Microwave Ablation of the Pig Growth Plate: Proof of Concept for Minimally Invasive Epiphysiodesis.

BACKGROUND: Different surgical methods for epiphysiodesis of limb length discrepancy (LLD) have been described. Although these methods are variably effective, they are associated with morbidity (pain and limp) and potential complications. Microwave ablation is a less-invasive opportunity to halt growth by selectively destroying the growth plate via thermal energy to treat LLD in children. QUESTIONS/PURPOSES: In this proof-of-concept study using an in vivo pig model, we asked: (1) What is the durability of response 2 to 4 months after microwave ablation of the tibial growth plate as measured by length and angulation of the tibia via a CT scan? (2) Was articular cartilage maintained as measured by standard histologic staining for articular cartilage viability? METHODS: To develop an in vivo protocol for microwave ablation, we placed microwave antennas adjacent to the proximal tibia growth plate in the cadaveric hindlimbs of 18 3-month-old pigs. To determine the suitable time, we varied ablation from 90 to 270 seconds at 65-W power settings. After sectioning the tibia, we visually assessed for discoloration (implying growth plate destruction) that included the central growth plate but did not encroach into the epiphysis in a manner that could disrupt the articular surface. Using this information, we then performed microwave ablation on three live female pigs (3.5 to 4 months old) to evaluate physiologic changes and durability of response. A postprocedure MRI was performed to ensure the intervention led to spatial growth plate alterations similar to that seen in cadavers. This was followed by serial CT, which was used to assess the potential effect on local bone and growth until the animals were euthanized 2 to 4 months after the procedure. We analyzed LLD, angular deformity, and bony deformity using CT scans of both tibias. The visibility of articular cartilage was compared with that of the contralateral tibia via standard histologic staining, and growth rates of the proximal tibial growth plate were compared via fluorochrome labeling. RESULTS: Eighteen cadaveric specimens showed ablation zones across the growth plate without visual damage to the articular surface. The three live pigs did not exhibit changes in gait or require notable pain medication after the procedure. Each animal demonstrated growth plate destruction, expected limb shortening (0.8, 1.2, and 1.5 cm), and bony cavitation around the growth plate. Slight valgus bone angulation (4º, 5º, and 12º) compared with the control tibia was noted. No qualitatively observable articular cartilage damage was encountered from the histologic comparison with the contralateral tibia for articular cartilage thickness and cellular morphology. CONCLUSION: A microwave antenna placed into a pig's proximal tibia growth plate can slow the growth of the tibia without apparent pain and alteration of gait and function. CLINICAL RELEVANCE: Further investigation and refinement of our animal model is ongoing and includes shorter ablation times and comparison of dynamic ablation (moving the antennae during the ablation) as well as static ablation of the tibia from a medial and lateral portal. These refinements and planned comparison with standard mechanical growth arrest in our pig model may lead to a similar approach to ablate growth plates in children with LLD.

Detecting Eating Episodes From Wrist Motion Using Daily Pattern Analysis.

This paper presents new methods to detect eating from wrist motion. Our main novelty is that we analyze a full day of wrist motion data as a single sample so that the detection of eating occurrences can benefit from diurnal context. We develop a two-stage framework to facilitate a feasible full-day analysis. The first-stage model calculates local probabilities of eating P(Ew) within windows of data, and the second-stage model calculates enhanced probabilities of eating P(Ed) by treating all P(Ew) within a single day as one sample. The framework also incorporates an augmentation technique, which involves the iterative retraining of the first-stage model. This allows us to generate a sufficient number of day-length samples from datasets of limited size. We test our methods on the publicly available Clemson All-Day (CAD) dataset and FreeFIC dataset, and find that the inclusion of day-length analysis substantially improves accuracy in detecting eating episodes. We also benchmark our results against several state-of-the-art methods. Our approach achieved an eating episode true positive rate (TPR) of 89% with 1.4 false positives per true positive (FP/TP), and a time weighted accuracy of 84%, which are the highest accuracies reported on the CAD dataset. Our results show that the daily pattern classifier substantially improves meal detections and in particular reduces transient false detections that tend to occur when relying on shorter windows to look for individual ingestion or consumption events.

State-level working memory and dysregulated eating in children and adolescents: An exploratory ecological momentary assessment study.

BACKGROUND: Children with loss of control (LOC) eating and overweight/obesity have relative deficiencies in trait-level working memory (WM), which may limit adaptive responding to intra- and extra-personal cues related to eating. Understanding of how WM performance relates to eating behavior in real-time is currently limited. METHODS: We studied 32 youth (ages 10-17 years) with LOC eating and overweight/obesity (LOC-OW; n = 9), overweight/obesity only (OW; n = 16), and non-overweight status (NW; n = 7). Youth completed spatial and numerical WM tasks requiring varying degrees of cognitive effort and reported on their eating behavior daily for 14 days via smartphone-based ecological momentary assessment. Linear mixed effects models estimated group-level differences in WM performance, as well as associations between contemporaneously completed measures of WM and dysregulated eating. RESULTS: LOC-OW were less accurate on numerical WM tasks compared to OW and NW (ps < .01); groups did not differ on spatial task accuracy (p = .41). Adjusting for between-subject effects (reflecting differences between individuals in their mean WM performance and its association with eating behavior), within-subject effects (reflecting variations in moment-to-moment associations) revealed that more accurate responding on the less demanding numerical WM task, compared to one's own average, was associated with greater overeating severity across the full sample (p = .013). There were no associations between WM performance and LOC eating severity (ps > .05). CONCLUSIONS: Youth with LOC eating and overweight/obesity demonstrated difficulties mentally retaining and manipulating numerical information in daily life, replicating prior laboratory-based research. Overeating may be related to improved WM, regardless of LOC status, but temporality and causality should be further explored. PUBLIC SIGNIFICANCE STATEMENT: Our findings suggest that youth with loss of control eating and overweight/obesity may experience difficulties mentally retaining and manipulating numerical information in daily life relative to their peers with overweight/obesity and normal-weight status, which may contribute to the maintenance of dysregulated eating and/or elevated body weight. However, it is unclear whether these individual differences are related to eating behavior on a moment-to-moment basis.

Ethical, legal, and social implications of digital health: A needs assessment from the Society of Behavioral Medicine to inform capacity building for behavioral scientists.

The ethical, legal, and social implications (ELSIs) of digital health are important when researchers and practitioners are using technology to collect, process, or store personal health data. Evidence underscores a strong need for digital health ELSI training, yet little is known about the specific ELSI topic areas that researchers and practitioners would most benefit from learning. To identify ELSI educational needs, a needs assessment survey was administered to the members of the Society of Behavioral Medicine (SBM). We sought to identify areas of ELSI proficiency and training need, and also evaluate interest and expertise in ELSI topics by career level and prior ELSI training history. The 14-item survey distributed to SBM members utilized the Digital Health Checklist tool (see recode.health/tools) and included items drawn from the four-domain framework: data management, access and usability, privacy and risk to benefit assessment. Respondents (N = 66) were majority faculty (74.2%) from psychology or public health. Only 39.4% reported receiving "formal" ELSI training. ELSI topics of greatest interest included practices that supported participant engagement, and dissemination and implementation of digital tools beyond the research setting. Respondents were least experienced in managing "bystander" data, having discussions about ELSIs, and reviewing terms of service agreements and privacy policies with participants and patients. There is opportunity for formalized ELSI training across career levels. Findings serve as an evidence base for continuous and ongoing evaluation of ELSI training needs to support scientists in conducting ethical and impactful digital health research.

New technologies are increasingly used in research and practice, which introduce new ethical, legal, and social implications (ELSIs). While there are scholars who study ELSIs in research, it is important that behavioral scientists have ELSI training in order to identify and mitigate possible harms and maximize benefits among their patients/participants, particularly when using technologies that collect personal health information. ELSI training opportunities are limited and, because ELSI is a broad complicated field, we know very little about the specific topics that researchers/practitioners would benefit from learning. To understand ELSI training needs specific to the field of digital health, we asked the members of the Society of Behavioral Medicine, a multidisciplinary nonprofit organization, to tell us about which ELSI areas they are most interested in. We found that 39.4% of members received formal ELSI training. Members were most interested in using technology to help patients/participants stay engaged in their treatments, and developing technologies that can be used outside of research (in the "real world"). Members were least experienced in reviewing terms of service/privacy policies and handling information collected from non-patient/participants (people in the backgrounds of voice recordings/videos). Training interests differed by career level (faculty vs. students), and so future ELSI trainings could be more beneficial if they were mindful of prior experiences.

An explanation for the accuracy of sensor-based measures of energy intake: Amount of food consumed matters more than dietary composition.

Understanding and intervening on eating behavior often necessitates measurement of energy intake (EI); however, commonly utilized and widely accepted methods vary in accuracy and place significant burden on users (e.g., food diaries), or are costly to implement (e.g., doubly labeled water). Thus, researchers have sought to leverage inexpensive and low-burden technologies such as wearable sensors for EI estimation. Paradoxically, one such methodology that estimates EI via smartwatch-based bite counting has demonstrated high accuracy in laboratory and free-living studies, despite only measuring the amount, not the composition, of food consumed. This secondary analysis sought to further explore this phenomenon by evaluating the degree to which EI can be explained by a sensor-based estimate of the amount consumed versus the energy density (ED) of the food consumed. Data were collected from 82 adults in free-living conditions (51.2% female, 31.7% racial and/or ethnic minority; Mage = 33.5, SD = 14.7) who wore a bite counter device on their wrist and used smartphone app to implement the Remote Food Photography Method (RFPM) to assess EI and ED for two weeks. Bite-based estimates of EI were generated via a previously validated algorithm. At a per-meal level, linear mixed effect models indicated that bite-based EI estimates accounted for 23.4% of the variance in RFPM-measured EI, while ED and presence of a beverage accounted for only 0.2% and 0.1% of the variance, respectively. For full days of intake, bite-based EI estimates and ED accounted for 41.5% and 0.2% of the variance, respectively. These results help to explain the viability of sensor-based EI estimation even in the absence of information about dietary composition.

A randomized trial examining the effect of yoga on dietary lapses and lapse triggers following behavioral weight loss treatment.

Background: Dietary lapses can hinder weight loss and yoga can improve self-regulation, which may protect against lapses. This study examined the effect of yoga on dietary lapses, potential lapse triggers (e.g., affective states, cravings, dietary temptations), and reasons for initiating eating following weight loss treatment. Methods: Sixty women with overweight/obesity (34.3 ± 3.9 kg/m2) were randomized to a 12 week yoga intervention (2x/week; YOGA) or contact-matched control (cooking/nutrition classes; CON) following a 12-week behavioral weight loss program. Participants responded to smartphone surveys (5x/day) over a 10-day period at baseline, 12, and 24 weeks to assess lapses and triggers. Results: At 24 weeks, YOGA and CON differed on several types of lapses (i.e., less eating past full, eating more than usual, loss of control when eating, self-identified overeating, difficulty stopping eating in YOGA), and YOGA was less likely to eat to feel better or in response to stress (ps < 0.05). YOGA also reported less stress and anxiety and more positive affect (ps < 0.01); dietary temptations and cravings did not differ from CON. Conclusion: Yoga resulted in fewer dietary lapses and improved affect among women with overweight/obesity following weight loss. While preliminary, findings suggest that yoga should be considered as a potential component of weight loss treatment to target dietary lapses.

Association of weight and shape concern with weight change and weight-related behaviors in behavioral weight loss treatment.

Weight and shape concern (WSC) is a facet of negative body image that is common among individuals with overweight/obesity seeking behavioral weight loss treatment (BWL), but remains understudied. This secondary analysis evaluates associations between WSC, weight change, and weight-related behaviors among individuals in a 24-week BWL. Adults (n = 32) with body mass index 25-50 kg/m2 completed a baseline WSC questionnaire, measured weight at 12 and 24 weeks, measured physical activity via accelerometer, and completed 24-hour dietary recalls. Adherence to self-monitoring dietary intake and weight were assessed. A series of linear mixed models were used to evaluate associations between baseline WSC and weight change, as well as weight-related behaviors. Results revealed no significant effect of WSC on weight change. There were significant WSC x time interactions, such that those rating WSC "very important" decreased self-weighing and the "low importance" group decreased their caloric intake during treatment. The "pretty important" group had greater minutes of activity than the "low importance" group. Findings indicated that WSC may impact weight-related behaviors that contribute to BWL success. This trial was pre-registered on ClinicalTrials.gov (NCT03739151).

Failed Extubation in Neonates After Cardiac Surgery: A Single-Center, Retrospective Study.

OBJECTIVES: To describe factors associated with failed extubation (FE) in neonates following cardiovascular surgery, and the relationship with clinical outcomes. DESIGN: Retrospective cohort study. SETTING: Twenty-bed pediatric cardiac ICU (PCICU) in an academic tertiary care children's hospital. PATIENTS: Neonates admitted to the PCICU following cardiac surgery between July 2015 and June 2018. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Patients who experienced FE were compared with patients who were successfully extubated. Variables associated with FE ( p < 0.05) from univariate analysis were considered for inclusion in multivariable logistic regression. Univariate associations of FE with clinical outcomes were also examined. Of 240 patients, 40 (17%) experienced FE. Univariate analyses revealed associations of FE with upper airway (UA) abnormality (25% vs 8%, p = 0.003) and delayed sternal closure (50% vs 24%, p = 0.001). There were weaker associations of FE with hypoplastic left heart syndrome (25% vs 13%, p = 0.04), postoperative ventilation greater than 7 days (33% vs 15%, p = 0.01), Society of Thoracic Surgeons-European Association for Cardio-Thoracic Surgery (STAT) category 5 operations (38% vs 21%, p = 0.02), and respiratory rate during spontaneous breathing trial (median 42 vs 37 breaths/min, p = 0.01). In multivariable analysis, UA abnormalities (adjusted odds ratio [AOR] 3.5; 95% CI, 1.4-9.0), postoperative ventilation greater than 7 days (AOR 2.3; 95% CI, 1.0-5.2), and STAT category 5 operations (AOR 2.4; 95% CI, 1.1-5.2) were independently associated with FE. FE was also associated with unplanned reoperation/reintervention during hospital course (38% vs 22%, p = 0.04), longer hospitalization (median 29 vs 16.5 d, p < 0.0001), and in-hospital mortality (13% vs 3%, p = 0.02). CONCLUSIONS: FE in neonates occurs relatively commonly following cardiac surgery and is associated with adverse clinical outcomes. Additional data are needed to further optimize periextubation decision-making in patients with multiple clinical factors associated with FE.

Classification of Lapses in Smokers Attempting to Stop: A Supervised Machine Learning Approach Using Data From a Popular Smoking Cessation Smartphone App.

INTRODUCTION: Smoking lapses after the quit date often lead to full relapse. To inform the development of real time, tailored lapse prevention support, we used observational data from a popular smoking cessation app to develop supervised machine learning algorithms to distinguish lapse from non-lapse reports. AIMS AND METHODS: We used data from app users with ≥20 unprompted data entries, which included information about craving severity, mood, activity, social context, and lapse incidence. A series of group-level supervised machine learning algorithms (eg, Random Forest, XGBoost) were trained and tested. Their ability to classify lapses for out-of-sample (1) observations and (2) individuals were evaluated. Next, a series of individual-level and hybrid algorithms were trained and tested. RESULTS: Participants (N = 791) provided 37 002 data entries (7.6% lapses). The best-performing group-level algorithm had an area under the receiver operating characteristic curve (AUC) of 0.969 (95% confidence interval [CI] = 0.961 to 0.978). Its ability to classify lapses for out-of-sample individuals ranged from poor to excellent (AUC = 0.482-1.000). Individual-level algorithms could be constructed for 39/791 participants with sufficient data, with a median AUC of 0.938 (range: 0.518-1.000). Hybrid algorithms could be constructed for 184/791 participants and had a median AUC of 0.825 (range: 0.375-1.000). CONCLUSIONS: Using unprompted app data appeared feasible for constructing a high-performing group-level lapse classification algorithm but its performance was variable when applied to unseen individuals. Algorithms trained on each individual's dataset, in addition to hybrid algorithms trained on the group plus a proportion of each individual's data, had improved performance but could only be constructed for a minority of participants. IMPLICATIONS: This study used routinely collected data from a popular smartphone app to train and test a series of supervised machine learning algorithms to distinguish lapse from non-lapse events. Although a high-performing group-level algorithm was developed, it had variable performance when applied to new, unseen individuals. Individual-level and hybrid algorithms had somewhat greater performance but could not be constructed for all participants because of the lack of variability in the outcome measure. Triangulation of results with those from a prompted study design is recommended prior to intervention development, with real-world lapse prediction likely requiring a balance between unprompted and prompted app data.

Applying a Social Determinants of Health Framework to Guide Digital Innovations That Reduce Disparities in Chronic Disease.

ABSTRACT: Chronic diseases are among the top causes of global death, disability, and health care expenditure. Digital health interventions (e.g., patient support delivered via technologies such as smartphones, wearables, videoconferencing, social media, and virtual reality) may prevent and mitigate chronic disease by facilitating accessible, personalized care. Although these tools have promise to reach historically marginalized groups, who are disproportionately affected by chronic disease, evidence suggests that digital health interventions could unintentionally exacerbate health inequities. This commentary outlines opportunities to harness recent advancements in technology and research design to drive equitable digital health intervention development and implementation. We apply "calls to action" from the World Health Organization Commission on Social Determinants of Health conceptual framework to the development of new, and refinement of existing, digital health interventions that aim to prevent or treat chronic disease by targeting intermediary, social, and/or structural determinants of health. Three mirrored "calls to action" are thus proposed for digital health research: a) develop, implement, and evaluate multilevel, context-specific digital health interventions; b) engage in intersectoral partnerships to advance digital health equity and social equity more broadly; and c) include and empower historically marginalized groups to develop, implement, and access digital health interventions. Using these "action items," we review several technological and methodological innovations for designing, evaluating, and implementing digital health interventions that have greater potential to reduce health inequities. We also enumerate possible challenges to conducting this work, including leading interdisciplinary collaborations, diversifying the scientific workforce, building trustworthy community relationships, and evolving health care and digital infrastructures.

Prediction Model with External Validation for Early Detection of Postoperative Pediatric Chylothorax.

Earlier diagnosis of chylothorax following pediatric cardiac surgery is associated with decreased duration of chylothorax. Pleural fluid testing is used to diagnosis chylothorax which may delay detection in patients who are not enterally fed at time of chylothorax onset. Our aim was to develop and externally validate a prediction model to detect chylothorax earlier than pleural fluid testing in pediatric patients following cardiac surgery. A multivariable logistic regression model was developed to detect chylothorax using a stepwise approach. The model was developed using data from patients < 18 years following cardiac surgery from Primary Children's Hospital, a tertiary-care academic center, between 2017 and 2020. External validation used a contemporary cohort (n = 171) from Lucille Packard Children's Hospital. A total of 763 encounters (735 patients) were analyzed, of which 72 had chylothorax. The final variables selected were chest tube output (CTO) the day after sternal closure (dichotomized at 15.6 mL/kg/day, and as a continuous variable) and delayed sternal closure. The highest odds of chylothorax were associated with CTO on post-sternal closure day 1 > 15.6 mL/kg/day (odds ratio 11.3, 95% CI 6,3, 21.3). The c-statistic for the internal and external validation datasets using the dichotomized CTO variable were 0.78 (95% CI 0.73, 0.82) and 0.84 (95% CI, 0.78, 0.9) and performance improved when using CTO as a continuous variable (OR 0.84, CI: 95% CI 0.80, 0.87). Using the models described, chylothorax after pediatric cardiac surgery may be detected earlier and without reliance on enteral feeds.

Characterizing emotional eating: Ecological momentary assessment with person-specific modeling.

Emotional eating is a topic of clinical importance, with links to weight regulation and wellness. However, issues of concept clarity and measurement can interfere with efforts to understand and intervene on emotional eating. One explanation for prior difficulties in defining emotional eating may be that this construct is not uniform across individuals. The current study critically examined emotional eating by combining ecological momentary assessment (EMA) with an idiographic analytic approach. The study examined the heterogeneity in the emotions and dysregulated eating behaviors often thought to underlie emotional eating, by establishing and comparing latent factor profiles across individuals. Ten community adults with overweight or obesity completed a 21-day EMA protocol, with 5 daily prompts to report on relevant emotions and eating behaviors. P-technique factor analysis was used to examine the data. Results suggested variability across individuals in the number of factors that emerged, the items that loaded on each factor, and the strength of loadings. Dysregulated eating was not found to covary with affective states strongly enough to produce a distinct "emotional eating" factor for any individual, nor did the correlations between factors suggest strong relationships between emotions and dysregulated eating for most participants, even in this sample with 90% of participants self-identifying as "emotional eaters." Findings are consistent with a growing body of literature questioning the validity of the "emotional eating" construct as currently defined and measured, and supports conceptualizing emotional eating as a locally heterogenous construct that varies between people. Combining EMA with an intra-individual modeling technique appears to be a promising approach for understanding emotional eating. Additional work with larger samples is needed to capture the full range in individual profiles.

Dietary lapses are associated with meaningful elevations in daily caloric intake and added sugar consumption during a lifestyle modification intervention.

Objective: Lapses from the dietary prescription in lifestyle modification interventions for overweight/obesity are common and impact weight loss outcomes. While it is expected that lapses influence weight via increased consumption, there are no studies that have evaluated how dietary lapses affect dietary intake during treatment. This study examined the association between daily lapses and daily energy and macronutrient intake during a lifestyle modification intervention. Methods: This study used an intensive longitudinal design to observe participants throughout a 6-month lifestyle modification intervention. Participants (n = 32) were adults with overweight/obesity (body mass index 25-50 kg/m2) and a diagnosed cardiovascular disease risk factor (e.g., hypertension) with a desire to lose weight. Participants underwent a gold-standard individual in-person lifestyle modification protocol consisting of 3 months of weekly sessions with 3 months of monthly sessions. Each participant's dietary prescription included a calorie target range that was based on their starting weight. Participants completed ecological momentary assessment (EMA; repeated daily smartphone surveys) every other week to self-report on dietary lapses and telephone-based 24-h dietary recalls every 6 weeks. Results: On days with EMA and recalled intake (n = 210 days), linear mixed models demonstrated significant associations between daily lapse and higher total daily caloric intake (B = 139.20, p < 0.05), more daily grams of added sugar (B = 16.24, p < 0.001), and likelihood of exceeding the daily calorie goal (B = 0.89, p < 0.05). The associations between daily lapse and intake of all other daily macronutrients were non-significant. Conclusions: This study contributes to literature suggesting that dietary lapses pose a threat to weight loss success. Results indicate that reducing lapse frequency could reduce overall caloric intake and added sugar consumption.

Analysis of inflammatory cytokines in the chest tube drainage of post-operative superior cavopulmonary connection patients.

INTRODUCTION: Prolonged pleural effusions are common post Fontan operation and are associated with morbidity. Fontan pleural effusions have elevated proinflammatory cytokines. Little is known about the chest tube drainage after a superior cavopulmonary connection. We examined the chest tube drainage and the inflammatory profiles in post-operative superior cavopulmonary connection patients. METHODS: This prospective cohort study enrolled 25 patients undergoing superior cavopulmonary connection and 10 age-similar controls. Data are also compared to 25 previously published Fontan patients and their 15 age-similar controls. Chest tube samples were analysed with a 17-cytokine BioPlex Assay. Descriptive statistics and univariate comparisons were made between groups. RESULTS: Duration of chest tube drainage was significantly shorter in superior cavopulmonary connection patients (median 4 days, [interquartile range 3-5 days]) versus Fontan patients (10 days, [7-11 days], p < 0.0001). Cytokine concentrations were higher on post-operative day 1 in superior cavopulmonary connection patients versus Fontan patients (all p ≤ 0.01), however levels were comparable to age-similar controls. While proinflammatory IL 8, MIP-1ß, and TNF-α concentrations increased in chest tube drainage of Fontan patients from post-operative day 1 to last chest tube day (all p < 0.0001), there was no change in these biomarkers in superior cavopulmonary connection patients, their controls, or Fontan controls. CONCLUSIONS: Our study demonstrates that after superior cavopulmonary connection, proinflammatory cytokines in the chest tube drainage remain similar to biventricular controls of both age groups, unlike the significant rise over time observed in Fontan patients. Inflammation within the chest tube drainage is likely not innate to single ventricle patients.

Combining passive eating monitoring and ecological momentary assessment to characterize dietary lapses from a lifestyle modification intervention.

Dietary lapses (i.e., specific instances of nonadherence to recommended dietary goals) contribute to suboptimal weight loss outcomes during lifestyle modification programs. Passive eating monitoring could enhance lapse measurement via objective assessment of eating characteristics that could be markers for lapse (e.g., more bites consumed). The purpose of this study was to evaluate if passively-inferred eating characteristics (i.e., bites, eating duration, and eating rate), measured via wrist-worn device, could distinguish dietary lapses from non-lapse eating. Adults (n = 25) with overweight/obesity received a 24-week lifestyle modification intervention. Participants completed ecological momentary assessment (EMA; repeated smartphone surveys) biweekly to self-report on dietary lapses and non-lapse eating episodes. Participants wore a wrist device that captured continuous wrist motion. Previously-validated algorithms inferred eating episodes from wrist data, and calculated bite count, duration, and rate (seconds per bite). Mixed effects logistic regressions revealed no simple effects of bite count, duration, or eating rate on the likelihood of dietary lapse. Moderation analyses revealed that eating episodes in the evening were more likely to be lapses if they involved fewer bites (B = -0.16, p < .05), were shorter (B = -0.54, p < .05), or had a slower rate (B = 1.27, p < .001). Statistically significant interactions between eating characteristics (Bs = -0.30 to -0.08, ps < .001) revealed two distinct patterns. Eating episodes that were 1. smaller, slower, and shorter than average, or 2. larger, quicker, and longer than average were associated with increased probability of lapse. This study is the first to use objective eating monitoring to characterize dietary lapses throughout a lifestyle modification intervention. Results demonstrate the potential of sensors to identify non-adherence using only patterns of passively-sensed eating characteristics, thereby minimizing the need for self-report in future studies. CLINICAL TRIALS REGISTRY NUMBER: NCT03739151.

Preliminary evidence of contextual factors' influence on weight loss treatment outcomes: implications for future research.

BACKGROUND/OBJECTIVES: Behavioral health interventions, including behavioral obesity treatment, typically target psychosocial qualities of the individual (e.g., knowledge, self-efficacy) that are largely treated as persistent, over momentary contextual factors (e.g., affect, environmental conditions). The variance in treatment outcomes that can be attributable to these two sources is rarely quantified but may help inform future research and treatment development efforts. SUBJECTS/METHODS: The intraclass correlation coefficient (ICC) for weekly weight loss was calculated in three studies involving 10-12 weeks of behavioral obesity treatment delivered to adults via in-person group sessions, mobile application, or website. The ICC explains the proportion of variance between vs. within individuals, and was used to infer the contribution of individual vs. contextual factors to weekly weight loss. The analytic approach involved unconditional linear mixed effect models with a random effect for subject. RESULTS: The ICCs were very low, ranging from 0.01 to 0.06, suggesting that momentary contextual factors may influence obesity treatment outcomes to a substantial degree. CONCLUSIONS: This study yielded preliminary evidence that the influence of contextual factors in behavioral obesity treatment may be underappreciated. Future research is needed to simultaneously identify and quantify sources of within- and between-subjects variance to optimize treatment approaches.

Oral Aversion in Infants With Congenital Heart Disease: A Single-Center Retrospective Cohort Study.

OBJECTIVES: Neonates undergoing cardiac surgery are at risk for oral aversion (OA). OA is not well described outside of the index hospitalization and impacts patients and families. We evaluated the prevalence of OA at 1 year old after neonatal cardiopulmonary bypass (CPB) surgery. DESIGN: Retrospective cohort study. SETTING: Single quaternary care hospital. SUBJECTS: Our cohort included 157 neonates who underwent CPB surgery from 2014 to 2017 and had follow-up data available at 1 year old. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Three feeding experts reviewed the medical record to define children with OA; 30% of charts were evaluated in triplicate for validation. Neonates with and without OA were compared in univariate analysis, and risk factors for OA were explored in a limited multivariable analysis. OA was present at 1 year in 37 patients (23.6%) and other feeding difficulties were present in an additional 29 patients (18.5%). Thirty-eight patients (24.2%) had a feeding tube, including 12 (7.6%) with a gastrostomy tube. Factors associated with OA at 1 year included total ICU days, duration of mechanical ventilation, total number of nil per os days, and number of postoperative days (PODs) until oral feeding initiation (all p < 0.0001). Number of POD until oral feeding initiation remained independently associated with OA at 1 year in multivariable analysis (adjusted odds ratio, 1.08; 95% CI, 1.04-1.12; p < 0.0001). Infants with any oral intake at discharge had lower odds of OA at 1 year (0.21; 95% CI, 0.08-0.5; p = 0.0003). At hospital discharge, 132 patients (84.1%) were taking some oral feeds, and 128 patients (81.5%) received tube feeding. CONCLUSIONS: OA and other feeding difficulties are common at 1 year old in neonates undergoing CPB surgery. Delayed exposure to oral intake may be a modifiable risk factor for OA and efforts to improve early oral feeding could lead to better functional outcomes.

Momentary predictors of dietary lapse from a mobile health weight loss intervention.

Identifying factors that influence risk of dietary lapses (i.e., instances of dietary non-adherence) is important because lapses contribute to suboptimal weight loss outcomes. Existing research examining lapse risk factors has had methodological limitations, including retrospective recall biases, subjective operationalizations of lapse, and has investigated lapses among participants in gold-standard behavioral weight loss programs (which are not accessible to most Americans). The current study will address these limitations by being the first to prospectively assess several risk factors of lapse (objectively operationalized) in the context of a commercial mobile health (mHealth) intervention, a highly popular and accessible method of weight loss. N = 159 adults with overweight or obesity enrolled in an mHealth commercial weight loss program completed ecological momentary assessments (EMAs) of 15 risk factors and lapses (defined as exceeding a point target for a meal/snack) over a 2-week period. N = 9 participants were excluded due to low EMA compliance, resulting in a sample of N = 150. Dietary lapses were predicted by momentary increases in urges to deviate from one's eating plan (b = .55, p < .001), cravings (b = .55, p < .001), alcohol consumption (b = .51, p < .001), and tiredness (b = .19, p < .001), and decreases in confidence related to meeting dietary goals (b = -.21, p < .001) and planning food intake (b = -.15, p < .001). This study was among the first to identify prospective predictors of lapse in the context of a commercial mHealth weight loss program. Findings can inform mHealth weight loss programs, including just-in-time interventions that measure these risk factors, calculate when risk of lapse is high, and deliver momentary interventions to prevent lapses.

Optimizing a Just-in-Time Adaptive Intervention to Improve Dietary Adherence in Behavioral Obesity Treatment: Protocol for a Microrandomized Trial.

BACKGROUND: Behavioral obesity treatment (BOT) is a gold standard approach to weight loss and reduces the risk of cardiovascular disease. However, frequent lapses from the recommended diet stymie weight loss and prevent individuals from actualizing the health benefits of BOT. There is a need for innovative treatment solutions to improve adherence to the prescribed diet in BOT. OBJECTIVE: The aim of this study is to optimize a smartphone-based just-in-time adaptive intervention (JITAI) that uses daily surveys to assess triggers for dietary lapses and deliver interventions when the risk of lapse is high. A microrandomized trial design will evaluate the efficacy of any interventions (ie, theory-driven or a generic alert to risk) on the proximal outcome of lapses during BOT, compare the effects of theory-driven interventions with generic risk alerts on the proximal outcome of lapse, and examine contextual moderators of interventions. METHODS: Adults with overweight or obesity and cardiovascular disease risk (n=159) will participate in a 6-month web-based BOT while using the JITAI to prevent dietary lapses. Each time the JITAI detects elevated lapse risk, the participant will be randomized to no intervention, a generic risk alert, or 1 of 4 theory-driven interventions (ie, enhanced education, building self-efficacy, fostering motivation, and improving self-regulation). The primary outcome will be the occurrence of lapse in the 2.5 hours following randomization. Contextual moderators of intervention efficacy will also be explored (eg, location and time of day). The data will inform an optimized JITAI that selects the theory-driven approach most likely to prevent lapses in a given moment. RESULTS: The recruitment for the microrandomized trial began on April 19, 2021, and is ongoing. CONCLUSIONS: This study will optimize a JITAI for dietary lapses so that it empirically tailors the provision of evidence-based intervention to the individual and context. The finalized JITAI will be evaluated for efficacy in a future randomized controlled trial of distal health outcomes (eg, weight loss). TRIAL REGISTRATION: ClinicalTrials.gov NCT04784585; http://clinicaltrials.gov/ct2/show/NCT04784585. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/33568.

Comparing ecological momentary assessment to sensor-based approaches in predicting dietary lapse.

Ecological momentary assessment (EMA; brief self-report surveys) of dietary lapse risk factors (e.g., cravings) has shown promise in predicting and preventing dietary lapse (nonadherence to a dietary prescription), which can improve weight loss interventions. Passive sensors also can measure lapse risk factors and may offer advantages over EMA (e.g., objective, automatic, semicontinuous data collection), but currently can measure only a few lapse predictors, a notable limitation. This study preliminarily compared the burden and accuracy of commercially available sensors versus established EMA in lapse prediction. N = 23 adults with overweight/obesity completed a 6-week commercial app-based weight loss program. Participants wore a Fitbit, enabled GPS tracking, completed EMA, and reported on EMA and sensor burden poststudy via a 5-point Likert scale. Sensed risk factors were physical activity and sleep (accelerometer), geolocation (GPS), and time, from which 233 features (measurable characteristics of sensor signals) were extracted. EMA measured 19 risk factors, lapse, and categorized GPS into meaningful geolocations. Two supervised binary classification models (LASSO) were created: the sensor model predicted lapse with 63% sensitivity (true prediction rate of lapse) and 60% specificity (true prediction rate of non-lapse) and EMA model with 59% sensitivity and 72% specificity. EMA model accuracy was higher, but self-reported EMA burden (M = 2.96, SD = 1.02) also was higher (M = 1.50, SD = 0.94). EMA model accuracy was superior, but EMA burden was higher than sensor burden. Findings highlight the promise of sensors in contributing to lapse prediction, and future research may use EMA, sensors, or both depending on prioritization of accuracy versus participant burden.