Abstract Brain age prediction using machine‐learning techniques has recently attracted growing attention, as it has the potential to serve as a biomarker for characterizing the typical brain development and neuropsychiatric disorders. Yet one long‐standing problem is that the predicted brain age is overestimated in younger subjects and underestimated in older. There is a plethora of claims as to the bias origins, both methodologically and in data itself. With a large neuroanatomical dataset ( N = 2,026; 6–89 years of age) from multiple shared datasets, we show this bias is neither data‐dependent nor specific to particular method including deep neural network. We present an alternative account that offers a statistical explanation for the bias and describe a simple, yet efficient, method using general linear model to adjust the bias. We demonstrate the effectiveness of bias adjustment with a large multi‐modal neuroimaging data ( N = 804; 8–21 years of age) for both healthy controls and post‐traumatic stress disorders patients obtained from the Philadelphia Neurodevelopmental Cohort.
Abstract Objectives Elevated glucose variability may be one mechanism that increases risk for significant psychological and physiological health conditions among individuals with binge‐spectrum eating disorders (B‐EDs), given the impact of eating disorder (ED) behaviors on blood glucose levels. This study aimed to characterize glucose variability among individuals with B‐EDs compared with age‐matched, sex‐matched, and body mass index‐matched controls, and investigate the association between frequency of ED behaviors and glucose variability. Methods Participants were 52 individuals with B‐EDs and 22 controls who wore continuous glucose monitors to measure blood glucose levels and completed ecological momentary assessment surveys to measure ED behaviors for 1 week. Independent samples t ‐tests compared individuals with B‐EDs and controls and multiple linear regression models examined the association between ED behaviors and glucose variability. Results Individuals with B‐EDs demonstrated numerically higher glucose variability than controls ( t = 1.42, p = .08, d = 0.43), although this difference was not statistically significant. When controlling for covariates, frequency of ED behaviors was significantly, positively associated with glucose variability ( t = 3.17, p = .003) with medium effect size ( f 2 = 0.25). Post hoc analyses indicated that binge eating frequency was significantly associated with glucose variability, while episodes of 5+ hours without eating were not. Discussion Glucose variability among individuals with B‐EDs appears to be positively associated with engagement in ED behaviors, particularly binge eating. Glucose variability may be an important mechanism by which adverse health outcomes occur at elevated rates in B‐EDs and warrants future study. Public Significance This study suggests that some individuals with binge ED and bulimia nervosa may experience elevated glucose variability, a physiological symptom that is linked to a number of adverse health consequences. The degree of elevation in glucose variability is positive associated with frequency of eating disorder behaviors, especially binge eating.
Despite interest in financial incentive programs, evidence regarding the feasibility, acceptability, and effectiveness of deposit contracts (ie, use of participants' own money as a financial reward) for increasing physical activity (PA) is limited. Furthermore, evidence regarding the use of feedback within incentive programs is limited.To evaluate: (1) the feasibility and acceptability of deposit contracts for increasing objectively measured PA and (2) the effects of deposit contracts with or without ongoing feedback on PA.Participants (n = 24) were exposed to 3 conditions (1) self-monitoring, (2) incentive, and (3) incentive with feedback in an ABACABAC design, with the order of incentive conditions counterbalanced across participants.Effect sizes suggest that individuals had a modest increase in PA during the incentive conditions compared with self-monitoring. Presentation order moderated results, such that individuals exposed to incentives with feedback first performed more poorly across both incentive conditions. In addition, individuals often cited the deposit contract as a reason for not enrolling, and those who did participate reported inadequate acceptability of the incentives and feedback.Results suggest that while deposit contracts may engender modest increases in PA, this type of incentive may not be feasible or acceptable for promoting PA.
Abstract Objective Adjunctive mobile health (mHealth) technologies offer promise for improving treatment response to enhanced cognitive‐behavior therapy (CBT‐E) among individuals with binge‐spectrum eating disorders, but research on the key “active” components of these technologies has been very limited. The present study will use a full factorial design to (1) evaluate the optimal combination of complexity of two commonly used mHealth components (i.e., self‐monitoring and microinterventions) alongside CBT‐E and (2) test whether the optimal complexity level of these interventions is moderated by baseline self‐regulation. Secondary aims of the present study include evaluating target engagement associated with each level of these intervention components and quantifying the component interaction effects (i.e., partially additive, fully additive, or synergistic effects). Method Two hundred and sixty‐four participants with binge‐spectrum eating disorders will be randomized to six treatment conditions determined by the combination of self‐monitoring condition (i.e., standard self‐monitoring or skills monitoring) and microinterventions condition (i.e., no microinterventions, automated microinterventions, or just‐in‐time adaptive interventions) as an augmentation to 16 sessions of CBT‐E. Treatment outcomes will be measured using the Eating Disorder Examination and compared by treatment condition using multilevel models. Results Results will clarify the “active” components in mHealth interventions for binge eating. Discussion The present study will provide critical insight into the efficacy of commonly used digital intervention components (i.e., skills monitoring and microinterventions) alongside CBT‐E. Furthermore, results of this study may inform personalization of digital intervention intensity based on patient profiles of self‐regulation. Public Significance This study will examine the relative effectiveness of commonly used components of application‐based interventions as an augmentation to cognitive‐behavioral therapy for binge eating. Findings from this study will inform the development of an optimized digital intervention for individuals with binge eating.
Objective: Weight suppression (WS) is related to a wide variety of eating disorder characteristics. However, individuals with eating disorders usually reach their highest premorbid weight while still developing physically. Therefore, a more sensitive index of individual differences in highest premorbid weight may be one that compares highest premorbid z-BMI to current z-BMI (called developmental weight suppression (DWS) here). Method: We compared the relationships between traditional weight suppression (TWS) and DWS and a wide variety of measures related to bulimic psychopathology in 91 females (M age, 25.2; 60.5% White), with clinical or sub-clinical bulimia nervosa. Results: TWS and DWS were correlated (r = .40). TWS was significantly related to only one of 23 outcome variables whereas DWS showed significant or near-significant relationships to 14 outcomes. DWS showed consistent positive relations with behavioral outcomes (e.g., binge eating) but consistent negative relations with cognitive/affective outcomes (e.g., weight concerns). Conclusions: Findings indicated a much more consistent relationship between the novel DWS measure and bulimic characteristics than with the traditional weight suppression measure. DWS showed both positive and negative relations with bulimic symptoms, though these findings require replication to confirm their validity. Consistent evidence indicated that the two WS measures served as mutual suppressor variables.
The design of interface based on 8 bit micro computer unit is introduced. Through TCP/IP protocol stack hierarchical,the migration ability is improved. The proposed microcontroller systems embedded TCP/IP protocol many advantages,such as lower cost,fewer component,higher transport speed,and easier to use. The system is fit for all of the network transmission systems,and also adapted to data collecting and data monitoring.
