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BackgroundUnmanaged cancer symptoms and treatment-related side effects can compromise long-term clinical outcomes and health-related quality of life. Health information technologies such as web-based platforms offer the possibility to supplement existing care and optimize symptom management. ObjectiveThis paper describes the development and usability of a web-based symptom management platform for cancer patients and survivors that will be implemented within a large health system. MethodsA web-based symptom management platform was designed and evaluated via one-on-one usability testing sessions. The System Usability Scale (SUS), After Scenario Questionnaire (ASQ), and qualitative analysis of semistructured interviews were used to assess program usability. ResultsTen cancer survivors and five cancer center staff members participated in usability testing sessions. The mean score on the SUS was 86.6 (SD 14.0), indicating above average usability. The mean score on the ASQ was 2.5 (SD 2.1), indicating relatively high satisfaction with the usability of the program. Qualitative analyses identified valued features of the program and recommendations for further improvements. ConclusionsCancer survivors and oncology care providers reported high levels of acceptability and usability in the initial development of a web-based symptom management platform for cancer survivors. Future work will test the effectiveness of this web-based platform.

BackgroundNearly half of the patients with breast cancer experience clinically significant mental distress within the first year of receiving their cancer diagnosis. There is an urgent need to identify scalable and cost-efficient ways of delivering empirically supported mental health interventions to patients with breast cancer. ObjectiveThe aim of this study was to evaluate the feasibility of in-clinic recruitment for a mobile phone app study and to evaluate the usability and preliminary impact of a suite of mental health apps (IntelliCare) with phone coaching on psychosocial distress symptoms in patients recently diagnosed with breast cancer. MethodsThis pilot study adopted a within-subject, 7-week pre-post study design. A total of 40 patients with breast cancer were recruited at a US National Cancer Institute–designated clinical cancer center. Self-reported distress (Patient Health Questionnaire-4) and mood symptoms (Patient-Reported Outcomes Measurement Information System depression and anxiety scales) were assessed at baseline and postintervention. App usability was assessed at postintervention. ResultsThe minimum recruitment threshold was met. There was a significant decrease in general distress symptoms, as well as symptoms of depression and anxiety, from baseline to postintervention. Overall, participants reported high levels of ease of app use and learning. Scores for app usefulness and satisfaction were reinforced by some qualitative feedback suggesting that tailoring the apps more for patients with breast cancer could enhance engagement. ConclusionsThere is a dire need for scalable, supportive interventions in cancer. The results from this study inform how scalable mobile phone–delivered programs with additional phone support can be used to support patients with breast cancer. International Registered Report Identifier (IRRID)RR2-10.2196/11452

BackgroundIs someone at home, at their friend’s place, at a restaurant, or enjoying the outdoors? Knowing the semantic location of an individual matters for delivering medical interventions, recommendations, and other context-aware services. This knowledge is particularly useful in mental health care for monitoring relevant behavioral indicators to improve treatment delivery. Local search-and-discovery services such as Foursquare can be used to detect semantic locations based on the global positioning system (GPS) coordinates, but GPS alone is often inaccurate. Mobile phones can also sense other signals (such as movement, light, and sound), and the use of these signals promises to lead to a better estimation of an individual’s semantic location. ObjectiveWe aimed to examine the ability of mobile phone sensors to estimate semantic locations, and to evaluate the relationship between semantic location visit patterns and depression and anxiety. MethodsA total of 208 participants across the United States were asked to log the type of locations they visited daily, using their mobile phones for a period of 6 weeks, while their phone sensor data was recorded. Using the sensor data and Foursquare queries based on GPS coordinates, we trained models to predict these logged locations, and evaluated their prediction accuracy on participants that models had not seen during training. We also evaluated the relationship between the amount of time spent in each semantic location and depression and anxiety assessed at baseline, in the middle, and at the end of the study. ResultsWhile Foursquare queries detected true semantic locations with an average area under the curve (AUC) of 0.62, using phone sensor data alone increased the AUC to 0.84. When we used Foursquare and sensor data together, the AUC further increased to 0.88. We found some significant relationships between the time spent in certain locations and depression and anxiety, although these relationships were not consistent. ConclusionsThe accuracy of location services such as Foursquare can significantly benefit from using phone sensor data. However, our results suggest that the nature of the places people visit explains only a small part of the variation in their anxiety and depression symptoms.

U.S. health care systems are tasked with alleviating the burden of mental health, but are frequently underprepared and lack workforce and resource capacity to deliver services to all in need. Digital mental health interventions (DMHIs) can increase access to evidence-based mental health care. However, DMHIs commonly do not fit into the day-to-day activities of the people who engage with them, resulting in a research-to-practice gap for DMHI implementation. For health care settings, differences between digital and traditional mental health services make alignment and integration challenging. Specialized attention is needed to improve the implementation of DMHIs in health care settings so that these services yield high uptake, engagement, and sustainment. The purpose of this article is to enhance efforts to integrate DMHIs in health care settings by proposing implementation strategies, selected and operationalized based on the discrete strategies established in the Expert Recommendations for Implementing Change project, that align to DMHI-specific barriers in these settings. Guidance is offered in how these strategies can be applied to DMHI implementation across four phases commonly distinguished in implementation science using the Exploration, Preparation, Implementation, Sustainment Framework. Next steps to advance research in this area and improve the research-to-practice gap for implementing DMHIs are recommended. Applying implementation strategies to DMHI implementation will enable psychologists to systematically evaluate this process, which can yield an enhanced understanding of the factors that facilitate implementation success and improve the translation of DMHIs from controlled trials to real-world settings. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Objective: To recognize gaps between students' expectations of college counseling centers and centers' current practices and to identify strategies to improve care from both clinicians and students. Participants: As part of an ongoing study on mobile technologies for stress management, we gathered data from counseling center clinicians and students at two large Midwestern universities. Methods: Clinicians completed online questionnaires on their current practices and interest in digital mental health tools. Students and clinicians completed co-design workshops. In subsequent individual interviews, students identified barriers to care. Results: Both students and clinicians recognized a need for stress management and mental health support outside of counseling sessions. Conclusions: Students and clinicians recognize barriers to face-to-face therapy and are eager to collaborate to identify opportunities to address barriers to mental health services. We discuss digital mental health tools as a potential opportunity for support and benefits of including students in discussions of treatment resources.

This trial examined whether a stepped care program for depression, which initiated treatment with internet cognitive behavioral therapy, including telephone and messaging support, and stepped up non-responders to telephone-administered cognitive behavioral therapy (tCBT), was noninferior, less costly to deliver, and as acceptable to patients compared to tCBT alone. Adults with a diagnosis of major depressive episode (MDE) were randomized to receive up to 20 weeks of stepped care or tCBT. Stepped care (n = 134) was noninferior to tCBT (n = 136) with an end-of-treatment effect size of d = 0.03 and a 6-month post-treatment effect size of d = -0.07 [90% CI 0.29 to 0.14]. Therapist time in stepped care was 5.26 (SD = 3.08) hours versus 10.16 (SD 4.01) for tCBT (p

BackgroundTreatments for depression and anxiety have several behavioral and psychological targets and rely on varied strategies. Digital mental health treatments often employ feature-rich approaches addressing several targets and strategies. These treatments, often optimized for desktop computer use, are at odds with the ways people use smartphone applications. Smartphone use tends to focus on singular functions with easy navigation to desired tools. The IntelliCare suite of apps was developed to address the discrepancy between need for diverse behavioral strategies and constraints imposed by typical app use. Each app focuses on one strategy for a limited subset of clinical aims all pertinent to depression and anxiety. This study presents the uptake and usage of apps from the IntelliCare suite following an open deployment on a large app marketplace.MethodsThirteen lightweight apps, including 12 interactive apps and one Hub app that coordinates use across those interactive apps, were developed and made free to download on the Google Play store. De-identified app usage data from the first year of IntelliCare suite deployment were analyzed for this study.ResultsIn the first year of public availability, 5,210 individuals downloaded one or more of the IntelliCare apps, for a total of 10,131 downloads. Nearly a third of these individuals (31.8%) downloaded more than one of these apps. The modal number of launches for each of the apps was 1, however the mean number of app launches per app ranged from 3.10 to 16.98, reflecting considerable variability in the use of each app.ConclusionsThe use rate of the IntelliCare suite of apps is higher than public deployments of other comparable digital resources. Our findings suggest that people will use multiple apps and provides support for the concept of app suites as a useful strategy for providing diverse behavioral strategies.

BackgroundSmartphones offer the hope that depression can be detected using passively collected data from the phone sensors. The aim of this study was to replicate and extend previous work using geographic location (GPS) sensors to identify depressive symptom severity.MethodsWe used a dataset collected from 48 college students over a 10-week period, which included GPS phone sensor data and the Patient Health Questionnaire 9-item (PHQ-9) to evaluate depressive symptom severity at baseline and end-of-study. GPS features were calculated over the entire study, for weekdays and weekends, and in 2-week blocks.ResultsThe results of this study replicated our previous findings that a number of GPS features, including location variance, entropy, and circadian movement, were significantly correlated with PHQ-9 scores (r's ranging from -0.43 to -0.46, p-values

The Cognitive and Behavioral Response to Stress Scale (CB-RSS) is a self-report measure of the use and helpfulness of several cognitive and behavioral skills. Unlike other measures that focus on language specific to terms used in therapy, the CB-RSS was intended to tap the strategies in ways that might be understandable to those who had not undergone therapy. The measure was included in a clinical trial of cognitive-behavioral therapy for depression and completed by 325 participants at baseline and end of treatment (18 weeks). Psychometric properties of the scale were assessed through iterative exploratory and confirmatory factor analyses. These analyses identified two subscales, cognitive and behavioral skills, each with high reliability. Validity was addressed by investigating relationships with depression symptoms, positive affect, perceived stress, and coping self-efficacy. End of treatment scores predicted changes in all outcomes, with the largest relationships between baseline CB-RSS scales and coping self-efficacy. These findings suggest that the CB-RSS is a useful tool to measure cognitive and behavioral skills both at baseline (prior to treatment) as well as during the course of treatment.

The COVID-19 pandemic has drastically increased use of remote-delivered mental health services. This study identifies advantages and limitations of remote-delivered services on college campuses to inform mental health delivery post-pandemic. Methods: Clinicians (n = 30) were asked to evaluate COVID-19's impact on their work, environment, and wellness in an online survey. Qualitative data was coded using a thematic analysis approach, while quantitative data was analyzed using descriptive statistics. Results: Many clinicians reported benefits of remote services, including increased accessibility, greater convenience, no change in therapeutic alliance, and decreased stress for clinicians. Clinicians also experienced challenges such as social isolation, technological difficulties, and personal/family concerns. Clinicians envisioned a hybrid service combining online and in-person activities post-pandemic. Conclusions: Overall, remote-delivered mental health services on college campuses have potential in increasing treatment quality while highlighting a necessity for further research in hybrid mental health delivery. Supplemental data for this article can be accessed online at https://doi.org/10.1080/07448481.2022.2038178. [ABSTRACT FROM AUTHOR]

Background: Blended care therapy models are intended to increase the efficiency and effectiveness of evidence-based psychotherapy by combining synchronous and asynchronous components of care. Objective: This retrospective cohort study evaluated the clinical effects of synchronous video therapy sessions and asynchronous guided practice session elements on anxiety and depression in a blended care therapy program, with a novel focus on asynchronous provider feedback messages. Methods: Participants were adults (N=33,492) with clinical symptoms of anxiety (Generalized Anxiety Disorder 7-item scale [GAD-7] score of ≥8) and depression (Patient Health Questionnaire 9-item scale [PHQ-9] score of ≥10) at intake. Symptom trajectories were evaluated via individual growth curve models. Time-varying covariates evaluated effects of synchronous video session attendance and the presence or absence of each asynchronous guided practice session element occurring within 7 days and 8-14 days prior to each clinical outcome assessment. Guided practice session elements included client digital lesson completion, client digital exercise completion, and feedback messages sent by providers. Results: Approximately 86.6% (29,012/33,492) of clients met criteria for clinical improvement by end of care (median 6, IQR 4-8 synchronous sessions). Synchronous video session attendance and client digital lesson completion in the past 7 days and in the past 8-14 days were each uniquely and significantly associated with lower GAD-7 scores (video session effects: b session7=–0.82, b session8-14=–0.58, P values<.001; digital lesson effects: b lesson7=–0.18, b lesson8-14=–0.26, P values <.001) and PHQ-9 scores (video session effects: b session7=–0.89, b session8-14=–0.67, P values <.001; digital lesson effects: b lesson7=–0.12, b lesson8-14=–0.30, P values <.001). Client digital exercise completion in the past 8-14 days was significantly associated with lower GAD-7 scores (b exercise8-14=–0.10; P <.001) but exercise completion in the 7 days prior to clinical outcome assessment was not (b exercise7=0.00; P =.89). Exercise completion in the past 7 days was significantly associated with lower PHQ-9 scores (b exercise7=–0.16; P <.001) but exercise completion in the past 8-14 days was not (b exercise8-14=–0.05; P =.09). Provider feedback messaging in the past 7 days and in the past 8-14 days was significantly associated with lower GAD-7 and PHQ-9 scores, respectively (GAD-7: b feedback7=–0.12, P <.001; b feedback8-14=–0.07, P =.004; PHQ-9: b feedback7=–0.15, P <.001; b feedback8-14=–0.08, P =.01). Conclusions: Provider feedback between synchronous therapy sessions provided significant benefit for symptom reduction, beyond the effects of client digital engagement and synchronous video sessions. When guided practice sessions are well integrated into care, blended care therapy provides meaningful improvements upon the traditional, synchronous session–only therapy model. Provider guidance and feedback for clients between synchronous sessions support more efficient and effective mental health care overall. [ABSTRACT FROM AUTHOR]

Background: eHealth interventions have proven to be valuable resources for users with diverse mental and behavioral health concerns. As these technologies continue to proliferate, both academic researchers and commercial app creators are leveraging the use of features that foster a sense of social connection on these digital platforms. Yet, the literature often insufficiently represents the functionality of these key social features, resulting in a lack of understanding of how they are being implemented. Objective: This study aimed to conduct a methodical review of commercially available eHealth apps to establish the SoCAP (social communication, affiliation, and presence) taxonomy of social features in eHealth apps. Our goal was to examine what types of social features are being used in eHealth apps and how they are implemented. Methods: A scoping review of commercially available eHealth apps was conducted to develop a taxonomy of social features. First, a shortlist of the 20 highest-rated eHealth apps was derived from One Mind PsyberGuide, a nonprofit organization with trained researchers who rate apps based on their (1) credibility, (2) user experience, and (3) transparency. Next, both mobile- and web-based versions of each app were double-coded by 2 trained raters to derive a list of social features. Subsequently, the social features were organized by category and tested on other apps to ensure their completeness. Results: Four main categories of social features emerged: (1) communication features (videoconferencing, discussion boards, etc), (2) social presence features (chatbots, reminders, etc), (3) affiliation and identity features (avatars, profiles, etc), and (4) other social integrations (social network and other app integrations). Our review shows that eHealth apps frequently use resource-intensive interactions (eg, videoconferencing with a clinician and phone calls from a facilitator), which may be helpful for participants with high support needs. Furthermore, among commercially available eHealth apps, there is a strong reliance on automated features (eg, avatars, personalized multimedia, and tailored content) that enhance a sense of social presence without requiring a high level of input from a clinician or staff member. Conclusions: The SoCAP taxonomy includes a comprehensive list of social features and brief descriptions of how these features work. This classification system will provide academic and commercial eHealth app creators with an understanding of the various social features that are commonly implemented, which will allow them to apply these features to enhance their own apps. Future research may include comparing the synergistic effects of various combinations of these social features. [ABSTRACT FROM AUTHOR]