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Journal Description

JMIR Mental Health (JMH, ISSN 2368-7959) is a PubMed-indexed, peer-reviewed sister journal of JMIR, the leading eHealth journal by Impact Factor. (The projected inofficial impact factor for JMIR Mental Health is about 3.0)

JMIR Mental Health focusses on digital health and Internet interventions, technologies and electronic innovations (software and hardware) for mental health, addictions, online counselling and behaviour change. This includes formative evaluation and system descriptions, theoretical papers, review papers, viewpoint/vision papers, and rigorous evaluations.

JMIR Mental Health publishes even faster and has a broader scope with including papers which are more technical or more formative/developmental than what would be published in the Journal of Medical Internet Research

JMIR Mental Health features a rapid and thorough peer-review process, professional copyediting, professional production of PDF, XHTML, and XML proofs. The journal is indexed in PubMed, PubMed Central, and ESCI (Emerging Sources Citation Index).

JMIR Mental Health adheres to the same quality standards as JMIR and all articles published here are also cross-listed in the Table of Contents of JMIR, the worlds' leading medical journal in health sciences / health services research and health informatics.

 

Recent Articles:

  • Source: iStock by Getty Images; Copyright: Dean Mitchell; URL: https://www.istockphoto.com/gb/photo/happy-father-and-daughter-using-digital-tablet-at-home-gm472122447-33241530; License: Licensed by the authors.

    Using Mobile Technology to Provide Personalized Reminiscence for People Living With Dementia and Their Carers: Appraisal of Outcomes From a...

    Abstract:

    Background: Dementia is an international research priority. Reminiscence is an intervention that prompts memories and has been widely used as a therapeutic approach for people living with dementia. We developed a novel iPad app to support home-based personalized reminiscence. It is crucial that technology-enabled reminiscence interventions are appraised. Objective: We sought to measure the effect of technology-enabled reminiscence on mutuality (defined as the level of “closeness” between an adult living with dementia and their carer), quality of carer and patient relationship, and subjective well-being. Methods: A 19-week personalized reminiscence intervention facilitated by a program of training and a bespoke iPad app was delivered to people living with dementia and their family carers at their own homes. Participants (N=60) were recruited in dyads from a cognitive rehabilitation team affiliated with a large UK health care organization. Each dyad comprised a person living with early to moderate dementia and his or her family carer. Outcome measurement data were collected at baseline, midpoint, and intervention closure. Results: Participants living with dementia attained statistically significant increases in mutuality, quality of carer and patient relationship, and subjective well-being (P<.001 for all 3) from baseline to endpoint. Carers attained nonsignificant increases in mutuality and quality of carer and patient relationship and a nonsignificant decrease in subjective well-being. Conclusions: Our results indicate that individual-specific reminiscence supported by an iPad app may be efficient in the context of early to moderate dementia. A robust randomized controlled trial of technology-enabled personalized reminiscence is warranted.

  • Elderly woman using virtual reality. Source: Image created by the Authors; Copyright: The Authors; URL: http://mental.jmir.org/2018/3/e55/; License: Licensed by JMIR.

    Digital Technology for Caregivers of People With Psychosis: Systematic Review

    Abstract:

    Background: Psychotic disorders are severe mental health conditions that adversely affect the quality of life and life expectancy. Schizophrenia, the most common and severe form of psychosis affects 21 million people globally. Informal caregivers (families) are known to play an important role in facilitating patient recovery outcomes, although their own health and well-being could be adversely affected by the illness. The application of novel digital interventions in mental health care for patient groups is rapidly expanding; interestingly, however, far less is known about their role with family caregivers. Objective: This study aimed to systematically identify the application of digital interventions that focus on informal caregivers of people with psychosis and describe their outcomes. Methods: We completed a search for relevant papers in four electronic databases (EMBASE, MEDLINE, PsycINFO, and Web of Science). The search also included the Cochrane database and manual search of reference lists of relevant papers. The search was undertaken in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses reporting guidelines. Results: The search identified 9 studies derived from 8 unique datasets. Most studies were assessments of feasibility and were undertaken in the United States. Interventions were predominately Web-based, with a focus on improving the caregivers’ knowledge and understanding about psychosis. Conclusions: This study offers preliminary support for the feasibility and acceptability of digital interventions for psychosis in informal caregiver populations. However, the findings underpin a clear need for greater development in the range of caregiver-focused digital approaches on offer and robust evaluation of their outcomes. The use of digital approaches with caregiver populations seemingly lags someway behind the significant developments observed in patient groups.

  • Source: Image created by the Authors; Copyright: Alicia Heraz; URL: http://mental.jmir.org/2018/3/e10104/; License: Creative Commons Attribution + Noncommercial + NoDerivatives (CC-BY-NC-ND).

    Recognition of Emotions Conveyed by Touch Through Force-Sensitive Screens: Observational Study of Humans and Machine Learning Techniques

    Abstract:

    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.

  • A smartphone application requesting user's consent to allow data collection (montage). Source: The Authors / Placeit.net; Copyright: Daniel Di Matteo; URL: http://mental.jmir.org/2018/3/e56/; License: Licensed by the authors.

    Patient Willingness to Consent to Mobile Phone Data Collection for Mental Health Apps: Structured Questionnaire

    Abstract:

    Background: It has become possible to use data from a patient’s mobile phone as an adjunct or alternative to the traditional self-report and interview methods of symptom assessment in psychiatry. Mobile data–based assessment is possible because of the large amounts of diverse information available from a modern mobile phone, including geolocation, screen activity, physical motion, and communication activity. This data may offer much more fine-grained insight into mental state than traditional methods, and so we are motivated to pursue research in this direction. However, passive data retrieval could be an unwelcome invasion of privacy, and some may not consent to such observation. It is therefore important to measure patients’ willingness to consent to such observation if this approach is to be considered for general use. Objective: The aim of this study was to measure the ownership rates of mobile phones within the patient population, measure the patient population’s willingness to have their mobile phone used as an experimental assessment tool for their mental health disorder, and, finally, to determine how likely patients would be to provide consent for each individual source of mobile phone–collectible data across the variety of potential data sources. Methods: New patients referred to a tertiary care mood and anxiety disorder clinic from August 2016 to October 2017 completed a survey designed to measure their mobile phone ownership, use, and willingness to install a mental health monitoring app and provide relevant data through the app. Results: Of the 82 respondents, 70 (85%) reported owning an internet-connected mobile phone. When asked about installing a hypothetical mobile phone app to assess their mental health disorder, 41% (33/80) responded with complete willingness to install with another 43% (34/80) indicating potential willingness to install such an app. Willingness to give permissions for specific types of data varied by data source, with respondents least willing to consent to audio recording and analysis (19% [15/80] willing respondents, 31% [25/80] potentially willing) and most willing to consent to observation of the mobile phone screen being on or off (46% [36/79] willing respondents and 23% [18/79] potentially willing). Conclusions: The patients surveyed had a high incidence of ownership of internet-connected mobile phones, which suggests some plausibility for the general approach of mental health state inference through mobile phone data. Patients were also relatively willing to consent to data collection from sources that were less personal but expressed less willingness for the most personal communication and location data.

  • Source: Shutterstock; Copyright: Rawpixel.com; URL: https://www.shutterstock.com/image-photo/women-use-mobile-phone-connection-social-573088405?irgwc=1&utm_medium=Affiliate&utm_campaign=TinEye&utm_source=77643&utm_term=; License: Licensed by the authors.

    The Effortless Assessment of Risk States (EARS) Tool: An Interpersonal Approach to Mobile Sensing

    Abstract:

    Background: To predict and prevent mental health crises, we must develop new approaches that can provide a dramatic advance in the effectiveness, timeliness, and scalability of our interventions. However, current methods of predicting mental health crises (eg, clinical monitoring, screening) usually fail on most, if not all, of these criteria. Luckily for us, 77% of Americans carry with them an unprecedented opportunity to detect risk states and provide precise life-saving interventions. Smartphones present an opportunity to empower individuals to leverage the data they generate through their normal phone use to predict and prevent mental health crises. Objective: To facilitate the collection of high-quality, passive mobile sensing data, we built the Effortless Assessment of Risk States (EARS) tool to enable the generation of predictive machine learning algorithms to solve previously intractable problems and identify risk states before they become crises. Methods: The EARS tool captures multiple indices of a person’s social and affective behavior via their naturalistic use of a smartphone. Although other mobile data collection tools exist, the EARS tool places a unique emphasis on capturing the content as well as the form of social communication on the phone. Signals collected include facial expressions, acoustic vocal quality, natural language use, physical activity, music choice, and geographical location. Critically, the EARS tool collects these data passively, with almost no burden on the user. We programmed the EARS tool in Java for the Android mobile platform. In building the EARS tool, we concentrated on two main considerations: (1) privacy and encryption and (2) phone use impact. Results: In a pilot study (N=24), participants tolerated the EARS tool well, reporting minimal burden. None of the participants who completed the study reported needing to use the provided battery packs. Current testing on a range of phones indicated that the tool consumed approximately 15% of the battery over a 16-hour period. Installation of the EARS tool caused minimal change in the user interface and user experience. Once installation is completed, the only difference the user notices is the custom keyboard. Conclusions: The EARS tool offers an innovative approach to passive mobile sensing by emphasizing the centrality of a person’s social life to their well-being. We built the EARS tool to power cutting-edge research, with the ultimate goal of leveraging individual big data to empower people and enhance mental health.

  • Source: The Authors / Placeit.net; Copyright: JMIR Publications; URL: http://mental.jmir.org/2018/3/e54/; License: Creative Commons Attribution (CC-BY).

    A Mobile App–Based Intervention for Depression: End-User and Expert Usability Testing Study

    Abstract:

    Background: Despite the growing number of mental health apps available for smartphones, the perceived usability of these apps from the perspectives of end users or health care experts has rarely been reported. This information is vital, particularly for self-guided mHealth interventions, as perceptions of navigability and quality of content are likely to impact participant engagement and treatment compliance. Objective: The aim of this study was to conduct a usability evaluation of a personalized, self-guided, app-based intervention for depression. Methods: Participants were administered the System Usability Scale and open-ended questions as part of a semistructured interview. There were 15 participants equally divided into 3 groups: (1) individuals with clinical depression who were the target audience for the app, (2) mental health professionals, and (3) researchers who specialize in the area of eHealth interventions and/or depression research. Results: The end-user group rated the app highly, both in quantitative and qualitative assessments. The 2 expert groups highlighted the self-monitoring features and range of established psychological treatment options (such as behavioral activation and cognitive restructuring) but had concerns that the amount and layout of content may be difficult for end users to navigate in a self-directed fashion. The end-user data did not confirm these concerns. Conclusions: Encouraging participant engagement via self-monitoring and feedback, as well as personalized messaging, may be a viable way to maintain participation in self-guided interventions. Further evaluation is necessary to determine whether levels of engagement with these features enhance treatment effects.

  • Computerized Coordinated Anxiety Learning Management (CALM) program. Source: Image created by the Authors; Copyright: The Authors; URL: http://mental.jmir.org/2018/3/e10277; License: Fair use/fair dealings.

    Adapting Coordinated Anxiety Learning and Management for Veterans Affairs Community-Based Outpatient Clinics: Iterative Approach

    Abstract:

    Background: A national priority at the US Department of Veterans Affairs (VA) is to increase the availability and accessibility of evidence-based psychotherapies (EBPs) across all VA medical facilities. Yet many veterans, particularly those who use remote outpatient VA clinics, still do not receive much needed evidence-based treatment. Strategies are needed for supporting mental health providers at rural VA community-based outpatient clinics (CBOCs) as they translate their clinical training to routine practice. The Coordinated Anxiety Learning Management (CALM) program is a computer-delivered program that supports the delivery of cognitive behavioral therapy (CBT) by providers in outpatient settings to patients with depression and anxiety, including posttraumatic stress disorder. Objective: The objectives of our study were to (1) adapt an existing computer-based program to rural VA CBOCs through feedback from key stakeholder focus groups; (2) develop a prototype of the adapted program; and (3) determine the adapted program’s acceptability and feasibility. Mental health stakeholders included VA leaders (n=4) in the implementation of EBPs, VA experts (n=4) in CBT, VA CBOC mental health providers (n=8), and veterans (n=8) diagnosed with a mental health condition treated using the CALM program and receiving treatment in a VA CBOC. Methods: An iterative approach comprising 3 waves of focus group discussions was used to develop a modified prototype of CALM. Following each wave of focus group discussions, template analysis was used to rapidly communicate stakeholder recommendations and feedback to the design team. The original program was first adapted through a process of data collection, design modification, and product development. Next, a prototype was developed. Finally, the redesigned program was tested for acceptability and feasibility through a live demonstration. Results: Key stakeholders suggested modifications to the original CALM program that altered its modules’ appearance by incorporating veteran-centric content. These modifications likely have no impact on the integrity of the original CALM program, but have altered its content to reflect better the demographic characteristics and experiences of rural veterans. Feedback from stakeholder groups indicates that changes will help VA patients identify with the program content, potentially enhancing their treatment engagement. Conclusions: The development model was effective for economically gathering actionable recommendations from stakeholders to adapt a computer-based program, and it can result in the development of an acceptable and feasible computer-delivered intervention. Results have implications for developing computer-based programs targeting behavior change more broadly and enhancing engagement in EBP.

  • Woman in hospital listening to a mindfulness program on her smartphone. Source: iStock by Getty Images; Copyright: LightFieldStudios; URL: https://www.istockphoto.com/au/photo/senior-woman-with-smartphone-in-hospital-gm865136324-143600135; License: Licensed by the authors.

    Digital Characteristics and Dissemination Indicators to Optimize Delivery of Internet-Supported Mindfulness-Based Interventions for People With a Chronic...

    Abstract:

    Background: Internet-supported mindfulness-based interventions (MBIs) are increasingly being used to support people with a chronic condition. Characteristics of MBIs vary greatly in their mode of delivery, communication patterns, level of facilitator involvement, intervention period, and resource intensity, making it difficult to compare how individual digital features may optimize intervention adherence and outcomes. Objective: The aims of this review were to (1) provide a description of digital characteristics of internet-supported MBIs and examine how these relate to evidence for efficacy and adherence to the intervention and (2) gain insights into the type of information available to inform translation of internet-supported MBIs to applied settings. Methods: MEDLINE Complete, PsycINFO, and CINAHL databases were searched for studies assessing an MBI delivered or accessed via the internet and engaging participants in daily mindfulness-based activities such as mindfulness meditations and informal mindfulness practices. Only studies using a comparison group of alternative interventions (active compactor), usual care, or wait-list were included. Given the broad definition of chronic conditions, specific conditions were not included in the original search to maximize results. The search resulted in 958 articles, from which 11 articles describing 10 interventions met the inclusion criteria. Results: Internet-supported MBIs were more effective than usual care or wait-list groups, and self-guided interventions were as effective as facilitator-guided interventions. Findings were informed mainly by female participants. Adherence to interventions was inconsistently defined and prevented robust comparison between studies. Reporting of factors associated with intervention dissemination, such as population representativeness, program adoption and maintenance, and costs, was rare. Conclusions: More comprehensive descriptions of digital characteristics need to be reported to further our understanding of features that may influence engagement and behavior change and to improve the reproducibility of MBIs. Gender differences in determinants and patterns of health behavior should be taken into account at the intervention design stage to accommodate male and female preferences. Future research could compare MBIs with established evidence-based therapies to identify the population groups that would benefit most from internet-supported programs. Trial Registration: PROSPERO CRD42017078665; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=78665 (Archived by WebCite at http://www.webcitation.org/71ountJpu)

  • Screenshot from the internet intervention, WalkAlong (montage). Source: The Authors / Placeit.net; Copyright: JMIR Publications; URL: http://mental.jmir.org/2018/3/e50/; License: Creative Commons Attribution (CC-BY).

    A Process Evaluation of a Web-Based Mental Health Portal (WalkAlong) Using Google Analytics

    Abstract:

    Background: Despite the increasing amount of research on Web-based mental health interventions with proven efficacy, high attrition rates decrease their effectiveness. Continued process evaluations should be performed to maximize the target population’s engagement. Google Analytics has been used to evaluate various health-related Web-based programs and may also be useful for Web-based mental health programs. Objective: The objective of our study was to evaluate WalkAlong.ca, a youth-oriented mental health web-portal, using Google Analytics to inform the improvement strategy for the platform and to demonstrate the use of Google Analytics as a tool for process evaluation of Web-based mental health interventions. Methods: Google Analytics was used to monitor user activity during WalkAlong’s first year of operation (Nov 13, 2013-Nov 13, 2014). Selected Google Analytic variables were overall website engagement including pages visited per session, utilization rate of specific features, and user access mode and location. Results: The results included data from 3076 users viewing 29,299 pages. Users spent less average time on Mindsteps (0 minute 35 seconds) and self-exercises (1 minute 08 seconds), which are important self-help tools, compared with that on the Screener tool (3 minutes 4 seconds). Of all visitors, 82.3% (4378/5318) were desktop users, followed by 12.7 % (677/5318) mobile phone and 5.0% (263/5318) tablet users. Both direct traffic (access via URL) and referrals by email had more than 7 pages viewed per session and longer than average time of 6 minutes per session. The majority of users (67%) accessed the platform from Canada. Conclusions: Engagement and feature utilization rates are higher among people who receive personal invitations to visit the site. Low utilization rates with specific features offer a starting place for further exploration of users in order to identify the root cause. The data provided by Google Analytics, although informative, can be supplemented by other evaluation methods (ie, qualitative methods) in order to better determine the modifications required to improve user engagement. Google Analytics can play a vital role in highlighting the preferences of those using Web-based mental health tools.

  • Source: The Authors / Placeit.net; Copyright: JMIR Publications; URL: http://mental.jmir.org/2018/3/e10092; License: Creative Commons Attribution (CC-BY).

    Using Smartphone Apps to Promote Psychiatric Rehabilitation in a Peer-Led Community Support Program: Pilot Study

    Abstract:

    Background: Management of severe and persistent mental illness is a complex, resource-intensive challenge for individuals, their families, treaters, and the health care system at large. Community-based rehabilitation, in which peer specialists provide support for individuals managing their own condition, has demonstrated effectiveness but has only been implemented in specialty centers. It remains unclear how the peer-based community rehabilitation model could be expanded, given that it requires significant resources to both establish and maintain. Objective: Here, we describe the results from a study of one such program implemented within Waverley Place, a community support program at McLean Hospital, emphasizing psychiatric rehabilitation for individuals with severe and persistent mental illness, as well as describing the challenges encountered during the implementation of the program. Key questions were whether the patients could, and would, successfully use the app. Methods: The smartphone app offered multiple features relevant to psychiatric rehabilitation, including daily task lists, activity tracking, and text messaging with peer specialists. A 90-day program of activities, goals, and content specific to the community support program was created on the basis of a prior pilot, in collaboration between members of the app development team (WellFrame), and peers, clinical, and research staff associated with the program. Hospital research staff recruited patients into the study, monitored peer and patient engagement, and handled all raw data acquired from the study. Results: Of 100 people approached for the study, a total of 13 provided consent, of which 10 downloaded and used the app. Two patients were unable to complete the app installation. Five used the app regularly as part of their daily lives for at least 20 days of the 90-day program. We were unable to identify any specific factors (eg, clinical or demographic) that affected willingness to consent or engage with the app platform in the very limited sample, although the individuals with significant app use were generally satisfied with the experience. Conclusions: Smartphone apps may become a useful tool for psychiatric rehabilitation, addressing both psychiatric and co-occurring medical problems. Individualizing functions to each patient and facilitating connection with a certified peer specialist may be an important feature of useful apps. Unlike prior reports emphasizing that patients with schizophrenia will adopt smartphone platforms, we found that implementation of digital tools into existing community support programs for severe and persistent mental illness has many challenges yet to be fully overcome to realize the potential benefits such apps could have to promote systematization and cost reduction for psychiatric rehabilitation.

  • Source: Image created by the authors; Copyright: The Authors; URL: http://mental.jmir.org/2018/3/e10420/; License: Licensed by JMIR.

    Comparing Internet-Based Cognitive Behavioral Therapy With Standard Care for Women With Fear of Birth: Randomized Controlled Trial

    Abstract:

    Background: Although many pregnant women report fear related to the approaching birth, no consensus exists on how fear of birth should be handled in clinical care. Objective: This randomized controlled trial aimed to compare the efficacy of a guided internet-based self-help program based on cognitive behavioral therapy (guided ICBT) with standard care on the levels of fear of birth in a sample of pregnant women reporting fear of birth. Methods: This nonblinded, multicenter randomized controlled trial with a parallel design was conducted at three study centers (hospitals) in Sweden. Recruitment commenced at the ultrasound screening examination during gestational weeks 17-20. The therapist-guided ICBT intervention was inspired by the Unified protocol for transdiagnostic treatment of emotional disorders and consisted of 8 treatment modules and 1 module for postpartum follow-up. The aim was to help participants observe and understand their fear of birth and find new ways of coping with difficult thoughts and emotions. Standard care was offered in the three different study regions. The primary outcome was self-assessed levels of fear of birth, measured using the Fear of Birth Scale. Results: We included 258 pregnant women reporting clinically significant levels of fear of birth (guided ICBT group, 127; standard care group, 131). Of the 127 women randomized to the guided ICBT group, 103 (81%) commenced treatment, 60 (47%) moved on to the second module, and only 13 (10%) finished ≥4 modules. The levels of fear of birth did not differ between the intervention groups postintervention. At 1-year postpartum follow-up, participants in the guided ICBT group exhibited significantly lower levels of fear of birth (U=3674.00, z=−1.97, P=.049, Cohen d=0.28, 95% CI –0.01 to 0.57). Using the linear mixed models analysis, an overall decrease in the levels of fear of birth over time was found (P≤ .001), along with a significant interaction between time and intervention, showing a larger reduction in fear of birth in the guided ICBT group over time (F1,192.538=4.96, P=.03). Conclusions: Fear of birth decreased over time in both intervention groups; while the decrease was slightly larger in the guided ICBT group, the main effect of time alone, regardless of treatment allocation, was most evident. Poor treatment adherence to guided ICBT implies low feasibility and acceptance of this treatment. Trial Registration: ClinicalTrials.gov NCT02306434; https://clinicaltrials.gov/ct2/show/NCT02306434 (Archived by WebCite at http://www.webcitation.org/70sj83qat)

  • The app created on the smart watch. Source: The Authors; Copyright: Min Hooi Yong; URL: http://mental.jmir.org/2018/3/e10153/; License: Creative Commons Attribution (CC-BY).

    Emotion Recognition Using Smart Watch Sensor Data: Mixed-Design Study

    Abstract:

    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.

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    Date Submitted: Sep 19, 2018

    Open Peer Review Period: Sep 20, 2018 - Nov 15, 2018

    Background: Mental health recovery denotes an individual’s experience of gaining a sense of personal control, striving towards one’s life goals, and meeting one’s needs. Although people with ser...

    Background: Mental health recovery denotes an individual’s experience of gaining a sense of personal control, striving towards one’s life goals, and meeting one’s needs. Although people with serious mental illness own and use electronic devices for general purposes, knowledge of their current use and interest in future use for supporting mental health recovery remains limited. Objective: To identify the applications of smartphones, tablets, and computers that mental health service recipients use and want to use to support their recovery. Methods: As a pilot study, we surveyed a convenience sample of 66 mental health service recipients with serious mental illness. The survey assessed current use and interest in the applications of mobile and computer devices to support recovery. Results: Listening to music (60%), accessing the internet (59%), calling (59%), and texting (54%) were the top functions regularly used by participants on their device to support their recovery. Participants expressed interest in learning how to use apps for anxiety/stress management (45%), mood management (45%), monitoring mental health symptoms (43%), cognitive behavioral therapy (40%), sleep (38%), and dialectical behavior therapy (38%) to support their recovery. Conclusions: Mental health service recipients currently use general functions, such as listening to music and calling friends, to support recovery. Nevertheless, they report interest in trying more specific illness-management functions.

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    Date Submitted: Sep 13, 2018

    Open Peer Review Period: Sep 14, 2018 - Nov 10, 2018

    Background: Medically unexplained physical symptoms (MUPS) are a substantial health problem in primary care, with a high burden for patients, general practitioners and the health care system. Most stu...

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    Date Submitted: Sep 7, 2018

    Open Peer Review Period: Sep 11, 2018 - Nov 7, 2018

    Background: Alzheimer’s disease (AD) is the most common cause of dementia. The number of AD patients is increasing worldwide, mainly due to the aging of the population. The lack of pharmaceutical i...

    Background: Alzheimer’s disease (AD) is the most common cause of dementia. The number of AD patients is increasing worldwide, mainly due to the aging of the population. The lack of pharmaceutical interventions able to delay or treat AD underlines the potential of non-pharmacological strategies. As an estimated one third of dementia cases might be attributable to modifiable lifestyle factors, multi-domain lifestyle interventions show promise as a way to maintain or improve brain health. Offering such programs online would enable large-scale implementation. An overview of multi-domain online lifestyle programs for brain health is currently lacking though, which hampers the field to compare and improve programs in order to develop effective and sustainable innovations. Objective: We aim to provide a comprehensive overview and meta-analysis of online multi-domain lifestyle programs aimed at optimizing brain health in healthy elderly adult populations. Methods: Electronic databases (PubMed, EMBASE.com, PsycINFO) were searched for online lifestyle programs which were included when the program 1) aimed to optimize brain health; 2) focused on multiple lifestyle factors; 3) was completely online; 4) existed of multiple sessions; 5) focused on a healthy adult population. We extracted and compared program characteristics (target population, duration, frequency, tailoring, platform, availability) and results of program evaluations (effectiveness, user-evaluations and adherence). Studies using a controlled design were included in a random effects meta-analysis on effectiveness on brain health outcomes. The quality of these studies was assessed using the PEDro scale. Results: The systematic literature search resulted in 44 documents describing 14 online lifestyle programs, which together address a multitude of lifestyle factors. Physical and cognitive activities were included in all programs. The majority of the programs was not publicly available and restricted to research settings (6/14, 43%) or available after payment (2/14, 14%). Studies on user-evaluations were reported for 8 (57%) programs, of which only 3 studies described their methods. Five studies evaluated the effectiveness of 4 programs, of which 3 used a controlled design, hence eligible for the meta-analysis (N=449; studies of moderate-to-high quality on PEDro scale). Pooled results showed a significant small to medium effect of the online multi-domain lifestyle interventions on outcome measures for brain health (global cognition score, subjective cognitive score, lifestyle risk score; std. mean diff. 0.45, 95% CI [0.12-0.78]) with a high heterogeneity across studies (I2 = 75%, p=0.02). Conclusions: We found 14 online multi-domain lifestyle programs aimed at optimizing brain health. The programs showed heterogeneity in both characteristics and effectiveness evaluation, which limited direct comparisons. Despite this heterogeneity, the results from this meta-analysis suggest that these programs can positively influence brain health outcomes and therefore have potential to contribute to the prevention of dementia.

  • Methodology for Clinical Trials of Virtual Reality in Healthcare: Recommendations from an International Working Group

    Date Submitted: Aug 19, 2018

    Open Peer Review Period: Aug 20, 2018 - Oct 16, 2018

    Background: Therapeutic virtual reality (VR) has emerged as an efficacious treatment modality for a wide range of health conditions. However, in spite of encouraging outcomes from early stage research...

    Background: Therapeutic virtual reality (VR) has emerged as an efficacious treatment modality for a wide range of health conditions. However, in spite of encouraging outcomes from early stage research, a consensus is needed for how best to develop and evaluate VR treatments within a scientific framework. Objective: We sought to develop a methodological framework with input from an international working group to guide the design, implementation, analysis, interpretation, and communication of trials that develop and test VR treatments. Methods: A group of 21 international experts was recruited based upon contributions to the VR literature. The resulting Virtual Reality Committee of Outcomes Research Experts (VR-CORE) held iterative meetings to seek consensus regarding best practices for development and testing of VR treatments. Results: The interactions were transcribed, and key themes were identified to support a scientific framework to support methodology best practices for clinical VR trials. Using the Food and Drug Administration (FDA) Phase I-III pharmacotherapy model as guidance, a framework emerged to support three phases of VR clinical study designs, herein named VR1, VR2, and VR3. VR1 studies focus on content development by working with patients and providers through principles of human centered design. VR2 trials conduct early testing with a focus on feasibility, acceptability, tolerability and initial clinical efficacy. VR3 trials are randomized, controlled studies to evaluate efficacy versus a control condition. Best practice recommendations for each trial are provided. Conclusions: Patients, providers, payers and regulators may consider this best practice framework when assessing the validity of VR treatments.

  • Game-based Digital Biomarkers: Mental Health Modeling using Behavioural Traces from Commercial Games

    Date Submitted: Aug 15, 2018

    Open Peer Review Period: Aug 20, 2018 - Oct 16, 2018

    Background: Currently, assessment for mental health is done by experts using interview techniques, questionnaires, and test batteries, and following standardized manuals; however, there would be myria...

    Background: Currently, assessment for mental health is done by experts using interview techniques, questionnaires, and test batteries, and following standardized manuals; however, there would be myriad benefits if behavioural correlates could predict mental health and be used for population screening or prevalence estimations. A variety of digital sources of data (e.g., online search data, social media posts) have been previously proposed as candidates for digital phenotyping—the digital quantification of disease phenotypes—in the context of mental health. Playing games on computers, gaming consoles, or mobile devices (i.e., digital gaming) has become a leading leisure activity of choice and yields rich data from a variety of sources. Objective: In this paper, we argue that game-based data from commercial off-the-shelf games have potential to be used as a digital biomarker to assess and model mental health and health decline. Although there is great potential in games developed specifically for mental health assessment (e.g., Sea Hero Quest), we focus on data gathered “in-the-wild” from playing commercial off-the-shelf games designed primarily for entertainment. Methods: In this paper, we argue that the behavioural traces left behind by natural interactions with digital games can be modeled using computational approaches for big data. To support our argument, we present an investigation of existing data sources, a categorization of observable traits from game data, and examples of potentially useful digital biomarkers. Results: Our investigation reveals different types of data that are generated from play, and the sources from which these data can be accessed. Based on these insights, we created a framework with six categories of observable traits that can be derived from game-based data, including: usage, cognitive performance, motor performance, social, affective, and content/preference. For each category, we describe the data type, the game-based sources from which it can be derived, its importance for mental health modeling, and any existing statistical associations with mental health that have been demonstrated in prior work. We close with a discussion on the limitations and potential of data from commercial off-the-shelf games for use as a digital biomarker of mental health. Conclusions: When people play commercial digital games, they produce significant volumes of high-resolution data—data that is not just related to play frequency, but that includes performance data reflecting low-level cognitive and motor processing, text-based data that is indicative of affective state, social data that reveals networks of relationships, content choice data that implies preferred genres, and contextual data that divulges where, when, and with whom they are playing. These data provide a source for quantification of disease phenotypes of mental health. Produced by engaged human behaviour, game data have potential to be leveraged for population screening or prevalence estimations, leading toward at-scale, non-intrusive assessment of mental health.

  • Digital Games vs Mindfulness Apps: Which is More Effective for Post-Work Recovery?

    Date Submitted: Aug 15, 2018

    Open Peer Review Period: Aug 20, 2018 - Oct 16, 2018

    Background: Post-work recovery is essential for the dissipation of work stress, and consequently wellbeing. Evidence suggests that activities that are immersive, active and engaging are especially eff...

    Background: Post-work recovery is essential for the dissipation of work stress, and consequently wellbeing. Evidence suggests that activities that are immersive, active and engaging are especially effective at promoting recovery. Previous research has suggested that playing digital games might be effective in promoting recovery, but little is known about how they compare to other popular mobile activities, such as mindfulness apps, which are specifically designed to support wellbeing. Objective: This research aimed to investigate and compare the effectiveness of a digital game and a mindfulness app in promoting post-work recovery, first in a lab setting and then in a field study. Methods: Study 1 was a lab experiment (n=45) in which participants’ need for recovery was induced by a work task, before undertaking one of three break tasks: a digital game (Block! Hexa Puzzle), a mindfulness app (Headspace) or a non-media control with a fidget spinner (a physical toy). Recovery in the form of how energised participants felt (energetic arousal) was compared before and after the break task, and how recovered participants felt (recovery experience) was compared across the conditions. Study 2 was a field study with working professionals (n=20), for which participants either played the digital game or used the mindfulness app once arriving home from work over a period of five working days. Measures of energetic and tense arousal were taken before and after the task, and recovery experience was measured after the task, along with measures of enjoyment and job strain. Results: A 3x2 mixed ANOVA identified that the digital game condition increased energetic arousal (indicative of improved recovery) whereas the other two conditions decreased energetic arousal (F2,42=3.76, p<.05). However, there were no differences between the conditions in Recovery Experience (F2,42=.01, p=.99). In Study 2, a multi-level model comparison approach identified that neither intervention nor day of the week had a significant impact on how energised participants felt. However, for those in the digital game condition, daily recovery experience increased during the course of the study, whereas for those in the mindfulness condition it decreased (F1,20=2.1489, p<0.01). Follow up interviews with participants identified three core themes: Detachment and Restoration, Fluctuations and Differences, and finally, Routine and Scheduling. These suggested that the activities differed in how much they allowed individuals to detach from work, but there were also differences across days and participants, and in some ways, the benefit of the activities came from simply having an enforced routine. Conclusions: This work suggests that digital games may be effective in promoting post-work recovery in lab contexts, even without a high need for recovery (Study 1) and in the real world, although the effect in this case may be accumulative rather than instant (Study 2).

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