Digital Intervention Barriers Scale–7 (DIBS-7): Development, Evaluation, and Preliminary Validation

Background The translation of mental health services into digital formats, deemed digital mental health interventions (DMHIs), has the potential to address long-standing obstacles to accessing care. However, DMHIs have barriers of their own that impact enrollment, adherence, and attrition in these programs. Unlike in traditional face-to-face therapy, there is a paucity of standardized and validated measures of barriers in DMHIs. Objective In this study, we describe the preliminary development and evaluation of such a scale, the Digital Intervention Barriers Scale-7 (DIBS-7). Methods Following an iterative QUAN → QUAL mixed methods approach, item generation was guided by qualitative analysis of feedback from participants (n=259) who completed a DMHI trial for anxiety and depression and identified barriers related to self-motivation, ease of use, acceptability, and comprehension of tasks. Item refinement was achieved through DMHI expert review. A final item pool was administered to 559 treatment completers (mean age 23.02 years; 438/559, 78.4% female; 374/559, 69.9% racially or ethnically minoritized). Exploratory factor analyses and confirmatory factor analyses were estimated to determine the psychometric properties of the measure. Finally, criterion-related validity was examined by estimating partial correlations between the DIBS-7 mean score and constructs related to treatment engagement in DMHIs. Results Statistical analyses estimated a 7-item unidimensional scale with high internal consistency (α=.82, ω=0.89). Preliminary criterion-related validity was supported by significant partial correlations between the DIBS-7 mean score and treatment expectations (pr=–0.25), number of modules with activity (pr=–0.55), number of weekly check-ins (pr=–0.28), and treatment satisfaction (pr=–0.71). Conclusions Overall, these results provide preliminary support for the use of the DIBS-7 as a potentially useful short scale for clinicians and researchers interested in measuring an important variable often associated with treatment adherence and outcomes in DMHIs.

[1]  M. Hennink,et al.  Sample sizes for saturation in qualitative research: A systematic review of empirical tests. , 2021, Social science & medicine.

[2]  K. Sobowale,et al.  Considerations of diversity, equity, and inclusion in mental health apps: A scoping review of evaluation frameworks. , 2021, Behaviour research and therapy.

[3]  P. Cuijpers,et al.  Digital interventions for the treatment of depression: A meta-analytic review. , 2021, Psychological bulletin.

[4]  Meredith R. Boyd,et al.  Testing multiple measures of adherence within a self-guided online intervention for depression and anxiety: Lessons learned and recommendations (Preprint) , 2021, JMIR Mental Health.

[5]  D. Mukamel,et al.  Barriers to and Facilitators of User Engagement With Digital Mental Health Interventions: Systematic Review , 2021, Journal of medical Internet research.

[6]  B. Chorpita,et al.  Feasibility of a web-based program for universal prevention of anxiety and depression in university students: An open trial , 2021, Journal of American college health : J of ACH.

[7]  U. Müssener.  Digital encounters: Human interactions in mHealth behavior change interventions , 2021, Digital health.

[8]  Ashley B. Batastini,et al.  Are videoconferenced mental and behavioral health services just as good as in-person? A meta-analysis of a fast-growing practice. , 2020, Clinical psychology review.

[9]  Jorge E. Palacios,et al.  A Machine Learning Approach to Understanding Patterns of Engagement With Internet-Delivered Mental Health Interventions , 2020, JAMA network open.

[10]  R. Morriss,et al.  The Acceptability and Usability of Digital Health Interventions for Adults With Depression, Anxiety, and Somatoform Disorders: Qualitative Systematic Review and Meta-Synthesis , 2019, Journal of medical Internet research.

[11]  Y. Khazaal,et al.  Mobile Apps for Mental Health Issues: Meta-Review of Meta-Analyses , 2020, JMIR mHealth and uHealth.

[12]  D. Chavira,et al.  Use of Technology to Provide Mental Health Care for Racial and Ethnic Minorities: Evidence, Promise, and Challenges , 2019 .

[13]  M. Fuller-Tyszkiewicz,et al.  Attrition and adherence in smartphone-delivered interventions for mental health problems: A systematic and meta-analytic review. , 2019, Journal of consulting and clinical psychology.

[14]  John Wilbanks,et al.  Indicators of retention in remote digital health studies: a cross-study evaluation of 100,000 participants , 2019, npj Digital Medicine.

[15]  John F. Hunter,et al.  Use of Digital Mental Health for Marginalized and Underserved Populations , 2019, Current Treatment Options in Psychiatry.

[16]  M. Lambert,et al.  The merits of implementing routine outcome monitoring in clinical practice. , 2018, Clinical Psychology: Science and Practice.

[17]  S. Carpenter,et al.  Ten Steps in Scale Development and Reporting: A Guide for Researchers , 2018 .

[18]  R. Kessler,et al.  Socio-economic variations in the mental health treatment gap for people with anxiety, mood, and substance use disorders: results from the WHO World Mental Health (WMH) surveys , 2017, Psychological Medicine.

[19]  J. M. Furr,et al.  Remotely Delivering Real-Time Parent Training to the Home: An Initial Randomized Trial of Internet-Delivered Parent–Child Interaction Therapy (I-PCIT) , 2017, Journal of consulting and clinical psychology.

[20]  N. King,et al.  Applied Qualitative Research in Psychology , 2017 .

[21]  C. Whittington,et al.  Annual Research Review: Digital health interventions for children and young people with mental health problems – a systematic and meta‐review , 2017, Journal of child psychology and psychiatry, and allied disciplines.

[22]  J. Mellor-Clark,et al.  Leading Horses to Water: Lessons from a Decade of Helping Psychological Therapy Services Use Routine Outcome Measurement to Improve Practice , 2016, Administration and Policy in Mental Health and Mental Health Services Research.

[23]  G. Andersson.  Internet-Delivered Psychological Treatments. , 2016, Annual review of clinical psychology.

[24]  Peter M Yellowlees,et al.  The Empirical Evidence for Telemedicine Interventions in Mental Disorders. , 2016, Telemedicine journal and e-health : the official journal of the American Telemedicine Association.

[25]  Alan E. Kazdin,et al.  Technology-Based Interventions and Reducing the Burdens of Mental Illness: Perspectives and Comments on the Special Series , 2015 .

[26]  L. Palinkas.  Qualitative and Mixed Methods in Mental Health Services and Implementation Research , 2014, Journal of clinical child and adolescent psychology : the official journal for the Society of Clinical Child and Adolescent Psychology, American Psychological Association, Division 53.

[27]  V. Savalei,et al.  When can categorical variables be treated as continuous? A comparison of robust continuous and categorical SEM estimation methods under suboptimal conditions. , 2012, Psychological methods.

[28]  Jordi Alonso,et al.  The COSMIN checklist for evaluating the methodological quality of studies on measurement properties: A clarification of its content , 2010, BMC medical research methodology.

[29]  H. Christensen,et al.  Adherence in Internet Interventions for Anxiety and Depression: Systematic Review , 2009, Journal of medical Internet research.

[30]  E. Kaltenthaler,et al.  The acceptability to patients of computerized cognitive behaviour therapy for depression: a systematic review , 2008, Psychological Medicine.

[31]  D. Mohr,et al.  Barriers to psychotherapy among depressed and nondepressed primary care patients , 2006, Annals of behavioral medicine : a publication of the Society of Behavioral Medicine.

[32]  Duane T. Wegener,et al.  Evaluating the use of exploratory factor analysis in psychological research. , 1999 .

[33]  A. Kazdin,et al.  Barriers to Treatment Participation Scale: evaluation and validation in the context of child outpatient treatment. , 1997, Journal of child psychology and psychiatry, and allied disciplines.

[34]  R. Ryan,et al.  Initial motivations for alcohol treatment: relations with patient characteristics, treatment involvement, and dropout. , 1995, Addictive behaviors.

[35]  Peter Cabrera-Nguyen.  Author Guidelines for Reporting Scale Development and Validation Results in the Journal of the Society for Social Work and Research , 2010, Journal of the Society for Social Work and Research.