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

Digital interventions to address mental health needs in colleges: Perspectives of student stakeholders

OBJECTIVE: The need for clinical services in U.S. colleges exceeds the supply. Digital Mental health Interventions (DMHIs) are a potential solution, but successful implementation depends on stakeholder acceptance. This study investigated the relevance of DMHIs from students\u27 perspectives. METHODS: In 2020-2021, an online cross-sectional survey using mixed methods was conducted with 479 students at 23 colleges and universities. Respondents reported views and use of standard mental health services and DMHIs and rated the priority of various DMHIs to be offered through campus services. Qualitative data included open-ended responses. FINDINGS: Among respondents, 91% reported having experienced mental health problems, of which 91% reported barriers to receiving mental health services. Students highlighted therapy and counseling as desired and saw flexible access to services as important. With respect to DMHIs, respondents had the most experience with physical health apps (46%), mental health questionnaires (41%), and mental well-being apps (39%). Most were unaware of or had not used apps or self-help programs for mental health problems. Students were most likely to report the following DMHIs as high priorities: a crisis text line (76%), telehealth (66%), websites for connecting to services (62%), and text/messaging with counselors (62%). They considered a self-help program with coach support to be convenient but some also perceived such services to be possibly less effective than in-person therapy. CONCLUSIONS: Students welcome DMHIs on campus and indicate preference for mental health services that include human support. The findings, with particular focus on characteristics of the DMHIs prioritized, and students\u27 awareness and perceptions of scalable DMHIs emphasized by policymakers, should inform schools looking to implement DMHIs

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 (β [SE], -0.44 [0.10]; d = -0.40; t1387 = -4.23; P \u3c .001), as well as over the follow-up period (β [SE], -0.39 [0.12]; d = -0.35; t1387 = -3.30; P \u3c .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 \u3c .001), depression (β [SE], -1.34 [0.53]; d = -0.22; t1387 = -2.52; P = .01), and clinical impairment (β [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 \u3c .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

Adapting a mobile app to support patients with anorexia nervosa following post-acute care: perspectives from eating disorder treatment center stakeholders

Introduction Anorexia nervosa (AN) is a harmful, life-threatening illness. Patients with severe AN often receive acute treatment but, upon discharge, experience high relapse rates. Evidence-based, outpatient treatment following acute care is critical to preventing relapse; however, numerous barriers (e.g., location, financial limitations, low availability of providers) preclude individuals from accessing treatment. mHealth technologies may help to address these barriers, but research on such digital approaches for those with AN is limited. Further, such technologies should be developed with all relevant stakeholder input considered from the outset. As such, the present study aimed to garner feedback from eating disorder (ED) treatment center providers on (1) the process of discharging patients to outpatient services, (2) their experiences with technology as a treatment tool, and (3) how future mHealth technologies may be harnessed to offer the most benefit to patients in the post-acute period. Methods Participants (N = 11, from 7 ED treatment centers across the United States) were interviewed. To analyze the data for this study, each interview was manually transcribed and analyzed using components of Braun and Clarke's six-phase thematic analysis framework (Braun & Clarke, 2006). Results Participants indicated proactively securing outpatient care for their patients, but mentioned several barriers their patients face in accessing evidence-based ED treatment. All participants had some experience using various technologies for treatment (e.g., teletherapy, self-monitoring apps), and mentioned a high level of interest in the development of a new app to be used by patients recently discharged from acute treatment for AN. Participants also offered suggestions of effective and relevant content for a potential app and adjunctive social networking component for post-acute care of AN. Discussion Overall, participants expressed positive attitudes toward the integration of an app into the care flow, suggesting the high potential benefit of harnessing technology to support individuals recovering from AN

The Challenges in Designing a Prevention Chatbot for Eating Disorders : Observational Study

Background: Chatbots have the potential to provide cost-effective mental health prevention programs at scale and increase interactivity, ease of use, and accessibility of intervention programs. Objective: The development of chatbot prevention for eating disorders (EDs) is still in its infancy. Our aim is to present examples of and solutions to challenges in designing and refining a rule-based prevention chatbot program for EDs, targeted at adult women at risk for developing an ED. Methods: Participants were 2409 individuals who at least began to use an EDs prevention chatbot in response to social media advertising. Over 6 months, the research team reviewed up to 52,129 comments from these users to identify inappropriate responses that negatively impacted users experience and technical glitches. Problems identified by reviewers were then presented to the entire research team, who then generated possible solutions and implemented new responses. Results: The most common problem with the chatbot was a general limitation in understanding and responding appropriately to unanticipated user responses. We developed several workarounds to limit these problems while retaining some interactivity. Conclusions: Rule-based chatbots have the potential to reach large populations at low cost but are limited in understanding and responding appropriately to unanticipated user responses. They can be most effective in providing information and simple conversations. Workarounds can reduce conversation errors.Funding Agencies|National Eating Disorders Association through the Feeding Hope Fund; National Institute of Mental Health [R01 MH123482, K08 MH120341, R01 MH115128]; National Heart, Lung, and Blood Institute [T32 HL130357]; Swedish Research Council [2018-06585]; National Health and Medical Research Council [APP1170937]</p

Digital interventions to address mental health needs in colleges : Perspectives of student stakeholders

Objective: The need for clinical services in U.S. colleges exceeds the supply. Digital Mental health Interventions (DMHIs) are a potential solution, but successful implementation depends on stakeholder acceptance. This study investigated the relevance of DMHIs from students perspectives. Methods: In 2020-2021, an online cross-sectional survey using mixed methods was conducted with 479 students at 23 colleges and universities. Respondents reported views and use of standard mental health services and DMHIs and rated the priority of various DMHIs to be offered through campus services. Qualitative data included open-ended responses. Findings: Among respondents, 91% reported having experienced mental health problems, of which 91% reported barriers to receiving mental health services. Students highlighted therapy and counseling as desired and saw flexible access to services as important. With respect to DMHIs, respondents had the most experience with physical health apps (46%), mental health questionnaires (41%), and mental well-being apps (39%). Most were unaware of or had not used apps or self-help programs for mental health problems. Students were most likely to report the following DMHIs as high priorities: a crisis text line (76%), telehealth (66%), websites for connecting to services (62%), and text/messaging with counselors (62%). They considered a self-help program with coach support to be convenient but some also perceived such services to be possibly less effective than in-person therapy. Conclusions: Students welcome DMHIs on campus and indicate preference for mental health services that include human support. The findings, with particular focus on characteristics of the DMHIs prioritized, and students awareness and perceptions of scalable DMHIs emphasized by policymakers, should inform schools looking to implement DMHIs.Funding Agencies|National Institute of Mental HealthUnited States Department of Health &amp; Human ServicesNational Institutes of Health (NIH) - USANIH National Institute of Mental Health (NIMH) [K08 MH120341]; Swedish Research CouncilSwedish Research CouncilEuropean Commission [2018-06585]; National Heart, Lung, and Blood InstituteUnited States Department of Health &amp; Human ServicesNational Institutes of Health (NIH) - USANIH National Heart Lung &amp; Blood Institute (NHLBI) [T32 HL130357]</p

Adapting a mobile app to support patients with anorexia nervosa following post-acute care: perspectives from eating disorder treatment center stakeholders

IntroductionAnorexia nervosa (AN) is a harmful, life-threatening illness. Patients with severe AN often receive acute treatment but, upon discharge, experience high relapse rates. Evidence-based, outpatient treatment following acute care is critical to preventing relapse; however, numerous barriers (e.g., location, financial limitations, low availability of providers) preclude individuals from accessing treatment. mHealth technologies may help to address these barriers, but research on such digital approaches for those with AN is limited. Further, such technologies should be developed with all relevant stakeholder input considered from the outset. As such, the present study aimed to garner feedback from eating disorder (ED) treatment center providers on (1) the process of discharging patients to outpatient services, (2) their experiences with technology as a treatment tool, and (3) how future mHealth technologies may be harnessed to offer the most benefit to patients in the post-acute period.MethodsParticipants (N = 11, from 7 ED treatment centers across the United States) were interviewed. To analyze the data for this study, each interview was manually transcribed and analyzed using components of Braun and Clarke's six-phase thematic analysis framework (Braun &amp; Clarke, 2006).ResultsParticipants indicated proactively securing outpatient care for their patients, but mentioned several barriers their patients face in accessing evidence-based ED treatment. All participants had some experience using various technologies for treatment (e.g., teletherapy, self-monitoring apps), and mentioned a high level of interest in the development of a new app to be used by patients recently discharged from acute treatment for AN. Participants also offered suggestions of effective and relevant content for a potential app and adjunctive social networking component for post-acute care of AN.DiscussionOverall, participants expressed positive attitudes toward the integration of an app into the care flow, suggesting the high potential benefit of harnessing technology to support individuals recovering from AN

Digital Overload among College Students : Implications for Mental Health App Use

Mental health phone applications (apps) provide cost-effective, easily accessible support for college students, yet long-term engagement is often low. Digital overload, defined as information burden from technological devices, may contribute to disengagement from mental health apps. This study aimed to explore the influence of digital overload and phone use preferences on mental health app use among college students, with the goal of informing how notifications could be designed to improve engagement in mental health apps for this population. A semi-structured interview guide was developed to collect quantitative data on phone use and notifications as well as qualitative data on digital overload and preferences for notifications and phone use. Interview transcripts from 12 college students were analyzed using thematic analysis. Participants had high daily phone use and received large quantities of notifications. They employed organization and management strategies to filter information and mitigate the negative effects of digital overload. Digital overload was not cited as a primary barrier to mental health app engagement, but participants ignored notifications for other reasons. Findings suggest that adding notifications to mental health apps may not substantially improve engagement unless additional factors are considered, such as users motivation and preferences.Funding Agencies|National Institute of Mental HealthUnited States Department of Health &amp; Human ServicesNational Institutes of Health (NIH) - USANIH National Institute of Mental Health (NIMH) [R01 MH115128]; National Health and Medical Research CouncilNational Health and Medical Research Council of Australia [NHMRC APP1170937]; National Institutes of HealthUnited States Department of Health &amp; Human ServicesNational Institutes of Health (NIH) - USA [K08 MH120341, K01 DK120778]; Na-tional Institutes of HealthUnited States Department of Health &amp; Human ServicesNational Institutes of Health (NIH) - USA [K01 DK116925]; National Heart, Lung, and Blood InstituteUnited States Department of Health &amp; Human ServicesNational Institutes of Health (NIH) - USANIH National Heart Lung &amp; Blood Institute (NHLBI) [T32 HL130357]; Swedish Research CouncilSwedish Research CouncilEuropean Commission [2018-06585]</p