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OBJECTIVE: In this study, we examined students' fitness, body mass index (BMI), and demographics as predictors of observed time on-task (TOT) behaviors as an indicator of behavioral inattention. METHODS: We collected demographics, fitness estimates, and BMI from 2020 fourth-graders (Mean age = 8.6 (SD = 0.5); 47% girls; 49% white) from 28 schools. We measured TOT through momentary time sampling observations. Three-level linear models were conducted to determine whether characteristics predicted differences in TOT. We tested interactions between characteristics and TOT. RESULTS: Older students exhibited greater percent of TOT (estimate = 2.34, SE = 1.02, df = 919, t = 2.30, p < .05). Additionally, boys spent less percent TOT (estimate = -3.59, SE = 1.03, df = 906, t = -3.49, p < .05). There were no differences by race/ethnicity, SES, BMI, fitness, or time of day and percent TOT. Furthermore, none of the interactions were statistically significant (p > .15). CONCLUSIONS: Girls and older students spent more TOT. These findings are of interest to educators and psychologists working on the development of research-based guidelines aimed to support elementary students' engagement in the classroom.

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BACKGROUND: Adaptive behavioral interventions are individualized interventions that vary support based on a person's evolving needs. Digital technologies enable these adaptive interventions to function at scale. Adaptive interventions show great promise for producing better results compared with static interventions related to health outcomes. Our central thesis is that adaptive interventions are more likely to succeed at helping individuals meet and maintain behavioral targets if its elements can be iteratively improved via data-driven testing (ie, optimization). Control systems engineering is a discipline focused on decision making in systems that change over time and has a wealth of methods that could be useful for optimizing adaptive interventions. OBJECTIVE: The purpose of this paper was to provide an introductory tutorial on when and what to do when using control systems engineering for designing and optimizing adaptive mobile health (mHealth) behavioral interventions. OVERVIEW: We start with a review of the need for optimization, building on the multiphase optimization strategy (MOST). We then provide an overview of control systems engineering, followed by attributes of problems that are well matched to control engineering. Key steps in the development and optimization of an adaptive intervention from a control engineering perspective are then summarized, with a focus on why, what, and when to do subtasks in each step. IMPLICATIONS: Control engineering offers exciting opportunities for optimizing individualization and adaptation elements of adaptive interventions. Arguably, the time is now for control systems engineers and behavioral and health scientists to partner to advance interventions that can be individualized, adaptive, and scalable. This tutorial should aid in creating the bridge between these communities.

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BACKGROUND: Control systems engineering methods, particularly, system identification (system ID), offer an idiographic (i.e., person-specific) approach to develop dynamic models of physical activity (PA) that can be used to personalize interventions in a systematic, scalable way. The purpose of this work is to: (1) apply system ID to develop individual dynamical models of PA (steps/day measured using Fitbit Zip) in the context of a goal setting and positive reinforcement intervention informed by Social Cognitive Theory; and (2) compare insights on potential tailoring variables (i.e., predictors expected to influence steps and thus moderate the suggested step goal and points for goal achievement) selected using the idiographic models to those selected via a nomothetic (i.e., aggregated across individuals) approach. METHOD: A personalized goal setting and positive reinforcement intervention was deployed for 14 weeks. Baseline PA measured in weeks 1-2 was used to inform personalized daily step goals delivered in weeks 3-14. Goals and expected reward points (granted upon goal achievement) were pseudo-randomly assigned using techniques from system ID, with goals ranging from their baseline median steps/day up to 2.5× baseline median steps/day, and points ranging from 100 to 500 (i.e., $0.20-$1.00). Participants completed a series of daily self-report measures. Auto Regressive with eXogenous Input (ARX) modeling and multilevel modeling (MLM) were used as the idiographic and nomothetic approaches, respectively. RESULTS: Participants (N = 20, mean age = 47.25 ±â€¯6.16 years, 90% female) were insufficiently active, overweight (mean BMI = 33.79 ±â€¯6.82 kg/m2) adults. Results from ARX modeling suggest that individuals differ in the factors (e.g., perceived stress, weekday/weekend) that influence their observed steps/day. In contrast, the nomothetic model from MLM suggested that goals and weekday/weekend were the key variables that were predictive of steps. Assuming the ARX models are more personalized, the obtained nomothetic model would have led to the identification of the same predictors for 5 of the 20 participants, suggesting a mismatch of plausible tailoring variables to use for 75% of the sample. CONCLUSION: The idiographic approach revealed person-specific predictors beyond traditional MLM analyses and unpacked the inherent complexity of PA; namely that people are different and context matters. System ID provides a feasible approach to develop personalized dynamical models of PA and inform person-specific tailoring variable selection for use in adaptive behavioral interventions.

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Adaptive interventions are an emerging class of behavioral interventions that allow for individualized tailoring of intervention components over time to a person's evolving needs. The purpose of this study was to evaluate an adaptive step goal + reward intervention, grounded in Social Cognitive Theory delivered via a smartphone application (Just Walk), using a mixed modeling approach. Participants (N = 20) were overweight (mean BMI = 33.8 ± 6.82 kg/m2), sedentary adults (90% female) interested in participating in a 14-week walking intervention. All participants received a Fitbit Zip that automatically synced with Just Walk to track daily steps. Step goals and expected points were delivered through the app every morning and were designed using a pseudo-random multisine algorithm that was a function of each participant's median baseline steps. Self-report measures were also collected each morning and evening via daily surveys administered through the app. The linear mixed effects model showed that, on average, participants significantly increased their daily steps by 2650 (t = 8.25, p < 0.01) from baseline to intervention completion. A non-linear model with a quadratic time variable indicated an inflection point for increasing steps near the midpoint of the intervention and this effect was significant (t2 = -247, t = -5.01, p < 0.001). An adaptive step goal + rewards intervention using a smartphone app appears to be a feasible approach for increasing walking behavior in overweight adults. App satisfaction was high and participants enjoyed receiving variable goals each day. Future mHealth studies should consider the use of adaptive step goals + rewards in conjunction with other intervention components for increasing physical activity.

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OBJECTIVE: To determine whether population-specific normative data should be employed when screening neurocognitive functioning as part of physician fitness for duty evaluations. If so, to provide such norms based on the evidence currently available. METHODS: A comparison of published data from four sources was analyzed. Data from the two physician samples were then entered into a meta-analysis to obtain full information estimates and generate provisional norms for physicians. RESULTS: Two-way analysis of variance (Study x Index) revealed a significant main effect and an interaction. Results indicate differences in mean levels of performance and standard deviation for physicians. CONCLUSIONS: Reliance on general population normative data results in under-identification of potential neuropsychological difficulties. Population specific normative data are needed to effectively evaluate practicing physicians.

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Over the last ten years, HCI researchers have introduced a range of novel ways to support health behavior change, from glanceable displays to sophisticated game dynamics. Yet, this research has not had as much impact as its originality warrants. A key reason for this is that common forms of evaluation used in HCI make it difficult to effectively accumulate-and use-knowledge across research projects. This paper proposes a strategy for HCI research on behavior change that retains the field's focus on novel technical contributions while enabling accumulation of evidence that can increase impact of individual research projects both in HCI and the broader behavior-change science. The core of this strategy is an emphasis on the discovery of causal effects of individual components of behavior-change technologies and the precise ways in which those effects vary with individual differences, design choices, and contexts in which those technologies are used.

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Schools often offer healthy fruits and vegetables (FV) and healthy entrées. However, children may resist these efforts due to a lack of familiarity with the offerings. While numerous exposures with a food increase its liking, it may be that an exposure to a variety of FV at home leads to greater willingness to select other foods - even those that are unrelated to those eaten at home. As an initial test of this possibility, this study was designed to examine how self-reports of exposure and consumption of various FV were associated with the selection of FV and lunch entrées at school. Participants (n = 59) were a convenience sample of elementary children. A median split was used to place students into high- and low-exposure groups for self-reports of both exposure and consumption at home. The primary dependent variables were self-reports of selecting FV at school; the children's absolute and relative ratings of eight 'healthier' lunch entrées; and self-reports of selecting these entrées. These entrées were recently added to the school menu and, therefore, tended to be less familiar to children. Food ratings were collected through taste exposures conducted at school. Results indicate that children who reported more frequent exposure to FV at home consumed a wider variety of FV at school and were more likely to report selecting 'healthier' entrées at school lunch. These data suggest that exposure to, and the consumption of, a variety of FV may make children more willing to select a wider range of FV and other healthy entrées.

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PURPOSE: To identify factors associated with physician performance in a comprehensive competence assessment. METHOD: The authors conducted a retrospective analysis of 683 physicians referred for assessment at the Center for Personalized Education for Physicians from 2000 to 2010, who were evaluated as either safe or unsafe to return to practice. Multivariate logistic regression was used to determine factors predictive of unsafe assessment outcome. Covariates included personal characteristics (e.g., age), practice context (e.g., solo practice), and referral information (e.g., previous board license action). RESULTS: Older physicians were more likely to have unsafe assessment outcomes (odds ratio [OR] = 1.07; P < .001). Board-certified individuals were less likely to have poor assessment outcomes (OR = 0.40; P = .003) than uncertified individuals. Physicians in solo practice were more likely (OR = 2.15; P = .037) to be deemed unsafe than physicians in other settings. Physicians with a practice scope that matched their training were less likely (OR = 0.29; P = .023) to have unsafe assessment outcomes than those whose did not. Physicians with current or previous board action (suspension, revocation, limitation, or stipulation) were more likely to be deemed unsafe (OR = 2.47; P = .003) than those without. CONCLUSIONS: Findings suggest that important predictors of physician performance on competence assessment include personal characteristics, practice context, and reasons for assessment referral. These findings have implications for development of policies and programs designed to assess risk of poor physician performance and quality of care improvement efforts through organizational/practice design or remedial education.

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INTRODUCTION: Limited information exists to describe physicians who return to practice after absences from patient care. The Center for Personalized Education for Physicians (CPEP) is an independent, not-for-profit organization that provides clinical competency assessment and educational programs for physicians, including those reentering practice. This article studies the medical licensure status, performance, and correlates between physician characteristics and performance on initial assessment. METHODS: Sixty-two physicians who left practice voluntarily and without discipline or sanction and who were returning to practice in the same discipline as their previous practice participated in the CPEP reentry program. Physicians completed an objective clinical skills assessment including clinical interviews by specialty-matched board-certified physicians, simulated patient encounters, a documentation exercise, and a cognitive function screen. Physicians were rated from 1 (no or limited educational needs) to 4 (global, pervasive deficits). Performance scores were compared based on select physician characteristics. RESULTS: Twenty-five (40.3%) participants were female; participants' average age was 53.7 years (female 48.1 years; male 57.5 years). Physicians left practice for family issues (30.6%), health issues (27.4%), retirement or nonmedical career change (17.7%), and change to medical administration (14.5%). Females were more likely than males to have left practice for child rearing (P < 0.0001). Approximately one-quarter (24.2%) of participants achieved a performance rating of 1 (best-performing group); 35.5% achieved a rating of 2; 33% achieved a rating of 3; 6.5% achieved a rating of 4 (worst-performing group). Years out of practice and increasing physician age predicted poorer performance (P = 0.0403, P = 0.0440). A large proportion of physicians presenting without an active license achieved active licensure; how many of these physicians actually returned to practice is not known. DISCUSSION: Physicians who leave practice are a heterogeneous group. Most participants' performance warranted some formal education; few demonstrated global educational needs. The data from this study justify mandates that physicians demonstrate competence through an objective testing process prior to returning to practice. Emerging patterns regarding the performance of the reentering physician may help guide future policy.

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INTRODUCTION: Limited information exists to describe physicians who return to practice after absences from patient care. The Center for Personalized Education for Physicians (CPEP) is an independent, not-for-profit organization that provides clinical competency assessment and educational programs for physicians, including those reentering practice. This article studies the medical licensure status, performance, and correlates between physician characteristics and performance on initial assessment. METHODS: Sixty-two physicians who left practice voluntarily and without discipline or sanction and who were returning to practice in the same discipline as their previous practice participated in the CPEP reentry program. Physicians completed an objective clinical skills assessment including clinical interviews by specialty-matched board-certified physicians, simulated patient encounters, a documentation exercise, and a cognitive function screen. Physicians were rated from 1 (no or limited educational needs) to 4 (global, pervasive deficits). Performance scores were compared based on select physician characteristics. RESULTS: Twenty-five (40.3%) participants were female; participants' average age was 53.7 years (female 48.1 years; male 57.5 years). Physicians left practice for family issues (30.6%), health issues (27.4%), retirement or nonmedical career change (17.7%), and change to medical administration (14.5%). Females were more likely than males to have left practice for child rearing (P < 0.0001). Approximately one-quarter (24.2%) of participants achieved a performance rating of 1 (best-performing group); 35.5% achieved a rating of 2; 33% achieved a rating of 3; 6.5% achieved a rating of 4 (worst-performing group). Years out of practice and increasing physician age predicted poorer performance (P = 0.0403, P = 0.0440). A large proportion of physicians presenting without an active license achieved active licensure; how many of these physicians actually returned to practice is not known. DISCUSSION: Physicians who leave practice are a heterogeneous group. Most participants' performance warranted some formal education; few demonstrated global educational needs. The data from this study justify mandates that physicians demonstrate competence through an objective testing process prior to returning to practice. Emerging patterns regarding the performance of the reentering physician may help guide future policy.

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PURPOSE: Research suggests that there are concerns about the neuropsychological functioning of physicians who undergo physician competency evaluation. Academic health center faculty often participate in the evaluation and remediation of these physicians. The purpose of this study was to compare the cognitive abilities between a group of physicians referred for competency evaluations and a control group. METHOD: Using the MicroCog, a computerized neuropsychological screen originally designed for physicians, the authors compared the cognitive performance of 267 physicians referred for competency evaluations with a control group of 68 recruited physicians. Physicians referred for competency evaluations took the MicroCog as a part of their evaluation at CPEP, the national Center for Personalized Education for Physicians, from January 1997 to January 2004. The control group comprised practicing physicians whose competency was not in question. RESULTS: Compared with the control group, the competency evaluation group had a greater proportion of physicians with scores suggesting possible cognitive impairment and performed significantly lower on scores of processing speed, processing accuracy, and cognitive proficiency. The control group of physicians performed significantly better than the age- and education-corrected normative sample. CONCLUSIONS: Because there were significant neuropsychological differences between physicians referred for competency evaluations and physicians whose competency was not in question, it is important that neuropsychological screening be included as part of physicians' competency evaluations.

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