Differentiation between atypical anorexia nervosa and anorexia nervosa using machine learning.

OBJECTIVE: Body mass index (BMI) is the primary criterion differentiating anorexia nervosa (AN) and atypical anorexia nervosa despite prior literature indicating few differences between disorders. Machine learning (ML) classification provides us an efficient means of accurately distinguishing between two meaningful classes given any number of features. The aim of the present study was to determine if ML algorithms can accurately distinguish AN and atypical AN given an ensemble of features excluding BMI, and if not, if the inclusion of BMI enables ML to accurately classify between the two. METHODS: Using an aggregate sample from seven studies consisting of individuals with AN and atypical AN who completed baseline questionnaires (N = 448), we used logistic regression, decision tree, and random forest ML classification models each trained on two datasets, one containing demographic, eating disorder, and comorbid features without BMI, and one retaining all features and BMI. RESULTS: Model performance for all algorithms trained with BMI as a feature was deemed acceptable (mean accuracy = 74.98%, mean area under the receiving operating characteristics curve [AUC] = 74.75%), whereas model performance diminished without BMI (mean accuracy = 59.37%, mean AUC = 59.98%). DISCUSSION: Model performance was acceptable, but not strong, if BMI was included as a feature; no other features meaningfully improved classification. When BMI was excluded, ML algorithms performed poorly at classifying cases of AN and atypical AN when considering other demographic and clinical characteristics. Results suggest a reconceptualization of atypical AN should be considered. PUBLIC SIGNIFICANCE: There is a growing debate about the differences between anorexia nervosa and atypical anorexia nervosa as their diagnostic differentiation relies on BMI despite being similar otherwise. We aimed to see if machine learning could distinguish between the two disorders and found accurate classification only if BMI was used as a feature. This finding calls into question the need to differentiate between the two disorders.

Dynamic relationships among feeling fat, fear of weight gain, and eating disorder symptoms in an eating disorder sample.

Feeling fat and fear of weight gain are key cognitive-affective symptoms that are theorized to maintain eating disorders (EDs). Little research has examined the dynamic relationships among feeling fat, fear of weight gain, emotions, cognitions, and ED behaviors. Furthermore, it is unknown if these relations vary by ED diagnosis (e.g., anorexia nervosa (AN) vs other ED). The current study (N = 94 ED participants; AN n = 64) utilized ecological momentary assessments collected four times a day for 18 days (72 timepoints) asking about feeling fat, fear of weight gain, emotions (i.e., anxiety, guilt), cognitions (i.e., feelings of having overeaten, thoughts about dieting), and ED behaviors (i.e., vomiting, diuretic/laxative use, excessive exercise, body checking, self-weighing, binge-eating, restriction) at stressful timepoints (contemporaneous [mealtime], and prospective/temporal [next-meal]). Multilevel modeling was used to test for between and within-person associations. Higher feeling fat and fear of weight gain independently predicted higher next-meal emotions (i.e., anxiety, guilt), cognitions (i.e., feelings of having overeaten, thoughts about dieting, fear of weight gain, feeling fat), and ED behaviors (i.e., body checking, self-weighing [feeling fat]). There were relationships in the opposite direction, such that some emotions, cognitions, and ED behaviors prospectively predicted feeling fat and fear of weight gain, suggesting existence of a reciprocal cycle. Some differences were found via diagnosis. Findings pinpoint specific dynamic and cyclical relationships among feeling fat, fear of weight gain, and specific ED symptoms, and suggest the need for more research on how feeling fat, fear of weight gain and cognitive-affective-behavioral aspects of ED operate. Future research can test if treatment interventions targeted at feeling fat and fear of weight gain may disrupt these cycles.

Evidence-Based Assessments for Transdiagnostic Eating Disorder Symptoms: Guidelines for Current Use and Future Directions.

Eating disorders are severe and often chronic mental illnesses that are associated with high impairment and mortality rates. Recent estimates suggest that eating disorder prevalence rates are on the rise, indicating an increased need for accurate assessment and detection. The current review provides an overview of transdiagnostic eating disorder assessments, including interview, self-report, health and primary care screeners, and technology-based and objective assessments. We focused on assessments that are transdiagnostic in nature and exhibit high impact in the field. We provide recommendations for how these assessments should be used in research and clinical settings. We also discuss considerations that are crucial for assessment, including the use of a categorical versus dimensional diagnostic framework, assessment of eating disorders in related fields (i.e., anxiety and depression), and measurement-based care for eating disorders. Finally, we provide suggestions for future research, including the need for more research on short transdiagnostic screeners for use in health care settings, standardized assessments for ecological momentary assessment, development of state-based assessment of eating disorder symptoms, and consideration of assessment across multiple timescales.

Development of an App for Tracking Family Engagement With Early Intervention Services: Focus Groups and Pilot Evaluation Study.

BACKGROUND: Expedient access to early intervention (EI) systems has been identified as a priority for children with developmental delays, identified disabilities, and other special health care needs. Despite the mandated availability of EI, it remains challenging for families to navigate referral processes and establish appropriate services. Such challenges disproportionately affect families from traditionally underserved communities. Mobile health apps can improve clinical outcomes, increase accessibility to health services, and promote adherence to health-related interventions. Though promising, the implementation of apps within routine care is in its infancy, with limited research examining the components of what makes an effective app or how to reach families most impacted by inequities in health care delivery. OBJECTIVE: In study 1, we conducted focus groups to access a broad range of perspectives on the process of navigating the EI system, with the dual goals of identifying ways in which a patient-facing app might facilitate this process and identifying barriers to use with traditionally underrepresented and underserved groups. In study 2, focus group findings informed the development of a patient-facing app, which was subsequently tested with a pilot sample of 5 families. METHODS: In study 1, the focus groups included 29 participants from 4 shareholder groups. Targeted sampling was used to recruit participants from traditionally underrepresented groups. Focus group questions sought information about barriers families experience as they navigate the EI system, ideal features of a patient-facing app designed to track family engagement with the EI system, and potential barriers. Focus group procedures were informed by the Consolidated Framework for Implementation Research framework. In study 2, a pilot app was developed. The app was tested with a sample of 5 families of young children involved in the EI system. Families provided information on app functionality and usability. RESULTS: Qualitative analysis revealed a desire for increased communication and information about the process of accessing EI services, potential utility of an app for communication purposes, and clear recommendations for app features. Insights from focus groups were used to inform the development of the Family on Track app and related implementation supports. App features included survey customization, timing and delivery of prompts, and questions related to barriers and service satisfaction. Implementation supports include a visual guide for app installation, resources related to common family questions, and availability of study personnel to guide families through installation and provide ongoing support. Field testing provided preliminary information about app usability, including identifying future directions. CONCLUSIONS: The results of this study could support the development of a new way for the EI system to communicate and connect with families, provide families with a means to communicate satisfaction and frustration, and access the supports they need to be active participants in their child's care.

Trends in U.S. self-reported health and self-care behaviors during the COVID-19 pandemic.

IMPORTANCE: The COVID-19 pandemic represents a unique stressor in Americans' daily lives and access to health services. However, it remains unclear how the pandemic impacted perceived health status and engagement in health-related behaviors. OBJECTIVE: To assess changes in self-reported health outcomes during the COVID-19 pandemic, and to explore trends in health-related behaviors that may underlie the observed health changes. DESIGN: Interrupted time series stratified by age, gender, race/ethnicity, educational attainment, household income, and employment status. SETTING: United States. PARTICIPANTS: All adult respondents to the 2016-2020 Behavioral Risk Factor Surveillance System (N = 2,146,384). EXPOSURE: Survey completion following the U.S. public health emergency declaration (March-December 2020). January 2019 to February 2020 served as our reference period. MAIN OUTCOMES AND MEASURES: Self-reported health outcomes included the number of days per month that respondents spent in poor mental health, physical health, or when poor health prevented their usual activities of daily living. Self-reported health behaviors included the number of hours slept per day, number of days in the past month where alcohol was consumed, participation in any exercise, and current smoking status. RESULTS: The national rate of days spent in poor physical health decreased overall (-1.00 days, 95% CI: -1.10 to -0.90) and for all analyzed subgroups. The rate of poor mental health days or days when poor health prevented usual activities did not change overall but exhibited substantial heterogeneity by subgroup. We also observed overall increases in mean sleep hours per day (+0.09, 95% CI 0.05 to 0.13), the percentage of adults who report any exercise activity (+3.28%, 95% CI 2.48 to 4.09), increased alcohol consumption days (0.27, 95% CI 0.18 to 0.37), and decreased smoking prevalence (-1.11%, 95% CI -1.39 to -0.83). CONCLUSIONS AND RELEVANCE: The COVID-19 pandemic had deleterious but heterogeneous effects on mental health, days when poor health prevented usual activities, and alcohol consumption. In contrast, the pandemic's onset was associated with improvements in physical health, mean hours of sleep per day, exercise participation, and smoking status. These findings highlight the need for targeted outreach and interventions to improve mental health in individuals who may be disproportionately affected by the pandemic.

Paisley: Preliminary validation of a novel app-based e-Screener for ASD in children 18-36 months.

The purpose of this study was to assess the validity of an autism e-screener, Paisley, when utilized in a clinical research setting via a tablet application. The Paisley application used a series of play-based activities, all of which incorporated varying aspects of the ASD-PEDS. Participants included children (18-36 months; n = 198) referred for evaluation of autism spectrum disorder (ASD) and community providers (n = 66) with differing levels of familiarity with ASD. Community providers administered the Paisley application to children who then completed a comprehensive psychological evaluation. Based on comprehensive evaluation, 75% of children met diagnostic criteria for ASD. Paisley scores were significantly higher for children diagnosed with ASD (15.06) versus those not diagnosed (9.34). The newly determined cutoff ASD-PEDS cutoff score of 13 had significantly higher specificity and positive predictive value than the originally proposed cutoff of 11. Results support the use of Paisley by community providers to identify autism risk in toddlers. Limitations and strengths of the work, as well as opportunities for future clinical validation, are described.

A Randomized Trial of the Accuracy of Novel Telehealth Instruments for the Assessment of Autism in Toddlers.

PURPOSE: Telemedicine approaches to autism (ASD) assessment have become increasingly common, yet few validated tools exist for this purpose. This study presents results from a clinical trial investigating two approaches to tele-assessment for ASD in toddlers. METHODS: 144 children (29% female) between 17 and 36 months of age (mean = 2.5 years, SD = 0.33 years) completed tele-assessment using either the TELE-ASD-PEDS (TAP) or an experimental remote administration of the Screening Tool for Autism in Toddlers (STAT). All children then completed traditional in-person assessment with a blinded clinician, using the Mullen Scales of Early Learning (MSEL), Vineland Adaptive Behavior Scales, 3rd Edition (VABS-3), and Autism Diagnostic Observation Schedule (ADOS-2). Both tele-assessment and in-person assessment included a clinical interview with caregivers. RESULTS: Results indicated diagnostic agreement for 92% of participants. Children diagnosed with ASD following in-person assessment who were missed by tele-assessment (n = 8) had lower scores on tele- and in-person ASD assessment tools. Children inaccurately identified as having ASD by tele-assessment (n = 3) were younger than other children and had higher developmental and adaptive behavior scores than children accurately diagnosed with ASD by tele-assessment. Diagnostic certainty was highest for children correctly identified as having ASD via tele-assessment. Clinicians and caregivers reported satisfaction with tele-assessment procedures. CONCLUSION: This work provides additional support for the use of tele-assessment for identification of ASD in toddlers, with both clinicians and families reporting broad acceptability. Continued development and refinement of tele-assessment procedures is recommended to optimize this approach for the needs of varying clinicians, families, and circumstances.

Patient perspectives on mesh-related complications after hernia repair.

BACKGROUND: To explore the thoughts, feelings, and experiences of patients with mesh-related complications after hernia repair. The rate of long-term mesh-related complications requiring procedural intervention after abdominal core surgery, including hernia repair, is unknown. Determining this rate is challenging due to its anticipated low chance of occuring and historically poor systematic long-term follow-up in patients' hernia repair. The lived experience of these patients is also not well understood. METHODS: Purposive sampling was used to identify patients who have experienced mesh-related complications after hernia repair, and semistructured interviews were conducted. Descriptive thematic analysis was used to identify, analyze, and report common patterns across the data set related to the patient experience of mesh-related complications. RESULTS: Eight patients who had undergone a hernia repair with mesh and had at least 1 mesh-related complication after their repair requiring operation, an additional procedure, or medical treatment were included in the study and completed semistructured interviews over the phone. Five domains emerged from the interviews: indicators of mesh-related complications, knowledge of potential surgical complications, relationship/satisfcation with surgeon and/or surgical team, psychosocial impact of hernia repair and mesh-related complications, and function. CONCLUSION: Despite the widespread use of mesh in abdominal wall operations, little is known regarding the patient experience of mesh-related complications. The themes identified in the present study provide insight into the patient experience of mesh-related complications and can inform the future development of a patient-reported outcome measure to determine the true incidence of mesh-related complications and the impact of these complications on quality of life.

An eye-tracking study examining the relationship between males' eating disorder symptomatology, body mass index, and expectations about character behaviour in text.

Eating disorder prevalence is increasing in males, perhaps more rapidly than in females. Theorists have proposed that cognitive biases are important factors underpinning disordered eating, especially those related to food, body, and perfectionism. We investigated these factors in relation to males' eating disorder symptomatology in the general population by using eye-tracking during reading as a novel and implicit measure. 180 males' eye movements were monitored while they read scenarios (third-person in Experiment 1 (n = 90, 18-38(Mage = 21.50, SD = 3.65)); second-person in Experiment 2 (n = 90, 18-35(Mage = 20.50, SD = 2.22))) describing characters' emotional responses (e.g. upset) to food-, body image-, and perfectionism-related events. Participants' eating disorder symptomatology was then assessed, and body mass index (BMI) was calculated. Results showed processing of characters' emotional responses (detected via eye-tracking) to body- and perfectionism-related events for third-person scenarios was related to eating disorder symptomatology. Processing of characters' emotional responses to body-related events for second-person scenarios was related to males' BMI. The moment-to-moment processing of characters' emotional responses to food-related scenarios was not related to eating disorder symptomatology or BMI. Findings support theories that include body- and perfectionism-related cognitive biases as underlying mechanisms of eating disorder symptomatology and the use of implicit measures of cognitive processes underlying males' eating disorder symptomatology.