The Digital Therapeutics Real-World Evidence Framework: An Approach for Guiding Evidence-Based Digital Therapeutics Design, Development, Testing, and Monitoring.

Digital therapeutics (DTx) are a promising way to provide safe, effective, accessible, sustainable, scalable, and equitable approaches to advance individual and population health. However, developing and deploying DTx is inherently complex in that DTx includes multiple interacting components, such as tools to support activities like medication adherence, health behavior goal-setting or self-monitoring, and algorithms that adapt the provision of these according to individual needs that may change over time. While myriad frameworks exist for different phases of DTx development, no single framework exists to guide evidence production for DTx across its full life cycle, from initial DTx development to long-term use. To fill this gap, we propose the DTx real-world evidence (RWE) framework as a pragmatic, iterative, milestone-driven approach for developing DTx. The DTx RWE framework is derived from the 4-phase development model used for behavioral interventions, but it includes key adaptations that are specific to the unique characteristics of DTx. To ensure the highest level of fidelity to the needs of users, the framework also incorporates real-world data (RWD) across the entire life cycle of DTx development and use. The DTx RWE framework is intended for any group interested in developing and deploying DTx in real-world contexts, including those in industry, health care, public health, and academia. Moreover, entities that fund research that supports the development of DTx and agencies that regulate DTx might find the DTx RWE framework useful as they endeavor to improve how DTxcan advance individual and population health.

Development of an assessment method for freely moving nonhuman primates' eating behavior using manual and deep learning analysis.

Purpose: Although eating is imperative for survival, few comprehensive methods have been developed to assess freely moving nonhuman primates' eating behavior. In the current study, we distinguished eating behavior into appetitive and consummatory phases and developed nine indices to study them using manual and deep learning-based (DeepLabCut) techniques. Method: The indices were utilized to three rhesus macaques by different palatability and hunger levels to validate their utility. To execute the experiment, we designed the eating behavior cage and manufactured the artificial food. The total number of trials was 3, with 1 trial conducted using natural food and 2 trials using artificial food. Result: As a result, the indices of highest utility for hunger effect were approach frequency and consummatory duration. Appetitive composite score and consummatory duration showed the highest utility for palatability effect. To elucidate the effects of hunger and palatability, we developed 2D visualization plots based on manual indices. These 2D visualization methods could intuitively depict the palatability perception and hunger internal state. Furthermore, the developed deep learning-based analysis proved accurate and comparable with manual analysis. When comparing the time required for analysis, deep learning-based analysis was 24-times faster than manual analysis. Moreover, temporal and spatial dynamics were visualized via manual and deep learning-based analysis. Based on temporal dynamics analysis, the patterns were classified into four categories: early decline, steady decline, mid-peak with early incline, and late decline. Heatmap of spatial dynamics and trajectory-related visualization could elucidate a consumption posture and a higher spatial occupancy of food zone in hunger and with palatable food. Discussion: Collectively, this study describes a newly developed and validated multi-phase method for assessing freely moving nonhuman primate eating behavior using manual and deep learning-based analyses. These effective tools will prove valuable in food reward (palatability effect) and homeostasis (hunger effect) research.

The Impact of Intervention Design on User Engagement in Digital Therapeutics Research: Factorial Experiment With a Mixed Methods Study.

BACKGROUND: User engagement is crucial for digital therapeutics (DTx) effectiveness; due to variations in the conceptualization of engagement and intervention design, assessment and retention of engagement remain challenging. OBJECTIVE: We investigated the influence of the perceived acceptability of experimental intervention components and satisfaction with core intervention components in DTx on user engagement, while also identifying potential barriers and facilitators to user engagement. METHODS: We conducted a mixed methods study with a 2 × 2 factorial design, involving 12 outpatients with atopic dermatitis. Participants were randomized into 4 experimental groups based on push notification ("basic" or "advanced") and human coach ("on" or "off") experimental intervention components. All participants engaged in self-monitoring and learning courses as core intervention components within an app-based intervention over 8 weeks. Data were collected through in-app behavioral data, physician- and self-reported questionnaires, and semistructured interviews assessed at baseline, 4 weeks, and 8 weeks. Descriptive statistics and thematic analysis were used to evaluate user engagement, perceived acceptability of experimental intervention components (ie, push notification and human coach), satisfaction with core intervention components (ie, self-monitoring and learning courses), and intervention effectiveness through clinical outcomes. RESULTS: The primary outcome indicated that group 4, provided with "advanced-level push notifications" and a "human coach," showed higher completion rates for self-monitoring forms and learning courses compared to the predetermined threshold of clinical significance. Qualitative data analysis revealed three key themes: (1) perceived acceptability of the experimental intervention components, (2) satisfaction with the core intervention components, and (3) suggestions for improvement in the overall intervention program. Regarding clinical outcomes, the Perceived Stress Scale and Dermatology Life Quality Index scores presented the highest improvement in group 4. CONCLUSIONS: These findings will help refine the intervention and inform the design of a subsequent randomized trial to test its effectiveness. Furthermore, this design may serve as a model for broadly examining and optimizing overall engagement in DTx and for future investigation into the complex relationship between engagement and clinical outcomes. TRIAL REGISTRATION: Clinical Research Information Service KCT0007675; http://tinyurl.com/2m8rjrmv.

Promoting Self-Efficacy of Individuals With Autism in Practicing Social Skills in the Workplace Using Virtual Reality and Physiological Sensors: Mixed Methods Study.

BACKGROUND: Individuals with autism often experience heightened anxiety in workplace environments because of challenges in communication and sensory overload. As these experiences can result in negative self-image, promoting their self-efficacy in the workplace is crucial. Virtual reality (VR) systems have emerged as promising tools for enhancing the self-efficacy of individuals with autism in navigating social scenarios, aiding in the identification of anxiety-inducing situations, and preparing for real-world interactions. However, there is limited research exploring the potential of VR to enhance self-efficacy by facilitating an understanding of emotional and physiological states during social skills practice. OBJECTIVE: This study aims to develop and evaluate a VR system that enabled users to experience simulated work-related social scenarios and reflect on their behavioral and physiological data through data visualizations. We intended to investigate how these data, combined with the simulations, can support individuals with autism in building their self-efficacy in social skills. METHODS: We developed WorkplaceVR, a comprehensive VR system designed for engagement in simulated work-related social scenarios, supplemented with data-driven reflections of users' behavioral and physiological responses. A within-subject deployment study was subsequently conducted with 14 young adults with autism to examine WorkplaceVR's feasibility. A mixed methods approach was used, compassing pre- and postsystem use assessments of participants' self-efficacy perceptions. RESULTS: The study results revealed WorkplaceVR's effectiveness in enhancing social skills and self-efficacy among individuals with autism. First, participants exhibited a statistically significant increase in perceived self-efficacy following their engagement with the VR system (P=.02). Second, thematic analysis of the interview data confirmed that the VR system and reflections on the data fostered increased self-awareness among participants about social situations that trigger their anxiety, as well as the behaviors they exhibit during anxious moments. This increased self-awareness prompted the participants to recollect their related experiences in the real world and articulate anxiety management strategies. Furthermore, the insights uncovered motivated participants to engage in self-advocacy, as they wanted to share the insights with others. CONCLUSIONS: This study highlights the potential of VR simulations enriched with physiological and behavioral sensing as a valuable tool for augmenting self-efficacy in workplace social interactions for individuals with autism. Data reflection facilitated by physiological sensors helped participants with autism become more self-aware of their emotions and behaviors, advocate for their characteristics, and develop positive self-beliefs.

CETP and APOA2 polymorphisms are associated with weight loss and healthy eating behavior changes in response to digital lifestyle modifications.

Response to digital healthcare lifestyle modifications is highly divergent. This study aimed to examine the association between single nucleotide polymorphism (SNP) genotypes and clinical efficacy of a digital healthcare lifestyle modification. We genotyped 97 obesity-related SNPs from 45 participants aged 18-39 years, who underwent lifestyle modification via digital cognitive behavioral therapy for obesity for 8 weeks. Anthropometric, eating behavior phenotypes, and psychological measures were analyzed before and after the intervention to identify their clinical efficacy. CETP (rs9939224) SNP significantly predict "super-responders" with greater body mass index (BMI) reduction (p = 0.028; GG - 2.91%, GT - 9.94%), while APOA2 (rs5082) appeared to have some potential for predicting "poor-responders" with lower BMI reduction (p = 0.005; AA - 6.17%, AG + 2.05%, and GG + 5.11%). These SNPs was also associated with significant differences in eating behavior changes, healthy diet proportions, health diet diversity, emotional and restrained eating behavior changes. Furthermore, classification using gene-gene interactions between rs9939224 and rs5082 significantly predicted the best response, with a greater decrease in BMI (p = 0.038; - 11.45% for the best response group (CEPT GT/TT × APOA2 AA) vs. + 2.62% for the worst response group (CEPT GG × APOA2 AG/GG)). CETP and APOA2 SNPs can be used as candidate markers to predict the efficacy of digital healthcare lifestyle modifications based on genotype-based precision medicine.Trial registration: NCT03465306, ClinicalTrials.gov. Registered March, 2018.

Advancing Understanding of Just-in-Time States for Supporting Physical Activity (Project JustWalk JITAI): Protocol for a System ID Study of Just-in-Time Adaptive Interventions.

BACKGROUND: Just-in-time adaptive interventions (JITAIs) are designed to provide support when individuals are receptive and can respond beneficially to the prompt. The notion of a just-in-time (JIT) state is critical for JITAIs. To date, JIT states have been formulated either in a largely data-driven way or based on theory alone. There is a need for an approach that enables rigorous theory testing and optimization of the JIT state concept. OBJECTIVE: The purpose of this system ID experiment was to investigate JIT states empirically and enable the empirical optimization of a JITAI intended to increase physical activity (steps/d). METHODS: We recruited physically inactive English-speaking adults aged ≥25 years who owned smartphones. Participants wore a Fitbit Versa 3 and used the study app for 270 days. The JustWalk JITAI project uses system ID methods to study JIT states. Specifically, provision of support systematically varied across different theoretically plausible operationalizations of JIT states to enable a more rigorous and systematic study of the concept. We experimentally varied 2 intervention components: notifications delivered up to 4 times per day designed to increase a person's steps within the next 3 hours and suggested daily step goals. Notifications to walk were experimentally provided across varied operationalizations of JIT states accounting for need (ie, whether daily step goals were previously met or not), opportunity (ie, whether the next 3 h were a time window during which a person had previously walked), and receptivity (ie, a person previously walked after receiving notifications). Suggested daily step goals varied systematically within a range related to a person's baseline level of steps per day (eg, 4000) until they met clinically meaningful targets (eg, averaging 8000 steps/d as the lower threshold across a cycle). A series of system ID estimation approaches will be used to analyze the data and obtain control-oriented dynamical models to study JIT states. The estimated models from all approaches will be contrasted, with the ultimate goal of guiding rigorous, replicable, empirical formulation and study of JIT states to inform a future JITAI. RESULTS: As is common in system ID, we conducted a series of simulation studies to formulate the experiment. The results of our simulation studies illustrated the plausibility of this approach for generating informative and unique data for studying JIT states. The study began enrolling participants in June 2022, with a final enrollment of 48 participants. Data collection concluded in April 2023. Upon completion of the analyses, the results of this study are expected to be submitted for publication in the fourth quarter of 2023. CONCLUSIONS: This study will be the first empirical investigation of JIT states that uses system ID methods to inform the optimization of a scalable JITAI for physical activity. TRIAL REGISTRATION: ClinicalTrials.gov NCT05273437; https://clinicaltrials.gov/ct2/show/NCT05273437. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/52161.

Prediction of Diagnosis and Treatment Response in Adolescents With Depression by Using a Smartphone App and Deep Learning Approaches: Usability Study.

BACKGROUND: Lack of quantifiable biomarkers is a major obstacle in diagnosing and treating depression. In adolescents, increasing suicidality during antidepressant treatment further complicates the problem. OBJECTIVE: We sought to evaluate digital biomarkers for the diagnosis and treatment response of depression in adolescents through a newly developed smartphone app. METHODS: We developed the Smart Healthcare System for Teens At Risk for Depression and Suicide app for Android-based smartphones. This app passively collected data reflecting the social and behavioral activities of adolescents, such as their smartphone usage time, physical movement distance, and the number of phone calls and text messages during the study period. Our study consisted of 24 adolescents (mean age 15.4 [SD 1.4] years, 17 girls) with major depressive disorder (MDD) diagnosed with Kiddie Schedule for Affective Disorders and Schizophrenia for School-Age Children-Present and Lifetime Version and 10 healthy controls (mean age 13.8 [SD 0.6] years, 5 girls). After 1 week's baseline data collection, adolescents with MDD were treated with escitalopram in an 8-week, open-label trial. Participants were monitored for 5 weeks, including the baseline data collection period. Their psychiatric status was measured every week. Depression severity was measured using the Children's Depression Rating Scale-Revised and Clinical Global Impressions-Severity. The Columbia Suicide Severity Rating Scale was administered in order to assess suicide severity. We applied the deep learning approach for the analysis of the data. Deep neural network was employed for diagnosis classification, and neural network with weighted fuzzy membership functions was used for feature selection. RESULTS: We could predict the diagnosis of depression with training accuracy of 96.3% and 3-fold validation accuracy of 77%. Of the 24 adolescents with MDD, 10 responded to antidepressant treatments. We predicted the treatment response of adolescents with MDD with training accuracy of 94.2% and 3-fold validation accuracy of 76%. Adolescents with MDD tended to move longer distances and use smartphones for longer periods of time compared to controls. The deep learning analysis showed that smartphone usage time was the most important feature in distinguishing adolescents with MDD from controls. Prominent differences were not observed in the pattern of each feature between the treatment responders and nonresponders. The deep learning analysis revealed that the total length of calls received as the most important feature predicting antidepressant response in adolescents with MDD. CONCLUSIONS: Our smartphone app demonstrated preliminary evidence of predicting diagnosis and treatment response in depressed adolescents. This is the first study to predict the treatment response of adolescents with MDD by examining smartphone-based objective data with deep learning approaches.

Characteristics and Potential Challenges of Digital-Based Interventions for Children and Young People: Scoping Review.

BACKGROUND: Digital health technologies are becoming increasingly available to children and young people and their families. However, there are no scoping reviews that provide both an overview of the characteristics of digital interventions for children and young people and potential challenges to be considered when developing and implementing them. OBJECTIVE: This study aimed to systematically review scientific publications to identify the current characteristics and potential complications of digital interventions for children and young people. METHODS: This scoping review was conducted using the framework of Arksey and O'Malley and adheres to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines for scoping reviews. A search of 5 databases (PubMed, Scopus, Embase, MEDLINE, and CINAHL) and Google Scholar was performed for eligible clinical trials published between January 1, 2018, and August 19, 2022. RESULTS: The initial search of the 5 databases yielded 3775 citations; duplicates and those not meeting the inclusion criteria were eliminated. In total, 34 articles were included in the final review and relevant information, such as the descriptive characteristics and potential challenges, were classified. Mental health (26/34, 76%) was the most common target for the digital intervention for children and young people, exceeding physical health (8/34, 24%) by more than 3 times. In addition, a substantial number of digital interventions were dedicated solely to children and young people. Digital interventions for children and young people were more likely to be delivered via computers (17/34, 50%) rather than smartphones (13/34, 38%). More than one-third of the studies (13/34, 38%) applied cognitive behavioral theory as the theory of digital interventions. The duration of the digital intervention for children and young people was more likely to vary depending on the target user rather than the target disease. Intervention components were classified into 5 categories: guidance, task and activity, reminder and monitoring, supportive feedback, and reward system. Potential challenges were subcategorized into ethical, interpersonal, and societal challenges. For ethical challenges, the consent of children and young people or caregivers, potential risk of adverse events, and data privacy issues were considered. For interpersonal challenges, the engagement of children and young people was affected by the preference or barrier of caregivers to participate in studies. For societal challenges, restricted ethnicity in recruitment, limited availability of digital technology, differences in internet use patterns between girls and boys, unified clinical settings, and language barriers were described. CONCLUSIONS: We identified potential challenges and provided suggestions about ethical, interpersonal, and societal aspects to consider when developing and deploying digital-based interventions for children and young people. Our findings provide a thorough overview of the published literature and may serve as a comprehensive, informative foundation for the development and implementation of digital-based interventions for children and young people.

Willingness to Use and Pay for Digital Health Care Services According to 4 Scenarios: Results from a National Survey.

BACKGROUND: Smartphones and their associated technology have evolved to an extent where these devices can be used to provide digital health interventions. However, few studies have been conducted on the willingness to use (WTU) and willingness to pay (WTP) for digital health interventions. OBJECTIVE: The purpose of this study was to investigate how previous service experience, the content of the services, and individuals' health status affect WTU and WTP. METHODS: We conducted a nationwide web-based survey in 3 groups: nonusers (n=506), public service users (n=368), and private service users (n=266). Participants read scenarios about an imagined health status (such as having a chronic illness) and the use of digital health intervention models (self-management, expert management, and medical management). They were then asked to respond to questions on WTU and WTP. RESULTS: Public service users had a greater intention to use digital health intervention services than nonusers and private service users: scenario A (health-risk situation and self-management), nonusers=odd ratio [OR] .239 (SE .076; P<.001) and private service users=OR .138 (SE .044; P<.001); scenario B (health-risk situation and expert management), nonusers=OR .175 (SE .040; P<.001) and private service users=OR .219 (SE .053; P<.001); scenario C (chronic disease situation and expert management), nonusers=OR .413 (SE .094; P<.001) and private service users=OR .401 (SE .098; P<.001); and scenario D (chronic disease situation and medical management), nonusers=OR .480 (SE .120; P=.003) and private service users=OR .345 (SE .089; P<.001). In terms of WTP, in scenarios A and B, those who used the public and private services had a higher WTP than those who did not (scenario A: ß=-.397, SE .091; P<.001; scenario B: ß=-.486, SE .098; P<.001). In scenario C, private service users had greater WTP than public service users (ß=.264, SE .114; P=.02), whereas public service users had greater WTP than nonusers (ß=-.336, SE .096; P<.001). In scenario D, private service users were more WTP for the service than nonusers (ß=-.286, SE .092; P=.002). CONCLUSIONS: We confirmed that the WTU and WTP for digital health interventions differed based on individuals' prior experience with health care services, health status, and demographics. Recently, many discussions have been made to expand digital health care beyond the early adapters and fully into people's daily lives. Thus, more understanding of people's awareness and acceptance of digital health care is needed.

Survey for Government Policies Regarding Strategies for the Commercialization and Globalization of Digital Therapeutics.

PURPOSE: This study was conducted to build a direction for government policies regarding strategies for the commercialization of digital therapeutics in Korea, as well as its globalization. MATERIALS AND METHODS: The study included 37 participants from the Korea Digital Health Industry Association (KODHIA). The data was based on a survey conducted in 2020 targeting employees of companies engaged in the digital health industry in Korea. Participants were asked about their involvement in product development of digital therapeutics and their opinion about the growing motivator for digital therapeutics in Korea and the global market. RESULTS: According to our data, among subjects not involved in making digital therapeutics products, the main reason for not being involved was the lack of experts (73.9%) and difficulty in licensing (73.9%). Responses concerning the priority area in need of national support were R&D funding (43.2%), and the next was licensing guidance and simplifying regulations (24.3%). Possible difficulties of overseas market expansion were the unfamiliarity in digital therapeutics technology verification and licensing structures of foreign countries (73%), and concerns regarding the level of recognition of clinical trials and technology in Korea from overseas (70.3%). Overall, respondents were hesitant in starting a related business due to the lack of government support and the complexity of the regulation process. Moreover, concerns about global market entry were similar. Being unfamiliar with the novel process and worrying about the achievement despite existing challenges were the biggest drawback. CONCLUSION: For the digital therapeutics industry to evolve domestically and internationally, government support and guidance are essential.

Machine Learning Analysis to Identify Digital Behavioral Phenotypes for Engagement and Health Outcome Efficacy of an mHealth Intervention for Obesity: Randomized Controlled Trial.

BACKGROUND: The digital health care community has been urged to enhance engagement and clinical outcomes by analyzing multidimensional digital phenotypes. OBJECTIVE: This study aims to use a machine learning approach to investigate the performance of multivariate phenotypes in predicting the engagement rate and health outcomes of digital cognitive behavioral therapy. METHODS: We leveraged both conventional phenotypes assessed by validated psychological questionnaires and multidimensional digital phenotypes within time-series data from a mobile app of 45 participants undergoing digital cognitive behavioral therapy for 8 weeks. We conducted a machine learning analysis to discriminate the important characteristics. RESULTS: A higher engagement rate was associated with higher weight loss at 8 weeks (r=-0.59; P<.001) and 24 weeks (r=-0.52; P=.001). Applying the machine learning approach, lower self-esteem on the conventional phenotype and higher in-app motivational measures on digital phenotypes commonly accounted for both engagement and health outcomes. In addition, 16 types of digital phenotypes (ie, lower intake of high-calorie food and evening snacks and higher interaction frequency with mentors) predicted engagement rates (mean R2 0.416, SD 0.006). The prediction of short-term weight change (mean R2 0.382, SD 0.015) was associated with 13 different digital phenotypes (ie, lower intake of high-calorie food and carbohydrate and higher intake of low-calorie food). Finally, 8 measures of digital phenotypes (ie, lower intake of carbohydrate and evening snacks and higher motivation) were associated with a long-term weight change (mean R2 0.590, SD 0.011). CONCLUSIONS: Our findings successfully demonstrated how multiple psychological constructs, such as emotional, cognitive, behavioral, and motivational phenotypes, elucidate the mechanisms and clinical efficacy of a digital intervention using the machine learning method. Accordingly, our study designed an interpretable digital phenotype model, including multiple aspects of motivation before and during the intervention, predicting both engagement and clinical efficacy. This line of research may shed light on the development of advanced prevention and personalized digital therapeutics. TRIAL REGISTRATION: ClinicalTrials.gov NCT03465306; https://clinicaltrials.gov/ct2/show/NCT03465306.

Mental health of people with distorted body weight perception using medicinal remedies: A representative study / Salud mental de personas con percepción distorsionada del peso que usan remedios medicinales: un estudio representativo

We aimed to examine the prevalence of distorted body weight perception (BWP) and the choice of weight control strategies to investigate the associations between the psychological features and the different strategies for controlling body weight. Method: We used a representative nationwide 39-item survey to randomly select 1,000 participants. The extrapolated number (eN) to the whole national population was also reported. Self-BWP, weight control strategies, and obesity-related psychological conditions including anxiety, self-esteem, body satisfaction, obesity-related quality-of-life (QoL), and eating attitudes were assessed. Results: Among men, 39.30% (eN = 5,887,137) underestimated, whereas 24.90% (eN = 3,290,847) of women overestimated their weight. In contrast to 2% (eN = 271,745) of men, 15.20% (eN = 2,012,262) of women sought medical support to control their weight. Men and women who used medical support for weight management and women who overestimated their weight reported the most unfavorable psychological conditions (anxiety, self-esteem, body satisfaction, QoL, and eating attitudes; p < .05). Conclusions: A prevalent burden of psychological problems related to distorted BWP and weight control strategies was revealed. People with distorted BWP and using medical procedures for their weight control could be at a higher risk of psychological disorders. Therefore, body weight-related psychological problems call for urgent public health policies. (AU)

Examinar la prevalencia de percepción distorsionada del peso corporal (BWP) y elección de estrategias de control de peso para investigar asociaciones entre características psicológicas y diferentes estrategias para controlar el peso corporal. Método: Encuesta representativa de 39 ítems a nivel nacional para seleccionar al azar a 1,000 participantes. Se informó número extrapolado (eN) a toda la población nacional. Se evaluaron auto-BWP, estrategias de control de peso y condiciones psicológicas relacionadas con obesidad, ansiedad, autoestima, satisfacción corporal, calidad de vida (QoL) relacionada con la obesidad y actitudes alimentarias. Resultados: Entre los hombres, el 39,30% (eN = 5,887,137) subestimó, mientras que el 24,90% (eN = 3,290,847) de mujeres sobreestimó su peso. A diferencia del 2% (eN = 271,745) de los hombres, el 15,20% (eN = 2,012,262) de mujeres buscó apoyo médico para controlar su peso. Hombres y mujeres que utilizaron apoyo médico para el control de peso y mujeres que sobreestimaron su peso informaron condiciones psicológicas más desfavorables (ansiedad, autoestima, satisfacción corporal, calidad de vida y actitudes alimentarias; p < 0,05). Conclusiones: Preponderancia de carga de problemas psicológicos relacionadas con BWP distorsionadas y estrategias para el control de peso. Personas con BWP distorsionadas usando procedimientos médicos para el control de peso podrían tener mayor riesgo de trastornos psicológicos. Problemas psicológicos relacionados con peso corporal exigen políticas de salud pública. (AU)

Digital Therapeutics for Obesity and Eating-Related Problems.

In recent years, digital technologies have rapidly advanced and are being applied to remedy medical problems. These technologies allow us to monitor and manage our physical and mental health in our daily lives. Since lifestyle modification is the cornerstone of the management of obesity and eating behavior problems, digital therapeutics (DTx) represent a powerful and easily accessible treatment modality. This review discusses the critical issues to consider for enhancing the efficacy of DTx in future development initiatives. To competently adapt and expand public access to DTx, it is important for various stakeholders, including health professionals, patients, and guardians, to collaborate with other industry partners and policy-makers in the ecosystem.

Mental health of people with distorted body weight perception using medicinal remedies: A representative study.

We aimed to examine the prevalence of distorted body weight perception (BWP) and the choice of weight control strategies to investigate the associations between the psychological features and the different strategies for controlling body weight. METHOD: We used a representative nationwide 39-item survey to randomly select 1,000 participants. The extrapolated number (eN) to the whole national population was also reported. Self-BWP, weight control strategies, and obesity-related psychological conditions including anxiety, self-esteem, body satisfaction, obesity-related quality-of-life (QoL), and eating attitudes were assessed. RESULTS: Among men, 39.30% (eN = 5,887,137) underestimated, whereas 24.90% (eN = 3,290,847) of women overestimated their weight. In contrast to 2% (eN = 271,745) of men, 15.20% (eN = 2,012,262) of women sought medical support to control their weight. Men and women who used medical support for weight management and women who overestimated their weight reported the most unfavorable psychological conditions (anxiety, self-esteem, body satisfaction, QoL, and eating attitudes; p < .05). CONCLUSIONS: A prevalent burden of psychological problems related to distorted BWP and weight control strategies was revealed. People with distorted BWP and using medical procedures for their weight control could be at a higher risk of psychological disorders. Therefore, body weight-related psychological problems call for urgent public health policies.

Examinar la prevalencia de percepción distorsionada del peso corporal (BWP) y elección de estrategias de control de peso para investigar asociaciones entre características psicológicas y diferentes estrategias para controlar el peso corporal. Método: Encuesta representativa de 39 ítems a nivel nacional para seleccionar al azar a 1,000 participantes. Se informó número extrapolado (eN) a toda la población nacional. Se evaluaron auto-BWP, estrategias de control de peso y condiciones psicológicas relacionadas con obesidad, ansiedad, autoestima, satisfacción corporal, calidad de vida (QoL) relacionada con la obesidad y actitudes alimentarias. Resultados: Entre los hombres, el 39,30% (eN = 5,887,137) subestimó, mientras que el 24,90% (eN = 3,290,847) de mujeres sobreestimó su peso. A diferencia del 2% (eN = 271,745) de los hombres, el 15,20% (eN = 2,012,262) de mujeres buscó apoyo médico para controlar su peso. Hombres y mujeres que utilizaron apoyo médico para el control de peso y mujeres que sobreestimaron su peso informaron condiciones psicológicas más desfavorables (ansiedad, autoestima, satisfacción corporal, calidad de vida y actitudes alimentarias; p <  0,05). Conclusiones: Preponderancia de carga de problemas psicológicos relacionadas con BWP distorsionadas y estrategias para el control de peso. Personas con BWP distorsionadas usando procedimientos médicos para el control de peso podrían tener mayor riesgo de trastornos psicológicos. Problemas psicológicos relacionados con peso corporal exigen políticas de salud pública.

Multidimensional Cognitive Behavioral Therapy for Obesity Applied by Psychologists Using a Digital Platform: Open-Label Randomized Controlled Trial.

BACKGROUND: Developing effective, widely useful, weight management programs is a priority in health care because obesity is a major health problem. OBJECTIVE: This study developed and investigated a new, comprehensive, multifactorial, daily, intensive, psychologist coaching program based on cognitive behavioral therapy (CBT) modules. The program was delivered via the digital health care mobile services Noom Coach and InBody. METHODS: This was an open-label, active-comparator, randomized controlled trial. A total of 70 female participants with BMI scores above 24 kg/m2 and no clinical problems besides obesity were randomized into experimental and control groups. The experimental (ie, digital CBT) group (n=45) was connected with a therapist intervention using a digital health care service that provided daily feedback and assignments for 8 weeks. The control group (n=25) also used the digital health care service, but practiced self-care without therapist intervention. The main outcomes of this study were measured objectively at baseline, 8 weeks, and 24 weeks and included weight (kg) as well as other body compositions. Differences between groups were evaluated using independent t tests and a per-protocol framework. RESULTS: Mean weight loss at 8 weeks in the digital CBT group was significantly higher than in the control group (-3.1%, SD 4.5, vs -0.7%, SD 3.4, P=.04). Additionally, the proportion of subjects who attained conventional 5% weight loss from baseline in the digital CBT group was significantly higher than in the control group at 8 weeks (32% [12/38] vs 4% [1/21], P=.02) but not at 24 weeks. Mean fat mass reduction in the digital CBT group at 8 weeks was also significantly greater than in the control group (-6.3%, SD 8.8, vs -0.8%, SD 8.1, P=.02). Mean leptin and insulin resistance in the digital CBT group at 8 weeks was significantly reduced compared to the control group (-15.8%, SD 29.9, vs 7.2%, SD 35.9, P=.01; and -7.1%, SD 35.1, vs 14.4%, SD 41.2, P=.04). Emotional eating behavior (ie, mean score) measured by questionnaire (ie, the Dutch Eating Behavior Questionnaire) at 8 weeks was significantly improved compared to the control group (-2.8%, SD 34.4, vs 21.6%, SD 56.9, P=.048). Mean snack calorie intake in the digital CBT group during the intervention period was significantly lower than in the control group (135.9 kcal, SD 86.4, vs 208.2 kcal, SD 166.3, P=.02). Lastly, baseline depression, anxiety, and self-esteem levels significantly predicted long-term clinical outcomes (24 weeks), while baseline motivation significantly predicted both short-term (8 weeks) and long-term clinical outcomes. CONCLUSIONS: These findings confirm that technology-based interventions should be multidimensional and are most effective with human feedback and support. This study is innovative in successfully developing and verifying the effects of a new CBT approach with a multidisciplinary team based on digital technologies rather than standalone technology-based interventions. TRIAL REGISTRATION: ClinicalTrials.gov NCT03465306; https://clinicaltrials.gov/ct2/show/NCT03465306.

Food Craving, Seeking, and Consumption Behaviors: Conceptual Phases and Assessment Methods Used in Animal and Human Studies.

What drives us to eat? It is one of the most fundamental questions in the obesity research field which have been investigated for centuries. Numerous novel in vivo technologies in the neuroscience field allows us to reevaluate the multiple components and phases of food-related behaviors. Focused on the cognitive, executive, behavioral and temporal aspects, food-related behaviors can be distinguished into appetitive phase (food craving→food seeking) and consummatory phase (food consumption). Food craving phase is an internal state or stage in which the animal has the motivation to eat the food but there is no actual food specific behaviors or actions. Food seeking phase entails repeated behaviors with a food searching purpose until the animal discovers the food (or food-related cue) and the approach behavior stage after the discovery of food. Food consumption phase is the step that the animal grabs, chews and intake the food. This review will specifically focus on characteristics and evaluation methods for each phase of food-related behavior in rodent, non-human primates and human.