The utility of behavioral biometrics in user authentication and demographic characteristic detection: a scoping review.

BACKGROUND: Objective measures of screen time are necessary to better understand the complex relationship between screen time and health outcomes. However, current objective measures of screen time (e.g., passive sensing applications) are limited in identifying the user of the mobile device, a critical limitation in children's screen time research where devices are often shared across a family. Behavioral biometrics, a technology that uses embedded sensors on modern mobile devices to continuously authenticate users, could be used to address this limitation. OBJECTIVE: The purpose of this scoping review was to summarize the current state of behavioral biometric authentication and synthesize these findings within the scope of applying behavioral biometric technology to screen time measurement. METHODS: We systematically searched five databases (Web of Science Core Collection, Inspec in Engineering Village, Applied Science & Technology Source, IEEE Xplore, PubMed), with the last search in September of 2022. Eligible studies were on the authentication of the user or the detection of demographic characteristics (age, gender) using built-in sensors on mobile devices (e.g., smartphone, tablet). Studies were required to use the following methods for authentication: motion behavior, touch, keystroke dynamics, and/or behavior profiling. We extracted study characteristics (sample size, age, gender), data collection methods, data stream, model evaluation metrics, and performance of models, and additionally performed a study quality assessment. Summary characteristics were tabulated and compiled in Excel. We synthesized the extracted information using a narrative approach. RESULTS: Of the 14,179 articles screened, 122 were included in this scoping review. Of the 122 included studies, the most highly used biometric methods were touch gestures (n = 76) and movement (n = 63), with 30 studies using keystroke dynamics and 6 studies using behavior profiling. Of the studies that reported age (47), most were performed exclusively in adult populations (n = 34). The overall study quality was low, with an average score of 5.5/14. CONCLUSION: The field of behavioral biometrics is limited by the low overall quality of studies. Behavioral biometric technology has the potential to be used in a public health context to address the limitations of current measures of screen time; however, more rigorous research must be performed in child populations first. SYSTEMATIC REVIEW REGISTRATION: The protocol has been pre-registered in the Open Science Framework database ( https://doi.org/10.17605/OSF.IO/92YCT ).

Feasibility and preliminary efficacy of structured programming and a parent intervention to mitigate accelerated summer BMI gain: a pilot study.

BACKGROUND: This study assessed the initial feasibility and preliminary efficacy of providing children a free summer day camp and a parent intervention to improve self-regulation and mitigate accelerated summer BMI gain. METHODS: This pilot 2x2 factorial randomized control trial used a mixed-methods design to evaluate providing children a free summer day camp (SCV), a parent intervention (PI), and the combination of these two strategies (SCV+PI) to mitigate accelerated summer body mass index (BMI) gain. Progression criteria for feasibility and efficacy were assessed to determine if a full-scale trial was warranted. Feasibility criteria included recruitment capability (≥80 participants recruited) retention (≥70% participants retained), compliance (≥80% of participants attending the summer program with children attending ≥60% of program days, and ≥80% of participants completing goal setting calls with ≥60% of weeks syncing their child's Fitbit), and treatment fidelity (≥80% of summer program days delivered for ≥9 h/day, and ≥80% of participant texts delivered). Efficacy criteria were assessed via achieving a clinically meaningful impact on zBMI (i.e., ≥0.15). Changes in BMI were estimated using intent-to-treat and post hoc dose-response analyses via multilevel mixed-effects regressions. RESULTS: For recruitment, capability and retention progression criteria were met with a total of 89 families participating and 24 participants randomized to the PI group, 21 randomized to the SCV group, 23 randomized to the SCV+PI group, and 21 randomized to the control. However, fidelity and compliance progression criteria were not achieved due to COVID-19 and lack of transportation. Progression criteria for efficacy was also not achieved as intent-to-treat analyses did not show changes in BMI gain that were clinically meaningful. Post hoc dose-response analyses showed that for each day (0 to 29) of summer programming children attended they gained -0.009 (95CI= -0.018, -0.001) less in BMI z score. CONCLUSIONS: Engagement in both the SCV and PI was not ideal due to COVID-19 and lack of transportation. Providing children with structured summer programming to mitigate accelerated summer BMI gain may be an effective strategy. However, because feasibility and efficacy progression criteria were not met, a larger trial is not warranted until further pilot work is completed to ensure children attend the programming. TRIAL REGISTRATION: The trial reported herein was prospectively registered at ClinicalTrials.gov. Trial #: NCT04608188.

Early-stage studies to larger-scale trials: investigators' perspectives on scaling-up childhood obesity interventions.

BACKGROUND: Pilot/feasibility studies play an important role in the development and refinement of behavioral interventions by providing information about feasibility, acceptability, and potential efficacy. Despite their importance and wide-spread use, the approaches taken by behavioral scientists to scale-up early-stage studies to larger-scale trials has received little attention. The aim of our study was to understand the role that pilot studies play in the development and execution of larger-scale trials. METHODS: We conducted interviews with childhood obesity researchers who had published pilot behavioral interventions and larger-scale trials of the same or similar interventions. Questions were asked about the role of pilot studies in developing larger-scale trials and the challenges encountered when scaling-up an intervention based upon pilot findings. Data were coded and analyzed using an inductive analytic approach to identify themes. RESULTS: Twenty-four interventionists (54% women, 37-70 years old, mean 20 years since terminal degree) completed a total of 148 pilot studies across their careers (mean 6.4, range 1-20), of which 59% were scaled-up. Scaling was described as resource intensive and pilot work was considered essential to successfully competing for funding by 63% of the sample (n = 15). When asked to define a high-quality pilot study, interventionists described studies that allowed them to evaluate two independent factors: components of their intervention (e.g., acceptability, feasibility) and study parameters (e.g., sample size, measures). Interventionists expressed that more process implementation measures, different study designs, and additional iterations could improve decisions to scale-up. Most agreed that pilot studies were likely to produce inflated estimates of potential efficacy though only nine interventionists provided potential solutions for decreasing inflated measures of efficacy. Suggested major causes of inflated effects included high levels of oversight in pilot studies (e.g., researcher support), reliance on subjective measures, and utilizing convenience or highly motivated samples. Potential solutions included designing pilots for real-world implementation, only conducting randomized controlled pilot studies, and pre-registering pilot studies. CONCLUSIONS: Pilot studies purposes are multifaceted and deemed essential to obtaining funding for larger-scale trials. Clarifying the form and function of preliminary, early-stage research may enhance the productive utilization of early-stage studies and reduced drops in efficacy when transitioning to larger scale studies.

Identifying effective intervention strategies to reduce children's screen time: a systematic review and meta-analysis.

BACKGROUND: Excessive screen time ([Formula: see text] 2 h per day) is associated with childhood overweight and obesity, physical inactivity, increased sedentary time, unfavorable dietary behaviors, and disrupted sleep. Previous reviews suggest intervening on screen time is associated with reductions in screen time and improvements in other obesogenic behaviors. However, it is unclear what study characteristics and behavior change techniques are potential mechanisms underlying the effectiveness of behavioral interventions. The purpose of this meta-analysis was to identify the behavior change techniques and study characteristics associated with effectiveness in behavioral interventions to reduce children's (0-18 years) screen time. METHODS: A literature search of four databases (Ebscohost, Web of Science, EMBASE, and PubMed) was executed between January and February 2020 and updated during July 2021. Behavioral interventions targeting reductions in children's (0-18 years) screen time were included. Information on study characteristics (e.g., sample size, duration) and behavior change techniques (e.g., information, goal-setting) were extracted. Data on randomization, allocation concealment, and blinding was extracted and used to assess risk of bias. Meta-regressions were used to explore whether intervention effectiveness was associated with the presence of behavior change techniques and study characteristics. RESULTS: The search identified 15,529 articles, of which 10,714 were screened for relevancy and 680 were retained for full-text screening. Of these, 204 studies provided quantitative data in the meta-analysis. The overall summary of random effects showed a small, beneficial impact of screen time interventions compared to controls (SDM = 0.116, 95CI 0.08 to 0.15). Inclusion of the Goals, Feedback, and Planning behavioral techniques were associated with a positive impact on intervention effectiveness (SDM = 0.145, 95CI 0.11 to 0.18). Interventions with smaller sample sizes (n < 95) delivered over short durations (< 52 weeks) were associated with larger effects compared to studies with larger sample sizes delivered over longer durations. In the presence of the Goals, Feedback, and Planning behavioral techniques, intervention effectiveness diminished as sample size increased. CONCLUSIONS: Both intervention content and context are important to consider when designing interventions to reduce children's screen time. As interventions are scaled, determining the active ingredients to optimize interventions along the translational continuum will be crucial to maximize reductions in children's screen time.

The impact of summer programming on the obesogenic behaviors of children: behavioral outcomes from a quasi-experimental pilot trial.

BACKGROUND: Children from low-income families experience accelerated BMI gain and learning loss during summer. Healthy Summer Learners (HSL) addresses accelerated BMI gain and academic learning loss during summer by providing academic- and health-focused programming. This manuscript reports the effects of HSL on underlying obesogenic behaviors (i.e., physical activity, screen time, sleep, diet) that lead to accelerated summer BMI gain, a necessary first step to informing a future randomized controlled trial of HSL. METHODS: In the summer of 2018 and 2019 using a quasi-experimental study design, 180 children (90 per summer, 7.9 years [SD = 1.0], 94% non-Hispanic Black, 40% male) at two schools (i.e., one per summer) who were struggling academically (25-75% on a standardized reading test) were provided a free, school-based 6-week health- and academic-focused summer program (i.e., HSL, n = 60), a 4- to 6-week academic-focused summer program (i.e., 21st Century Summer Learning program (21C), n = 60), or no summer program (n = 60). Children wore the Fitbit Charge 2™ over a 10-week period during the summers (June-Aug) of 2018-2019. Differences within (within child days attend vs. not attend) and between (differences between groups attend vs. not attend) were evaluated using mixed effects linear regression. RESULTS: Regression estimates indicated that, on days attending, HSL children experienced a greater reduction in sedentary minutes (- 58.6 [95% CI = - 92.7, - 24.4]) and a greater increase in moderate-to-vigorous physical activity (MVPA) (36.2 [95% CI = 25.1, 47.3]) and steps (2799.2 [95% CI = 2114.2, 3484.2]) compared to 21C children. However, both HSL and 21C children were more active (i.e., greater MVPA, total steps) and less sedentary (i.e., less sedentary minutes and total screen time) and displayed better sleeping patterns (i.e., earlier and less variability in sleep onset and offset) on days they attended than children in the control. CONCLUSIONS: HSL produced greater changes in physical activity than 21C. However, attendance at either HSL or 21C leads to more healthy obesogenic behaviors. Based on the behavioral data in this pilot study, a larger trial may be warranted. These results must be considered along with the pending primary outcomes (i.e., academics and BMI z-score) of the HSL pilot to determine if a full-scale trial is warranted. TRIAL REGISTRATION: NIH-NCT03321071. Registered 25 October 2017.

Classification of physical activity intensities using a wrist-worn accelerometer in 8-12-year-old children.

BACKGROUND: Population-specific accelerometer cut-points are required to accurately determine the accumulation of physical activity of various intensities. OBJECTIVES: A calibration study was conducted (i) to determine the cut-points for the ActiGraph GT3X+, non-dominant, wrist-mounted accelerometer in children aged 8-12 years and (ii) to compare classification accuracies among the accelerometer's three axes and vector magnitude (VM) values. METHODS: Forty-five children aged 8-12 years performed up to seven activities while wearing accelerometers on their non-dominant wrist. Activities were performed in a summer day camp setting, represented free-living activities, and lasted for 10 min with minutes 5-8.5 used for analysis. Direct observation and percentage of heart rate reserve were used to determine activity intensity. RESULTS: Receiver operator characteristic (ROC) analyses resulted in area under the curve values of all three axes and VM ranging 0.82-0.89, 0.80-0.83, 0.62-0.67 and 0.86-0.89 for light, moderate, vigorous and moderate-to-vigorous activity intensities. Additionally, regression analyses resulted in prediction equations with R2 values ranging from 0.70 to 0.77. CONCLUSION: Results found comparable activity intensity classification accuracies from the ActiGraph GT3X+ wrist-worn accelerometer to previously published studies. Based on ROC and regression analyses, activity intensities can be distilled from this accelerometer using axis 1, axis 2 or VM values with similar classification accuracy.

Time spent in sedentary behavior and changes in childhood BMI: a longitudinal study from ages 9 to 15 years.

OBJECTIVE: To determine if time spent in objectively measured sedentary behavior is associated with a change in body mass index (BMI) between ages 9 and 15 years, adjusting for moderate-to-vigorous physical activity (MVPA). DESIGN: Prospective observational study of children at ages 9 (2000), 11 (2002), 12 (2003) and 15 years (2006). Longitudinal quantile regression was used to model the influence of predictors on changes at the 10th, 25th, 50th, 75th and 90th BMI percentiles over time. SUBJECTS: Participants were enrolled in the National Institute of Child Health and Human Development (NICHD) Study of Early Child Care and Youth Development and include both boys and girls (n=789). MEASUREMENTS: Objectively measured BMI (kg m(-2)) was the outcome variable and objectively measured sedentary behavior was the main predictor. Adjustment was also made for MVPA, gender, race, maternal education, hours of sleep and healthy eating index. RESULTS: Increases in BMI were observed at all percentiles, with the greatest increase observed at the 90th BMI percentile. Spending more time in sedentary behavior (h per day) was associated with additional increases in BMI at the 90th, 75th and 50th BMI percentiles, independent of MVPA and the other covariates (90th percentile=0.59, 95% confidence interval (95% CI): 0.19-0.98 kg m(-2); 75th percentile=0.48, 95% CI: 0.25-0.72 kg m(-2); and 50th percentile=0.19, 95% CI: 0.05-0.33 kg m(-2)). No associations were observed between sedentary behavior and changes at the 25th and 10th BMI percentiles. CONCLUSION: Sedentary behavior was associated with greater increases in BMI at the 90th, 75th and 50th BMI percentiles between ages 9 and 15 years, independent of MVPA. Preventing an increase in sedentary behavior from childhood to adolescence may contribute to reducing the number of children classified as obese.