Family Cancer Caregiver Use of and Benefit from an Internet-delivered Insomnia Intervention: Results from a Single-Group Feasibility Trial.

Background: Cancer caregivers are more likely to report clinically significant symptoms of insomnia than cancer patients and the general population, yet research has been limited regarding cognitive-behavioral therapy for insomnia (CBT-I) among this population. Methods: To better understand cancer caregivers' engagement with and benefit from CBT-I, cancer caregivers were enrolled in a nonrandomized pilot feasibility trial of an evidence-based Internet-delivered insomnia program. Thirteen caregivers completed mixed-methods assessments prior to receiving the insomnia program and after the nine-week intervention period. Results: Compared to the five caregivers who did not complete any intervention Cores, the eight caregivers who completed at least one of the intervention Cores tended to report more sleep impairment (insomnia symptom severity; minutes of sleep onset latency and wake after sleep onset), less physical and emotional strain from caregiving, and less maladaptive sleep beliefs at the baseline assessment. These caregivers who used the program also showed large improvements in their insomnia symptoms. Caregivers' qualitative feedback about their experience with the program identified potential areas that might be modified to improve caregivers' engagement with and benefit from Internet-delivered insomnia programs. Conclusions: Findings suggest that family cancer caregivers can use and benefit from a fully-automated Internet-delivered CBT-I program, even without caregiving-specific tailoring. Further rigorous research is needed to better understand whether and how program modifications may allow more caregivers to initiate and engage with this program.

Investigating psychological mechanisms linking pain severity to depression symptoms in women cancer survivors at a cancer center with a rural catchment area.

PURPOSE: Women cancer survivors, especially those in rural areas, with high levels of depression may be acutely susceptible to pain due to the ways they think, feel, and behave. The current study seeks to elucidate the relationship between symptoms of depression and pain severity in women cancer survivors, by examining the putative mediators involved in this relationship, specifically their self-efficacy for managing their health, how overwhelmed they were from life's responsibilities, and relational burden. METHODS: Self-report data were collected from 183 cancer survivors of breast, cervical, ovarian, or endometrial/uterine cancer, who were between 6 months and 3 years post-active therapy. RESULTS: Women cancer survivors with higher (vs. lower) symptoms of depression had more severe pain. Individual mediation analyses revealed that survivors with higher levels of depression felt more overwhelmed by life's responsibilities and had lower self-efficacy about managing their health, which was associated with greater pain severity. When all mediators were simultaneously entered into the same model, feeling overwhelmed by life's responsibilities significantly mediated the link between survivors' symptoms of depression and their pain severity. CONCLUSIONS: The relationship between symptoms of depression and pain severity in women cancer survivors may be attributed in part to their self-efficacy and feeling overwhelmed by life's responsibilities. Early and frequent assessment of psychosocial factors involved in pain severity for women cancer survivors may be important for managing their pain throughout the phases of cancer survivorship.

Mobile sensing to advance tumor modeling in cancer patients: A conceptual framework.

As mobile and wearable devices continue to grow in popularity, there is strong yet unrealized potential to harness people's mobile sensing data to improve our understanding of their cellular and biologically-based diseases. Breakthrough technical innovations in tumor modeling, such as the three dimensional tumor microenvironment system (TMES), allow researchers to study the behavior of tumor cells in a controlled environment that closely mimics the human body. Although patients' health behaviors are known to impact their tumor growth through circulating hormones (cortisol, melatonin), capturing this process is a challenge to rendering realistic tumor models in the TMES or similar tumor modeling systems. The goal of this paper is to propose a conceptual framework that unifies researchers from digital health, data science, oncology, and cellular signaling, in a common cause to improve cancer patients' treatment outcomes through mobile sensing. In support of our framework, existing studies indicate that it is feasible to use people's mobile sensing data to approximate their underlying hormone levels. Further, it was found that when cortisol is cycled through the TMES based on actual patients' cortisol levels, there is a significant increase in pancreatic tumor cell growth compared to when cortisol levels are at normal healthy levels. Taken together, findings from these studies indicate that continuous monitoring of people's hormone levels through mobile sensing may improve experimentation in the TMES, by informing how hormones should be introduced. We hope our framework inspires digital health researchers in the psychosocial sciences to consider how their expertise can be applied to advancing outcomes across levels of inquiry, from behavioral to cellular.

A multi-dimensional assessment of financial hardship of cancer patients using existing health system data.

BACKGROUND: Existing financial hardship screening does not capture the multifaceted and dynamic nature of the problem. The use of existing health system data is a promising way to enable scalable and sustainable financial hardship screening. METHODS: We used existing data from 303 adult patients with cancer at the University of Virginia Comprehensive Cancer Center (2016-2018). All received distress screening and had a valid financial assistance screening based solely on household size-adjusted income. We constructed a composite index that integrates multiple existing health system data (Epic, distress screening, and cancer registry) to assess comprehensive financial hardship (e.g., material conditions, psychological responses, and coping behaviors). We examined differences of at-risk patients identified by our composite index and by existing single-dimension criterion. Dynamics of financial hardship over time, by age, and cancer type, were examined by fractional probit models. RESULTS: At-risk patients identified by the composite index were generally younger, better educated, and had a higher annual household income, though they had lower health insurance coverage. Identified periods to intervene for most patients are before formal diagnosis, 2 years, and 6 years after diagnosis. Within 2 years of diagnosis and more than 4 years after diagnosis appear critical for subgroups of patients who may suffer from financial hardship disparities. CONCLUSION: Existing health system data provides opportunities to systematically measure and track financial hardship in a systematic, scalable and sustainable way. We find that the dimensions of financial hardship can exhibit different patterns over time and across patient subgroups, which can guide targeted interventions. The scalability of the algorithm is limited by existing data availability.

Associations between context and affect within the daily lives of cancer caregivers.

PURPOSE: This study used ecological momentary assessment (EMA) to test the association between activity, location, and social company contexts with cancer caregivers' in-the-moment affect to identify precisely when and where to deliver psychological interventions for caregivers. METHODS: Current cancer caregivers (N = 25) received 8 EMA prompts per day for 7 consecutive days. At each prompt, caregivers reported their current positive affect and negative affect, as well as what they were doing, where they were located, and who they were with. Multilevel logistic regressions tested the associations between caregivers' contexts with their own person-mean-centered state (concurrent momentary level) and trait (overall weekly average) positive or negative affect. RESULTS: Caregivers reported lower state negative affect, as well as higher state positive affect, when socializing (ps < .001), when at a public location (ps < .03), and when around their friends, family, spouse/partner, or care recipient (i.e., person with cancer, ps < .02), relative to when not endorsing the context. Caregivers also reported lower state negative affect when eating/drinking or engaging in leisure (ps < .01; but no parallel effects for state positive affect). Caregivers reported higher state negative affect while working, when at their workplace, or when around work colleagues (ps < .001) and lower state positive affect when at home or alone (ps < .03). CONCLUSIONS: Results suggest the pertinence of a behavioral activation framework to mitigate the emotional strain of caregiving. Interventions that facilitate caregivers' ability to socialize with a range of friends and family, including their loved one with cancer, outside of the home may have the strongest positive emotional impact.

Assessing Emotion Polyregulation in Daily Life: Who Uses It, When Is It Used, and How Effective Is It?

Most research on emotion regulation has focused on understanding individual emotion regulation strategies. Preliminary research, however, suggests that people often use several strategies to regulate their emotions in a given emotional scenario (polyregulation). The present research examined who uses polyregulation, when polyregulation is used, and how effective polyregulation is when it is used. College students (N = 128; 65.6% female; 54.7% White) completed an in-person lab visit followed by a 2-week ecological momentary assessment protocol with six randomly timed survey prompts per day for up 2 weeks. At baseline, participants completed measures assessing past-week depression symptoms, social anxiety-related traits, and trait emotion dysregulation. During each randomly timed prompt, participants reported up to eight strategies used to change their thoughts or feelings, negative and positive affect, motivation to change emotions, their social context, and how well they felt they were managing their emotions. In pre-registered analyses examining the 1,423 survey responses collected, polyregulation was more likely when participants were feeling more intensely negative and when their motivation to change their emotions was stronger. Neither sex, psychopathology-related symptoms and traits, social context, nor subjective effectiveness was associated with polyregulation, and state affect did not moderate these associations. This study helps address a key gap in the literature by assessing emotion polyregulation in daily life. Supplementary Information: The online version contains supplementary material available at 10.1007/s42761-022-00166-x.

Evaluating the impact of patients' psychological and physical problems on their interest in participating in research at a cancer center with a rural catchment area.

BACKGROUND: Cancer patients' participation in research trials is essential to improving their care and treatment. In a large sample of adults recently diagnosed with cancer, the aim of the current study is to investigate the relationship between cancer patients' interest in research and the psychological and physical problems they experience resulting from their cancer diagnosis. METHOD: We analyzed data from 906 cancer patients collected during routine clinical care. Correlational analyses focused on the relationship between patients' psychological and physical problems and their interest in research. Hierarchical binary logistic regression analyses tested whether patients' psychological/physical problems as a block predicted their interest in research, above and beyond their sociodemographic characteristics. RESULTS: Higher levels of patients' anxiety, fear of cancer treatment, difficulty managing emotions, worry, suicidal/homicidal ideation, fatigue, problems related to physical appearance, sleep difficulty, and changes to weight/appetite, were associated with a greater interest in research. Patients' psychological/physical problems, as a block, incrementally predicted their interest in research (Δχ2 = 24.34, df = 14, p = .04, Δr2=0.05), though none of the individual psychological/physical problems were found to be significant predictors. A higher level of education was significantly positively associated with an increased likelihood of being interested in research (OR = 1.26, 95%CI = 1.09, 1.46, p = .001). CONCLUSION: Cancer patients' problems stemming from their diagnosis can be important factors in their decision to participate in research. Depending on the needs of the trial in question, researchers may want to account for patients' level of symptom burden in deciding who to approach for enrollment in a research trial.

Outcomes of a Caregiver-Focused Short Message Service (SMS) Intervention to Reduce Intake of Sugar-Sweetened Beverages in Rural Caregivers and Adolescents.

This study examined enrollment, retention, engagement, and behavior changes from a caregiver short message service (SMS) component of a larger school-based sugar-sweetened beverage (SSB) reduction intervention. Over 22 weeks, caregivers of seventh graders in 10 Appalachian middle schools received a two-way SMS Baseline Assessment and four monthly follow-up assessments to report their and their child's SSB intake and select a personalized strategy topic. Between assessments, caregivers received two weekly one-way messages: one information or infographic message and one strategy message. Of 1873 caregivers, 542 (29%) enrolled by completing the SMS Baseline Assessment. Three-quarters completed Assessments 2-5, with 84% retained at Assessment 5. Reminders, used to encourage adherence, improved completion by 19-40%, with 18-33% completing after the first two reminders. Most caregivers (72-93%) selected a personalized strategy and an average of 28% viewed infographic messages. Between Baseline and Assessment 5, daily SSB intake frequency significantly (p < 0.01) declined for caregivers (-0.32 (0.03), effect size (ES) = 0.51) and children (-0.26 (0.01), ES = 0.53). Effect sizes increased when limited to participants who consumed SSB twice or more per week (caregivers ES = 0.65, children ES = 0.67). Findings indicate that an SMS-delivered intervention is promising for engaging rural caregivers of middle school students and improving SSB behaviors.

Retention and engagement of rural caregivers of adolescents in a short message service intervention to reduce sugar-sweetened beverage intake.

Objective: This study investigates a 6-month short message service (SMS) intervention to reduce adolescent sugar-sweetened beverage (SSB) intake. The objectives are to describe caregiver retention and SMS engagement as well as explore differences by caregiver characteristics. Methods: Caregivers completed a baseline survey then messages were sent two times per week. Message types included the following: SSB intake assessments, educational information, infographic URLs, and strategies. Engagement was measured through interaction with these messages and included: assessment completion, reminders needed, number of strategies chosen, and URLs clicked. Results: Caregivers (n = 357) had an average baseline SSB intake of 23.9 (SD = 26.8) oz/day. Of those, 89% were retained. Caregivers with a greater income and education were retained at a higher rate. Average engagement included: 4.1 (SD = 1.3) of 5 assessments completed with few reminders needed [4.1 (SD = 3.7) of 14 possible], 3.2 (SD = 1.1) of 4 strategies selected, and 1.2 (SD = 1.6) of 5 URLs clicked. Overall, average engagement was relatively high, even where disparities were found. Demographic characteristics that were statistically related to lower engagement included younger age, lower income, lower educational attainment, single caregivers, lower health literacy. Furthermore, caregivers with a reduced intention to change SSB behaviors completed fewer assessments and needed more reminders. Higher baseline SSB intake was associated with lower engagement across all indicators except URL clicks. Conclusions: Results can be used to develop targeted retention and engagement strategies (e.g., just-in-time and/or adaptive interventions) in rural SMS interventions for identified demographic subsets. Trial registration: Clincialtrials.gov: NCT03740113.

Effects of an internet-delivered insomnia intervention for older adults: A secondary analysis on symptoms of depression and anxiety.

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 ≥ 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].

Understanding the Relationship Between Mood Symptoms and Mobile App Engagement Among Patients With Breast Cancer Using Machine Learning: Case Study.

BACKGROUND: Health interventions delivered via smart devices are increasingly being used to address mental health challenges associated with cancer treatment. Engagement with mobile interventions has been associated with treatment success; however, the relationship between mood and engagement among patients with cancer remains poorly understood. A reason for this is the lack of a data-driven process for analyzing mood and app engagement data for patients with cancer. OBJECTIVE: This study aimed to provide a step-by-step process for using app engagement metrics to predict continuously assessed mood outcomes in patients with breast cancer. METHODS: We described the steps involved in data preprocessing, feature extraction, and data modeling and prediction. We applied this process as a case study to data collected from patients with breast cancer who engaged with a mobile mental health app intervention (IntelliCare) over 7 weeks. We compared engagement patterns over time (eg, frequency and days of use) between participants with high and low anxiety and between participants with high and low depression. We then used a linear mixed model to identify significant effects and evaluate the performance of the random forest and XGBoost classifiers in predicting weekly mood from baseline affect and engagement features. RESULTS: We observed differences in engagement patterns between the participants with high and low levels of anxiety and depression. The linear mixed model results varied by the feature set; these results revealed weak effects for several features of engagement, including duration-based metrics and frequency. The accuracy of predicting depressed mood varied according to the feature set and classifier. The feature set containing survey features and overall app engagement features achieved the best performance (accuracy: 84.6%; precision: 82.5%; recall: 64.4%; F1 score: 67.8%) when used with a random forest classifier. CONCLUSIONS: The results from the case study support the feasibility and potential of our analytic process for understanding the relationship between app engagement and mood outcomes in patients with breast cancer. The ability to leverage both self-report and engagement features to analyze and predict mood during an intervention could be used to enhance decision-making for researchers and clinicians and assist in developing more personalized interventions for patients with breast cancer.

Feasibility of ecological momentary assessment to study depressive symptoms among cancer caregivers.

OBJECTIVE: Ecological momentary assessment (EMA) may help with the development of more targeted interventions for caregivers' depression, yet the use of this method has been limited among cancer caregivers. This study aimed to demonstrate the feasibility of EMA among cancer caregivers and the use of EMA data to understand affective correlates of caregiver depressive symptoms. METHODS: Caregivers (N = 25) completed a depressive symptom assessment (Patient Health Questionnaire-8) and then received eight EMA survey prompts per day for 7 days. EMA surveys assessed affect on the orthogonal dimensions of valence and arousal. Participants completed feedback surveys regarding the EMA protocol at the conclusion of the week-long study. RESULTS: Of 32 caregivers approached, 25 enrolled and participated (78%), which exceeded the a priori feasibility cutoff of 55%. The prompt completion rate (59%, or 762 of 1,286 issued) did not exceed the a priori cutoff of 65%, although completion was not related to caregivers' age, employment status, physical health quality of life, caregiving stress, or depressive symptoms or the patients' care needs (ps > 0.22). Caregivers' feedback about their study experience was generally positive. Mixed-effects location scale modeling showed caregivers' higher depressive symptoms were related to overall higher reported negative affect and lower positive affect, but not to affective variability. CONCLUSIONS: Findings from this feasibility study refute potential concerns that an EMA design is too burdensome for distressed caregivers. Clinically, findings suggest the potential importance of not only strategies to reduce overall levels of negative affect, but also to increase opportunities for positive affect.

Using Graph Representation Learning to Predict Salivary Cortisol Levels in Pancreatic Cancer Patients.

Cortisol is a glucocorticoid hormone that is critical to immune system functioning. Studies show that prolonged exposure to high levels of cortisol can lead to a range of physical health ailments including the progression of tumor growth. The ability to monitor cortisol levels over time can therefore be used to facilitate decision-making during cancer treatment. However, collecting serum or saliva samples to monitor cortisol in situ is inconvenient, costly, and impractical. In this paper, we propose a general predictive modeling process that uses passively sensed actigraphy data to predict underlying salivary cortisol levels using graph representation learning. We compare machine learning models with handcrafted feature engineering and with graph representation learning, which includes Graph2Vec, FeatherGraph, GeoScattering and NetLSD. Our preliminary results generated from data from 10 newly diagnosed pancreatic cancer patients demonstrate that machine learning models with graph representation learning can outperform the handcrafted feature engineering to predict salivary cortisol levels.

Developing a Process for the Analysis of User Journeys and the Prediction of Dropout in Digital Health Interventions: Machine Learning Approach.

BACKGROUND: User 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. OBJECTIVE: The 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. METHODS: Steps 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. RESULTS: Accuracy 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. CONCLUSIONS: The 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.

Recruiting breast cancer patients for mHealth research: Obstacles to clinic-based recruitment for a mobile phone app intervention study.

BACKGROUND: Nearly half of newly diagnosed breast cancer patients will report clinically significant symptoms of depression and/or anxiety within the first year of diagnosis. Research on the trajectory of distress in cancer patients suggests that targeting patients early in the diagnostic pathway could be particularly impactful. Given the recent rise of smartphone adoption, apps are a convenient and accessible platform from which to deliver mental health support; however, little research has examined their potential impact among newly diagnosed cancer patients. One reason is likely due to the obstacles associated with in-clinic recruitment of newly diagnosed cancer patients for mHealth pilot studies. METHODS: This article draws from our experiences of a recently completed pilot study to test a suite of mental health apps in newly diagnosed breast cancer patients. Recruitment strategies included in-clinic pamphlets, flyers, and direct communication with clinicians. Surgical oncologists and research staff members approached eligible patients after a medical appointment. Research team members met with patients to provide informed consent and review the study schedule. RESULTS: Four domains of in-clinic recruitment challenges emerged: (a) coordination with clinic staff, (b) perceived burden among breast cancer patients, (c) limitations regarding the adoption and use of technology, and (d) availability of resources. Potential solutions are provided for each challenge. CONCLUSION: Recruitment of newly diagnosed cancer patients is a major challenge to conducting mobile intervention studies for researchers on a pilot-study budget. To realize the impact of mobile interventions for the most vulnerable cancer patient populations, health researchers must address barriers to in-clinic recruitment to provide vital preliminary data in proposals of large-scale research projects.

The Model of Gamification Principles for Digital Health Interventions: Evaluation of Validity and Potential Utility.

BACKGROUND: Although 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. OBJECTIVE: The 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. METHODS: A 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. RESULTS: Apps 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). CONCLUSIONS: Overall, 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.

Use of Mental Health Apps by Patients With Breast Cancer in the United States: Pilot Pre-Post Study.

BACKGROUND: Nearly 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. OBJECTIVE: The 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. METHODS: This 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. RESULTS: The 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. CONCLUSIONS: There 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.

Developing Mental or Behavioral Health Mobile Apps for Pilot Studies by Leveraging Survey Platforms: A Do-it-Yourself Process.

BACKGROUND: Behavioral 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. OBJECTIVE: This 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). METHODS: This 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. RESULTS: This 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. CONCLUSIONS: As 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.

A Novel Mobile Phone App Intervention With Phone Coaching to Reduce Symptoms of Depression in Survivors of Women's Cancer: Pre-Post Pilot Study.

BACKGROUND: Psychological 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. OBJECTIVE: This 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. METHODS: In 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. RESULTS: There 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. CONCLUSIONS: This 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.

Examining the relationship between changes in personality and depression in older adult cancer survivors.

Objectives: Despite widespread agreement that personality traits change across the lifespan into older adulthood, the association between changes in personality and depression among older adult cancer survivors is unknown. It was hypothesized that older adults with (vs. without) a past cancer diagnosis would experience an increase in neuroticism, and decreases in conscientiousness, agreeableness, openness, and extraversion, and that changes in these traits would mediate the relationship between receiving a cancer diagnosis and change in depression. Two hypotheses were tested in a cancer survivor sample. First, that increased chronic stressors and decreased physical health would mediate the link between personality change and increased depression. Second, that personality change would mediate the link between changes in chronic stressors/health and increased depression.Method: Secondary data analysis utilizing three waves of data from the Health and Retirement Study. Data was compiled from 5,217 participants, among whom 707 received a cancer diagnosis.Results: Older adults with (vs. without) a cancer diagnosis decreased in conscientiousness, which was associated with increased depression. Among cancer survivors, worsening chronic stressors/health mediated many pathways between personality change and an increased depression. Increased neuroticism mediated the link between worsening health/chronic stressors and increased depression.Conclusion: With the exception of conscientiousness, changes in personality did not mediate the link between cancer survivor status and depression. Among older adult cancer survivors, changes in personality traits may increase depression through worsening physical health and chronic stressors, potentially informing targeted interventions. Interventions that target increased neuroticism may be particularly useful in older adult cancer survivors.