TY - JOUR AU - Berrouiguet, Sofian AU - Billot, Romain AU - Larsen, Erik Mark AU - Lopez-Castroman, Jorge AU - Jaussent, Isabelle AU - Walter, Michel AU - Lenca, Philippe AU - Baca-García, Enrique AU - Courtet, Philippe PY - 2019/05/07 TI - An Approach for Data Mining of Electronic Health Record Data for Suicide Risk Management: Database Analysis for Clinical Decision Support JO - JMIR Ment Health SP - e9766 VL - 6 IS - 5 KW - clinical decision support system KW - data mining KW - electronic health KW - mobile phone KW - prevention KW - suicide KW - suicide attempts N2 - Background: In an electronic health context, combining traditional structured clinical assessment methods and routine electronic health?based data capture may be a reliable method to build a dynamic clinical decision-support system (CDSS) for suicide prevention. Objective: The aim of this study was to describe the data mining module of a Web-based CDSS and to identify suicide repetition risk in a sample of suicide attempters. Methods: We analyzed a database of 2802 suicide attempters. Clustering methods were used to identify groups of similar patients, and regression trees were applied to estimate the number of suicide attempts among these patients. Results: We identified 3 groups of patients using clustering methods. In addition, relevant risk factors explaining the number of suicide attempts were highlighted by regression trees. Conclusions: Data mining techniques can help to identify different groups of patients at risk of suicide reattempt. The findings of this study can be combined with Web-based and smartphone-based data to improve dynamic decision making for clinicians. UR - https://mental.jmir.org/2019/5/e9766/ UR - http://dx.doi.org/10.2196/mental.9766 UR - http://www.ncbi.nlm.nih.gov/pubmed/31066693 ID - info:doi/10.2196/mental.9766 ER - TY - JOUR AU - Lemey, Christophe AU - Larsen, Erik Mark AU - Devylder, Jordan AU - Courtet, Philippe AU - Billot, Romain AU - Lenca, Philippe AU - Walter, Michel AU - Baca-García, Enrique AU - Berrouiguet, Sofian PY - 2019/04/25 TI - Clinicians? Concerns About Mobile Ecological Momentary Assessment Tools Designed for Emerging Psychiatric Problems: Prospective Acceptability Assessment of the MEmind App JO - J Med Internet Res SP - e10111 VL - 21 IS - 4 KW - acceptability KW - feasibility studies KW - mobile applications KW - ecological momentary assessment KW - decision support systems, clinical KW - internet KW - outpatients KW - young adult KW - prodromal symptoms KW - mental health N2 - Background: Many mental disorders are preceded by a prodromal phase consisting of various attenuated and unspecific symptoms and functional impairment. Electronic health records are generally used to capture these symptoms during medical consultation. Internet and mobile technologies provide the opportunity to monitor symptoms emerging in patients? environments using ecological momentary assessment techniques to support preventive therapeutic decision making. Objective: The objective of this study was to assess the acceptability of a Web-based app designed to collect medical data during appointments and provide ecological momentary assessment features. Methods: We recruited clinicians at 4 community psychiatry departments in France to participate. They used the app to assess patients and to collect data after viewing a video of a young patient?s emerging psychiatric consultation. We then asked them to answer a short anonymous self-administered questionnaire that evaluated their experience, the acceptability of the app, and their habit of using new technologies. Results: Of 24 practitioners invited, 21 (88%) agreed to participate. Most of them were between 25 and 45 years old, and greater age was not associated with poorer acceptability. Most of the practitioners regularly used new technologies, and 95% (20/21) connected daily to the internet, with 70% (15/21) connecting 3 times a day or more. However, only 57% (12/21) reported feeling comfortable with computers. Of the clinicians, 86% (18/21) would recommend the tool to their colleagues and 67% (14/21) stated that they would be interested in daily use of the app. Most of the clinicians (16/21, 76%) found the interface easy to use and useful. However, several clinicians noted the lack of readability (8/21, 38%) and the need to improve ergonometric features (4/21, 19%), in particular to facilitate browsing through various subsections. Some participants (5/21, 24%) were concerned about the storage of medical data and most of them (11/21, 52%) seemed to be uncomfortable with this. Conclusions: We describe the first step of the development of a Web app combining an electronic health record and ecological momentary assessment features. This online tool offers the possibility to assess patients and to integrate medical data easily into face-to-face conditions. The acceptability of this app supports the feasibility of its broader implementation. This app could help to standardize assessment and to build up a strong database. Used in conjunction with robust data mining analytic techniques, such a database would allow exploration of risk factors, patterns of symptom evolution, and identification of distinct risk subgroups. UR - https://www.jmir.org/2019/4/e10111/ UR - http://dx.doi.org/10.2196/10111 UR - http://www.ncbi.nlm.nih.gov/pubmed/31021327 ID - info:doi/10.2196/10111 ER - TY - JOUR AU - Cho, Youngjun AU - Julier, J. Simon AU - Bianchi-Berthouze, Nadia PY - 2019/04/09 TI - Instant Stress: Detection of Perceived Mental Stress Through Smartphone Photoplethysmography and Thermal Imaging JO - JMIR Ment Health SP - e10140 VL - 6 IS - 4 KW - stress detection KW - mobile applications KW - photoplethysmography KW - thermography KW - psychophysiology KW - heart rate variability KW - physiological computing KW - affective computing KW - machine learning N2 - Background: A smartphone is a promising tool for daily cardiovascular measurement and mental stress monitoring. A smartphone camera?based photoplethysmography (PPG) and a low-cost thermal camera can be used to create cheap, convenient, and mobile monitoring systems. However, to ensure reliable monitoring results, a person must remain still for several minutes while a measurement is being taken. This is cumbersome and makes its use in real-life situations impractical. Objective: We proposed a system that combines PPG and thermography with the aim of improving cardiovascular signal quality and detecting stress responses quickly. Methods: Using a smartphone camera with a low-cost thermal camera added on, we built a novel system that continuously and reliably measures 2 different types of cardiovascular events: (1) blood volume pulse and (2) vasoconstriction/dilation-induced temperature changes of the nose tip. 17 participants, involved in stress-inducing mental workload tasks, measured their physiological responses to stressors over a short time period (20 seconds) immediately after each task. Participants reported their perceived stress levels on a 10-cm visual analog scale. For the instant stress inference task, we built novel low-level feature sets representing cardiovascular variability. We then used the automatic feature learning capability of artificial neural networks to improve the mapping between the extracted features and the self-reported ratings. We compared our proposed method with existing hand-engineered features-based machine learning methods. Results: First, we found that the measured PPG signals presented high quality cardiac cyclic information (mean pSQI: 0.755; SD 0.068). We also found that the measured thermal changes of the nose tip presented high-quality breathing cyclic information and filtering helped extract vasoconstriction/dilation-induced patterns with fewer respiratory effects (mean pSQI: from 0.714 to 0.157). Second, we found low correlations between the self-reported stress scores and the existing metrics of the cardiovascular signals (ie, heart rate variability and thermal directionality) from short measurements, suggesting they were not very dependent upon one another. Third, we tested the performance of the instant perceived stress inference method. The proposed method achieved significantly higher accuracies than existing precrafted features-based methods. In addition, the 17-fold leave-one-subject-out cross-validation results showed that combining both modalities produced higher accuracy than using PPG or thermal imaging only (PPG+Thermal: 78.33%; PPG: 68.53%; Thermal: 58.82%). The multimodal results are comparable to the state-of-the-art stress recognition methods that require long-term measurements. Finally, we explored effects of different data labeling strategies on the sensitivity of our inference methods. Our results showed the need for separation of and normalization between individual data. Conclusions: The results demonstrate the feasibility of using smartphone-based imaging for instant stress detection. Given that this approach does not need long-term measurements requiring attention and reduced mobility, we believe it is more suitable for mobile mental health care solutions in the wild. UR - https://mental.jmir.org/2019/4/e10140/ UR - http://dx.doi.org/10.2196/10140 UR - http://www.ncbi.nlm.nih.gov/pubmed/30964440 ID - info:doi/10.2196/10140 ER - TY - JOUR AU - Birk, Valentin Max AU - Mandryk, Lee Regan PY - 2019/01/08 TI - Improving the Efficacy of Cognitive Training for Digital Mental Health Interventions Through Avatar Customization: Crowdsourced Quasi-Experimental Study JO - J Med Internet Res SP - e10133 VL - 21 IS - 1 KW - cognitive therapy KW - computer-assisted therapy KW - video games KW - attentional bias KW - cognitive bias KW - motivation N2 - Background: The success of internet-based mental health interventions in practice?that is, in the wild?depends on the uptake and retention of the application and the user?s focused attention in the moment of use. Incorporating game-based motivational design into digital interventions delivered in the wild has been shown to increase uptake and retention in internet-based training; however, there are outstanding questions about the potential of game-based motivational strategies to increase engagement with a task in the moment of use and the effect on intervention efficacy. Objective: Designers of internet-based interventions need to know whether game-based motivational design strategies can increase in-the-moment engagement and thus improve digital interventions. The aim of this study was to investigate the effects of 1 motivational design strategy (avatar customization) in an example mental health intervention (computerized cognitive training for attention bias modification). Methods: We assigned 317 participants to either a customized avatar or an assigned avatar condition. After measuring state anxiety (State-Trait Anxiety Inventory), we randomly assigned half of the participants in each condition to either an attentional retraining condition (Attention Bias Modification Training) or a control condition. After training, participants were exposed to a negative mood induction using images with strong negative valance (International Affective Picture System), after which we measured state anxiety again. Results: Avatar customization decreased posttraining state anxiety when controlling for baseline state anxiety for those in the attentional retraining condition; however, those who did not train experienced decreased resilience to the negative mood induction (F1,252=6.86, P=.009, ?p2=.027). This interaction effect suggests that customization increased task engagement with the intervention in the moment of use. Avatar customization also increased avatar identification (F5,252=12.46, P<.001, R2=.23), regardless of condition (F1,252=.79, P=.38). Avatar identification reduced anxiety after the negative mood induction for participants who underwent training but increased poststimulus anxiety for participants who did not undergo training, further suggesting that customization increases engagement in the task (F1,252=6.19, P=.01). The beneficial effect of avatar customization on training was driven by participants who were low in their basic satisfaction of relatedness (F10,248=18.5, P<.001, R2=.43), which is important because these are the participants who are most likely in need of digital interventions for mental health. Conclusions: Our results suggest that applying motivational design?specifically avatar customization?is a viable strategy to increase engagement and subsequently training efficacy in a computerized cognitive task. UR - https://www.jmir.org/2019/1/e10133/ UR - http://dx.doi.org/10.2196/10133 UR - http://www.ncbi.nlm.nih.gov/pubmed/30622095 ID - info:doi/10.2196/10133 ER - TY - JOUR AU - Babbage, Camilla AU - Jackson, Margaret Georgina AU - Nixon, Elena PY - 2018/12/18 TI - Desired Features of a Digital Technology Tool for Self-Management of Well-Being in a Nonclinical Sample of Young People: Qualitative Study JO - JMIR Ment Health SP - e10067 VL - 5 IS - 4 KW - adolescence KW - young people KW - well-being KW - self-management KW - digital technology KW - E-health KW - coping strategies KW - mental health, help-seeking KW - qualitative N2 - Background: Adaptive coping behaviors can improve well-being for young people experiencing life stressors, while maladaptive coping can increase vulnerability to mental health problems in youth and into adulthood. Young people could potentially benefit from the use of digital technology tools to enhance their coping skills and overcome barriers in help-seeking behaviors. However, little is known about the desired digital technology use for self-management of well-being among young people in the general population. Objective: This is a small, qualitative study aimed at exploring what young people desire from digital technology tools for the self-management of their well-being. Methods: Young people aged 12-18 years were recruited from the general community to take part in semistructured interviews. Recorded data from the interviews were transcribed and analyzed using inductive thematic analysis. Results: In total, 14 participants were recruited and completed the study, with a mean age of 14.6 years (female n=3). None of the participants reported using any digital tools specifically designed to manage well-being. However, as indicated through the emerged themes, young people used digital technology to reduce their stress levels and manage their mood, mainly through games, music, and videos. Overall, identified themes showed that young people were keen on using such tools and desired certain facets and features of an ideal tool for self-management of well-being. Themes related to these facets indicated what young people felt a tool should do to improve well-being, including being immersed in a stress-free environment, being uplifting, and that such a tool would direct them to resources based on their needs. The feature-based themes suggested that young people wanted the tool to be flexible and enable engagement with others while also being sensitive to privacy. Conclusions: The young people interviewed in this study did not report engaging with digital technology specialized to improving well-being but instead used media already accessed in their daily lives in order to self-manage their psychological states. As a result, the variety of coping strategies reported and digital tools used was limited to the resources that were already being used for recreational and social purposes. These findings contribute to the scarce research into young people?s preferred use of digital technology tools for the self-management of their well-being. However, this was a small-scale study and the current participant sample is not representative of the general youth population. Therefore, the results are only tentative and warrant further investigation. UR - http://mental.jmir.org/2018/4/e10067/ UR - http://dx.doi.org/10.2196/10067 UR - http://www.ncbi.nlm.nih.gov/pubmed/30563820 ID - info:doi/10.2196/10067 ER - TY - JOUR AU - Berrouiguet, Sofian AU - Ramírez, David AU - Barrigón, Luisa María AU - Moreno-Muñoz, Pablo AU - Carmona Camacho, Rodrigo AU - Baca-García, Enrique AU - Artés-Rodríguez, Antonio PY - 2018/12/10 TI - Combining Continuous Smartphone Native Sensors Data Capture and Unsupervised Data Mining Techniques for Behavioral Changes Detection: A Case Series of the Evidence-Based Behavior (eB2) Study JO - JMIR Mhealth Uhealth SP - e197 VL - 6 IS - 12 KW - behavioral changes KW - data mining KW - mental disorders KW - sensors KW - wearables N2 - Background: The emergence of smartphones, wearable sensor technologies, and smart homes allows the nonintrusive collection of activity data. Thus, health-related events, such as activities of daily living (ADLs; eg, mobility patterns, feeding, sleeping, ...) can be captured without patients? active participation. We designed a system to detect changes in the mobility patterns based on the smartphone?s native sensors and advanced machine learning and signal processing techniques. Objective: The principal objective of this work is to assess the feasibility of detecting mobility pattern changes in a sample of outpatients with depression using the smartphone?s sensors. The proposed method processed the data acquired by the smartphone using an unsupervised detection technique. Methods: In this study, 38 outpatients from the Hospital Fundación Jiménez Díaz Psychiatry Department (Madrid, Spain) participated. The Evidence-Based Behavior (eB2) app was downloaded by patients on the day of recruitment and configured with the assistance of a physician. The app captured the following data: inertial sensors, physical activity, phone calls and message logs, app usage, nearby Bluetooth and Wi-Fi connections, and location. We applied a change-point detection technique to location data on a sample of 9 outpatients recruited between April 6, 2017 and December 14, 2017. The change-point detection was based only on location information, but the eB2 platform allowed for an easy integration of additional data. The app remained running in the background on patients? smartphone during the study participation. Results: The principal outcome measure was the identification of mobility pattern changes based on an unsupervised detection technique applied to the smartphone?s native sensors data. Here, results from 5 patients? records are presented as a case series. The eB2 system detected specific mobility pattern changes according to the patients? activity, which may be used as indicators of behavioral and clinical state changes. Conclusions: The proposed technique could automatically detect changes in the mobility patterns of outpatients who took part in this study. Assuming these mobility pattern changes correlated with behavioral changes, we have developed a technique that may identify possible relapses or clinical changes. Nevertheless, it is important to point out that the detected changes are not always related to relapses and that some clinical changes cannot be detected by the proposed method. UR - https://mhealth.jmir.org/2018/12/e197/ UR - http://dx.doi.org/10.2196/mhealth.9472 UR - http://www.ncbi.nlm.nih.gov/pubmed/30530465 ID - info:doi/10.2196/mhealth.9472 ER - TY - JOUR AU - Pulantara, Wayan I. AU - Parmanto, Bambang AU - Germain, Anne PY - 2018/12/07 TI - Clinical Feasibility of a Just-in-Time Adaptive Intervention App (iREST) as a Behavioral Sleep Treatment in a Military Population: Feasibility Comparative Effectiveness Study JO - J Med Internet Res SP - e10124 VL - 20 IS - 12 KW - just-in-time adaptive intervention KW - insomnia KW - sleep KW - mHealth KW - mobile health KW - interactive Resilience Enhancing Sleep Tactics (iREST) KW - behavioral therapy KW - brief behavioral therapy for insomnia KW - cognitive behavioral therapy for insomnia N2 - Background: Although evidence-based cognitive behavioral sleep treatments have been shown to be safe and effective, these treatments have limited scalability. Mobile health tools can address this scalability challenge. iREST, or interactive Resilience Enhancing Sleep Tactics, is a mobile health platform designed to provide a just-in-time adaptive intervention (JITAI) in the assessment, monitoring, and delivery of evidence-based sleep recommendations in a scalable and personalized manner. The platform includes a mobile phone?based patient app linked to a clinician portal. Objective: The first aim of the pilot study was to evaluate the effectiveness of JITAI using the iREST platform for delivering evidence-based sleep interventions in a sample of military service members and veterans. The second aim was to explore the potential effectiveness of this treatment delivery form relative to habitual in-person delivery. Methods: In this pilot study, military service members and veterans between the ages of 18 and 60 years who reported clinically significant service-related sleep disturbances were enrolled as participants. Participants were asked to use iREST for a period of 4 to 6 weeks during which time they completed a daily sleep/wake diary. Through the clinician portal, trained clinicians offered recommendations consistent with evidence-based behavioral sleep treatments on weeks 2 through 4. To explore potential effectiveness, self-report measures were used, including the Insomnia Severity Index (ISI), the Pittsburgh Sleep Quality Index (PSQI), and the PSQI Addendum for Posttraumatic Stress Disorder. Results: A total of 27 participants completed the posttreatment assessments. Between pre- and postintervention, clinically and statistically significant improvements in primary and secondary outcomes were detected (eg, a mean reduction on the ISI of 9.96, t26=9.99, P<.001). At posttreatment, 70% (19/27) of participants met the criteria for treatment response and 59% (16/27) achieved remission. Comparing these response and remission rates with previously published results for in-person trials showed no significant differences. Conclusion: Participants who received evidence-based recommendations from their assigned clinicians through the iREST platform showed clinically significant improvements in insomnia severity, overall sleep quality, and disruptive nocturnal disturbances. These findings are promising, and a larger noninferiority clinical trial is warranted. UR - https://www.jmir.org/2018/12/e10124/ UR - http://dx.doi.org/10.2196/10124 UR - http://www.ncbi.nlm.nih.gov/pubmed/30530452 ID - info:doi/10.2196/10124 ER - TY - JOUR AU - Miyoshi, Brandão Newton Shydeo AU - Azevedo-Marques, De João Mazzoncini AU - Alves, Domingos AU - Azevedo-Marques, De Paulo Mazzoncini PY - 2018/12/07 TI - An eHealth Platform for the Support of a Brazilian Regional Network of Mental Health Care (eHealth-Interop): Development of an Interoperability Platform for Mental Care Integration JO - JMIR Ment Health SP - e10129 VL - 5 IS - 4 KW - eHealth KW - mental health KW - health information exchange KW - health information interoperability KW - medical record linkage KW - continuity of patient care N2 - Background: The electronic exchange of health-related data can support different professionals and services to act in a more coordinated and transparent manner and make the management of health service networks more efficient. Although mental health care is one of the areas that can benefit from a secure health information exchange (HIE), as it usually involves long-term and multiprofessional care, there are few published studies on this topic, particularly in low- and middle-income countries. Objective: The aim of this study was to design, implement, and evaluate an electronic health (eHealth) platform that allows the technical and informational support of a Brazilian regional network of mental health care. This solution was to enable HIE, improve data quality, and identify and monitor patients over time and in different services. Methods: The proposed platform is based on client-server architecture to be deployed on the Web following a Web services communication model. The interoperability information model was based on international and Brazilian health standards. To test platform usage, we have utilized the case of the mental health care network of the XIII Regional Health Department of the São Paulo state, Brazil. Data were extracted from 5 different sources, involving 26 municipalities, and included national demographic data, data from primary health care, data from requests for psychiatric hospitalizations performed by community services, and data obtained from 2 psychiatric hospitals about hospitalizations. Data quality metrics such as accuracy and completeness were evaluated to test the proposed solution. Results: The eHealth-Interop integration platform was designed, developed, and tested. It contains a built-in terminology server and a record linkage module to support patients? identification and deduplication. The proposed interoperability environment was able to integrate information in the mental health care network case with the support of 5 international and national terminologies. In total, 27,353 records containing demographic and clinical data were integrated into eHealth-Interop. Of these records, 34.91% (9548/27,353) were identified as patients who were present in more than 1 data source with different levels of accuracy and completeness. The data quality analysis was performed on 26 demographic attributes for each integrable patient record, totaling 248,248 comparisons. In general, it was possible to achieve an improvement of 18.40% (45,678/248,248) in completeness and 1.10% (2731/248,248) in syntactic accuracy over the test dataset after integration and deduplication. Conclusions: The proposed platform established an eHealth solution to fill the gap in the availability and quality of information within a network of health services to improve the continuity of care and the health services management. It has been successfully applied in the context of mental health care and is flexible to be tested in other areas of care. UR - http://mental.jmir.org/2018/4/e10129/ UR - http://dx.doi.org/10.2196/10129 UR - http://www.ncbi.nlm.nih.gov/pubmed/30530455 ID - info:doi/10.2196/10129 ER - TY - JOUR AU - Doherty, Kevin AU - Barry, Marguerite AU - Marcano-Belisario, José AU - Arnaud, Bérenger AU - Morrison, Cecily AU - Car, Josip AU - Doherty, Gavin PY - 2018/11/27 TI - A Mobile App for the Self-Report of Psychological Well-Being During Pregnancy (BrightSelf): Qualitative Design Study JO - JMIR Ment Health SP - e10007 VL - 5 IS - 4 KW - engagement KW - mental health KW - mHealth KW - midwifery KW - perinatal depression KW - pregnancy KW - self-report KW - well-being KW - mobile phone N2 - Background: Maternal mental health impacts both parental well-being and childhood development. In the United Kingdom, 15% of women are affected by depression during pregnancy or within 1 year of giving birth. Suicide is a leading cause of perinatal maternal mortality, and it is estimated that >50% of perinatal depression cases go undiagnosed. Mobile technologies are potentially valuable tools for the early recognition of depressive symptoms, but complex design challenges must be addressed to enable their use in public health screening. Objective: The aim of this study was to explore the issues and challenges surrounding the use of mobile phones for the self-report of psychological well-being during pregnancy. Methods: This paper presents design research carried out as part of the development of BrightSelf, a mobile app for the self-report of psychological well-being during pregnancy. Design sessions were carried out with 38 participants, including pregnant women, mothers, midwives, and other health professionals. Overall, 19 hours of audio were fully transcribed and used as the basis of thematic analysis. Results: The study highlighted anxieties concerning the pregnancy journey, challenges surrounding current approaches to the appraisal of well-being in perinatal care, and the midwife-patient relationship. Designers should consider the framing of perinatal mental health technologies, the experience of self-report, supporting self-awareness and disclosure, providing value to users through both self-report and supplementary features, and designing for longitudinal engagement. Conclusions: This study highlights the needs, motivations, and anxieties of women with respect to technology use in pregnancy and implications for the design of mobile health technologies. UR - http://mental.jmir.org/2018/4/e10007/ UR - http://dx.doi.org/10.2196/10007 UR - http://www.ncbi.nlm.nih.gov/pubmed/30482742 ID - info:doi/10.2196/10007 ER - TY - JOUR AU - Rabbi, Mashfiqui AU - Aung, SH Min AU - Gay, Geri AU - Reid, Cary M. AU - Choudhury, Tanzeem PY - 2018/10/26 TI - Feasibility and Acceptability of Mobile Phone?Based Auto-Personalized Physical Activity Recommendations for Chronic Pain Self-Management: Pilot Study on Adults JO - J Med Internet Res SP - e10147 VL - 20 IS - 10 KW - chronic pain KW - machine learning KW - personalization KW - chronic back pain KW - reinforcement learning N2 - Background: Chronic pain is a globally prevalent condition. It is closely linked with psychological well-being, and it is often concomitant with anxiety, negative affect, and in some cases even depressive disorders. In the case of musculoskeletal chronic pain, frequent physical activity is beneficial. However, reluctance to engage in physical activity is common due to negative psychological associations (eg, fear) between movement and pain. It is known that encouragement, self-efficacy, and positive beliefs are effective to bolster physical activity. However, given that the majority of time is spent away from personnel who can give such encouragement, there is a great need for an automated ubiquitous solution. Objective: MyBehaviorCBP is a mobile phone app that uses machine learning on sensor-based and self-reported physical activity data to find routine behaviors and automatically generate physical activity recommendations that are similar to existing behaviors. Since the recommendations are based on routine behavior, they are likely to be perceived as familiar and therefore likely to be actualized even in the presence of negative beliefs. In this paper, we report the preliminary efficacy of MyBehaviorCBP based on a pilot trial on individuals with chronic back pain. Methods: A 5-week pilot study was conducted on people with chronic back pain (N=10). After a week long baseline period with no recommendations, participants received generic recommendations from an expert for 2 weeks, which served as the control condition. Then, in the next 2 weeks, MyBehaviorCBP recommendations were issued. An exit survey was conducted to compare acceptance toward the different forms of recommendations and map out future improvement opportunities. Results: In all, 90% (9/10) of participants felt positive about trying the MyBehaviorCBP recommendations, and no participant found the recommendations unhelpful. Several significant differences were observed in other outcome measures. Participants found MyBehaviorCBP recommendations easier to adopt compared to the control (?int=0.42, P<.001) on a 5-point Likert scale. The MyBehaviorCBP recommendations were actualized more (?int=0.46, P<.001) with an increase in approximately 5 minutes of further walking per day (?int=4.9 minutes, P=.02) compared to the control. For future improvement opportunities, participants wanted push notifications and adaptation for weather, pain level, or weekend/weekday. Conclusions: In the pilot study, MyBehaviorCBP?s automated approach was found to have positive effects. Specifically, the recommendations were actualized more, and perceived to be easier to follow. To the best of our knowledge, this is the first time an automated approach has achieved preliminary success to promote physical activity in a chronic pain context. Further studies are needed to examine MyBehaviorCBP?s efficacy on a larger cohort and over a longer period of time. UR - http://www.jmir.org/2018/10/e10147/ UR - http://dx.doi.org/10.2196/10147 UR - http://www.ncbi.nlm.nih.gov/pubmed/30368433 ID - info:doi/10.2196/10147 ER - TY - JOUR AU - Payton, Cobb Fay AU - Yarger, Kvasny Lynette AU - Pinter, Thomas Anthony PY - 2018/10/23 TI - Text Mining Mental Health Reports for Issues Impacting Today?s College Students: Qualitative Study JO - JMIR Ment Health SP - e10032 VL - 5 IS - 4 KW - text mining KW - mental health KW - college students KW - information and communication technologies N2 - Background: A growing number of college students are experiencing personal circumstances or encountering situations that feel overwhelming and negatively affect their academic studies and other aspects of life on campus. To meet this growing demand for counseling services, US colleges and universities are offering a growing variety of mental health services that provide support and services to students in distress. Objective: In this study, we explore mental health issues impacting college students using a corpus of news articles, foundation reports, and media stories. Mental health concerns within this population have been on the rise. Uncovering the most salient themes articulated in current news and literature reports can better enable higher education institutions to provide health services to its students. Methods: We used SAS Text Miner to analyze 165 references that were published from 2010 to 2015 and focused on mental health among college students. Key clusters were identified to reveal the themes that were most significant to the topic. Results: The final cluster analysis yielded six themes in students? mental health experiences in higher education (ie, age, race, crime, student services, aftermath, victim). Two themes, increasing demand for student services provided by campus counseling centers (113/165, 68.5%) and the increased mental health risks faced by racial and ethnic minorities (30/165, 18.2%), dominated the discourse. Conclusions: Higher education institutions are actively engaged in extending mental health services and offering targeted outreach to students of color. Cluster analysis identified that institutions are devoting more and innovative resources in response to the growing number students who experience mental health concerns. However, there is a need to focus on proactive approaches to mitigate the causes of mental health and the aftermath of a negative experience, particularly violence and sexual assault. Such strategies can potentially influence how students navigate their health information seeking and how information and communication technologies, including mobile apps, can partially address the needs of college students. UR - http://mental.jmir.org/2018/4/e10032/ UR - http://dx.doi.org/10.2196/10032 UR - http://www.ncbi.nlm.nih.gov/pubmed/30355565 ID - info:doi/10.2196/10032 ER - TY - JOUR AU - Palmius, Niclas AU - Saunders, A. Kate E. AU - Carr, Oliver AU - Geddes, R. John AU - Goodwin, M. Guy AU - De Vos, Maarten PY - 2018/10/22 TI - Group-Personalized Regression Models for Predicting Mental Health Scores From Objective Mobile Phone Data Streams: Observational Study JO - J Med Internet Res SP - e10194 VL - 20 IS - 10 KW - behavioral features KW - depression KW - geolocation KW - group-personalized model KW - interindividual variability KW - mental health KW - mental illness KW - objective behavioral markers N2 - Background: Objective behavioral markers of mental illness, often recorded through smartphones or wearable devices, have the potential to transform how mental health services are delivered and to help users monitor their own health. Linking objective markers to illness is commonly performed using population-level models, which assume that everyone is the same. The reality is that there are large levels of natural interindividual variability, both in terms of response to illness and in usual behavioral patterns, as well as intraindividual variability that these models do not consider. Objective: The objective of this study was to demonstrate the utility of splitting the population into subsets of individuals that exhibit similar relationships between their objective markers and their mental states. Using these subsets, ?group-personalized? models can be built for individuals based on other individuals to whom they are most similar. Methods: We collected geolocation data from 59 participants who were part of the Automated Monitoring of Symptom Severity study at the University of Oxford. This was an observational data collection study. Participants were diagnosed with bipolar disorder (n=20); borderline personality disorder (n=17); or were healthy controls (n=22). Geolocation data were collected using a custom Android app installed on participants? smartphones, and participants weekly reported their symptoms of depression using the 16-item quick inventory of depressive symptomatology questionnaire. Population-level models were built to estimate levels of depression using features derived from the geolocation data recorded from participants, and it was hypothesized that results could be improved by splitting individuals into subgroups with similar relationships between their behavioral features and depressive symptoms. We developed a new model using a Dirichlet process prior for splitting individuals into groups, with a Bayesian Lasso model in each group to link behavioral features with mental illness. The result is a model for each individual that incorporates information from other similar individuals to augment the limited training data available. Results: The new group-personalized regression model showed a significant improvement over population-level models in predicting mental health severity (P<.001). Analysis of subgroups showed that different groups were characterized by different features derived from raw geolocation data. Conclusions: This study demonstrates the importance of handling interindividual variability when developing models of mental illness. Population-level models do not capture nuances in how different individuals respond to illness, and the group-personalized model demonstrates a potential way to overcome these limitations when estimating mental state from objective behavioral features. UR - https://www.jmir.org/2018/10/e10194/ UR - http://dx.doi.org/10.2196/10194 UR - http://www.ncbi.nlm.nih.gov/pubmed/30348626 ID - info:doi/10.2196/10194 ER - TY - JOUR AU - Bradshaw, L. Emma AU - Sahdra, K. Baljinder AU - Calvo, A. Rafael AU - Mrvaljevich, Alex AU - Ryan, M. Richard PY - 2018/10/22 TI - Users? Intrinsic Goals Linked to Alcohol Dependence Risk Level and Engagement With a Health Promotion Website (Hello Sunday Morning): Observational Study JO - JMIR Ment Health SP - e10022 VL - 5 IS - 4 KW - alcohol dependence KW - aspirations KW - goals KW - self-determination theory KW - website engagement N2 - Background: Hello Sunday Morning (HSM) is a self-guided health promotion website with the aim to improve drinking culture. Members are encouraged to sign up for a 3-month period of alcohol abstention and record and track their progress and goals. Objective: This study used self-determination theory (SDT) to examine the nature of goals subscribed by HSM users to test the extent to which intrinsic goal pursuit was linked to lower alcohol dependency risk and higher engagement with the HSM website. Methods: HSM users (N=2216; 59.75%, 1324/2216, females; aged 18-79 years) completed the World Health Organization?s Alcohol Use Disorders Identification Test (WHO-AUDIT, which measures alcohol dependence risk level) at sign-up and at 4 and 6 months after sign-up. In addition, the website had a goals-subscription feature that allowed participants to share their goals. Two independent raters classified the goals according to a coding system we devised based on SDT, which proposes that intrinsic goals (eg, growth, relationships, community, and health) better promote positive outcomes than extrinsic goals (eg, wealth, fame, and image). Results: Although there was substantial (1016/2216, 45.84%) attrition of HSM users from sign-up to 6 months, the attrition rate could not be attributed to alcohol dependency risk because people in different WHO-AUDIT risk zones were equally likely to be missing at 4 and 6 months after sign-up. The SDT-driven coding of goals yielded the following categories: wealth and image (extrinsic goals); relationships, personal growth, community engagement, and physical health (intrinsic goals); and alcohol use-related goals (which were hard to classify as either extrinsic or intrinsic). Alcohol dependence risk level correlated positively with goals related to money (r=.16), personal growth (r=.17), relationships (r=.10), and alcohol use (r=.25). Website engagement correlated negatively with alcohol dependence risk level (r=.10) and positively with relationship (r=.10) and community goals (r=.12). Conclusions: HSM users with higher alcohol dependence risk tended to engage with the website less, but to the extent that they did, they tended to subscribe to goals related to alcohol use and improving their personal growth, relationships, and finances. In line with SDT, engagement with goals?particularly the intrinsic goals of connecting with close-others and the broader community?related to increased website engagement. Web-based tools intended to promote healthy behaviors in users may be effective in engaging their users if the users? experience on the website supports the pursuit of intrinsic goals. UR - http://mental.jmir.org/2018/4/e10022/ UR - http://dx.doi.org/10.2196/10022 UR - http://www.ncbi.nlm.nih.gov/pubmed/30348624 ID - info:doi/10.2196/10022 ER - TY - JOUR AU - Pung, Alison AU - Fletcher, Louise Susan AU - Gunn, Maree Jane PY - 2018/09/27 TI - Mobile App Use by Primary Care Patients to Manage Their Depressive Symptoms: Qualitative Study JO - J Med Internet Res SP - e10035 VL - 20 IS - 9 KW - mobile apps KW - depression KW - health care KW - general practice N2 - Background: Mobile apps are emerging as tools with the potential to revolutionize the treatment of mental health conditions such as depression. At the forefront of the community health sector, general practitioners are in a unique position to guide the integration of technology and depression management; however, little is currently known about how primary care patients with depressive symptoms are currently using apps. Objective: The objective of our study was to explore the natural patterns of mobile app use among patients with depressive symptoms to facilitate the understanding of the potential role for mobile apps in managing depressive symptoms in the community. Methods: Semistructured phone interviews were conducted with primary care patients in Victoria, Australia, who reported symptoms of depression and were enrolled in a larger randomized controlled trial of depression care. Interviews explored current depression management strategies and the use of mobile apps (if any). Interviews were audio-recorded and transcribed verbatim. Inductive thematic analysis was iteratively conducted using QSR NVivo 11 Pro to identify emergent themes. Results: A total of 16 participants, aged between 20 to 58 years, took part in the interviews with 11 reporting the use of at least one mobile app to manage depressive symptoms and 5 reporting no app use. A variety of apps were described including relaxation, mindfulness, cognitive, exercise, gaming, social media, and well-being apps to aid with depressive symptoms. Among users, there were the following 4 main patterns of app use: skill acquisition, social connectedness, inquisitive trial, and safety netting. Factors that influenced app use included accessibility, perceptions of technology, and personal compatibility. Health care providers also had a role in initiating app use. Conclusions: Mobile apps are being utilized for self-management of depressive symptoms by primary care patients. This study provided insight into the natural patterns and perspectives of app use, which enhanced the understanding of how this technology may be integrated into the toolbox for the management of depression. Trial Registration: Australian New Zealand Clinical Trials Registry ACTRN12616000537459; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=367152 (Archived at WebCite at http://www.webcitation.org/71Vf06X2T) UR - http://www.jmir.org/2018/9/e10035/ UR - http://dx.doi.org/10.2196/10035 UR - http://www.ncbi.nlm.nih.gov/pubmed/30262449 ID - info:doi/10.2196/10035 ER - TY - JOUR AU - Heraz, Alicia AU - Clynes, Manfred PY - 2018/08/30 TI - Recognition of Emotions Conveyed by Touch Through Force-Sensitive Screens: Observational Study of Humans and Machine Learning Techniques JO - JMIR Ment Health SP - e10104 VL - 5 IS - 3 KW - emotional artificial intelligence KW - human-computer interaction KW - smartphone KW - force-sensitive screens KW - mental health KW - positive computing KW - artificial intelligence KW - emotions KW - emotional intelligence N2 - Background: Emotions affect our mental health: they influence our perception, alter our physical strength, and interfere with our reason. Emotions modulate our face, voice, and movements. When emotions are expressed through the voice or face, they are difficult to measure because cameras and microphones are not often used in real life in the same laboratory conditions where emotion detection algorithms perform well. With the increasing use of smartphones, the fact that we touch our phones, on average, thousands of times a day, and that emotions modulate our movements, we have an opportunity to explore emotional patterns in passive expressive touches and detect emotions, enabling us to empower smartphone apps with emotional intelligence. Objective: In this study, we asked 2 questions. (1) As emotions modulate our finger movements, will humans be able to recognize emotions by only looking at passive expressive touches? (2) Can we teach machines how to accurately recognize emotions from passive expressive touches? Methods: We were interested in 8 emotions: anger, awe, desire, fear, hate, grief, laughter, love (and no emotion). We conducted 2 experiments with 2 groups of participants: good imagers and emotionally aware participants formed group A, with the remainder forming group B. In the first experiment, we video recorded, for a few seconds, the expressive touches of group A, and we asked group B to guess the emotion of every expressive touch. In the second experiment, we trained group A to express every emotion on a force-sensitive smartphone. We then collected hundreds of thousands of their touches, and applied feature selection and machine learning techniques to detect emotions from the coordinates of participant? finger touches, amount of force, and skin area, all as functions of time. Results: We recruited 117 volunteers: 15 were good imagers and emotionally aware (group A); the other 102 participants formed group B. In the first experiment, group B was able to successfully recognize all emotions (and no emotion) with a high 83.8% (769/918) accuracy: 49.0% (50/102) of them were 100% (450/450) correct and 25.5% (26/102) were 77.8% (182/234) correct. In the second experiment, we achieved a high 91.11% (2110/2316) classification accuracy in detecting all emotions (and no emotion) from 9 spatiotemporal features of group A touches. Conclusions: Emotions modulate our touches on force-sensitive screens, and humans have a natural ability to recognize other people?s emotions by watching prerecorded videos of their expressive touches. Machines can learn the same emotion recognition ability and do better than humans if they are allowed to continue learning on new data. It is possible to enable force-sensitive screens to recognize users? emotions and share this emotional insight with users, increasing users? emotional awareness and allowing researchers to design better technologies for well-being. UR - http://mental.jmir.org/2018/3/e10104/ UR - http://dx.doi.org/10.2196/10104 UR - http://www.ncbi.nlm.nih.gov/pubmed/30166276 ID - info:doi/10.2196/10104 ER - TY - JOUR AU - Pratap, Abhishek AU - Renn, N. Brenna AU - Volponi, Joshua AU - Mooney, D. Sean AU - Gazzaley, Adam AU - Arean, A. Patricia AU - Anguera, A. Joaquin PY - 2018/08/09 TI - Using Mobile Apps to Assess and Treat Depression in Hispanic and Latino Populations: Fully Remote Randomized Clinical Trial JO - J Med Internet Res SP - e10130 VL - 20 IS - 8 KW - mobile apps KW - smartphone KW - depression KW - Hispanics KW - Latinos KW - clinical trial KW - cognition KW - problem solving KW - mHealth KW - minority groups N2 - Background: Most people with mental health disorders fail to receive timely access to adequate care. US Hispanic/Latino individuals are particularly underrepresented in mental health care and are historically a very difficult population to recruit into clinical trials; however, they have increasing access to mobile technology, with over 75% owning a smartphone. This technology has the potential to overcome known barriers to accessing and utilizing traditional assessment and treatment approaches. Objective: This study aimed to compare recruitment and engagement in a fully remote trial of individuals with depression who either self-identify as Hispanic/Latino or not. A secondary aim was to assess treatment outcomes in these individuals using three different self-guided mobile apps: iPST (based on evidence-based therapeutic principles from problem-solving therapy, PST), Project Evolution (EVO; a cognitive training app based on cognitive neuroscience principles), and health tips (a health information app that served as an information control). Methods: We recruited Spanish and English speaking participants through social media platforms, internet-based advertisements, and traditional fliers in select locations in each state across the United States. Assessment and self-guided treatment was conducted on each participant's smartphone or tablet. We enrolled 389 Hispanic/Latino and 637 non-Hispanic/Latino adults with mild to moderate depression as determined by Patient Health Questionnaire-9 (PHQ-9) score?5 or related functional impairment. Participants were first asked about their preferences among the three apps and then randomized to their top two choices. Outcomes were depressive symptom severity (measured using PHQ-9) and functional impairment (assessed with Sheehan Disability Scale), collected over 3 months. Engagement in the study was assessed based on the number of times participants completed active surveys. Results: We screened 4502 participants and enrolled 1040 participants from throughout the United States over 6 months, yielding a sample of 348 active users. Long-term engagement surfaced as a key issue among Hispanic/Latino participants, who dropped from the study 2 weeks earlier than their non-Hispanic/Latino counterparts (P<.02). No significant differences were observed for treatment outcomes between those identifying as Hispanic/Latino or not. Although depressive symptoms improved (beta=?2.66, P=.006) over the treatment course, outcomes did not vary by treatment app. Conclusions: Fully remote mobile-based studies can attract a diverse participant pool including people from traditionally underserved communities in mental health care and research (here, Hispanic/Latino individuals). However, keeping participants engaged in this type of ?low-touch? research study remains challenging. Hispanic/Latino populations may be less willing to use mobile apps for assessing and managing depression. Future research endeavors should use a user-centered design to determine the role of mobile apps in the assessment and treatment of depression for this population, app features they would be interested in using, and strategies for long-term engagement. Trial Registration: Clinicaltrials.gov NCT01808976; https://clinicaltrials.gov/ct2/show/NCT01808976 (Archived by WebCite at http://www.webcitation.org/70xI3ILkz) UR - http://www.jmir.org/2018/8/e10130/ UR - http://dx.doi.org/10.2196/10130 UR - http://www.ncbi.nlm.nih.gov/pubmed/30093372 ID - info:doi/10.2196/10130 ER - TY - JOUR AU - Quiroz, Carlos Juan AU - Geangu, Elena AU - Yong, Hooi Min PY - 2018/08/08 TI - Emotion Recognition Using Smart Watch Sensor Data: Mixed-Design Study JO - JMIR Ment Health SP - e10153 VL - 5 IS - 3 KW - emotion recognition KW - accelerometer KW - supervised learning KW - psychology N2 - Background: Research in psychology has shown that the way a person walks reflects that person?s current mood (or emotional state). Recent studies have used mobile phones to detect emotional states from movement data. Objective: The objective of our study was to investigate the use of movement sensor data from a smart watch to infer an individual?s emotional state. We present our findings of a user study with 50 participants. Methods: The experimental design is a mixed-design study: within-subjects (emotions: happy, sad, and neutral) and between-subjects (stimulus type: audiovisual ?movie clips? and audio ?music clips?). Each participant experienced both emotions in a single stimulus type. All participants walked 250 m while wearing a smart watch on one wrist and a heart rate monitor strap on the chest. They also had to answer a short questionnaire (20 items; Positive Affect and Negative Affect Schedule, PANAS) before and after experiencing each emotion. The data obtained from the heart rate monitor served as supplementary information to our data. We performed time series analysis on data from the smart watch and a t test on questionnaire items to measure the change in emotional state. Heart rate data was analyzed using one-way analysis of variance. We extracted features from the time series using sliding windows and used features to train and validate classifiers that determined an individual?s emotion. Results: Overall, 50 young adults participated in our study; of them, 49 were included for the affective PANAS questionnaire and 44 for the feature extraction and building of personal models. Participants reported feeling less negative affect after watching sad videos or after listening to sad music, P<.006. For the task of emotion recognition using classifiers, our results showed that personal models outperformed personal baselines and achieved median accuracies higher than 78% for all conditions of the design study for binary classification of happiness versus sadness. Conclusions: Our findings show that we are able to detect changes in the emotional state as well as in behavioral responses with data obtained from the smartwatch. Together with high accuracies achieved across all users for classification of happy versus sad emotional states, this is further evidence for the hypothesis that movement sensor data can be used for emotion recognition. UR - http://mental.jmir.org/2018/3/e10153/ UR - http://dx.doi.org/10.2196/10153 UR - http://www.ncbi.nlm.nih.gov/pubmed/30089610 ID - info:doi/10.2196/10153 ER - TY - JOUR AU - Boonstra, W. Tjeerd AU - Nicholas, Jennifer AU - Wong, JJ Quincy AU - Shaw, Frances AU - Townsend, Samuel AU - Christensen, Helen PY - 2018/07/30 TI - Using Mobile Phone Sensor Technology for Mental Health Research: Integrated Analysis to Identify Hidden Challenges and Potential Solutions JO - J Med Internet Res SP - e10131 VL - 20 IS - 7 KW - passive sensing KW - mental health KW - ubiquitous computing KW - ethics KW - depression KW - mobile health KW - smartphone KW - wearable sensors N2 - Background: Mobile phone sensor technology has great potential in providing behavioral markers of mental health. However, this promise has not yet been brought to fruition. Objective: The objective of our study was to examine challenges involved in developing an app to extract behavioral markers of mental health from passive sensor data. Methods: Both technical challenges and acceptability of passive data collection for mental health research were assessed based on literature review and results obtained from a feasibility study. Socialise, a mobile phone app developed at the Black Dog Institute, was used to collect sensor data (Bluetooth, location, and battery status) and investigate views and experiences of a group of people with lived experience of mental health challenges (N=32). Results: On average, sensor data were obtained for 55% (Android) and 45% (iOS) of scheduled scans. Battery life was reduced from 21.3 hours to 18.8 hours when scanning every 5 minutes with a reduction of 2.5 hours or 12%. Despite this relatively small reduction, most participants reported that the app had a noticeable effect on their battery life. In addition to battery life, the purpose of data collection, trust in the organization that collects data, and perceived impact on privacy were identified as main factors for acceptability. Conclusions: Based on the findings of the feasibility study and literature review, we recommend a commitment to open science and transparent reporting and stronger partnerships and communication with users. Sensing technology has the potential to greatly enhance the delivery and impact of mental health care. Realizing this requires all aspects of mobile phone sensor technology to be rigorously assessed. UR - http://www.jmir.org/2018/7/e10131/ UR - http://dx.doi.org/10.2196/10131 UR - http://www.ncbi.nlm.nih.gov/pubmed/30061092 ID - info:doi/10.2196/10131 ER - TY - JOUR AU - Meng, Jingbo AU - Hussain, Ali Syed AU - Mohr, C. David AU - Czerwinski, Mary AU - Zhang, Mi PY - 2018/07/17 TI - Exploring User Needs for a Mobile Behavioral-Sensing Technology for Depression Management: Qualitative Study JO - J Med Internet Res SP - e10139 VL - 20 IS - 7 KW - mobile sensing KW - mental health KW - depression KW - counseling KW - user-centered design N2 - Background: Today, college students are dealing with depression at some of the highest rates in decades. As the primary mental health service provider, university counseling centers are limited in their capacity and efficiency to provide mental health care due to time constraints and reliance on students? self-reports. A mobile behavioral-sensing platform may serve as a solution to enhance the efficiency and accessibility of university counseling services. Objective: The main objectives of this study are to (1) understand the usefulness of a mobile sensing platform (ie, iSee) in improving counseling services and assisting students? self-management of their depression conditions, and (2) explore what types of behavioral targets (ie, meaningful information extracted from raw sensor data) and feedback to deliver from both clinician and students? perspectives. Methods: We conducted semistructured interviews with 9 clinicians and 12 students with depression recruited from a counseling center at a large Midwestern university. The interviews were 40-50 minutes long and were audio recorded and transcribed. The interview data were analyzed using thematic analysis with an inductive approach. Clinician and student interviews were analyzed separately for comparison. The process of extracting themes involved iterative coding, memo writing, theme revisits, and refinement. Results: From the clinician perspective, the mobile sensing platform helps to improve counseling service by providing objective evidence for clinicians and filling gaps in clinician-patient communication. Clinicians suggested providing students with their sensed behavioral targets organized around personalized goals. Clinicians also recommended delivering therapeutic feedback to students based on their sensed behavioral targets, including positive reinforcement, reflection reminders, and challenging negative thoughts. From the student perspective, the mobile sensing platform helps to ease continued self-tracking practices. Students expressed their need for integrated behavioral targets to understand correlations between behaviors and depression. They also pointed out that they would prefer to avoid seeing negative feedback. Conclusions: Although clinician and student participants shared views on the advantages of iSee in supporting university counseling, they had divergent opinions on the types of behavioral targets and feedback to be provided via iSee. This exploratory work gained initial insights into the design of a mobile sensing platform for depression management and informed a more conclusive research project for the future. UR - http://www.jmir.org/2018/7/e10139/ UR - http://dx.doi.org/10.2196/10139 UR - http://www.ncbi.nlm.nih.gov/pubmed/30021710 ID - info:doi/10.2196/10139 ER - TY - JOUR AU - Lambert, D. Jeffrey AU - Greaves, J. Colin AU - Farrand, Paul AU - Price, Lisa AU - Haase, M. Anne AU - Taylor, H. Adrian PY - 2018/07/16 TI - Web-Based Intervention Using Behavioral Activation and Physical Activity for Adults With Depression (The eMotion Study): Pilot Randomized Controlled Trial JO - J Med Internet Res SP - e10112 VL - 20 IS - 7 KW - psychological therapy KW - mood KW - anxiety KW - exercise KW - eHealth KW - feasibility KW - acceptability N2 - Background: Physical activity is a potentially effective treatment for depression and depressive relapse. However, promoting physical activity in people with depression is challenging. Interventions informed by theory and evidence are therefore needed to support people with depression to become more physically active. eMotion is a Web-based intervention combining behavioral activation and physical activity promotion for people in the community with symptoms of depression. Objective: The objectives were to assess the feasibility and acceptability of delivering eMotion to people in the community with symptoms of depression and to explore outcomes. Methods: Participants with elevated depressive symptoms were recruited from the community through various methods (eg, social media) and randomized to eMotion or a waiting list control group for 8 weeks. eMotion is an administratively supported weekly modular program that helps people use key behavior change techniques (eg, graded tasks, action planning, and self-monitoring) to re-engage in routine, pleasurable, and necessary activities, with a focus on physical activities. Feasibility data were collected that included the following: recruitment and trial retention rates; fidelity of intervention delivery, receipt, and enactment; and acceptability of the intervention and data collection procedures. Data were collected for the primary (depression) and secondary outcomes (eg, anxiety, physical activity, fidelity, and client satisfaction) at baseline and 2 months postrandomization using self-reported Web-based questionnaires and accelerometers. Delivery fidelity (logins, modules accessed, time spent) was tracked using Web usage statistics. Exploratory analyses were conducted on the primary and secondary outcomes. Results: Of the 183 people who contacted the research team, 62 were recruited and randomized. The mean baseline score was 14.6 (SD 3.2) on the 8-item Patient Health Questionnaire depression scale (PHQ-8). Of those randomized, 52 participants provided accelerometer-recorded physical activity data at baseline that showed a median of 35.8 (interquartile range [IQR] 0.0-98.6) minutes of moderate-to-vigorous physical activity (MVPA) recorded in at least 10-minute bouts per week, with only 13% (7/52) people achieving guideline levels (150 minutes of MVPA per week). In total, 81% (50/62) of participants provided follow-up data for the primary outcome (PHQ-8), but only 39% (24/62) provided follow-up accelerometer data. Within the intervention group, the median number of logins, modules accessed, and total minutes spent on eMotion was 3 (IQR 2.0-8.0), 3 (IQR 2.0-5.0), and 41.3 (IQR 18.9-90.4), respectively. Acceptability was mixed. Exploratory data analysis showed that PHQ-8 levels were lower for the intervention group than for the control group at 2 months postrandomization (adjusted mean difference ?3.6, 95% CI ?6.1 to ?1.1). Conclusions: It was feasible to deliver eMotion in UK communities to inactive populations. eMotion has the potential to be effective and is ready for testing in a full-scale trial. Further work is needed to improve engagement with both the intervention and data collection procedures. Trial Registration: ClinicalTrials.gov NCT03084055; https://clinicaltrials.gov/ct2/show/NCT03084055 (Archived by WebCite at http://www.webcitation.org/6zoyM8UXa) UR - http://www.jmir.org/2018/7/e10112/ UR - http://dx.doi.org/10.2196/10112 UR - http://www.ncbi.nlm.nih.gov/pubmed/30012547 ID - info:doi/10.2196/10112 ER - TY - JOUR AU - Boukhechba, Mehdi AU - Chow, Philip AU - Fua, Karl AU - Teachman, A. Bethany AU - Barnes, E. Laura PY - 2018/07/04 TI - Predicting Social Anxiety From Global Positioning System Traces of College Students: Feasibility Study JO - JMIR Ment Health SP - e10101 VL - 5 IS - 3 KW - mental health KW - mHealth KW - mobility KW - GPS KW - social anxiety disorder N2 - Background: Social anxiety is highly prevalent among college students. Current methodologies for detecting symptoms are based on client self-report in traditional clinical settings. Self-report is subject to recall bias, while visiting a clinic requires a high level of motivation. Assessment methods that use passively collected data hold promise for detecting social anxiety symptoms and supplementing self-report measures. Continuously collected location data may provide a fine-grained and ecologically valid way to assess social anxiety in situ. Objective: The objective of our study was to examine the feasibility of leveraging noninvasive mobile sensing technology to passively assess college students? social anxiety levels. Specifically, we explored the different relationships between mobility and social anxiety to build a predictive model that assessed social anxiety from passively generated Global Positioning System (GPS) data. Methods: We recruited 228 undergraduate participants from a Southeast American university. Social anxiety symptoms were assessed using self-report instruments at a baseline laboratory session. An app installed on participants? personal mobile phones passively sensed data from the GPS sensor for 2 weeks. The proposed framework supports longitudinal, dynamic tracking of college students to evaluate the relationship between their social anxiety and movement patterns in the college campus environment. We first extracted the following mobility features: (1) cumulative staying time at each different location, (2) the distribution of visits over time, (3) the entropy of locations, and (4) the frequency of transitions between locations. Next, we studied the correlation between these features and participants? social anxiety scores to enhance the understanding of how students? social anxiety levels are associated with their mobility. Finally, we used a neural network-based prediction method to predict social anxiety symptoms from the extracted daily mobility features. Results: Several mobility features correlated with social anxiety levels. Location entropy was negatively associated with social anxiety (during weekdays, r=?0.67; and during weekends, r=?0.51). More (vs less) socially anxious students were found to avoid public areas and engage in less leisure activities during evenings and weekends, choosing instead to spend more time at home after school (4 pm-12 am). Our prediction method based on extracted mobility features from GPS trajectories successfully classified participants as high or low socially anxious with an accuracy of 85% and predicted their social anxiety score (on a scale of 0-80) with a root-mean-square error of 7.06. Conclusions: Results indicate that extracting and analyzing mobility features may help to reveal how social anxiety symptoms manifest in the daily lives of college students. Given the ubiquity of mobile phones in our society, understanding how to leverage passively sensed data has strong potential to address the growing needs for mental health monitoring and treatment. UR - http://mental.jmir.org/2018/3/e10101/ UR - http://dx.doi.org/10.2196/10101 UR - http://www.ncbi.nlm.nih.gov/pubmed/29973337 ID - info:doi/10.2196/10101 ER - TY - JOUR AU - Rohani, Adam Darius AU - Tuxen, Nanna AU - Quemada Lopategui, Andrea AU - Kessing, Vedel Lars AU - Bardram, Eyvind Jakob PY - 2018/06/28 TI - Data-Driven Learning in High-Resolution Activity Sampling From Patients With Bipolar Depression: Mixed-Methods Study JO - JMIR Ment Health SP - e10122 VL - 5 IS - 2 KW - activities KW - behavior KW - behavioral activation KW - bipolar disorder KW - circadian rhythm KW - depression KW - hourly planning KW - psychotherapy KW - statistics N2 - Background: Behavioral activation is a pen and paper-based therapy form for treating depression. The patient registers their activity hourly, and together with the therapist, they agree on a plan to change behavior. However, with the limited clinical personnel, and a growing patient population, new methods are needed to advance behavioral activation. Objective: The objectives of this paper were to (1) automatically identify behavioral patterns through statistical analysis of the paper-based activity diaries, and (2) determine whether it is feasible to move the behavioral activation therapy format to a digital solution. Methods: We collected activity diaries from seven patients with bipolar depression, covering in total 2,480 hours of self-reported activities. A pleasure score, on a 1-10 rating scale, was reported for each activity. The activities were digitalized into 6 activity categories, and statistical analyses were conducted. Results: Across all patients, movement-related activities were associated with the highest pleasure score followed by social activities. On an individual level, through a nonparametric Wilcoxon Signed-Rank test, one patient had a statistically significant larger amount of spare time activities when feeling bad (z=?2.045, P=.041). Through a within-subject analysis of covariance, the patients were found to have a better day than the previous, if that previous day followed their diurnal rhythm (?=.265, P=.029). Furthermore, a second-order trend indicated that two hours of daily social activity was optimal for the patients (?2=?0.08, t (63)=?1.22, P=.23). Conclusions: The data-driven statistical approach was able to find patterns within the behavioral traits that could assist the therapist in as well as help design future technologies for behavioral activation. UR - http://mental.jmir.org/2018/2/e10122/ UR - http://dx.doi.org/10.2196/10122 UR - http://www.ncbi.nlm.nih.gov/pubmed/29954726 ID - info:doi/10.2196/10122 ER - TY - JOUR AU - Morris, R. Robert AU - Kouddous, Kareem AU - Kshirsagar, Rohan AU - Schueller, M. Stephen PY - 2018/06/26 TI - Towards an Artificially Empathic Conversational Agent for Mental Health Applications: System Design and User Perceptions JO - J Med Internet Res SP - e10148 VL - 20 IS - 6 KW - conversational agents KW - mental health KW - empathy KW - crowdsourcing KW - peer support N2 - Background: Conversational agents cannot yet express empathy in nuanced ways that account for the unique circumstances of the user. Agents that possess this faculty could be used to enhance digital mental health interventions. Objective: We sought to design a conversational agent that could express empathic support in ways that might approach, or even match, human capabilities. Another aim was to assess how users might appraise such a system. Methods: Our system used a corpus-based approach to simulate expressed empathy. Responses from an existing pool of online peer support data were repurposed by the agent and presented to the user. Information retrieval techniques and word embeddings were used to select historical responses that best matched a user?s concerns. We collected ratings from 37,169 users to evaluate the system. Additionally, we conducted a controlled experiment (N=1284) to test whether the alleged source of a response (human or machine) might change user perceptions. Results: The majority of responses created by the agent (2986/3770, 79.20%) were deemed acceptable by users. However, users significantly preferred the efforts of their peers (P<.001). This effect was maintained in a controlled study (P=.02), even when the only difference in responses was whether they were framed as coming from a human or a machine. Conclusions: Our system illustrates a novel way for machines to construct nuanced and personalized empathic utterances. However, the design had significant limitations and further research is needed to make this approach viable. Our controlled study suggests that even in ideal conditions, nonhuman agents may struggle to express empathy as well as humans. The ethical implications of empathic agents, as well as their potential iatrogenic effects, are also discussed. UR - http://www.jmir.org/2018/6/e10148/ UR - http://dx.doi.org/10.2196/10148 UR - http://www.ncbi.nlm.nih.gov/pubmed/29945856 ID - info:doi/10.2196/10148 ER - TY - JOUR AU - DelPozo-Banos, Marcos AU - John, Ann AU - Petkov, Nicolai AU - Berridge, Mark Damon AU - Southern, Kate AU - LLoyd, Keith AU - Jones, Caroline AU - Spencer, Sarah AU - Travieso, Manuel Carlos PY - 2018/06/22 TI - Using Neural Networks with Routine Health Records to Identify Suicide Risk: Feasibility Study JO - JMIR Ment Health SP - e10144 VL - 5 IS - 2 KW - suicide prevention KW - risk assessment KW - electronic health records KW - routine data KW - machine learning KW - artificial neural networks N2 - Background: Each year, approximately 800,000 people die by suicide worldwide, accounting for 1?2 in every 100 deaths. It is always a tragic event with a huge impact on family, friends, the community and health professionals. Unfortunately, suicide prevention and the development of risk assessment tools have been hindered by the complexity of the underlying mechanisms and the dynamic nature of a person?s motivation and intent. Many of those who die by suicide had contact with health services in the preceding year but identifying those most at risk remains a challenge. Objective: To explore the feasibility of using artificial neural networks with routinely collected electronic health records to support the identification of those at high risk of suicide when in contact with health services. Methods: Using the Secure Anonymised Information Linkage Databank UK, we extracted the data of those who died by suicide between 2001 and 2015 and paired controls. Looking at primary (general practice) and secondary (hospital admissions) electronic health records, we built a binary feature vector coding the presence of risk factors at different times prior to death. Risk factors included: general practice contact and hospital admission; diagnosis of mental health issues; injury and poisoning; substance misuse; maltreatment; sleep disorders; and the prescription of opiates and psychotropics. Basic artificial neural networks were trained to differentiate between the suicide cases and paired controls. We interpreted the output score as the estimated suicide risk. System performance was assessed with 10x10-fold repeated cross-validation, and its behavior was studied by representing the distribution of estimated risk across the cases and controls, and the distribution of factors across estimated risks. Results: We extracted a total of 2604 suicide cases and 20 paired controls per case. Our best system attained a mean error rate of 26.78% (SD 1.46; 64.57% of sensitivity and 81.86% of specificity). While the distribution of controls was concentrated around estimated risks < 0.5, cases were almost uniformly distributed between 0 and 1. Prescription of psychotropics, depression and anxiety, and self-harm increased the estimated risk by ~0.4. At least 95% of those presenting these factors were identified as suicide cases. Conclusions: Despite the simplicity of the implemented system, the proposed methodology obtained an accuracy like other published methods based on specialized questionnaire generated data. Most of the errors came from the heterogeneity of patterns shown by suicide cases, some of which were identical to those of the paired controls. Prescription of psychotropics, depression and anxiety, and self-harm were strongly linked with higher estimated risk scores, followed by hospital admission and long-term drug and alcohol misuse. Other risk factors like sleep disorders and maltreatment had more complex effects. UR - http://mental.jmir.org/2018/2/e10144/ UR - http://dx.doi.org/10.2196/10144 UR - http://www.ncbi.nlm.nih.gov/pubmed/29934287 ID - info:doi/10.2196/10144 ER - TY - JOUR AU - Bauer, M. Amy AU - Baldwin, A. Scott AU - Anguera, A. Joaquin AU - Areán, A. Patricia AU - Atkins, C. David PY - 2018/06/19 TI - Comparing Approaches to Mobile Depression Assessment for Measurement-Based Care: Prospective Study JO - J Med Internet Res SP - e10001 VL - 20 IS - 6 KW - Patient Health Questionnaire KW - depression KW - mobile health KW - symptom assessment N2 - Background: To inform measurement-based care, practice guidelines suggest routine symptom monitoring, often on a weekly or monthly basis. Increasingly, patient-provider contacts occur remotely (eg, by telephone and Web-based portals), and mobile health tools can now monitor depressed mood daily or more frequently. However, the reliability and utility of daily ratings are unclear. Objective: This study aimed to examine the association between a daily depressive symptom measure and the Patient Health Questionnaire-9 (PHQ-9), the most widely adopted depression self-report measure, and compare how well these 2 assessment methods predict patient outcomes. Methods: A total of 547 individuals completed smartphone-based measures, including the Patient Health Questionnaire-2 (PHQ-2) modified for daily administration, the PHQ-9, and the Sheehan Disability Scale. Multilevel factor analyses evaluated the reliability of latent depression based on the PHQ-2 (for repeated measures) between weeks 2 and 4 and its correlation with the PHQ-9 at week 4. Regression models predicted week 8 depressive symptoms and disability ratings with daily PHQ-2 and PHQ-9. Results: The daily PHQ-2 and PHQ-9 are highly reliable (range: 0.80-0.88) and highly correlated (r=.80). Findings were robust across demographic groups (age, gender, and ethnic minority status). Daily PHQ-2 and PHQ-9 were comparable in predicting week 8 disability and were independent predictors of week 8 depressive symptoms and disability, though the unique contribution of the PHQ-2 was small in magnitude. Conclusions: Daily completion of the PHQ-2 is a reasonable proxy for the PHQ-9 and is comparable to the PHQ-9 in predicting future outcomes. Mobile assessment methods offer researchers and clinicians reliable and valid new methods for depression assessment that may be leveraged for measurement-based depression care. UR - http://www.jmir.org/2018/6/e10001/ UR - http://dx.doi.org/10.2196/10001 UR - http://www.ncbi.nlm.nih.gov/pubmed/29921564 ID - info:doi/10.2196/10001 ER - TY - JOUR AU - Ng, Ada AU - Reddy, Madhu AU - Zalta, K. Alyson AU - Schueller, M. Stephen PY - 2018/06/15 TI - Veterans? Perspectives on Fitbit Use in Treatment for Post-Traumatic Stress Disorder: An Interview Study JO - JMIR Ment Health SP - e10415 VL - 5 IS - 2 KW - fitness trackers KW - patient generated health data KW - consumer health informatics KW - stress disorders, post-traumatic KW - PTSD KW - mental health KW - veterans N2 - Background: The increase in availability of patient data through consumer health wearable devices and mobile phone sensors provides opportunities for mental health treatment beyond traditional self-report measurements. Previous studies have suggested that wearables can be effectively used to benefit the physical health of people with mental health issues, but little research has explored the integration of wearable devices into mental health care. As such, early research is still necessary to address factors that might impact integration including patients' motivations to use wearables and their subsequent data. Objective: The aim of this study was to gain an understanding of patients? motivations to use or not to use wearables devices during an intensive treatment program for post-traumatic stress disorder (PTSD). During this treatment, they received a complementary Fitbit. We investigated the following research questions: How did the veterans in the intensive treatment program use their Fitbit? What are contributing motivators for the use and nonuse of the Fitbit? Methods: We conducted semistructured interviews with 13 veterans who completed an intensive treatment program for PTSD. We transcribed and analyzed interviews using thematic analysis. Results: We identified three major motivations for veterans to use the Fitbit during their time in the program: increase self-awareness, support social interactions, and give back to other veterans. We also identified three major reasons certain features of the Fitbit were not used: lack of clarity around the purpose of the Fitbit, lack of meaning in the Fitbit data, and challenges in the veteran-provider relationship. Conclusions: To integrate wearable data into mental health treatment programs, it is important to understand the patient?s perspectives and motivations in using wearables. We also discuss how the military culture and PTSD may have contributed to our participants' behaviors and attitudes toward Fitbit usage. We conclude with possible approaches for integrating patient-generated data into mental health treatment settings that may address the challenges we identified. UR - http://mental.jmir.org/2018/2/e10415/ UR - http://dx.doi.org/10.2196/10415 UR - http://www.ncbi.nlm.nih.gov/pubmed/29907556 ID - info:doi/10.2196/10415 ER - TY - JOUR AU - Kornfield, Rachel AU - Sarma, K. Prathusha AU - Shah, V. Dhavan AU - McTavish, Fiona AU - Landucci, Gina AU - Pe-Romashko, Klaren AU - Gustafson, H. David PY - 2018/06/12 TI - Detecting Recovery Problems Just in Time: Application of Automated Linguistic Analysis and Supervised Machine Learning to an Online Substance Abuse Forum JO - J Med Internet Res SP - e10136 VL - 20 IS - 6 KW - self-help groups KW - substance-related disorders KW - supervised machine learning KW - social support KW - health communication N2 - Background: Online discussion forums allow those in addiction recovery to seek help through text-based messages, including when facing triggers to drink or use drugs. Trained staff (or ?moderators?) may participate within these forums to offer guidance and support when participants are struggling but must expend considerable effort to continually review new content. Demands on moderators limit the scalability of evidence-based digital health interventions. Objective: Automated identification of recovery problems could allow moderators to engage in more timely and efficient ways with participants who are struggling. This paper aimed to investigate whether computational linguistics and supervised machine learning can be applied to successfully flag, in real time, those discussion forum messages that moderators find most concerning. Methods: Training data came from a trial of a mobile phone-based health intervention for individuals in recovery from alcohol use disorder, with human coders labeling discussion forum messages according to whether or not authors mentioned problems in their recovery process. Linguistic features of these messages were extracted via several computational techniques: (1) a Bag-of-Words approach, (2) the dictionary-based Linguistic Inquiry and Word Count program, and (3) a hybrid approach combining the most important features from both Bag-of-Words and Linguistic Inquiry and Word Count. These features were applied within binary classifiers leveraging several methods of supervised machine learning: support vector machines, decision trees, and boosted decision trees. Classifiers were evaluated in data from a later deployment of the recovery support intervention. Results: To distinguish recovery problem disclosures, the Bag-of-Words approach relied on domain-specific language, including words explicitly linked to substance use and mental health (?drink,? ?relapse,? ?depression,? and so on), whereas the Linguistic Inquiry and Word Count approach relied on language characteristics such as tone, affect, insight, and presence of quantifiers and time references, as well as pronouns. A boosted decision tree classifier, utilizing features from both Bag-of-Words and Linguistic Inquiry and Word Count performed best in identifying problems disclosed within the discussion forum, achieving 88% sensitivity and 82% specificity in a separate cohort of patients in recovery. Conclusions: Differences in language use can distinguish messages disclosing recovery problems from other message types. Incorporating machine learning models based on language use allows real-time flagging of concerning content such that trained staff may engage more efficiently and focus their attention on time-sensitive issues. UR - http://www.jmir.org/2018/6/e10136/ UR - http://dx.doi.org/10.2196/10136 UR - http://www.ncbi.nlm.nih.gov/pubmed/29895517 ID - info:doi/10.2196/10136 ER - TY - JOUR AU - Schueller, M. Stephen AU - Neary, Martha AU - O'Loughlin, Kristen AU - Adkins, C. Elizabeth PY - 2018/06/11 TI - Discovery of and Interest in Health Apps Among Those With Mental Health Needs: Survey and Focus Group Study JO - J Med Internet Res SP - e10141 VL - 20 IS - 6 KW - mHealth KW - mental health KW - mobile apps KW - consumer preference KW - focus groups N2 - Background: A large number of health apps are available directly to consumers through app marketplaces. Little information is known, however, about how consumers search for these apps and which factors influence their uptake, adoption, and long-term use. Objective: The aim of this study was to understand what people look for when they search for health apps and the aspects and features of those apps that consumers find appealing. Methods: Participants were recruited from Northwestern University?s Center for Behavioral Intervention Technologies? research registry of individuals with mental health needs. Most participants (n=811) completed a survey asking about their use and interest in health and mental health apps. Local participants were also invited to participate in focus groups. A total of 7 focus groups were conducted with 30 participants that collected more detailed information about their use and interest in health and mental health apps. Results: Survey participants commonly found health apps through social media (45.1%, 366/811), personal searches (42.7%, 346/811), or word of mouth (36.9%, 299/811), as opposed to professional sources such as medical providers (24.6%, 200/811). From the focus groups, common themes related to uptake and use of health apps included the importance of personal use before adoption, specific features that users found desirable, and trusted sources either developing or promoting the apps. Conclusions: As the number of mental health and health apps continue to increase, it is imperative to better understand the factors that impact people?s adoption and use of such technologies. Our findings indicated that a number of factors?ease of use, aesthetics, and individual experience?drove adoption and use and highlighted areas of focus for app developers and disseminators. UR - http://www.jmir.org/2018/6/e10141/ UR - http://dx.doi.org/10.2196/10141 UR - http://www.ncbi.nlm.nih.gov/pubmed/29891468 ID - info:doi/10.2196/10141 ER - TY - JOUR AU - Grigorash, Alexander AU - O'Neill, Siobhan AU - Bond, Raymond AU - Ramsey, Colette AU - Armour, Cherie AU - Mulvenna, D. Maurice PY - 2018/06/11 TI - Predicting Caller Type From a Mental Health and Well-Being Helpline: Analysis of Call Log Data JO - JMIR Ment Health SP - e47 VL - 5 IS - 2 KW - data mining KW - machine learning KW - clustering KW - classification KW - mental health KW - suicide N2 - Background: This paper presents an analysis of call data records pertaining to a telephone helpline in Ireland among individuals seeking mental health and well-being support and among those who are in a suicidal crisis. Objective: The objective of our study was to examine whether rule sets generated from decision tree classification, trained using features derived from callers? several initial calls, could be used to predict what caller type they would become. Methods: Machine learning techniques were applied to the call log data, and five distinct patterns of caller behaviors were revealed, each impacting the helpline capacity in different ways. Results: The primary findings of this study indicate that a significant model (P<.001) for predicting caller type from call log data obtained from the first 8 calls is possible. This indicates an association between callers? behavior exhibited during initial calls and their behavior over the lifetime of using the service. Conclusions: These data-driven findings contribute to advanced workload forecasting for operational management of the telephone-based helpline and inform the literature on helpline caller behavior in general. UR - http://mental.jmir.org/2018/2/e47/ UR - http://dx.doi.org/10.2196/mental.9946 UR - http://www.ncbi.nlm.nih.gov/pubmed/29891472 ID - info:doi/10.2196/mental.9946 ER - TY - JOUR AU - Stawarz, Katarzyna AU - Preist, Chris AU - Tallon, Debbie AU - Wiles, Nicola AU - Coyle, David PY - 2018/06/06 TI - User Experience of Cognitive Behavioral Therapy Apps for Depression: An Analysis of App Functionality and User Reviews JO - J Med Internet Res SP - e10120 VL - 20 IS - 6 KW - mental health KW - mobile apps KW - cognitive behavioral therapy KW - depression KW - user experience KW - mHealth N2 - Background: Hundreds of mental health apps are available to the general public. With increasing pressures on health care systems, they offer a potential way for people to support their mental health and well-being. However, although many are highly rated by users, few are evidence-based. Equally, our understanding of what makes apps engaging and valuable to users is limited. Objective: The aim of this paper was to analyze functionality and user opinions of mobile apps purporting to support cognitive behavioral therapy for depression and to explore key factors that have an impact on user experience and support engagement. Methods: We systematically identified apps described as being based on cognitive behavioral therapy for depression. We then conducted 2 studies. In the first, we analyzed the therapeutic functionality of apps. This corroborated existing work on apps? fidelity to cognitive behavioral therapy theory, but we also extended prior work by examining features designed to support user engagement. Engagement features found in cognitive behavioral therapy apps for depression were compared with those found in a larger group of apps that support mental well-being in a more general sense. Our second study involved a more detailed examination of user experience, through a thematic analysis of publicly available user reviews of cognitive behavioral therapy apps for depression. Results: We identified 31 apps that purport to be based on cognitive behavioral therapy for depression. Functionality analysis (study 1) showed that they offered an eclectic mix of features, including many not based on cognitive behavioral therapy practice. Cognitive behavioral therapy apps used less varied engagement features compared with 253 other mental well-being apps. The analysis of 1287 user reviews of cognitive behavioral therapy apps for depression (study 2) showed that apps are used in a wide range of contexts, both replacing and augmenting therapy, and allowing users to play an active role in supporting their mental health and well-being. Users, including health professionals, valued and used apps that incorporated both core cognitive behavioral therapy and non-cognitive behavioral therapy elements, but concerns were also expressed regarding the unsupervised use of apps. Positivity was seen as important to engagement, for example, in the context of automatic thoughts, users expressed a preference to capture not just negative but also positive ones. Privacy, security, and trust were crucial to the user experience. Conclusions: Cognitive behavioral therapy apps for depression need to improve with respect to incorporating evidence-based cognitive behavioral therapy elements. Equally, a positive user experience is dependent on other design factors, including consideration of varying contexts of use. App designers should be able to clearly identify the therapeutic basis of their apps, but they should also draw on evidence-based strategies to support a positive and engaging user experience. The most effective apps are likely to strike a balance between evidence-based cognitive behavioral therapy strategies and evidence-based design strategies, including the possibility of eclectic therapeutic techniques. UR - http://www.jmir.org/2018/6/e10120/ UR - http://dx.doi.org/10.2196/10120 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/10120 ER - TY - JOUR AU - Bauer, M. Amy AU - Hodsdon, Sarah AU - Bechtel, M. Jared AU - Fortney, C. John PY - 2018/06/06 TI - Applying the Principles for Digital Development: Case Study of a Smartphone App to Support Collaborative Care for Rural Patients With Posttraumatic Stress Disorder or Bipolar Disorder JO - J Med Internet Res SP - e10048 VL - 20 IS - 6 KW - mHealth KW - mental health KW - primary health care KW - rural health KW - post-traumatic stress disorders KW - PTSD KW - bipolar disorder KW - depression N2 - Background: Despite a proliferation of patient-facing mobile apps for mental disorders, there is little literature guiding efforts to incorporate mobile tools into clinical care delivery and integrate patient-generated data into care processes for patients with complex psychiatric disorders. Objective: The aim of this study was to seek to gain an understanding of how to incorporate a patient-provider mobile health (mHealth) platform to support the delivery of integrated primary care?based mental health services (Collaborative Care) to rural patients with posttraumatic stress disorder and/or bipolar disorder. Methods: Using the Principles for Digital Development as a framework, we describe our experience designing, developing, and deploying a mobile system to support Collaborative Care. The system consists of a patient-facing smartphone app that integrates with a Web-based clinical patient registry used by behavioral health care managers and consulting psychiatrists. Throughout development, we engaged representatives from the system?s two user types: (1) providers, who use the Web-based registry and (2) patients, who directly use the mobile app. We extracted mobile metadata to describe the early adoption and use of the system by care managers and patients and report preliminary results from an in-app patient feedback survey that includes a System Usability Scale (SUS). Results: Each of the nine Principles for Digital Development is illustrated with examples. The first 10 patients to use the smartphone app have completed symptom measures on average every 14 days over an average period of 20 weeks. The mean SUS score at week 8 among four patients who completed this measure was 91.9 (range 72.5-100). We present lessons learned about the technical and training requirements for integration into practice that can inform future efforts to incorporate health technologies to improve care for patients with psychiatric conditions. Conclusions: Adhering to the Principles for Digital Development, we created and deployed an mHealth system to support Collaborative Care for patients with complex psychiatric conditions in rural health centers. Preliminary data among the initial users support high system usability and show promise for sustained use. On the basis of our experience, we propose five additional principles to extend this framework and inform future efforts to incorporate health technologies to improve care for patients with psychiatric conditions: design for public health impact, add value for all users, test the product and the process, acknowledge disruption, and anticipate variability. UR - http://www.jmir.org/2018/6/e10048/ UR - http://dx.doi.org/10.2196/10048 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/10048 ER - TY - JOUR AU - Heilemann, V. MarySue AU - Martinez, Adrienne AU - Soderlund, D. Patricia PY - 2018/05/02 TI - A Mental Health Storytelling Intervention Using Transmedia to Engage Latinas: Grounded Theory Analysis of Participants? Perceptions of the Story?s Main Character JO - J Med Internet Res SP - e10028 VL - 20 IS - 5 KW - depression KW - anxiety KW - transmedia storytelling KW - Internet KW - cell phone KW - mental health KW - eHealth KW - mood disorders KW - smartphone N2 - Background: Transmedia storytelling was used to attract English-speaking Latina women with elevated symptoms of depression and anxiety to engage in an intervention that included videos and a webpage with links to symptom management resources. However, a main character for the storyline who was considered dynamic, compelling, and relatable by the target group was needed. Objective: We conducted interviews with 28 English-speaking Latinas (target group) with elevated symptoms of depression or anxiety who participated in an Internet-accessible transmedia storytelling intervention. The objective of this study was to examine participants? perceptions of the lead character of the story. Development of this character was informed by deidentified data from previous studies with members of the target group. Critique of the character from a panel of therapists informed editing, as did input from women of the target group. Methods: All interviews were conducted via telephone, audio-recorded, and transcribed. Data analysis was guided by grounded theory methodology. Results: Participants embraced the main character, Catalina, related to her as a person with an emotional life and a temporal reality, reported that they learned from her and wanted more episodes that featured her and her life. Grounded theory analysis led to the development of one category (She ?just felt so real?: relating to Catalina as a real person with a past, present, and future) with 4 properties. Properties included (1) relating emotionally to Catalina?s vulnerability, (2) recognizing shared experiences, (3) needing to support others while simultaneously lacking self-support, and (4) using Catalina as a springboard for imagining alternative futures. Participants found Catalina?s efforts to pursue mental health treatment to be meaningful and led them to compare themselves to her and consider how they might pursue treatment themselves. Conclusions: When creating a story-based mental health intervention to be delivered through an app, regardless of type, careful development of the main character is valuable. Theoretical guidance, previous deidentified data from the target group, critique from key stakeholders and members of the target group, and preliminary testing are likely to enhance the main character?s relatability and appropriateness, which can increase sustained engagement. UR - http://www.jmir.org/2018/5/e10028/ UR - http://dx.doi.org/10.2196/10028 UR - http://www.ncbi.nlm.nih.gov/pubmed/29720357 ID - info:doi/10.2196/10028 ER -