Consolidated guidance for behavioral intervention pilot and feasibility studies.

BACKGROUND: In the behavioral sciences, conducting pilot and/or feasibility studies (PFS) is a key step that provides essential information used to inform the design, conduct, and implementation of a larger-scale trial. There are more than 160 published guidelines, reporting checklists, frameworks, and recommendations related to PFS. All of these publications offer some form of guidance on PFS, but many focus on one or a few topics. This makes it difficult for researchers wanting to gain a broader understanding of all the relevant and important aspects of PFS and requires them to seek out multiple sources of information, which increases the risk of missing key considerations to incorporate into their PFS. The purpose of this study was to develop a consolidated set of considerations for the design, conduct, implementation, and reporting of PFS for interventions conducted in the behavioral sciences. METHODS: To develop this consolidation, we undertook a review of the published guidance on PFS in combination with expert consensus (via a Delphi study) from the authors who wrote such guidance to inform the identified considerations. A total of 161 PFS-related guidelines, checklists, frameworks, and recommendations were identified via a review of recently published behavioral intervention PFS and backward/forward citation tracking of a well-known PFS literature (e.g., CONSORT Ext. for PFS). Authors of all 161 PFS publications were invited to complete a three-round Delphi survey, which was used to guide the creation of a consolidated list of considerations to guide the design, conduct, and reporting of PFS conducted by researchers in the behavioral sciences. RESULTS: A total of 496 authors were invited to take part in the three-round Delphi survey (round 1, N = 46; round 2, N = 24; round 3, N = 22). A set of twenty considerations, broadly categorized into six themes (intervention design, study design, conduct of trial, implementation of intervention, statistical analysis, and reporting) were generated from a review of the 161 PFS-related publications as well as a synthesis of feedback from the three-round Delphi process. These 20 considerations are presented alongside a supporting narrative for each consideration as well as a crosswalk of all 161 publications aligned with each consideration for further reading. CONCLUSION: We leveraged expert opinion from researchers who have published PFS-related guidelines, checklists, frameworks, and recommendations on a wide range of topics and distilled this knowledge into a valuable and universal resource for researchers conducting PFS. Researchers may use these considerations alongside the previously published literature to guide decisions about all aspects of PFS, with the hope of creating and disseminating interventions with broad public health impact.

The Day-Level Association Between Child Care Attendance and 24-Hour Movement Behaviors in Preschool-Aged Children.

BACKGROUND: Twenty-four hour movement behaviors (ie, physical activity [PA], screen time [ST], and sleep) are associated with children's health outcomes. Identifying day-level contextual factors, such as child care, that positively influence children's movement behaviors may help identify potential intervention targets, like improving access to child care programs. This study aimed to examine the between- and within-person effects of child care on preschoolers' 24-hour movement behaviors. METHODS: Children (N = 74, 4.7 [0.9] y, 48.9% girls, 63.3% White) wore an Axivity AX3 accelerometer on their nondominant wrist 24 hours per day for 14 days to measure PA and sleep. Parents completed surveys each night about their child's ST and child care attendance that day. Linear mixed effects models predicted day-level 24-hour movement behaviors from hours spent in child care. RESULTS: Children spent an average of 5.0 (2.9) hours per day in child care. For every additional hour of child care above their average, children had 0.3 hours (95% CI, -0.3 to -0.2) less ST that day. Between-person effects showed that compared with children who attended fewer overall hours of child care, children who attended more hours had less overall ST (B = -0.2 h; 95% CI, -0.4 to 0.0). Child care was not significantly associated with PA or sleep. CONCLUSIONS: Child care attendance was not associated with 24-hour PA or sleep; however, it was associated with less ST. More research utilizing objective measures of ST and more robust measures of daily schedules or structure is necessary to better understand how existing infrastructure may influence preschool-aged children's 24-hour movement behaviors. In addition, future research should consider how access to child care may influence child care attendance.

Balancing best practice and reality in behavioral intervention development: A survey of principal investigators funded by the National Institutes of Health.

Preliminary studies play a prominent role in the development of large-scale behavioral interventions. Though recommendations exist to guide the execution and interpretation of preliminary studies, these assume optimal scenarios which may clash with realities faced by researchers. The purpose of this study was to explore how principal investigators (PIs) balance expectations when conducting preliminary studies. We surveyed PIs funded by the National Institutes of Health to conduct preliminary behavioral interventions between 2000 and 2020. Four hundred thirty-one PIs (19% response rate) completed the survey (November 2021 to January 2022, 72% female, mean 21 years post-terminal degree). Most PIs were aware of translational models and believed preliminary studies should precede larger trials but also believed a single preliminary study provided sufficient evidence to scale. When asked about the relative importance of preliminary efficacy (i.e. changes in outcomes) and feasibility (i.e. recruitment, acceptance/adherence) responses varied. Preliminary studies were perceived as necessary to successfully compete for research funding, but among PIs who had peer-reviewed federal-level grants applications (n = 343 [80%]), responses varied about what should be presented to secure funding. Confusion surrounding the definition of a successful, informative preliminary study poses a significant challenge when developing behavior interventions. This may be due to a mismatch between expectations surrounding preliminary studies and the realities of the research enterprise in which they are conducted. To improve the quality of preliminary studies and advance the field of behavioral interventions, additional funding opportunities, more transparent criteria in grant reviews, and additional training for grant reviewers are suggested.

Initial testing of behavioral interventions can provide valuable information about the methods of the intervention and whether it is effective. However, recommendations that provide researchers with guidance on how to best conduct pilot studies assume ideal circumstances. The mismatch between what can be realistically accomplished in a preliminary study, and what researchers expect from preliminary studies creates confusion. As a result, it is difficult for researchers to judge the quality, relevance, and potential of preliminary studies. This study suggests more research funding opportunities, clearer rules for reviewing grant applications, and more training for the people who review these applications could help improve preliminary studies and create more effective health behavior programs.

Using the Nutrition Facts Label to Make Food Choices Is Associated with Healthier Eating among 8th and 11th-Grade Students: An Analysis of Statewide Representative Data from the 2019-2020 Texas School Physical Activity and Nutrition Survey.

BACKGROUND: Nutrition labels are a tool to inform and encourage the public to make healthier food choices, but little information is available about use in multi-ethnic adolescent populations in the U.S. The purpose of this study was to examine associations between the level of nutrition label usage and healthy/unhealthy eating behaviors among a statewide representative sample of 8th and 11th-grade students in Texas. METHODS: We analyzed cross-sectional associations between the Nutrition Facts label use and eating behaviors from a statewide sample of 8th and 11th-grade students in Texas, (n = 4730, weighted n = 710,731, mean age = 14.7 ± 1.6 years; 49% female, 51% Hispanic), who completed the 2019-2020 Texas School Physical Activity and Nutrition (TX SPAN) survey. Students self-reported their level of nutrition label usage to make food choices (5-point Likert scale from "Never" to "Always") and previous day consumption of 26 food items (13 healthy, 13 unhealthy). The 26 food items were used to calculate a Healthy Eating Index (HEI) score (0-100), a Healthy Foods Index (HFI) score (0-100), and an Unhealthy Foods Index (UFI) score (0-100). Weighted linear regression models were employed to examine the associations between self-reported use of nutrition labels to make food choices and HEI, HFI, and UFI scores. Marginal predicted means of HEI, HFI, and UFI scores were calculated post hoc from linear regression models. The odds of consuming specific individual food items for nutrition label usage were also calculated from weighted logistic regression models. All linear and logistic regression models were adjusted for grade, sex, Body Mass Index (BMI), race/ethnicity, economic disadvantage, and percentage of English language learners by school. RESULTS: A total of 11.0% of students reported always/almost always using nutrition labels to make food choices, 27.9% reported sometimes using them, while 61.0% indicated they never/almost never used nutrition labels to make food choices. The average HEI score among students in the sample was 47.7 ± 5.9. Nutrition Facts label usage was significantly and positively associated with HEI (b = 5.79, 95%CI: 4.45, 7.12) and HFI (b = 7.28, 95%CI:4.48, 10.07), and significantly and negatively associated with UFI (b = -4.30, 95%CI: -6.25, -2.34). A dose-response relationship was observed between nutrition label usage and HEI, HFI, and UFI scores, such that the strength of these associations increased with each one-point increase in nutrition label usage. Students who reported using nutrition labels always/almost always to make food choices had significantly higher odds of consuming healthy foods including baked meat, nuts, brown bread, vegetables, whole fruit, and yogurt (ORrange = 1.31-3.07), and significantly lower odds of consuming unhealthy foods including chips, cake, candy, and soda (ORrange = 0.48-0.68) compared to students who reported never/almost never using the Nutrition Facts label. CONCLUSIONS: Using the Nutrition Facts labels to make food choices is beneficially associated with healthy and unhealthy eating among 8th and 11th-grade students, although the proportion of students using nutrition labels to make their food choices was low. Public health efforts should be made to improve nutrition literacy and encourage nutrition label use among secondary students in the United States.

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.

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.

What does it mean to use the mean? The impact of different data handling strategies on the proportion of children classified as meeting 24-hr movement guidelines and associations with overweight and obesity.

Background: Despite the widespread endorsement of 24-hour movement guidelines (physical activity, sleep, screentime) for youth, no standardized processes for categorizing guideline achievement exists. The purpose of this study was to illustrate the impact of different data handling strategies on the proportion of children meeting 24-hour movement guidelines (24hrG) and associations with overweight and obesity. Methods: A subset of 524 children (ages 5-12yrs) with complete 24-hour behavior measures on at least 10 days was used to compare the impact of data handling strategies on estimates of meeting 24hrG. Physical activity and sleep were measured via accelerometry. Screentime was measured via parent self-report. Comparison of meeting 24hrG were made using 1) average of behaviors across all days (AVG-24hr), 2) classifying each day and evaluating the percentage meeting 24hrG from 10-100% of their measured days (DAYS-24hr), and 3) the average of a random sample of 4 days across 10 iterations (RAND-24hr). A second subset of children (N=475) with height and weight data was used to explore the influence of each data handling strategy on children meeting guidelines and the odds of overweight/obesity via logistic regression. Results: Classification for AVG-24hr resulted in 14.7% of participants meeting 24hrG. Classification for DAYS-24hr resulted in 63.5% meeting 24hrG on 10% of measured days with <1% meeting 24hrG on 100% of days. Classification for RAND-24hr resulted in 15.9% of participants meeting 24hrG. Across 10 iterations, 63.6% of participants never met 24hrG regardless of the days sampled, 3.4% always met 24hrG, with the remaining 33.0% classified as meeting 24hrG for at least one of the 10 random iterations of days. Using AVG-24hr as a strategy, meeting all three guidelines associated with lower odds of having overweight obesity (OR=0.38, p<0.05). The RAND-24hr strategy produced a range of odds from 0.27 to 0.56. Using the criteria of needing to meet 24hrG on 100% of days, meeting all three guidelines associated with the lowest odds of having overweight and obesity as well (OR=0.04, p<0.05). Conclusions: Varying estimates of meeting the 24hrG and the odds of overweight and obesity results from different data handling strategies and days sampled.

Elementary school staff perspectives on the implementation of physical activity approaches in practice: an exploratory sequential mixed methods study.

Introduction: A whole-of-school approach is best to promote physical activity before, during, and after school. However, multicomponent programming is often complex and difficult to deliver in school settings. There is a need to better understand how components of a whole-of-school approach are implemented in practice. The objectives of this mixed methods study were to: (1) qualitatively explore physical activity approaches and their implementation in elementary schools, (2) quantitatively assess implementation levels, and (3) examine associations between school-level physical activity promotion and academic ratings. Methods: We used an exploratory sequential mixed methods design. We conducted semi-structured qualitative interviews with elementary school staff from a Texas school district and used a directed content analysis to explore physical activity approaches and their implementation. Using qualitative findings, we designed a survey to quantitatively examine the implementation of physical activity approaches, which we distributed to elementary staff district wide. We used Pearson correlation coefficients to examine the association between the amount of physical activity opportunities present in individual schools and school-level academic ratings. Results: We completed 15 interviews (7 principals/assistant principals, 4 physical educators, and 4 classroom teachers). Elementary school teachers and staff indicated PE and recess implementation was driven from the top-down by state and district policies, while implementation of classroom-based approaches, before and after school programming, and active transport were largely driven from the bottom-up by teachers and school leaders. Teachers and staff also discussed implementation challenges across approaches. Survey respondents (n = 247 from 22 schools) indicated 54.6% of schools were implementing ≥135 min/week of physical education and 72.7% were implementing 30 min/day of recess. Classroom-based approaches were less common. Twenty-four percent of schools reported accessible before school programs, 72.7% reported accessible after school programs, and 27% promoted active transport. There was a direct association between the number of physical activity opportunities provided and school-level academic ratings r(22) = 0.53, p = 0.01. Conclusion: Schools provided physical activity opportunities consistent with a whole-of-school approach, although there was variability between schools and implementation challenges were present. Leveraging existing school assets while providing school-specific implementation strategies may be most beneficial for supporting successful physical activity promotion in elementary schools.

Observed and perceived benefits of providing physical activity opportunities in elementary schools: a qualitative study.

Introduction: Schools play an important role in promoting physical activity for youth. However, school-based physical activity opportunities often compete with other academic priorities, limiting their implementation. The purpose of this study was to qualitatively explore elementary school teacher and staff perspectives on providing physical activity opportunities and how they impact students and learning. Methods: We partnered with a school district in Texas to conduct semi-structured individual interviews. We used a purposeful sampling approach to recruit elementary teachers and staff knowledgeable about the physical activity opportunities provided at their school. Interviews included questions about participant opinions of providing physical activity opportunities and the types of opportunities provided. We analyzed data using a directed content analysis and iterative categorization approach. Results: Fifteen participants (4 teachers, 4 physical education teachers, 3 assistant principals, and 4 principals) completed interviews from 10 elementary schools. Participants discussed observed and perceived benefits when providing physical activity opportunities, which emerged into four themes and subthemes: (1) academic benefits (learning readiness, learning engagement, and academic performance); (2) social-emotional benefits (behavior, interpersonal and social skills, and classroom culture); (3) physical benefits (brain health, skill development, physical health); and (4) instructional benefits (quality teaching time, helpful teaching tools, and teacher-student relationships). Conclusions: Teachers and staff observed numerous benefits when students had opportunities to be physically active, including the positive impact on academic and social-emotional outcomes. Our findings highlight the alignment of physical activity with other school priorities. Physical activity programming can be used in ways to support academics, learning, behavior, and other important outcomes.

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.

The mysterious case of the disappearing pilot study: a review of publication bias in preliminary behavioral interventions presented at health behavior conferences.

BACKGROUND: The number of preliminary studies conducted and published has increased in recent years. However, there are likely many preliminary studies that go unpublished because preliminary studies are typically small and may not be perceived as methodologically rigorous. The extent of publication bias within preliminary studies is unknown but can prove useful to determine whether preliminary studies appearing in peer-reviewed journals are fundamentally different than those that are unpublished. The purpose of this study was to identify characteristics associated with publication in a sample of abstracts of preliminary studies of behavioral interventions presented at conferences. METHODS: Abstract supplements from two primary outlets for behavioral intervention research (Society of Behavioral Medicine and International Society of Behavioral Nutrition and Physical Activity) were searched to identify all abstracts reporting findings of behavioral interventions from preliminary studies. Study characteristics were extracted from the abstracts including year presented, sample size, design, and statistical significance. To determine if abstracts had a matching peer-reviewed publication, a search of authors' curriculum vitae and research databases was conducted. Iterative logistic regression models were used to estimate odds of abstract publication. Authors with unpublished preliminary studies were surveyed to identify reasons for nonpublication. RESULTS: Across conferences, a total of 18,961 abstracts were presented. Of these, 791 were preliminary behavioral interventions, of which 49% (388) were published in a peer-reviewed journal. For models with main effects only, preliminary studies with sample sizes greater than n = 24 were more likely to be published (range of odds ratios, 1.82 to 2.01). For models including interactions among study characteristics, no significant associations were found. Authors of unpublished preliminary studies indicated small sample sizes and being underpowered to detect effects as barriers to attempting publication. CONCLUSIONS: Half of preliminary studies presented at conferences go unpublished, but published preliminary studies appearing in peer-reviewed literature are not systematically different from those that remain unpublished. Without publication, it is difficult to assess the quality of information regarding the early-stage development of interventions. This inaccessibility inhibits our ability to learn from the progression of preliminary studies.

Evaluation of a device-agnostic approach to predict sleep from raw accelerometry data collected by Apple Watch Series 7, Garmin Vivoactive 4, and ActiGraph GT9X Link in children with sleep disruptions.

GOAL AND AIMS: Evaluate the performance of a sleep scoring algorithm applied to raw accelerometry data collected from research-grade and consumer wearable actigraphy devices against polysomnography. FOCUS METHOD/TECHNOLOGY: Automatic sleep/wake classification using the Sadeh algorithm applied to raw accelerometry data from ActiGraph GT9X Link, Apple Watch Series 7, and Garmin Vivoactive 4. REFERENCE METHOD/TECHNOLOGY: Standard manual PSG sleep scoring. SAMPLE: Fifty children with disrupted sleep (M = 8.5 years, range = 5-12 years, 42% Black, 64% male). DESIGN: Participants underwent to single night lab polysomnography while wearing ActiGraph, Apple, and Garmin devices. CORE ANALYTICS: Discrepancy and epoch-by-epoch analyses for sleep/wake classification (devices vs. polysomnography). ADDITIONAL ANALYTICS AND EXPLORATORY ANALYSES: Equivalence testing for sleep/wake classification (research-grade actigraphy vs. commercial devices). CORE OUTCOMES: Compared to polysomnography, accuracy, sensitivity, and specificity were 85.5, 87.4, and 76.8, respectively, for Actigraph; 83.7, 85.2, and 75.8, respectively, for Garmin; and 84.6, 86.2, and 77.2, respectively, for Apple. The magnitude and trend of bias for total sleep time, sleep efficiency, sleep onset latency, and wake after sleep were similar between the research and consumer wearable devices. IMPORTANT ADDITIONAL OUTCOMES: Equivalence testing indicated that total sleep time and sleep efficiency estimates from the research and consumer wearable devices were statistically significantly equivalent. CORE CONCLUSION: This study demonstrates that raw acceleration data from consumer wearable devices has the potential to be harnessed to predict sleep in children. While further work is needed, this strategy could overcome current limitations related to proprietary algorithms for predicting sleep in consumer wearable devices.

Scientists' perception of pilot study quality was influenced by statistical significance and study design.

OBJECTIVES: Preliminary studies play a key role in developing large-scale interventions but may be held to higher or lower scientific standards during the peer review process because of their preliminary study status. STUDY DESIGN AND SETTING: Abstracts from 5 published obesity prevention preliminary studies were systematically modified to generate 16 variations of each abstract. Variations differed by 4 factors: sample size (n = 20 vs. n = 150), statistical significance (P < 0.05 vs. P > 0.05), study design (single group vs. randomized 2 groups), and preliminary study status (presence/absence of pilot language). Using an online survey, behavioral scientists were provided with a randomly selected variation of each of the 5 abstracts and blinded to the existence of other variations. Respondents rated each abstract on aspects of study quality. RESULTS: Behavioral scientists (n = 271, 79.7% female, median age 34 years) completed 1,355 abstract ratings. Preliminary study status was not associated with perceived study quality. Statistically significant effects were rated as more scientifically significant, rigorous, innovative, clearly written, warranted further testing, and had more meaningful results. Randomized designs were rated as more rigorous, innovative, and meaningful. CONCLUSION: Findings suggest reviewers place a greater value on statistically significant findings and randomized control design and may overlook other important study characteristics.

Feasibility indicators in obesity-related behavioral intervention preliminary studies: a historical scoping review.

BACKGROUND: Behavioral interventions are often complex, operate at multiple levels, across settings, and employ a range of behavior change techniques. Collecting and reporting key indicators of initial trial and intervention feasibility is essential to decisions for progressing to larger-scale trials. The extent of reporting on feasibility indicators and how this may have changed over time is unknown. The aims of this study were to (1) conduct a historical scoping review of the reporting of feasibility indicators in behavioral pilot/feasibility studies related to obesity published through 2020, and (2) describe trends in the amount and type of feasibility indicators reported in studies published across three time periods: 1982-2006, 2011-2013, and 2018-2020. METHODS: A search of online databases (PubMed, Embase, EBSCOhost, Web of Science) for health behavior pilot/feasibility studies related to obesity published up to 12/31/2020 was conducted and a random sample of 600 studies, 200 from each of the three timepoints (1982-2006, 2011-2013, and 2018-2020), was included in this review. The presence/absence of feasibility indicators, including recruitment, retention, participant acceptability, attendance, compliance, and fidelity, were identified/coded for each study. Univariate logistic regression models were employed to assess changes in the reporting of feasibility indicators across time. RESULTS: A total of 16,365 unique articles were identified of which 6873 of these were reviewed to arrive at the final sample of 600 studies. For the total sample, 428 (71.3%) studies provided recruitment information, 595 (99.2%) provided retention information, 219 (36.5%) reported quantitative acceptability outcomes, 157 (26.2%) reported qualitative acceptability outcomes, 199 (33.2%) reported attendance, 187 (31.2%) reported participant compliance, 23 (3.8%) reported cost information, and 85 (14.2%) reported treatment fidelity outcomes. When compared to the Early Group (1982-2006), studies in the Late Group (2018-2020) were more likely to report recruitment information (OR=1.60, 95%CI 1.03-2.49), acceptability-related quantitative (OR=2.68, 95%CI 1.76-4.08) and qualitative (OR=2.32, 95%CI 1.48-3.65) outcomes, compliance outcomes (OR=2.29, 95%CI 1.49-3.52), and fidelity outcomes (OR=2.13, 95%CI 1.21, 3.77). CONCLUSION: The reporting of feasibility indicators within behavioral pilot/feasibility studies has improved across time, but key aspects of feasibility, such as fidelity, are still not reported in the majority of studies. Given the importance of behavioral intervention pilot/feasibility studies in the translational science spectrum, there is a need for improving the reporting of feasibility indicators.

Influence of pilot and small trials in meta-analyses of behavioral interventions: a meta-epidemiological study.

BACKGROUND: Pilot/feasibility or studies with small sample sizes may be associated with inflated effects. This study explores the vibration of effect sizes (VoE) in meta-analyses when considering different inclusion criteria based upon sample size or pilot/feasibility status. METHODS: Searches were to identify systematic reviews that conducted meta-analyses of behavioral interventions on topics related to the prevention/treatment of childhood obesity from January 2016 to October 2019. The computed summary effect sizes (ES) were extracted from each meta-analysis. Individual studies included in the meta-analyses were classified into one of the following four categories: self-identified pilot/feasibility studies or based upon sample size but not a pilot/feasibility study (N ≤ 100, N > 100, and N > 370 the upper 75th of sample size). The VoE was defined as the absolute difference (ABS) between the re-estimations of summary ES restricted to study classifications compared to the originally reported summary ES. Concordance (kappa) of statistical significance of summary ES between the four categories of studies was assessed. Fixed and random effects models and meta-regressions were estimated. Three case studies are presented to illustrate the impact of including pilot/feasibility and N ≤ 100 studies on the estimated summary ES. RESULTS: A total of 1602 effect sizes, representing 145 reported summary ES, were extracted from 48 meta-analyses containing 603 unique studies (avg. 22 studies per meta-analysis, range 2-108) and included 227,217 participants. Pilot/feasibility and N ≤ 100 studies comprised 22% (0-58%) and 21% (0-83%) of studies included in the meta-analyses. Meta-regression indicated the ABS between the re-estimated and original summary ES where summary ES ranged from 0.20 to 0.46 depending on the proportion of studies comprising the original ES were either mostly small (e.g., N ≤ 100) or mostly large (N > 370). Concordance was low when removing both pilot/feasibility and N ≤ 100 studies (kappa = 0.53) and restricting analyses only to the largest studies (N > 370, kappa = 0.35), with 20% and 26% of the originally reported statistically significant ES rendered non-significant. Reanalysis of the three case study meta-analyses resulted in the re-estimated ES rendered either non-significant or half of the originally reported ES. CONCLUSIONS: When meta-analyses of behavioral interventions include a substantial proportion of both pilot/feasibility and N ≤ 100 studies, summary ES can be affected markedly and should be interpreted with caution.

Expert Perspectives on Pilot and Feasibility Studies: A Delphi Study and Consolidation of Considerations for Behavioral Interventions.

Background: In the behavioral sciences, conducting pilot and/or feasibility studies (PFS) is a key step that provides essential information used to inform the design, conduct, and implementation of a larger-scale trial. There are more than 160 published guidelines, reporting checklists, frameworks, and recommendations related to PFS. All of these publications offer some form of guidance on PFS, but many focus on one or a few topics. This makes it difficult for researchers wanting to gain a broader understanding of all the relevant and important aspects of PFS and requires them to seek out multiple sources of information, which increases the risk of missing key considerations to incorporate into their PFS. The purpose of this study was to develop a consolidated set of considerations for the design, conduct, implementation, and reporting of PFS for interventions conducted in the behavioral sciences. Methods: To develop this consolidation, we undertook a review of the published guidance on PFS in combination with expert consensus (via a Delphi study) from the authors who wrote such guidance to inform the identified considerations. A total of 161 PFS-related guidelines, checklists, frameworks, and recommendations were identified via a review of recently published behavioral intervention PFS and backward/forward citation tracking of well-know PFS literature (e.g., CONSORT Ext. for PFS). Authors of all 161 PFS publications were invited to complete a three-round Delphi survey, which was used to guide the creation of a consolidated list of considerations to guide the design, conduct, and reporting of PFS conducted by researchers in the behavioral sciences. Results: A total of 496 authors were invited to take part in the Delphi survey, 50 (10.1%) of which completed all three rounds, representing 60 (37.3%) of the 161 identified PFS-related guidelines, checklists, frameworks, and recommendations. A set of twenty considerations, broadly categorized into six themes (Intervention Design, Study Design, Conduct of Trial, Implementation of Intervention, Statistical Analysis and Reporting) were generated from a review of the 161 PFS-related publications as well as a synthesis of feedback from the three-round Delphi process. These 20 considerations are presented alongside a supporting narrative for each consideration as well as a crosswalk of all 161 publications aligned with each consideration for further reading. Conclusion: We leveraged expert opinion from researchers who have published PFS-related guidelines, checklists, frameworks, and recommendations on a wide range of topics and distilled this knowledge into a valuable and universal resource for researchers conducting PFS. Researchers may use these considerations alongside the previously published literature to guide decisions about all aspects of PFS, with the hope of creating and disseminating interventions with broad public health impact.

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.

Impact of Virtual vs. In-Person School on Children Meeting the 24-h Movement Guidelines during the COVID-19 Pandemic.

The pandemic mitigation strategy of closing schools, while necessary, may have unintentionally impacted children's moderate-to-vigorous physical activity (MVPA), sleep, and time spent watching screens. In some locations, schools used hybrid attendance models, with some days during the week requiring in-person and others virtual attendance. This scenario offers an opportunity to evaluate the impact of attending in-person school on meeting the 24-h movement guidelines. Children (N = 690, 50% girls, K-5th) wore wrist-placed accelerometers for 14 days during October/November 2020. Parents completed daily reports on child time spent on screens and time spent on screens for school. The schools' schedule was learning for 2 days/week in-person and 3 days/week virtually. Using only weekdays (M-F), the 24-h movement behaviors were classified, and the probability of meeting all three was compared between in-person vs. virtual learning and across grades. Data for 4956 weekdays (avg. 7 d/child) were collected. In-person school was associated with a greater proportion (OR = 1.70, 95% CI: 1.33-2.18) of days that children were meeting the 24-h movement guidelines compared to virtual school across all grades. Students were more likely to meet the screen time (OR = 9.14, 95% CI: 7.05-11.83) and MVPA (OR = 1.50, 95% CI: 1.25-1.80) guidelines and less likely to meet the sleep (OR = 0.73, 95% CI: 0.62-0.86) guidelines on the in-person compared to the virtual school days. Structured environments, such as school, have a protective effect on children's movement behaviors, especially physical activity and screen time.

Fund behavioral science like the frameworks we endorse: the case for increased funding of preliminary studies by the National Institutes of Health.

Innovative, groundbreaking science relies upon preliminary studies (aka pilot, feasibility, proof-of-concept). In the behavioral sciences, almost every large-scale intervention is supported by a series of one or more rigorously conducted preliminary studies. The importance of preliminary studies was established by the National Institutes of Health (NIH) in 2014/2015 in two translational science frameworks (NIH Stage and ORBIT models). These frameworks outline the essential role preliminary studies play in developing the next generation of evidence-based behavioral prevention and treatment interventions. Data produced from preliminary studies are essential to secure funding from the NIH's most widely used grant mechanism for large-scale clinical trials, namely the R01. Yet, despite their unquestionable importance, the resources available for behavioral scientists to conduct rigorous preliminary studies are limited. In this commentary, we discuss ways the existing funding structure at the NIH, despite its clear reliance upon high-quality preliminary studies, inadvertently discourages and disincentivizes their pursuit by systematically underfunding them. We outline how multiple complementary and pragmatic steps via a small reinvestment of funds from larger trials could result in a large increase in funding for smaller preliminary studies. We make the case such a reinvestment has the potential to increase innovative science, increase the number of investigators currently funded, and would yield lasting benefits for behavioral science and scientists alike.