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Internet interventions, technologies and digital innovations for mental health and behavior change
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.
Editorial Board members are currently being recruited, please contact us if you are interested (jmir.editorial.office at gmail.com).
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Background: Access to evidence-based therapies for common mental health conditions is limited due to travel, scheduling, stigma, and provider availability. Internet-based self-care programs (ISPs) may...
Background: Access to evidence-based therapies for common mental health conditions is limited due to travel, scheduling, stigma, and provider availability. Internet-based self-care programs (ISPs) may mitigate these barriers; however, little is known about ISP implementation in integrated U.S. healthcare systems. Objective: The objective of this study was to elicit perspectives on implementing ISPs in primary care settings at the point of care with patients and providers in an integrated healthcare system. Methods: The objective was explored through qualitative analysis of semi-structured interviews with Veterans Health Administration (VHA) primary care and primary care mental health providers and administrators. Participants were identified using a reputation-based snowball design. Interviews focused on identifying determinants of practice for the use of ISPs at the point of care in VHA primary care. Investigators utilized thematic analysis to code interview transcripts and reach consensus on key themes through group discussion. Results: Twenty interviews with physicians, psychologists, social workers, and nurses were conducted and analyzed. Among this group, ISP use was low, but enthusiasm for the platform was uniformly high. Themes were organized into patient and provider level determinants of practice. Patient level determinants included literacy, age, Internet access, patient expectations, ISP fit with patient experiences, interest/motivation, and face-to-face human contact. Provider level themes included familiarity with ISPs, changes to traditional care delivery, face-to-face human contact, competing demands, and age. Conclusions: This exploration of perspectives on ISP implementation among VHA providers and administrators revealed key determinants of practice, which can be used to develop comprehensive strategies for the implementation of ISPs in primary care settings. Clinical Trial: none
Background: Depression is a leading cause of disease globally and is often characterized by a lack of social connection. With the rise of social media comes reports that Twitter users are seeking conn...
Background: Depression is a leading cause of disease globally and is often characterized by a lack of social connection. With the rise of social media comes reports that Twitter users are seeking connections about depression online. Objective: This study aimed to identify communities where Twitter users tweeted using the hashtag #MyDepressionLooksLike to connect about depression. Once identified, we wanted to understand which community characteristics correlated to Twitter-users turning to a virtual community to connect about depression. Methods: Tweets were collected using NCapture software from May 25th to June 1st, 2016 during Mental Health Awareness month (n=104) in the northeastern United States and Washington D.C. After mapping tweets, we used a Poisson multi-level regression model to predict tweets per community (county) offset by the population aged 15-44 years old, and adjusted for percent female, white, below poverty, and single-person households. We then compared predicted versus observed counts and calculated Tweeting Index Values (TIVs) based on the residuals. Last, we examined trends in community characteristics by TIV using Pearson’s correlation. Results: We found significant associations between tweet counts and area-level proportions of females, single-person households, and population aged 15-44 years. Tweeting Index Values (TIVs) were higher than expected (TIV 5) in western, inland areas of the study region. There were lower than expected tweets in the eastern, seaboard areas (TIV 1). When examining community characteristics and over- and under-tweeting by county, we observed a clear upward linear gradient in vacant housing and over-tweeting (r= 0.31, P< .001). However, we observed U-shaped relationships for most other community factors, suggesting that the same characteristics were correlated with both over- and under-tweeting. Conclusions: Lack of connection-building amenities in physical communities may lead to both the over- and under-tweeting by Twitter users seeking connections about depression. Higher rates of vacant housing are especially linked to higher levels of tweeting than expected. Future research could expand the spatiotemporal scope.
Background: There has been a growing trend in the delivery of mental health treatment via technology (i.e., eHealth). However, engagement with eHealth interventions is a concern and theoretically base...
Background: There has been a growing trend in the delivery of mental health treatment via technology (i.e., eHealth). However, engagement with eHealth interventions is a concern and theoretically based research in this area is sparse. Factors that influence engagement are poorly understood, especially in trauma survivors with symptoms of posttraumatic stress. Objective: The aim of this study was to examine engagement with a trauma recovery eHealth intervention using the Health Action Process Approach theoretical model. Outcome expectancy, perceived need, pre-treatment self-efficacy, and trauma symptoms influence the formation of intentions (motivational phase), followed by planning which mediates the translation of intentions into engagement (volitional phase). We hypothesized the mediational effect of planning would be moderated by level of treatment self-efficacy. Methods: Trauma survivors from around the U.S. used the eHealth intervention for two weeks. We collected baseline demographic, social cognitive predictors and distress symptoms and measured engagement subjectively and objectively throughout the intervention. Results: The motivational phase model explained 48% of the variance and outcome expectations (β = .36), perceived need (β = .32), pre-treatment self-efficacy (β = .13), and trauma symptoms (β = .21) were significant predictors of intention (N = 440). In the volitional phase, results of the moderated mediation model indicated for low levels of treatment self-efficacy, planning mediated the effects of intention on levels of engagement, B = 0.89, 95% CI[0.143, 2.605] (N = 115). Conclusions: Though many factors can affect engagement, these results offer a theoretical framework for understanding engagement with an eHealth intervention. This study highlighted the importance of perceived need, outcome expectations, self-efficacy and baseline distress symptoms in the formation of intentions to use the intervention. For those low in treatment self-efficacy, planning may play an important role in the translation of intentions into engagement. Results of this study may help bring some clarification to the question of what makes eHealth interventions work.
Background: The aim of the present study was to show the validity of a mobile based application (“Serenita”) , as a tool for measuring stress level quantitatively. In this interactive app, the use...
Background: The aim of the present study was to show the validity of a mobile based application (“Serenita”) , as a tool for measuring stress level quantitatively. In this interactive app, the user places his finger on the mobile`s camera lens, through which information related to the user’s blood flow, heart rate, and heart rate variability (HRV) is extracted. Physiological signals are then being filtered and processed through a certain machine- algorithm, resulting in a quantitative estimation of the user’s stress level. Method: a mixed sex group of 50 volunteers were recruited to participate in a standardized laboratory experiment, where a psychosocial stress protocol (Trier Social Stress Test-TSST) was implemented. Throughout the course of the experiment, physiological stress response was measured using both salivary cortisol level and Serenita app, hence, using a within subject design. Results: Serenita algorithm was able to effectively detect changes in the participant`s estimated stress level, as expected by the different experimental conditions and followed the robust physiological response pattern usually obtained by the TSST protocol. In addition, a cross correlation of .93 was obtained between the estimated stress level, using Serenita`s algorithm, and Cortisol level measures. Conclusions: these results serve a double validation for Serenita as an effective tool to quantitatively measure physiological stress response. This innovative technique bears important implications for the field of stress research and treatment, providing to the best of our knowledge the first clinically validated non-lab based quantitative physiological stress measurement tool. Objective: The aim of the present study was to show the validity of a mobile based application (“Serenita”) , as a tool for measuring stress level quantitatively. Methods: The current study was designed to validate and fine-tune the algorithms supporting the stress estimation function in this mobile application, under a clinical setting. In order to validate Serenita as an adequate stress estimator it was necessary to build a standardized experimental protocol able to i) effectively induce stress on a set of volunteers, ii) and properly quantify the stress variation. To this end, we adopted the Trier Social Stress Test (TSST -Kirschbaum, Pirke, & Hellhammer, 1993) as this well documented laboratory procedure, was shown to reliably induce stress in human research participants and used extensively in the field of stress studies (e.g., Kudielka, Hellhammer, Kirschbaum, Harmon-Jones, & Winkielman, 2007; Allen, Kennedy, Cryan, Dinan, & Clarke, 2014). The efficiency of TSST as a stress inducer, was explored not only through properly calibrated questionnaires but also with the analysis of the Cortisol, as physiological stress indicator, which is extensively used in clinical setting to determine stress levels and the response to stressful events. Similarly, the current study followed the typical experimental protocol. However, its novelty lies in combining traditional as solid stress inducer (TSST) and indicator (saliva cortisol), with an innovative digital-health assessment tool (Serenita application). Utilizing both tools to measure physiological stress in the course of the experiment, will not only comply with our research goal of establishing reliability and validity, but also will neutralize any potential variability in stress response that might be stemming from sex differences (e.g., Kirschbaum, Klauer, Filipp, & Hellhammer, 1995; Kelly, Tyrka, Anderson, Price, & Carpenter, 2008). Results: Serenita algorithm was able to effectively detect changes in the participant`s estimated stress level, as expected by the different experimental conditions and followed the robust physiological response pattern usually obtained by the TSST protocol. In addition, a cross correlation of .93 was obtained between the estimated stress level, using Serenita`s algorithm, and Cortisol level measures. Conclusions: these results serve a double validation for Serenita as an effective tool to quantitatively measure physiological stress response. This innovative technique bears important implications for the field of stress research and treatment, providing to the best of our knowledge the first clinically validated non-lab based quantitative physiological stress measurement tool. Conclusions: The present study aimed at investigating if the stress estimation algorithm, used by the Serenita app, was able to accurately estimate variations on stress levels. The TSST widely established as the standard protocol for stress induction was used to induce stress on a controlled clinical environment. The estimated stress levels show a high agreement rate with the expected stress response of the TSST. Furthermore, the analysis of salivary cortisol levels provided an objective measure of the real variation on stress levels, the average cortisol curve has a correlation index of 0.93 with the estimated stress provided by Serenita’s stress algorithm, supporting the stress estimation algorithm as a feasible way to estimate stress. Finally, in many stress monitoring applications it is useful to know, not just the relative change in stress along the time, but also to obtain a quantitative value for the stress level at any given time. Due to the highly subject-dependent nature of the basal level of stress (and cortisol) this task is relatively complex, however using a priori information such as age, gender, health condition, among others, it is possible to project the stress function into a bounded quantitative stress scale e.g. 0 − 100%. As far as we know this is the first time where an application is providing a quantitative and validated method comparable to measuring stress with a lab test. This tool could serve as a research tool in stress studies.
This article focuses on the ethical challenges presented by digital psychotherapy services that are direct-to-consumer and do not involve oversight by a professional mental health provider. These serv...
This article focuses on the ethical challenges presented by digital psychotherapy services that are direct-to-consumer and do not involve oversight by a professional mental health provider. These services include apps that connect users to peer counseling and counseling steered by artificial intelligence and conversational agents. These services can potentially assist in improving access to mental health care for the many people would otherwise not have the resources or ability to connect with a therapist. However, the lack of adequate regulation in this area exacerbates concerns over how safety, privacy, liability, and other ethical obligations to protect a client are addressed by these services. In the traditional therapeutic relationship, there are ethical obligations that serve to protect the interests of the client and provide warnings. In contrast, in a direct-to-consumer therapy app, there are not clear lines of accountability or associated ethical obligations to protect the user seeking mental health services. There is a need for increased oversight of direct-to-consumer non-professional psychotherapy services to better protect the consumer.
Background: Severe health anxiety (hypochondriasis), or illness anxiety disorder according to the DSM-5, is characterised by preoccupation with fear of suffering from a serious illness in spite of med...
Background: Severe health anxiety (hypochondriasis), or illness anxiety disorder according to the DSM-5, is characterised by preoccupation with fear of suffering from a serious illness in spite of medical reassurance. It is a debilitating, prevalent disorder associated with increased health care utilisation. Still, there is a lack of easily accessible specialised treatment for severe health anxiety. Objective: The present paper has two objectives; 1) to describe the development and setup of a new internet-delivered Acceptance and Commitment Therapy (iACT) programme for patients with severe health anxiety using self-referral and a video-based assessment, and 2) to examine the feasibility and potential clinical efficacy of iACT for severe health anxiety. Methods: Fifteen self-referred patients with severe health anxiety were diagnostically assessed by a video-based interview. They received 7 sessions of clinician-supported iACT comprising self-help texts, video clips, audio files and worksheets over 12 weeks. Self-report questionnaires were obtained at baseline, post-treatment and at 3-month follow-up (3MFU). The primary outcome was Whiteley-7 index measuring health anxiety severity. Depressive symptoms, health-related quality of life (HRQoL), life satisfaction and psychological flexibility were also assessed. A within-group design was employed. Means, standard deviations (SD) and effect sizes using the Standardized Response Mean were estimated. Post-treatment interviews were conducted to evaluate the patient experience of the usability and acceptability of the treatment setup and programme. Results: Self-referral and video-based assessment were well received. Most patients 12/15 (80%) completed the treatment, and only one patient dropped out. Post-treatment data were available for almost all patients 14/15 (93%) and 3MFU data for 12/15 (80%). Paired t-tests showed significant improvements on all outcome measures both at post-treatment and 3MFU except on one physical component subscale of HRQoL. Health anxiety symptoms decreased with 33.9 points at 3MFU (95% CI 13.6 to 54.3, t(11) = 3.66, P=0.004) with a large within-group effect size measured by the standardised response mean (SRM=1.06). Conclusions: Treatment adherence and potential efficacy suggest that iACT may be a feasible treatment for health anxiety. The uncontrolled design and small sample size limit the robustness of the findings. Therefore, the findings should be replicated in a randomised controlled trial. Potentially, iACT may increase availability and accessibility of specialised treatment for health anxiety. Clinical Trial: The study was approved by the Danish Data Protection Agency, Central Denmark Region (ID no. 1-16-02-427-14). URL: https://www.datatilsynet.dk/forside/