Understanding self-monitoring to inform a mobile intervention for binge eating and weight management: A proof-of-concept randomized trial.

OBJECTIVE: This study explored consumers' perspectives on self-monitoring, a common feature in behavioral interventions that helps inform consumers' progress and answer their questions, to learn what outcome metrics matter to consumers and whether self-selection of these metrics leads to greater engagement (i.e., compliance, satisfaction) in self-monitoring than monitoring only default options. METHODS: In a proof-of-concept randomized trial, 48 adult participants were randomly assigned to "clinician-determined monitoring" or "clinician + self-determined monitoring" conditions. Before starting monitoring, all participants shared outcomes that would matter to them in a mobile intervention for binge eating and weight management. Then, for 3 weeks, participants in the "clinician-determined" condition monitored their weight and binge-eating episodes, and participants in the "clinician + self-determined" condition monitored these and another metric of their choosing. After, satisfaction and compliance were assessed. RESULTS: Participants identified 116 metrics, grouped into 12 themes, that mattered to them. During monitoring, participants in the "clinician + self-determined" condition monitored 41 metrics. Surprisingly, participants in the "clinician-determined" condition also monitored metrics besides weight and binge eating. This resulted in a failure of our experimental manipulation, which represents a significant limitation of this research. No significant differences emerged in satisfaction or compliance between conditions. DISCUSSION: Although our proof-of-concept trial yielded null quantitative results, findings also suggested binge eating and weight management interventions may benefit from including an individually customizable monitoring option in addition to default metrics, warranting testing in future research. PUBLIC SIGNIFICANCE: Examining consumers' self-monitoring preferences for a mobile intervention for binge eating and weight management revealed a variety of metrics that matter to consumers, although binge eating and weight were still most valued. Findings from our proof-of-concept trial suggest design implications of encouraging an individually customizable monitoring option, in addition to default metrics, which needs to be tested in future research over a longer period and during actual mobile intervention delivery.

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.

Costs of Preparing to Implement a Family-Based Intervention to Prevent Pediatric Obesity in Primary Care: a Budget Impact Analysis.

The costs associated with implementing evidence-based programs for pediatric obesity contribute to a lack of widespread adoption. This study examined the costs of preparing to implement the Family Check-Up 4 Health (FCU4Health), a family-centered behavioral program for the prevention of pediatric obesity and excess weight gain in primary care. Budget impact analysis was used to estimate the cost of preparing to implement FCU4Health (i.e., the activities to prepare for, but prior to, offering the service to families). Electronic cost capture methods were used to prospectively track personnel time associated with implementation preparation activities. We also estimated the cost of replicating these preparatory activities to inform future decisions to adopt the program and associated budgetary planning. The total cost of FCU4Health implementation preparation in three clinics and developing delivery materials and infrastructure was $181,768, for an average of $60,589 per clinic. Over two thirds of the total cost were personnel related, the largest of which was associated with the time spent developing automated fidelity coding for monitoring (20%), developing and tailoring clinical materials (16%), and training FCU4Health coordinators (15%). Due to these development costs associated with an initial implementation, that we anticipate would not be repeated in full, we estimated the cost to prepare FCU4Health for implementation in a future initiative will range from $15,195 to $17,912 per clinic. This study is a critical step towards equipping decision-makers with comprehensive short-term information about expected costs that are incurred immediately after choosing to adopt an evidence-based program.

A budget impact analysis of cost to implement a whole child health focused, family-based intervention in primary care for children with elevated BMI.

BACKGROUND: Although the cost of implementing evidence-based interventions (EBIs) is a key determinant of adoption, lack of cost information is widespread. We previously evaluated the cost of preparing to implement Family Check-Up 4 Health (FCU4Health), an individually tailored, evidence-based parenting program that takes a whole child approach, with effects on both behavioral health and health behavior outcomes, in primary care settings. This study estimates the cost of implementation, including preparation. METHODS: We assessed the cost of FCU4Health across the preparation and implementation phases spanning 32 months and 1 week (October 1, 2016-June 13, 2019) in a type 2 hybrid effectiveness-implementation study. This family-level randomized controlled trial took place in Arizona with n = 113 predominantly low-income, Latino families with children ages > 5.5 to < 13 years. Using electronic cost capture and time-based activity-driven methods, budget impact analysis from the perspective of a future FCU4Health adopting entity-namely, ambulatory pediatric care clinicians-was used to estimate the cost of implementation. Labor costs were based on 2021 Bureau of Labor Statistics Occupational Employment Statistics, NIH-directed salary cap levels or known salaries, plus fringe benefits at a standard rate of 30%. Non-labor costs were based on actual amounts spent from receipts and invoices. RESULTS: The cost of FCU4Health implementation to 113 families was $268,886 ($2380 per family). Actual per family cost varied widely, as individual tailoring resulted in families receiving a range of 1-15 sessions. The estimated cost of replicating implementation for future sites ranged from $37,636-$72,372 ($333-$641 per family). Using our previously reported preparation costs (i.e., $174,489; $1544 per family), with estimated replication costs of $18,524-$21,836 ($164-$193 per family), the total cost of delivering FCU4Health was $443,375 ($3924 per family), with total estimated replication costs of $56,160-$94,208 ($497-$834 per family). CONCLUSIONS: This study provides a baseline for costs associated with implementation of an individually tailored parenting program. Results provide critical information for decision makers and a model for future economic analysis and can be used to inform optimization thresholds for implementation and, when necessary, benchmarks for program adaptation to promote scale-up. TRIAL REGISTRATION: This trial was prospectively registered on January 6, 2017, at ClinicalTrials.gov (NCT03013309).

Targeting subjective engagement in experimental therapeutics for digital mental health interventions.

Engagement is a multifaceted construct and a likely mechanism by which digital interventions achieve clinical improvements. To date, clinical research on digital mental health interventions (DMHIs) has overwhelmingly defined engagement and assessed its association with clinical outcomes through the objective/behavioral metrics of use of or interactions with a DMHI, such as number of log-ins or time spent using the technology. However, engagement also entails users' subjective experience. Research is largely lacking that tests the relationship between subjective metrics of engagement and clinical outcomes. The purpose of this study is to present a proof-of-concept exploratory evaluation of the association between subjective engagement measures of a mobile DMHI with changes in depression and anxiety. Adult primary care patients (N = 146) who screened positive for depression or anxiety were randomized to receive a DMHI, IntelliCare, immediately or following an 8-week waitlist. Subjective engagement was measured via the Usefulness, Satisfaction, and Ease of Use (USE) Questionnaire. Across both conditions, results showed that individuals who perceived a mobile intervention as more useful, easy to use and learn, and satisfying had greater improvements in depression and anxiety over eight weeks. Findings support our proposed experimental therapeutics framework that hypothesizes objective/behavioral and subjective engagement metrics as mechanisms that lead to changes in clinical outcomes, as well as support directing intervention design efforts for DMHIs to target the user experience.

An Exploratory Brief Head-To-Head Non-Inferiority Comparison of an Internet-Based and a Telephone-Delivered CBT Intervention for Adults with Depression.

BACKGROUND: Telephone-administered psychotherapy (tCBT) and internet-based treatments (iCBT) may overcome barriers to mental health treatment. TCBT has demonstrated efficacy similar to traditional psychotherapy, however, few studies have compared iCBT to efficacious interventions. This exploratory study examined the noninferiority of iCBT relative to tCBT. We also explored pretreatment moderators of outcome and assessed treatment dropout. METHODS: As a secondary exploratory analysis of a 304-participant randomized noninferiority trial, we compared iCBT, the first level of a stepped-care intervention, with tCBT on depression outcome after 5 weeks of treatment (prior to stepping). Multiple linear regression models were fit to examine moderators of 5-week depression. Differences in dropout were examined using Kaplan-Meier survival analysis. RESULTS: After 5 weeks of treatment, both interventions significantly reduced depression severity. The effect size difference between the two interventions was d=0.004 [90% CI=-0.19 to 0.19]; the CI did not cross the non-inferiority margin. Pretreatment depression was significantly associated with depression at week 5. The relationship between cognitive strategy usefulness and depression at week 5 differed between interventions, controlling for pretreatment depression. There was no significant difference in dropout between interventions. LIMITATIONS: Given the stepped-care trial design, iCBT and tCBT could not be compared at the end of treatment or follow-up. Analyses were exploratory and should be interpreted with caution. CONCLUSIONS: A large sample, powered for noninferiority, found iCBT no less efficacious than tCBT at reducing depression symptoms after five weeks of treatment.

Engagement and Clinical Improvement Among Older Adult Primary Care Patients Using a Mobile Intervention for Depression and Anxiety: Case Studies.

BACKGROUND: Technology-based mental health interventions are an increasingly attractive option for expanding access to mental health services within the primary care system. Older adults are among the groups that could potentially benefit from the growing ubiquity of technology-based mental health interventions; however, older adults are perceived to be averse to using technology and have reported barriers to use. OBJECTIVE: The aim of this paper is to present a case study of 3 participants from a clinical trial evaluating IntelliCare, an evidence-based mobile intervention for depression and anxiety, among adults recruited from primary care clinics. Our report of these 3 participants, who were aged 60 years or older, focuses on their engagement with the IntelliCare service (ie, app use, coach communication) and clinical changes in depression or anxiety symptoms over the intervention period. METHODS: The 3 case study participants were offered IntelliCare with coaching for 8 weeks. The intervention consisted of 5 treatment intervention apps that support a variety of psychological skills, a Hub app that contained psychoeducational content and administered weekly assessments, and coaching for encouragement, accountability, and technical assistance as needed. The 3 case study participants were selected to reflect the overall demographics of participants within the trial and because their interactions with IntelliCare provided a good illustration of varied experiences regarding engagement with the intervention. RESULTS: The 3 participants' unique experiences with the intervention are described. Despite potential barriers and experiencing some technical glitches, the participants showed proficient ability to use the apps, high levels of participation through frequent app use and coach interaction, and decreased depression and anxiety scores. At the end of the 8-week intervention, each of these 3 participants expressed great enthusiasm for the benefit of this program through feedback to their coach, and they each identified a number of ways they had seen improvements in themselves. CONCLUSIONS: These 3 cases provide examples of older individuals who engaged with and benefitted from the IntelliCare service. Although the results from these 3 cases may not generalize to others, they provide an important, informed perspective of the experiences that can contribute to our understanding of how older adults use and overcome barriers to mental health technologies. The findings also contribute toward the ultimate goal of ensuring that the IntelliCare intervention is appropriate for individuals of all ages.

Costs of preparing to implement a virtual reality job interview training programme in a community mental health agency: A budget impact analysis.

RATIONAL, AIMS AND OBJECTIVES: Individual Placement and Support (IPS) is an evidence based strategy for facilitating employment among adults with severe mental illness (SMI) where staff may lead mock job interviews to prepare clients for real-world interviews (a method with limited scalability and cost effectiveness). A virtual reality job interview training program (VR-JIT)-delivered via the internet-has demonstrated efficacy for increasing employment among adults with SMI. Now, VR-JIT is being implemented with a community mental health agency (CMHA) and evaluated for its effectiveness within IPS. This study is a budget impact analysis, evaluating the costs of preparing a CMHA to implement VR-JIT. METHOD: Implementation preparation occurred over 7 months from October 1, 2016 to April 30, 2017. CMHA staff (n = 15) and external research partners (n = 3) tracked their hours completing implementation preparation activities. Salaries plus a 28% fringe benefit rate were used to derive a per-hour salary amount for each individual and applied to each activity. Non-labor equipment costs were obtained from purchase receipts. A budget impact analysis evaluated the expenditures associated with preparing the CMHA to implement VRJIT. RESULTS: The total implementation preparation costs equaled $25,482. Labor costs equaled $22,882 and non-labor costs equaled $2,600. In total, 655 person-hours were spent preparing for VR-JIT implementation (e.g., preparing lab space, training, and supervising operations). CONCLUSIONS: This study presents an initial evaluation of the budget impact of preparing to implement VR-JIT in a CMHA. Cost considerations for future implementation preparation will be discussed. Given that the cost to prepare to implement an intervention can hinder its adoption, results provide an important analysis for decision-makers that may enhance uptake. Future work will determine the cost-effectiveness of VR-JIT implemented within IPS. This study is registered at http://clinicaltrials.gov, NCT = 03049813, "Virtual Reality Job Interview Training: An Enhancement to Supported Employment in Severe Mental Illness."

Effectiveness of a Digital Cognitive Behavior Therapy-Guided Self-Help Intervention for Eating Disorders in College Women: A Cluster Randomized Clinical Trial.

Importance: Eating disorders (EDs) are common, serious psychiatric disorders on college campuses, yet most affected individuals do not receive treatment. Digital interventions have the potential to bridge this gap. Objective: To determine whether a coached, digital, cognitive behavior therapy (CBT) intervention improves outcomes for college women with EDs compared with referral to usual care. Design, Setting, and Participants: This cluster randomized trial was conducted from 2014 to 2018 at 27 US universities. Women with binge-purge EDs (with both threshold and subthreshold presentations) were recruited from enrolled universities. The 690 participants were followed up for up to 2 years after the intervention. Data analysis was performed from February to September 2019. Interventions: Universities were randomized to the intervention, Student Bodies-Eating Disorders, a digital CBT-guided self-help program, or to referral to usual care. Main Outcomes and Measures: The main outcome was change in overall ED psychopathology. Secondary outcomes were abstinence from binge eating and compensatory behaviors, as well as ED behavior frequencies, depression, anxiety, clinical impairment, academic impairment, and realized treatment access. Results: A total of 690 women with EDs (mean [SD] age, 22.12 [4.85] years; 414 [60.0%] White; 120 [17.4%] Hispanic; 512 [74.2%] undergraduates) were included in the analyses. For ED psychopathology, there was a significantly greater reduction in the intervention group compared with the control group at the postintervention assessment (ß [SE], -0.44 [0.10]; d = -0.40; t1387 = -4.23; P < .001), as well as over the follow-up period (ß [SE], -0.39 [0.12]; d = -0.35; t1387 = -3.30; P < .001). There was not a significant difference in abstinence from any ED behaviors at the postintervention assessment (odds ratio, 1.48; 95% CI, 0.48-4.62; P = .50) or at follow-up (odds ratio, 1.51; 95% CI, 0.63-3.58; P = .36). Compared with the control group, the intervention group had significantly greater reductions in binge eating (rate ratio, 0.82; 95% CI, 0.70-0.96; P = .02), compensatory behaviors (rate ratio, 0.68; 95% CI, 0.54-0.86; P < .001), depression (ß [SE], -1.34 [0.53]; d = -0.22; t1387 = -2.52; P = .01), and clinical impairment (ß [SE], -2.33 [0.94]; d = -0.21; t1387 = -2.49; P = .01) at the postintervention assessment, with these gains sustained through follow-up for all outcomes except binge eating. Groups did not differ in terms of academic impairment. The majority of intervention participants (318 of 385 participants [83%]) began the intervention, whereas only 28% of control participants (76 of 271 participants with follow-up data available) sought treatment for their ED (odds ratio, 12.36; 95% CI, 8.73-17.51; P < .001). Conclusions and Relevance: In this cluster randomized clinical trial comparing a coached, digital CBT intervention with referral to usual care, the intervention was effective in reducing ED psychopathology, compensatory behaviors, depression, and clinical impairment through long-term follow-up, as well as realizing treatment access. No difference was found between the intervention and control groups for abstinence for all ED behaviors or academic impairment. Given its scalability, a coached, digital, CBT intervention for college women with EDs has the potential to address the wide treatment gap for these disorders. Trial Registration: ClinicalTrials.gov Identifier: NCT02076464.

Stepping Up: Predictors of 'Stepping' within an iCBT Stepped-Care Intervention for Depression.

Internet-based cognitive behavioral therapy (iCBT) may overcome barriers to mental health care and has proven efficacious. However, this approach currently exists outside the existing mental health care delivery system. Stepped care is a proposed framework for integrating digital mental health (DMH) into health systems by initiating iCBT and "stepping up" care to a more intensive intervention should iCBT prove ineffective. This study explores pre-treatment factors associated with reaching stepping criteria among patients receiving iCBT. This exploratory analysis of a stepped care arm of a larger randomized trial examined participants who were stepped to a more intensive intervention if they did not respond to iCBT. The association of pre-treatment factors on stepping were examined using Kruskal-Wallis and Chi-square tests. One-fifth of the 151 participants met criteria for stepping within the 20-week treatment period. Only pre-treatment depression severity and treatment preference were associated with increased likelihood of stepping (p = 0.049 and 0.048, respectively). The low number of individuals who stepped provides support for iCBT as an effective, low intensity treatment for depression. The modest association of pre-treatment depression and preference to not receive iCBT may be useful in identifying patients who are less likely to respond.

State-wide university implementation of an online platform for eating disorders screening and intervention.

The Internet-based Healthy Body Image (HBI) Program platform uses online screening to identify individuals at low risk for, high risk for, or with an eating disorder (ED) and then directs users to tailored, evidence-based online/mobile interventions or referral to in-person care to address individuals' risk/clinical status. We examined findings from the first state-wide deployment of HBI over the course of 3 years in Missouri public universities, sponsored by the Missouri Eating Disorders Council and the Missouri Mental Health Foundation. First, the screen was completed 2,454 times, with an average of 2.5% of the undergraduate student body on each campus taking the screen. Second, ED risk level in the participating students was high-over 56% of students screened were identified as being at high risk for ED onset or having a clinical/subclinical ED. Third, uptake for the HBI online/mobile interventions ranged from 44-51%, with higher rates of uptake in the high-risk compared with low-risk group. Fourth, results showed that, for students with a clinical/subclinical ED, use of the clinical mobile application Student Bodies-Eating Disorders intervention resulted in significantly decreased restrictive eating and binge eating. Neither vomiting nor diet pill/laxative use was found to decrease, but reports of these behaviors were very low. This is the first deployment of a comprehensive online platform for screening and delivering tailored interventions to a population of individuals with varying ED risk and symptom profiles in an organized care setting. Implications for future research and sustaining and broadening the reach of HBI are discussed. (PsycINFO Database Record (c) 2019 APA, all rights reserved).