A framework for applying natural language processing in digital health interventions

BACKGROUND: Digital health interventions (DHIs) are poised to reduce target symptoms in a scalable, affordable, and empirically supported way. DHIs that involve coaching or clinical support often collect text data from 2 sources: (1) open correspondence between users and the trained practitioners supporting them through a messaging system and (2) text data recorded during the intervention by users, such as diary entries. Natural language processing (NLP) offers methods for analyzing text, augmenting the understanding of intervention effects, and informing therapeutic decision making. OBJECTIVE: This study aimed to present a technical framework that supports the automated analysis of both types of text data often present in DHIs. This framework generates text features and helps to build statistical models to predict target variables, including user engagement, symptom change, and therapeutic outcomes. METHODS: We first discussed various NLP techniques and demonstrated how they are implemented in the presented framework. We then applied the framework in a case study of the Healthy Body Image Program, a Web-based intervention trial for eating disorders (EDs). A total of 372 participants who screened positive for an ED received a DHI aimed at reducing ED psychopathology (including binge eating and purging behaviors) and improving body image. These users generated 37,228 intervention text snippets and exchanged 4285 user-coach messages, which were analyzed using the proposed model. RESULTS: We applied the framework to predict binge eating behavior, resulting in an area under the curve between 0.57 (when applied to new users) and 0.72 (when applied to new symptom reports of known users). In addition, initial evidence indicated that specific text features predicted the therapeutic outcome of reducing ED symptoms. CONCLUSIONS: The case study demonstrates the usefulness of a structured approach to text data analytics. NLP techniques improve the prediction of symptom changes in DHIs. We present a technical framework that can be easily applied in other clinical trials and clinical presentations and encourage other groups to apply the framework in similar contexts

Evaluating psychometric properties of the Emotional Eating Scale Adapted for Children and Adolescents (EES-C) in a clinical sample of children seeking treatment for obesity: a case for the unidimensional model.

BackgroundThe Emotional Eating Scale - Adapted for Children and Adolescents (EES-C) assesses children's urge to eat in response to experiences of negative affect. Prior psychometric studies have demonstrated the high reliability, concurrent validity, and test-retest reliability of theoretically defined subconstructs among non-clinical samples of children and adolescents who were primarily healthy weight; however, no psychometric studies exist investigating the EES-C among clinical samples of children with overweight/obesity (OW/OB). Furthermore, studies conducted in different contexts have suggested a discordant number of subconstructs of emotions related to eating. The purpose of this study was to evaluate the validity of the EES-C in a clinical sample of children seeking weight-loss treatment.MethodUsing a hierarchical bi-factor approach, we evaluated the validity of the EES-C to measure a single general construct, a set of two separate correlated subconstructs, or a hierarchical arrangement of two constructs, and determined reliability in a clinical sample of treatment-seeking children with OW/OB aged 8-12 years (N = 147, mean age = 10.4 years.; mean BMI z = 2.0; female = 66%; Hispanic = 32%, White and other = 68%).ResultsComparison of factor-extraction methods suggested a single primary construct underlying EES-C in this clinical sample. The bi-factor indices provided clear evidence that most of the reliable variance in the total score (90.8 for bi-factor model with three grouping factors and 95.2 for bi-factor model with five grouping factors) was attributed to the general construct. After adjusting for relationships with the primary construct, remaining correlations among sets of items did not suggest additional reliable constructs.ConclusionResults suggest that the primary interpretive emphasis of the EES-C among treatment-seeking children with overweight or obesity should be placed on a single general construct, not on the 3- or 5- subconstructs as was previously suggested

Appetitive traits as targets for weight loss: The role of food cue responsiveness and satiety responsiveness.

Individuals with overweight or obesity (OW/OB) are at increased risk for significant physical and psychological comorbidities. The current treatment for OW/OB is behavioral weight loss, which provides psychoeducation on nutrition and physical activity, as well as behavior therapy skills. However, behavioral weight loss is not effective for the majority of the individuals who participate. Research suggests that overeating, or eating past nutritional needs, is one of the leading causes of weight gain. Accumulating evidence suggests that appetitive traits, such as food cue responsiveness and satiety responsiveness, are associated with overeating and weight in youth and adults. The following review presents the current literature on the relationship between food cue responsiveness, satiety responsiveness, overeating, and OW/OB. Research suggests that higher food cue responsiveness and lower satiety responsiveness are associated with overeating and OW/OB cross-sectionally and longitudinally. Emerging data suggest that food cue responsiveness and satiety responsiveness may exist along the same continuum and can be targeted to manage overeating and reduce weight. We have developed a treatment model targeting food cue responsiveness and satiety responsiveness to reduce overeating and weight and have preliminary feasibility, acceptability, and efficacy data, with testing currently being conducted in larger trials. Through programs targeting appetitive traits we hope to develop an alternative weight loss model to assist individuals with a propensity to overeat

Appetitive traits as targets for weight loss: The role of food cue responsiveness and satiety responsiveness.

Individuals with overweight or obesity (OW/OB) are at increased risk for significant physical and psychological comorbidities. The current treatment for OW/OB is behavioral weight loss, which provides psychoeducation on nutrition and physical activity, as well as behavior therapy skills. However, behavioral weight loss is not effective for the majority of the individuals who participate. Research suggests that overeating, or eating past nutritional needs, is one of the leading causes of weight gain. Accumulating evidence suggests that appetitive traits, such as food cue responsiveness and satiety responsiveness, are associated with overeating and weight in youth and adults. The following review presents the current literature on the relationship between food cue responsiveness, satiety responsiveness, overeating, and OW/OB. Research suggests that higher food cue responsiveness and lower satiety responsiveness are associated with overeating and OW/OB cross-sectionally and longitudinally. Emerging data suggest that food cue responsiveness and satiety responsiveness may exist along the same continuum and can be targeted to manage overeating and reduce weight. We have developed a treatment model targeting food cue responsiveness and satiety responsiveness to reduce overeating and weight and have preliminary feasibility, acceptability, and efficacy data, with testing currently being conducted in larger trials. Through programs targeting appetitive traits we hope to develop an alternative weight loss model to assist individuals with a propensity to overeat