children during sleep. METHODS: Children (n=82, 61% male, 43.9% Black) wore a wrist-placed Apple Watch Series 7 (AWS7) and ActiGraph GT9X during a polysomnogram. 3-Axis accelerometry data was extracted from AWS7 and the GT9X. Accelerometry heartrate estimates were derived from jerk (the rate of acceleration change), computed using the peak magnitude frequency in short time Fourier Transforms of Hilbert transformed jerk computed from acceleration magnitude. Heartrates from ECG traces were estimated from R-R intervals using R-pulse detection. Lin's Concordance Correlation Coefficient (CCC), mean absolute error (MAE) and mean absolute percent error (MAPE) assessed agreement with ECG estimated heartrate. Secondary analyses explored agreement by polysomnography sleep stage and a signal quality metric. RESULTS: The developed scripts are available on Github. For the GT9X, CCC was poor at -0.11 and MAE and MAPE were high at 16.8 (SD=14.2) beats/minute and 20.4% (SD=18.5%). For AWS7, CCC was moderate at 0.61 while MAE and MAPE were lower at 6.4 (SD=9.9) beats/minute and 7.3% (SD=10.3%). Accelerometry estimated heartrate for AWS7 was more closely related to ECG heartrate during N2, N3 and REM sleep than lights on, wake, and N1 and when signal quality was high. These patterns were not evident for the GT9X. CONCLUSIONS: Raw accelerometry data extracted from AWS7, but not the GT9X, can be used to estimate heartrate in children while they sleep. Future work is needed to explore the sources (i.e., hardware, software, etc.) of the GT9X's poor performance.

Food Insecurity, Federal Nutrition Support, and Parent Feeding Practices During COVID-19: A 1-Year Follow-up Study.

OBJECTIVES: COVID-19 caused stark increases in food insecurity. To maintain food provision, policy changes to the National School Lunch Program (NSLP) and Supplemental Nutrition Assistance Program (SNAP) were instated. This longitudinal study examined (1) food security patterns across the timeline of COVID-19; (2) the relationship among food security patterns, NSLP/SNAP use, and parent feeding practices; and (3) parent perceptions of NSLP/SNAP policy changes. METHODS: A total of 333 US parents completed online surveys during the COVID-19 pandemic: May 2020 (T1), September 2020 (T2), and May 2021 (T3). Food security and parent feeding practices were reported at each time point; pre-COVID-19 behaviors were retrospectively reported at T1. Use and perceptions of NSLP/SNAP policy changes were reported at T3. We examined associations between food security and parent feeding practices using repeated-measures mixed models. RESULTS: The percentage of parents with very low food security increased from pre-COVID-19 (9.6%) to T1 (29.1%) and remained elevated at T3 (16.8%). One-third (31.2%) of families fluctuated between food security and food insecurity, with 27.0% remaining food insecure at T3. Thirty percent of consistently food-insecure families reported not receiving school-provided meals, and 45% did not receive SNAP benefits. Most parents reported that pickup school meal sites (71.4%), Pandemic Electronic Benefit Transfer cards (51.4%), and increased SNAP benefits (79.6%) were beneficial. Initial changes in parent feeding practices reported at T1 returned to pre-COVID-19 levels by T3, yet concern for child overweight remained significantly elevated. CONCLUSION: Continued policy efforts to support food-insecure families via expanded food access in NSLP/SNAP are critical.

Barriers to Optimal Child Sleep among Families with Low Income: A Mixed-Methods Study to Inform Intervention Development.

This study gathered formative data on barriers to optimal child sleep to inform the development of a sleep intervention for parents of preschool-aged children in low-income households. Parents (n = 15, age: 34 ± 8 years, household income: $30,000 ± 17,845/year) reporting difficulties with their child's sleep participated in this study. Mixed methods included an online survey and semi-structured phone interview. Items assessed barriers/facilitators to optimal child sleep and intervention preferences. Interview transcripts were coded using inductive analyses and constant-comparison methods to generate themes. Derived themes were then mapped onto the Theoretical Domains Framework to contextualize barriers and inform future intervention strategies. Themes that emerged included: stimulating bedtime activities, child behavior challenges, variability in children's structure, parent work responsibilities, sleep-hindering environment, and parent's emotional capacity. Parent's intervention preferences included virtual delivery (preferred by 60% of parents) to reduce barriers and provide flexibility. Mixed preferences were observed for the group (47%) vs. individual (53%) intervention sessions. Parents felt motivated to try new intervention strategies given current frustrations, the potential for tangible results, and knowing others were in a similar situation. Future work will map perceived barriers to behavior change strategies using the Behavior Change Wheel framework to develop a parenting sleep intervention.

Use of guidelines, checklists, frameworks, and recommendations in behavioral intervention preliminary studies and associations with reporting comprehensiveness: a scoping bibliometric review.

BACKGROUND: Guidelines, checklists, frameworks, and recommendations (GCFRs) related to preliminary studies serve as essential resources to assist behavioral intervention researchers in reporting findings from preliminary studies, but their impact on preliminary study reporting comprehensiveness is unknown. The purpose of this study was to conduct a scoping bibliometric review of recently published preliminary behavioral-focused intervention studies to (1) examine the prevalence of GCFR usage and (2) determine the associations between GCFR usage and reporting feasibility-related characteristics. METHODS: A systematic search was conducted for preliminary studies of behavioral-focused interventions published between 2018 and 2020. Studies were limited to the top 25 journals publishing behavioral-focused interventions, text mined to identify usage of GCFRs, and categorized as either not citing GCFRs or citing ≥ 2 GCFRs (Citers). A random sample of non-Citers was text mined to identify studies which cited other preliminary studies that cited GCFRs (Indirect Citers) and those that did not (Never Citers). The presence/absence of feasibility-related characteristics was compared between Citers, Indirect Citers, and Never Citers via univariate logistic regression. RESULTS: Studies (n = 4143) were identified, and 1316 were text mined to identify GCFR usage (n = 167 Citers). A random sample of 200 studies not citing a GCFR were selected and categorized into Indirect Citers (n = 71) and Never Citers (n = 129). Compared to Never Citers, Citers had higher odds of reporting retention, acceptability, adverse events, compliance, cost, data collection feasibility, and treatment fidelity (ORrange = 2.62-14.15, p < 0.005). Citers also had higher odds of mentioning feasibility in purpose statements, providing progression criteria, framing feasibility as the primary outcome, and mentioning feasibility in conclusions (ORrange = 6.31-17.04, p < 0.005) and lower odds of mentioning efficacy in purpose statements, testing for efficacy, mentioning efficacy in conclusions, and suggesting future testing (ORrange = 0.13-0.54, p < 0.05). Indirect Citers had higher odds of reporting acceptability and treatment fidelity (ORrange = 2.12-2.39, p < 0.05) but lower odds of testing for efficacy (OR = 0.36, p < 0.05) compared to Never Citers. CONCLUSION: The citation of GCFRs is associated with greater reporting of feasibility-related characteristics in preliminary studies of behavioral-focused interventions. Researchers are encouraged to use and cite literature that provides guidance on design, implementation, analysis, and reporting to improve the comprehensiveness of reporting for preliminary studies.

Estimating Physical Activity and Sleep using the Combination of Movement and Heart Rate: A Systematic Review and Meta-Analysis.

The purpose of this meta-analysis was to quantify the difference in physical activity and sleep estimates assessed via 1) movement, 2) heart rate (HR), or 3) the combination of movement and HR (MOVE+HR) compared to criterion indicators of the outcomes. Searches in four electronic databases were executed September 21-24 of 2021. Weighted mean was calculated from standardized group-level estimates of mean percent error (MPE) and mean absolute percent error (MAPE) of the proxy signal compared to the criterion measurement method for physical activity, HR, or sleep. Standardized mean difference (SMD) effect sizes between the proxy and criterion estimates were calculated for each study across all outcomes, and meta-regression analyses were conducted. Two-One-Sided-Tests method were conducted to metaanalytically evaluate the equivalence of the proxy and criterion. Thirty-nine studies (physical activity k = 29 and sleep k = 10) were identified for data extraction. Sample size weighted means for MPE were -38.0%, 7.8%, -1.4%, and -0.6% for physical activity movement only, HR only, MOVE+HR, and sleep MOVE+HR, respectively. Sample size weighted means for MAPE were 41.4%, 32.6%, 13.3%, and 10.8% for physical activity movement only, HR only, MOVE+HR, and sleep MOVE+HR, respectively. Few estimates were statistically equivalent at a SMD of 0.8. Estimates of physical activity from MOVE+HR were not statistically significantly different from estimates based on movement or HR only. For sleep, included studies based their estimates solely on the combination of MOVE+HR, so it was impossible to determine if the combination produced significantly different estimates than either method alone.