Comparison of the Effects of Coaching and Receipt of App Recommendations on Depression, Anxiety, and Engagement in the IntelliCare Platform: Factorial Randomized Controlled Trial

Mohr, David C.; Schueller, Stephen M.; Tomasino, Kathryn Noth; Kaiser, Susan M.; Alam, Nameyeh; Karr, Chris; Vergara, J.; Gray, Elizabeth; Kwasny, Mary; Lattie, Emily G.

doi:10.2196/13609

Cited by 98 publications

(96 citation statements)

References 41 publications

(49 reference statements)

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“…A full description of the apps can be found elsewhere [27]. The IntelliCare apps have been shown to be effective and efficacious at reducing symptoms of depression and anxiety among adults in two research trials [28,27]. In the trials, all participants were expected to stay engaged with IntelliCare apps for 8 weeks and, were supported by a Coach who supported participants via text messaging to promote engagement.…”

Section: The Intellicare Platformmentioning

confidence: 99%

“…Below we outline five app use features that we believe represent interaction metrics of app engagement based on the described data sets, discussions with experts from the field, and our research to date [31,32].…”

Section: Proposed Interaction Featuresmentioning

confidence: 99%

“…The IRB and DSMB assured human subject protection, HIPAA, and GCP compliance adhering to commonly agreed standards set by international medical organizations. Details on the IntelliCare project and primary outcomes may be found in previous reports [27,28]. Data from the public deployment was acquired using similar measures and materials (apps) after users acknowledged that their deidentified data would be used for research purposes.…”

mentioning

confidence: 99%

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Characteristic Latent Features for Analyzing Digital Mental Health Interaction and Improved Explainability

Theilig

Knapp

Nicholas

et al. 2020

Preprint

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Background: Using smartphones and wearable sensor technology has sparked a broad engagement of data science and machine learning methods to leverage the complex, assorted amount of data. Despite verified processes, there is a reported underdevelopment of user engagement concepts, and the desire for high accuracy or significance has shown to lead to low explicability and irreproducibility. To overcome these issues, we aim to analyze principal characteristics of everyday behavior in digital mental health. Methods: We generated five latent features based on previous research, expert opinions from digital mental health, and informed by data. The features were analyzed with descriptive statistics and data visualization. We carried out two rounds of evaluations with data from 12,400 users of IntelliCare, a mental health platform with 12 apps. First, we focused to proof concept and second, we assessed reproducibility by drawing conclusion from distribution differences. User data was drawn from both research trials and public deployment on Google Play. Results: Our algorithms showed increased rationale for the basic usage of apps with different underlying behavioral strategies. Measures of the distribution of user’s allocated attention, the user’s circadian behavior, their consecutive commitment to a specific strategy, and users’ interaction trajectory curve are perceived as transferable to the public data set. Because distributions between research trial and public deployment were similar, consistency was shown regarding the underlying behavioral strategies: psychoeducation and goal setting are used as a catalyst to overcome the users’ primary obstacles, sleep hygiene is addressed most regularly, while regular emotional exposure is avoided. Relaxation as well as cognitive reframing have increased variance in commitment among public users, indicating the challenging nature of these apps. The relative course of the engagement (learning curve) is similar in research and public data. Conclusions: The deliberate, a-priori engineered features were reproducible across app users from both data sets. These features led to improved results as well as increased interpretability, providing an increased understanding of how people engage with multiple mental health apps over time. Since we based the generation of features on generic interaction proxies, these methods are applicable to other cases in artificial intelligence and digital health.

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Section: The Intellicare Platformmentioning

confidence: 99%

Section: Proposed Interaction Featuresmentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

Characteristic Latent Features for Analyzing Digital Mental Health Interaction and Improved Explainability

Theilig

Knapp

Nicholas

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…A full description of the apps can be found elsewhere [28]. The IntelliCare apps have been shown to be effective and efficacious at reducing symptoms of depression and anxiety among adults in two research trials [29,28]. In the trials, all participants were expected to stay engaged with IntelliCare apps for 8 weeks and, were supported by a Coach who supported participants via text messaging to promote engagement.…”

Section: The Intellicare Platformmentioning

confidence: 99%

Section: Proposed Interaction Featuresmentioning

confidence: 99%

Characteristic Latent Features for Analyzing Digital Mental Health Interaction and Improved Explainability

Theilig

Knapp

Nicholas

et al. 2020

Preprint

View full text Add to dashboard Cite

Background: Using smartphones and wearable sensor technology has sparked a broad engagement of data science and machine learning methods to leverage the complex, assorted amount of data. Despite verified processes, there is a reported underdevelopment of user engagement concepts, and the desire for high accuracy or significance has shown to lead to low explicability and irreproducibility. To overcome these issues, we aim to analyze principal characteristics of everyday behavior in digital mental health. Methods: We generated five latent features based on previous research, expert opinions from digital mental health, and informed by data. The features were analyzed with descriptive statistics and data visualization. We carried out two rounds of evaluations with data from 12,400 users of IntelliCare, a mental health platform with 12 apps. First, we focused to proof concept and second, we assessed reproducibility by drawing conclusion from distribution differences. User data was drawn from both research trials and public deployment on Google Play. Results: Our algorithms showed increased rationale for the basic usage of apps with different underlying behavioral strategies. Measures of the distribution of user’s allocated attention, the user’s circadian behavior, their consecutive commitment to a specific strategy, and users’ interaction trajectory are perceived as transferable to the public data set. Because distributions between research trial and public deployment were similar, consistency was shown regarding the underlying behavioral strategies: psychoeducation and goal setting are used as a catalyst to overcome the users’ primary obstacles, sleep hygiene is addressed most regularly, while regular self-reflective thinking is avoided. Relaxation as well as cognitive reframing have increased variance in commitment among public users, indicating the challenging nature of these apps. The relative course of the engagement (learning curve) is similar in research and public data. Conclusions: The deliberate, a-priori engineered features were reproducible across app users from both data sets. These features led to improved results as well as increased interpretability, providing an increased understanding of how people engage with multiple mental health apps over time. Since we based the generation of features on generic interaction proxies, these methods are applicable to other cases in artificial intelligence and digital health.

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Coached Mobile App Platform for the Treatment of Depression and Anxiety Among Primary Care Patients

et al. 2020

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IMPORTANCE Depression and anxiety are common and disabling. Primary care is the de facto site for treating these mental health problems but is typically underresourced to meet the burden of these demands.OBJECTIVE To evaluate the efficacy of a mobile intervention platform, IntelliCare, for addressing depression and anxiety among primary care patients.DESIGN, SETTING, AND PARTICIPANTS Two-arm randomized clinical trial at internal medicine clinics at the University of Arkansas for Medical Sciences. Adult primary care patients (N = 146) who screened positive for depression on the Patient Health Questionnaire-8 (PHQ; score Ն 10) or anxiety on the Generalized Anxiety Disorder-7 (GAD-7; score Ն 8) were recruited between July 17, 2018, and December 14, 2018. INTERVENTIONSThe coach-supported platform composed of a suite of apps, was delivered over 8 weeks. Wait list control participants received treatment as usual for 8 weeks, then the mobile platform.MAIN OUTCOMES AND MEASURES Primary outcomes were changes in depression (PHQ-9) and anxiety (GAD-7) during the intervention period. Secondary outcomes were differences in the proportion of patients who achieved recovery (PHQ-9/GAD-7 <5 or 50% improvement from baseline), sustainment of intervention effects during 2-month follow-up, and app use during the intervention period.RESULTS One hundred forty-six patients were included (119 of 146 were women [81.5%]; mean [SD] age, 42.3 [13.8] years). Of the 146 patients, 122 (83.6%) were diagnosed as having depression and 131 (89.7%) were diagnosed as having anxiety. A greater proportion of intervention vs wait list control participants achieved recovery from depression (n = 38 of 64 [59%] vs n = 18 of 58 [31%]; odds ratio, 3.25; 95% CI, 1.54-6.86) and anxiety (n = 37 of 65 [57%] vs n = 25 of 66 [38%]; odds ratio, 2.17; 95% CI, 1.08-4.36). Sustained effects were observed for depression (slope, 0.01; 95% CI, -0.09 to 0.10; P = .92) and anxiety scores (slope, 0.02; 95% CI, -0.08 to 0.12; P = .67) during follow-up. App use was high, with a median of 93 and 98 sessions among participants with depression and anxiety, respectively. CONCLUSIONS AND RELEVANCEIn this trial, a mobile intervention app was effective for depression and anxiety among primary care patients. Findings also support designing digital mental health interventions as platforms containing simple, brief apps that can be bundled by users to meet their needs.TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT03500536.

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Comparison of the Effects of Coaching and Receipt of App Recommendations on Depression, Anxiety, and Engagement in the IntelliCare Platform: Factorial Randomized Controlled Trial

Cited by 98 publications

References 41 publications

Characteristic Latent Features for Analyzing Digital Mental Health Interaction and Improved Explainability

Characteristic Latent Features for Analyzing Digital Mental Health Interaction and Improved Explainability

Characteristic Latent Features for Analyzing Digital Mental Health Interaction and Improved Explainability

Coached Mobile App Platform for the Treatment of Depression and Anxiety Among Primary Care Patients

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