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Objective To test whether an Internet-delivered cognitive behavioral therapy for insomnia (CBT-I) program for older adults attenuates symptoms of depression and anxiety. Methods Adults aged [greater than or equal to] 55 with insomnia were randomized to SHUTi-OASIS (Sleep Healthy Using the Internet for Older Adult Sufferers of Insomnia and Sleeplessness; N = 207) or Patient Education (PE; N = 104). Depression and anxiety were assessed (HADS-D and HADS-A, respectively) at baseline, post-assessment, and 6- and 12-month follow-ups. Results Multilevel modeling of HADS-D showed a condition by time interaction (F[3,779] = 3.23, p = .02): SHUTi-OASIS participants reported lower symptoms than PE at post-assessment. There was no such interaction effect for HADS-A (F[3,779] = 2.12, p = .10). Generalized linear modeling showed no moderation of effects by baseline symptom severity. Conclusions Participants randomized to Internet-delivered CBT-I showed stable depression and anxiety across time, while control participants' depressive symptoms briefly increased. CBT-I may help prevent development or worsening of psychological distress among older adults with insomnia. TRIAL REGISTRATION [Registered at ClinicalTrials.gov; identifier removed for anonymity], Author(s): Kelly M. Shaffer [sup.1] , Jessica G. Smith [sup.1] , Jillian V. Glazer [sup.1] , Fabian Camacho [sup.1] , Philip I. Chow [sup.1] , Meghan Mattos [sup.1] , Karen [...]

Cognitive-behavioral therapy for insomnia (CBT-I) shows treatment benefits among individuals with pain interference; however, effects of Internet-delivered CBT-I for this population are unknown. This secondary analysis used randomized clinical trial data from adults assigned to Internet-delivered CBT-I to compare changes in sleep by pre-intervention pain interference. Participants (N = 151) completed the Insomnia Severity Index (ISI) and sleep diaries [sleep onset latency (SOL); wake after sleep onset (WASO)] at baseline, post-assessment, 6- and 12-month follow-ups. Linear mixed-effects models showed no differences between pain interference groups (no, some, moderate/severe) for changes from baseline to any follow-up timepoint for ISI (p = .72) or WASO (p = .88). There was a small difference in SOL between those reporting some versus no or moderate/severe pain interference (p = .04). Predominantly comparable and sustained treatment benefits for both those with and without pain interference suggest that Internet-delivered CBT-I is promising for delivering accessible care to individuals with comorbid pain and insomnia., Author(s): Kelly M. Shaffer [sup.1] [sup.2] , Fabian Camacho [sup.1] , Holly R. Lord [sup.1] , Philip I. Chow [sup.1] , Tonya Palermo [sup.3] , Emily Law [sup.3] , Frances [...]

BackgroundUser dropout is a widespread concern in the delivery and evaluation of digital (ie, web and mobile apps) health interventions. Researchers have yet to fully realize the potential of the large amount of data generated by these technology-based programs. Of particular interest is the ability to predict who will drop out of an intervention. This may be possible through the analysis of user journey data—self-reported as well as system-generated data—produced by the path (or journey) an individual takes to navigate through a digital health intervention. ObjectiveThe purpose of this study is to provide a step-by-step process for the analysis of user journey data and eventually to predict dropout in the context of digital health interventions. The process is applied to data from an internet-based intervention for insomnia as a way to illustrate its use. The completion of the program is contingent upon completing 7 sequential cores, which include an initial tutorial core. Dropout is defined as not completing the seventh core. MethodsSteps of user journey analysis, including data transformation, feature engineering, and statistical model analysis and evaluation, are presented. Dropouts were predicted based on data from 151 participants from a fully automated web-based program (Sleep Healthy Using the Internet) that delivers cognitive behavioral therapy for insomnia. Logistic regression with L1 and L2 regularization, support vector machines, and boosted decision trees were used and evaluated based on their predictive performance. Relevant features from the data are reported that predict user dropout. ResultsAccuracy of predicting dropout (area under the curve [AUC] values) varied depending on the program core and the machine learning technique. After model evaluation, boosted decision trees achieved AUC values ranging between 0.6 and 0.9. Additional handcrafted features, including time to complete certain steps of the intervention, time to get out of bed, and days since the last interaction with the system, contributed to the prediction performance. ConclusionsThe results support the feasibility and potential of analyzing user journey data to predict dropout. Theory-driven handcrafted features increased the prediction performance. The ability to predict dropout at an individual level could be used to enhance decision making for researchers and clinicians as well as inform dynamic intervention regimens.

BackgroundAlthough gamification continues to be a popular approach to increase engagement, motivation, and adherence to behavioral interventions, empirical studies have rarely focused on this topic. There is a need to empirically evaluate gamification models to increase the understanding of how to integrate gamification into interventions. ObjectiveThe model of gamification principles for digital health interventions proposes a set of five independent yet interrelated gamification principles. This study aimed to examine the validity and reliability of this model to inform its use in Web- and mobile-based apps. MethodsA total of 17 digital health interventions were selected from a curated website of mobile- and Web-based apps (PsyberGuide), which makes independent and unbiased ratings on various metrics. A total of 133 independent raters trained in gamification evaluation techniques were instructed to evaluate the apps and rate the degree to which gamification principles are present. Multiple ratings (n≥20) were collected for each of the five gamification principles within each app. Existing measures, including the PsyberGuide credibility score, mobile app rating scale (MARS), and the app store rating of each app were collected, and their relationship with the gamification principle scores was investigated. ResultsApps varied widely in the degree of gamification implemented (ie, the mean gamification rating ranged from 0.17≤m≤4.65 out of 5). Inter-rater reliability of gamification scores for each app was acceptable (κ≥0.5). There was no significant correlation between any of the five gamification principles and the PsyberGuide credibility score (P≥.49 in all cases). Three gamification principles (supporting player archetypes, feedback, and visibility) were significantly correlated with the MARS score, whereas three principles (meaningful purpose, meaningful choice, and supporting player archetypes) were significantly correlated with the app store rating. One gamification principle was statistically significant with both the MARS and the app store rating (supporting player archetypes). ConclusionsOverall, the results support the validity and potential utility of the model of gamification principles for digital health interventions. As expected, there was some overlap between several gamification principles and existing app measures (eg, MARS). However, the results indicate that the gamification principles are not redundant with existing measures and highlight the potential utility of a 5-factor gamification model structure in digital behavioral health interventions. These gamification principles may be used to improve user experience and enhance engagement with digital health programs.

BackgroundBehavioral health researchers are increasingly recognizing the potential of mobile phone apps to deliver empirically supported treatments. However, current options for developing apps typically require large amounts of expertise or money. ObjectiveThis paper aims to describe a pragmatic do-it-yourself approach for researchers to create and pilot an Android mobile phone app using existing survey software (eg, Qualtrics survey platform). MethodsThis study was conducted at an academic research center in the United States focused on developing and evaluating behavioral health technologies. The process outlined in this paper was derived and condensed from the steps to building an existing app intervention, iCanThrive, which was developed to enhance mental well-being in women cancer survivors. ResultsThis paper describes an inexpensive, practical process that uses a widely available survey software, such as Qualtrics, to create and pilot a mobile phone intervention that is presented to participants as a Web viewer app that is downloaded from the Google Play store. Health researchers who are interested in using this process to pilot apps are encouraged to inquire about the survey platforms available to them, the level of security those survey platforms provide, and the regulatory guidelines set forth by their institution. ConclusionsAs app interventions continue to gain interest among researchers and consumers alike, it is important to find new ways to efficiently develop and pilot app interventions before committing a large amount of resources. Mobile phone app interventions are an important component to discovering new ways to reach and support individuals with behavioral or mental health disorders.

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

BackgroundPsychological distress is a major issue among survivors of women’s cancer who face numerous barriers to accessing in-person mental health treatments. Mobile phone app–based interventions are scalable and have the potential to increase access to mental health care among survivors of women’s cancer worldwide. ObjectiveThis study aimed to evaluate the acceptability and preliminary efficacy of a novel app-based intervention with phone coaching in a sample of survivors of women’s cancer. MethodsIn a single-group, pre-post, 6-week pilot study in the United States, 28 survivors of women’s cancer used iCanThrive, a novel app intervention that teaches skills for coping with stress and enhancing well-being, with added phone coaching. The primary outcome was self-reported symptoms of depression (Center for Epidemiologic Studies Depression Scale). Emotional self-efficacy and sleep disruption were also assessed at baseline, 6-week postintervention, and 4 weeks after the intervention period. Feedback obtained at the end of the study focused on user experience of the intervention. ResultsThere were significant decreases in symptoms of depression and sleep disruption from baseline to postintervention. Sleep disruption remained significantly lower at 4-week postintervention compared with baseline. The iCanThrive app was launched a median of 20.5 times over the intervention period. The median length of use was 2.1 min. Of the individuals who initiated the intervention, 87% (20/23) completed the 6-week intervention. ConclusionsThis pilot study provides support for the acceptability and preliminary efficacy of the iCanThrive intervention. Future work should validate the intervention in a larger randomized controlled study. It is important to develop scalable interventions that meet the psychosocial needs of different cancer populations. The modular structure of the iCanThrive app and phone coaching could impact a large population of survivors of women’s cancer.