Recent Articles:

• 

  Dementia

  Sep 11, 2018

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

  View Authors View Abstract

  Authors List:

  • Elizabeth A Laird  
  • Assumpta Ryan  
  • Claire McCauley  
  • Raymond B Bond  
  • Maurice D Mulvenna  
  • Kevin J Curran  
  • Brendan Bunting  
  • Finola Ferry  
  • Aideen Gibson  

  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.

  • JMIR Ment Health 2018;5(3):e57

  • Choose one of the following formats:

  • Share this article:

    Or, copy the article link to your clipboard:

    Article URL

  • Citation

    • END

      for: Endnote

    • BibTeX

      for: BibDesk and LaTeX

    • RIS

      for: RefMan, Procite, Endnote, RefWorks

    Add this article to your Mendeley library Add this article to your CiteULike library Copy Citation to Clipboard

  • Metrics

    Discover Social Media mentions by hovering over the donut. Click the 'details' link for a full report.

• 

  Psychotic Disorders

  Sep 5, 2018

  Digital Technology for Caregivers of People With Psychosis: Systematic Review

  View Authors View Abstract

  Authors List:

  • Juliana Onwumere  
  • Filipa Amaral  
  • Lucia R Valmaggia  

  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.

  • JMIR Ment Health 2018;5(3):e55

  • Choose one of the following formats:

  • Share this article:

    Or, copy the article link to your clipboard:

    Article URL

  • Citation

    • END

      for: Endnote

    • BibTeX

      for: BibDesk and LaTeX

    • RIS

      for: RefMan, Procite, Endnote, RefWorks

    Add this article to your Mendeley library Add this article to your CiteULike library Copy Citation to Clipboard

  • Metrics

    Discover Social Media mentions by hovering over the donut. Click the 'details' link for a full report.

• 

  Methods and New Tools in Mental...

  Aug 30, 2018

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

  View Authors View Abstract

  Authors List:

  • Alicia Heraz  
  • Manfred Clynes  

  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.

  • JMIR Ment Health 2018;5(3):e10104

  • Choose one of the following formats:

  • Share this article:

    Or, copy the article link to your clipboard:

    Article URL

  • Citation

    • END

      for: Endnote

    • BibTeX

      for: BibDesk and LaTeX

    • RIS

      for: RefMan, Procite, Endnote, RefWorks

    Add this article to your Mendeley library Add this article to your CiteULike library Copy Citation to Clipboard

  • Metrics

    Discover Social Media mentions by hovering over the donut. Click the 'details' link for a full report.

• 

  Methods and New Tools in Mental...

  Aug 29, 2018

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

  View Authors View Abstract

  Authors List:

  • Daniel Di Matteo  
  • Alexa Fine  
  • Kathryn Fotinos  
  • Jonathan Rose  
  • Martin Katzman  

  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.

  • JMIR Ment Health 2018;5(3):e56

  • Choose one of the following formats:

  • Share this article:

    Or, copy the article link to your clipboard:

    Article URL

  • Citation

    • END

      for: Endnote

    • BibTeX

      for: BibDesk and LaTeX

    • RIS

      for: RefMan, Procite, Endnote, RefWorks

    Add this article to your Mendeley library Add this article to your CiteULike library Copy Citation to Clipboard

  • Metrics

    Discover Social Media mentions by hovering over the donut. Click the 'details' link for a full report.

• 

  Viewpoints and Opinions on Mental Health

  Aug 28, 2018

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

  View Authors View Abstract

  Authors List:

  • Monika N Lind  
  • Michelle L Byrne  
  • Geordie Wicks  
  • Alec M Smidt  
  • Nicholas B Allen  

  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.

  • JMIR Ment Health 2018;5(3):e10334

  • Choose one of the following formats:

  • Share this article:

    Or, copy the article link to your clipboard:

    Article URL

  • Citation

    • END

      for: Endnote

    • BibTeX

      for: BibDesk and LaTeX

    • RIS

      for: RefMan, Procite, Endnote, RefWorks

    Add this article to your Mendeley library Add this article to your CiteULike library Copy Citation to Clipboard

  • Metrics

    Discover Social Media mentions by hovering over the donut. Click the 'details' link for a full report.

• 

  Depression and Mood Disorders

  Aug 23, 2018

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

  View Authors View Abstract

  Authors List:

  • Matthew Fuller-Tyszkiewicz  
  • Ben Richardson  
  • Britt Klein  
  • Helen Skouteris  
  • Helen Christensen  
  • David Austin  
  • David Castle  
  • Cathrine Mihalopoulos  
  • Renee O'Donnell  
  • Lilani Arulkadacham  
  • Adrian Shatte  
  • Anna Ware  

  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.

  • JMIR Ment Health 2018;5(3):e54

  • Choose one of the following formats:

  • Share this article:

    Or, copy the article link to your clipboard:

    Article URL

  • Citation

    • END

      for: Endnote

    • BibTeX

      for: BibDesk and LaTeX

    • RIS

      for: RefMan, Procite, Endnote, RefWorks

    Add this article to your Mendeley library Add this article to your CiteULike library Copy Citation to Clipboard

  • Metrics

    Discover Social Media mentions by hovering over the donut. Click the 'details' link for a full report.

• 

  Posttraumatic Stress Disorder (PTSD)

  Aug 22, 2018

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

  View Authors View Abstract

  Authors List:

  • Traci H Abraham  
  • Kathy Marchant-Miros  
  • Michael B McCarther  
  • Michelle G Craske  
  • Geoffrey M Curran  
  • Lisa K Kearney  
  • Carolyn Greene  
  • Jan A Lindsay  
  • Michael A Cucciare  

  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.

  • JMIR Ment Health 2018;5(3):e10277

  • Choose one of the following formats:

  • Share this article:

    Or, copy the article link to your clipboard:

    Article URL

  • Citation

    • END

      for: Endnote

    • BibTeX

      for: BibDesk and LaTeX

    • RIS

      for: RefMan, Procite, Endnote, RefWorks

    Add this article to your Mendeley library Add this article to your CiteULike library Copy Citation to Clipboard

  • Metrics

    Discover Social Media mentions by hovering over the donut. Click the 'details' link for a full report.

• 

  Mindfulness

  Aug 21, 2018

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

  View Authors View Abstract

  Authors List:

  • Lahiru Russell  
  • Anna Ugalde  
  • Donna Milne  
  • David Austin  
  • Patricia M Livingston  

  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)

  • JMIR Ment Health 2018;5(3):e53

  • Choose one of the following formats:

  • Share this article:

    Or, copy the article link to your clipboard:

    Article URL

  • Citation

    • END

      for: Endnote

    • BibTeX

      for: BibDesk and LaTeX

    • RIS

      for: RefMan, Procite, Endnote, RefWorks

    Add this article to your Mendeley library Add this article to your CiteULike library Copy Citation to Clipboard

  • Metrics

    Discover Social Media mentions by hovering over the donut. Click the 'details' link for a full report.

• 

  Methods and New Tools in Mental...

  Aug 20, 2018

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

  View Authors View Abstract

  Authors List:

  • Michael Jae Song  
  • John Ward  
  • Fiona Choi  
  • Mohammadali Nikoo  
  • Anastasia Frank  
  • Farhud Shams  
  • Katarina Tabi  
  • Daniel Vigo  
  • Michael Krausz  

  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.

  • JMIR Ment Health 2018;5(3):e50

  • Choose one of the following formats:

  • Share this article:

    Or, copy the article link to your clipboard:

    Article URL

  • Citation

    • END

      for: Endnote

    • BibTeX

      for: BibDesk and LaTeX

    • RIS

      for: RefMan, Procite, Endnote, RefWorks

    Add this article to your Mendeley library Add this article to your CiteULike library Copy Citation to Clipboard

  • Metrics

    Discover Social Media mentions by hovering over the donut. Click the 'details' link for a full report.

• 

  Psychotic Disorders

  Aug 15, 2018

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

  View Authors View Abstract

  Authors List:

  • Nora E Mueller  
  • Trishan Panch  
  • Cathaleene Macias  
  • Bruce M Cohen  
  • Dost Ongur  
  • Justin T Baker  

  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.

  • JMIR Ment Health 2018;5(3):e10092

  • Choose one of the following formats:

  • Share this article:

    Or, copy the article link to your clipboard:

    Article URL

  • Citation

    • END

      for: Endnote

    • BibTeX

      for: BibDesk and LaTeX

    • RIS

      for: RefMan, Procite, Endnote, RefWorks

    Add this article to your Mendeley library Add this article to your CiteULike library Copy Citation to Clipboard

  • Metrics

    Discover Social Media mentions by hovering over the donut. Click the 'details' link for a full report.

• 

  Anxiety and Stress Disorders

  Aug 10, 2018

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

  View Authors View Abstract

  Authors List:

  • Elisabet Rondung  
  • Elin Ternström  
  • Ingegerd Hildingsson  
  • Helen M Haines  
  • Örjan Sundin  
  • Johanna Ekdahl  
  • Annika Karlström  
  • Birgitta Larsson  
  • Birgitta Segeblad  
  • Rebecca Baylis  
  • Christine Rubertsson  

  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)

  • JMIR Ment Health 2018;5(3):e10420

  • Choose one of the following formats:

  • Share this article:

    Or, copy the article link to your clipboard:

    Article URL

  • Citation

    • END

      for: Endnote

    • BibTeX

      for: BibDesk and LaTeX

    • RIS

      for: RefMan, Procite, Endnote, RefWorks

    Add this article to your Mendeley library Add this article to your CiteULike library Copy Citation to Clipboard

  • Metrics

    Discover Social Media mentions by hovering over the donut. Click the 'details' link for a full report.

• 

  Methods and New Tools in Mental...

  Aug 8, 2018

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

  View Authors View Abstract

  Authors List:

  • Juan Carlos Quiroz  
  • Elena Geangu  
  • Min Hooi Yong  

  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.

  • JMIR Ment Health 2018;5(3):e10153

  • Choose one of the following formats:

  • Share this article:

    Or, copy the article link to your clipboard:

    Article URL

  • Citation

    • END

      for: Endnote

    • BibTeX

      for: BibDesk and LaTeX

    • RIS

      for: RefMan, Procite, Endnote, RefWorks

    Add this article to your Mendeley library Add this article to your CiteULike library Copy Citation to Clipboard

  • Metrics

    Discover Social Media mentions by hovering over the donut. Click the 'details' link for a full report.