Adolescent Dropout from Brief Digital Mental Health Interventions Within and Beyond Randomized Trials

2021 ◽  
Author(s):  
Katherine Cohen ◽  
Jessica L. Schleider
Keyword(s):  
Mental Health ◽  
Program Evaluation ◽  
Controlled Trial ◽  
Health Interventions ◽  
Completion Rates ◽  
Demographic Groups ◽  
Completion Status ◽  
Significant Difference ◽  
Mental Health Interventions ◽  
Time Required

Objective: Many adolescents struggle to access appropriate mental health care due to structural or psychological barriers. Among those who do access an intervention, retention is a pressing concern. As a result, adolescents are less likely to benefit from an intervention. Although traditional barriers to participation (e.g., location, cost) are hypothetically reduced or removed in internet interventions, low retention is still common, particularly in unguided programs (those not involving a clinician). It is therefore key to determine what factors are associated with dropout in digital mental health interventions with adolescents both within and beyond the context of research studies. Methods: We compare completion rates from two projects evaluating self-guided, online single-session mental health interventions (SSIs) for adolescents. One was a randomized controlled trial (RCT) in which participants were paid for participation. The other was a program evaluation project in which participants were not paid for participation. We additionally compare SSI completion rates across various demographic groups and across baseline hopelessness levels. Results: There was a statistically significant difference in SSI completion status between the RCT (84.75% full-completers) and the program evaluation (36.86% full-completers), X2 (2, N =2436) = 583.5, p < .05. There were no significant differences in the baseline hopelessness scores across completion statuses in either study. There were no significant differences in full-completion rates across demographic groups in either project. Conclusion: Adolescents may be more likely to complete a brief digital mental health intervention if they are paid for participation. Additionally, it is possible that the brevity of SSIs reduces demographic disparities related to retention by minimizing the time required to complete an intervention.

scholarly journals Employees’ Perspectives on the Facilitators and Barriers to Engaging With Digital Mental Health Interventions in the Workplace: Qualitative Study

2018 ◽  
Vol 5 (1) ◽  
pp. e8 ◽  
Author(s):  
Stephany Carolan ◽  
Richard O de Visser
Keyword(s):  
Mental Health ◽  
Controlled Trial ◽  
Health Intervention ◽  
Health Interventions ◽  
Mental Health Intervention ◽  
Depression And Anxiety ◽  
Occupational Setting ◽  
Mental Health Interventions ◽  
Facilitators And Barriers ◽  
Workplace Setting

Background Prevalence rates of work-related stress, depression, and anxiety are high, resulting in reduced productivity and increased absenteeism. There is evidence that these conditions can be successfully treated in the workplace, but take-up of psychological treatments among workers is low. Digital mental health interventions delivered in the workplace may be one way to address this imbalance, but although there is evidence that digital mental health is effective at treating stress, depression, and anxiety in the workplace, uptake of and engagement with these interventions remains a concern. Additionally, there is little research on the appropriateness of the workplace for delivering these interventions or on what the facilitators and barriers to engagement with digital mental health interventions in an occupational setting might be. Objective The aim of this research was to get a better understanding of the facilitators and barriers to engaging with digital mental health interventions in the workplace. Methods Semistructured interviews were held with 18 participants who had access to an occupational digital mental health intervention as part of a randomized controlled trial. The interviews were transcribed, and thematic analysis was used to develop an understanding of the data. Results Digital mental health interventions were described by interviewees as convenient, flexible, and anonymous; these attributes were seen as being both facilitators and barriers to engagement in a workplace setting. Convenience and flexibility could increase the opportunities to engage with digital mental health, but in a workplace setting they could also result in difficulty in prioritizing time and ensuring a temporal and spatial separation between work and therapy. The anonymity of the Internet could encourage use, but that benefit may be lost for people who work in open-plan offices. Other facilitators to engagement included interactive and interesting content and design features such as progress trackers and reminders to log in. The main barrier to engagement was the lack of time. The perfect digital mental health intervention was described as a website that combined a short interactive course that was accessed alongside time-unlimited information and advice that was regularly updated and could be dipped in and out of. Participants also wanted access to e-coaching support. Conclusions Occupational digital mental health interventions may have an important role in delivering health care support to employees. Although the advantages of digital mental health interventions are clear, they do not always fully translate to interventions delivered in an occupational setting and further work is required to identify ways of minimizing potential barriers to access and engagement. Trial Registration ClinicalTrials.gov: NCT02729987; https://clinicaltrials.gov/ct2/show/NCT02729987?term=NCT02729987& rank=1 (Archived at WebCite at http://www.webcitation.org/6wZJge9rt)

scholarly journals Employees� Perspectives on the Facilitators and Barriers to Engaging With Digital Mental Health Interventions in the Workplace: Qualitative Study (Preprint)

2017 ◽  
Author(s):  
Stephany Carolan ◽  
Richard O de Visser
Keyword(s):  
Mental Health ◽  
Controlled Trial ◽  
Health Intervention ◽  
Health Interventions ◽  
Mental Health Intervention ◽  
Depression And Anxiety ◽  
Occupational Setting ◽  
Mental Health Interventions ◽  
Facilitators And Barriers ◽  
Workplace Setting

BACKGROUND Prevalence rates of work-related stress, depression, and anxiety are high, resulting in reduced productivity and increased absenteeism. There is evidence that these conditions can be successfully treated in the workplace, but take-up of psychological treatments among workers is low. Digital mental health interventions delivered in the workplace may be one way to address this imbalance, but although there is evidence that digital mental health is effective at treating stress, depression, and anxiety in the workplace, uptake of and engagement with these interventions remains a concern. Additionally, there is little research on the appropriateness of the workplace for delivering these interventions or on what the facilitators and barriers to engagement with digital mental health interventions in an occupational setting might be. OBJECTIVE The aim of this research was to get a better understanding of the facilitators and barriers to engaging with digital mental health interventions in the workplace. METHODS Semistructured interviews were held with 18 participants who had access to an occupational digital mental health intervention as part of a randomized controlled trial. The interviews were transcribed, and thematic analysis was used to develop an understanding of the data. RESULTS Digital mental health interventions were described by interviewees as convenient, flexible, and anonymous; these attributes were seen as being both facilitators and barriers to engagement in a workplace setting. Convenience and flexibility could increase the opportunities to engage with digital mental health, but in a workplace setting they could also result in difficulty in prioritizing time and ensuring a temporal and spatial separation between work and therapy. The anonymity of the Internet could encourage use, but that benefit may be lost for people who work in open-plan offices. Other facilitators to engagement included interactive and interesting content and design features such as progress trackers and reminders to log in. The main barrier to engagement was the lack of time. The perfect digital mental health intervention was described as a website that combined a short interactive course that was accessed alongside time-unlimited information and advice that was regularly updated and could be dipped in and out of. Participants also wanted access to e-coaching support. CONCLUSIONS Occupational digital mental health interventions may have an important role in delivering health care support to employees. Although the advantages of digital mental health interventions are clear, they do not always fully translate to interventions delivered in an occupational setting and further work is required to identify ways of minimizing potential barriers to access and engagement. CLINICALTRIAL ClinicalTrials.gov: NCT02729987; https://clinicaltrials.gov/ct2/show/NCT02729987?term=NCT02729987& rank=1 (Archived at WebCite at http://www.webcitation.org/6wZJge9rt)

scholarly journals Surveying the Role of Analytics in Evaluating Digital Mental Health Interventions for Transition-Aged Youth: Scoping Review (Preprint)

2019 ◽  
Author(s):  
Brian Lo ◽  
Jenny Shi ◽  
Elisa Hollenberg ◽  
Alexxa Abi-Jaoudé ◽  
Andrew Johnson ◽  
...  
Keyword(s):  
Mental Health ◽  
Scoping Review ◽  
Care Delivery ◽  
Digital Health ◽  
Controlled Trial ◽  
Health Interventions ◽  
Health Apps ◽  
Mental Health Interventions ◽  
Usage Data

BACKGROUND Consumer-facing digital health interventions provide a promising avenue to bridge gaps in mental health care delivery. To evaluate these interventions, understanding how the target population uses a solution is critical to the overall validity and reliability of the evaluation. As a result, usage data (analytics) can provide a proxy for evaluating the engagement of a solution. However, there is paucity of guidance on how usage data or analytics should be used to assess and evaluate digital mental health interventions. OBJECTIVE This review aimed to examine how usage data are collected and analyzed in evaluations of mental health mobile apps for transition-aged youth (15-29 years). METHODS A scoping review was conducted using the Arksey and O’Malley framework. A systematic search was conducted on 5 journal databases using keywords related to usage and engagement, mental health apps, and evaluation. A total of 1784 papers from 2008 to 2019 were identified and screened to ensure that they included analytics and evaluated a mental health app for transition-aged youth. After full-text screening, 49 papers were included in the analysis. RESULTS Of the 49 papers included in the analysis, 40 unique digital mental health innovations were evaluated, and about 80% (39/49) of the papers were published over the past 6 years. About 80% involved a randomized controlled trial and evaluated apps with information delivery features. There were heterogeneous findings in the concept that analytics was ascribed to, with the top 3 being engagement, adherence, and acceptability. There was also a significant spread in the number of metrics collected by each study, with 35% (17/49) of the papers collecting only 1 metric and 29% (14/49) collecting 4 or more analytic metrics. The number of modules completed, the session duration, and the number of log ins were the most common usage metrics collected. CONCLUSIONS This review of current literature identified significant variability and heterogeneity in using analytics to evaluate digital mental health interventions for transition-aged youth. The large proportion of publications from the last 6 years suggests that user analytics is increasingly being integrated into the evaluation of these apps. Numerous gaps related to selecting appropriate and relevant metrics and defining successful or high levels of engagement have been identified for future exploration. Although long-term use or adoption is an important precursor to realizing the expected benefits of an app, few studies have examined this issue. Researchers would benefit from clarification and guidance on how to measure and analyze app usage in terms of evaluating digital mental health interventions for transition-aged youth. Given the established role of adoption in the success of health information technologies, understanding how to abstract and analyze user adoption for consumer digital mental health apps is also an emerging priority.

scholarly journals Determining Acceptance of e-mental Health Interventions in Digital Psychodiabetology: Using the Unified Theory of Acceptance and Use of Technology (Preprint)

2021 ◽  
Author(s):  
Mirjam Damerau ◽  
Martin Teufel ◽  
Venja Musche ◽  
Hannah Kohler ◽  
Adam Schweda ◽  
...  
Keyword(s):  
Mental Health ◽  
Regression Model ◽  
Health Interventions ◽  
Diabetic Patients ◽  
Unified Theory ◽  
Use Of Technology ◽  
Significant Difference ◽  
Mental Health Interventions ◽  
Explained Variance ◽  
Diabetes Patients

BACKGROUND Diabetes is a very common chronic disease, which confronts patients with massive physiological and psychological burdens. The digitalization of mental health care has generated effective e-mental health approaches, which bear indubitable practical value to patient treatment. However, before implementing and optimizing e-mental health tools, their acceptance and underlying barriers and resources should be determined first in order to be able to develop and establish effective patient-oriented interventions. OBJECTIVE This study aimed to assess the acceptance of e-mental health interventions in diabetes patients and to explore its underlying barriers and resources. METHODS A cross-sectional study was conducted in Germany over a period of two months in 2020 through an online survey recruited via online diabetes channels. Eligibility requirement was adult age (18 or above), a good command of the German language, internet access and a diagnosis of diabetes. Acceptance was measured using a modified questionnaire, which was based on the well-established Unified Theory of Acceptance and Use of Technology (UTAUT) and assessed health-related internet use, acceptance of e-mental health interventions and its barriers and resources. Mental health was measured using validated and established instruments, namely the Generalized Anxiety Disorder Scale-7, the Patient Health Questionnaire-2 and the Distress Thermometer. Additionally, socio-demographic and medical data regarding diabetes were asked RESULTS Of 340 participants starting the survey 76.8 % completed it, resulting in 261 participants and a final sample of 258 participants with complete datasets. The acceptance of e-mental health interventions in diabetes patients was overall moderate (M = 3.02, SD = 1.14). Sex and suffering from a mental disorder had a significant influence on acceptance (P < .001). In an extended UTAUT regression model (UTAUT predictors plus socio-demographics and mental health variables) acceptance was significantly predicted by distress (β = .11, P = .027) as well as by the UTAUT predictors performance expectancy (PE) (β = .50, P < .001), effort expectancy (EE) (β = .15, P = .001), and social influence (SI) (β = .28, P < .001). The comparison between an extended UTAUT regression model (13 predictors) and the UTAUT only regression model (PE, EE, SI) revealed no significant difference in explained variance (F10,244 = 1.567, P =.117). CONCLUSIONS This study supports the viability of the UTAUT model and its predictors in assessing acceptance of e-mental health interventions in diabetes patients. Three UTAUT predictors reached a notable amount of explained variance in acceptance of 75 %, indicating being a very useful and efficient method for measuring e-mental health intervention acceptance of diabetic patients. Due to the close link between acceptance and utilization, acceptance facilitating interventions focusing on these three UTAUT predictors should be fostered to bring forward the highly needed establishment of effective e-mental health interventions in psychodiabetology.

Predicting Acceptance of e-Mental Health Interventions in Patients with Obesity by using an extended Unified Theory of Acceptance Model (Preprint)

2021 ◽  
Author(s):  
Vanessa Rentrop ◽  
Mirjam Damerau ◽  
Adam Schweda ◽  
Jasmin Steinbach ◽  
Lynik Chantal Schüren ◽  
...  
Keyword(s):  
Mental Health ◽  
Health Interventions ◽  
Unified Theory ◽  
Obese Patients ◽  
Utaut Model ◽  
Significant Difference ◽  
Mental Health Interventions ◽  
Explained Variance ◽  
Health Burdens ◽  
The Impact

BACKGROUND The rapid increase in the number of overweight and obese people is a worldwide health problem. Obesity is often associated with physiological and mental health burdens. Due to several barriers of face-to-face psychotherapy, one promising approach is to exploit recent developments and implement innovative e-mental health interventions that offer various benefits to obese patients as well as for the healthcare system. OBJECTIVE This study aimed to assess the acceptance of e-mental health interventions in patients with obesity and explore its influencing predictors. In addition, the well-established, Unified Theory of Acceptance and Use of Technology model (UTAUT) will be compared with an extended UTAUT model in terms of variance explanation of acceptance. METHODS A cross-sectional online survey study was conducted from July 2020 to January 2021 in Germany. Eligibility requirement was adult age (18 or above), internet access, a good command of the German language, and a BMI > 30 kg/m2 (obesity). 448 patients with obesity (grade I, II and III) were recruited via specialized social media platforms. The impact of various socio-demographic, medical, and mental health characteristics were assessed. eHealth-related data and acceptance of e-mental health interventions were examined using a modified questionnaire, which is based on the UTAUT. RESULTS Acceptance of e-mental health interventions in obese patients was overall moderate (M = 3.18, SD = 1.11). There are significant differences in acceptance of e-mental health interventions among obese patients depending on the degree of obesity, age, gender, occupational status, and mental health status. In an extended UTAUT regression model acceptance was significantly predicted by the depression score (PHQ-8) (β = .07, P = .028), stress due to constant availability via mobile phone or email (β = .06, P = .024) and the confidence in using digital media (β = -.058, P = .042), as well as by the UTAUT core predictors performance expectancy (PE) (β = .45, P < .001), effort expectancy (EE) (β = .22, P < .001), and social influence (SI) (β = .27, P < .001). The comparison between an extended UTAUT model (16 predictors) and the restrictive UTAUT model (PE, EE, SI) revealed a significant difference in explained variance (F13,431= 2.366, P = .005). CONCLUSIONS The UTAUT model has proven to be a valuable instrument to predict the acceptance of e-mental health interventions in patients with obesity. Furthermore, when additional predictors were added, a significantly higher percentage of explained variance in acceptance could be achieved. Based on the strong association between acceptance and future utilization, new interventions should focus on these UTAUT predictors to promote the urgently needed establishment of effective e-mental health interventions for patients with obesity, who suffer from mental health burdens.

scholarly journals Surveying the Role of Analytics in Evaluating Digital Mental Health Interventions for Transition-Aged Youth: Scoping Review

10.2196/15942 ◽  
2020 ◽  
Vol 7 (6) ◽  
pp. e15942
Author(s):  
Brian Lo ◽  
Jenny Shi ◽  
Elisa Hollenberg ◽  
Alexxa Abi-Jaoudé ◽  
Andrew Johnson ◽  
...  
Keyword(s):  
Mental Health ◽  
Scoping Review ◽  
Care Delivery ◽  
Digital Health ◽  
Controlled Trial ◽  
Health Interventions ◽  
Health Apps ◽  
Mental Health Interventions ◽  
Usage Data

Background Consumer-facing digital health interventions provide a promising avenue to bridge gaps in mental health care delivery. To evaluate these interventions, understanding how the target population uses a solution is critical to the overall validity and reliability of the evaluation. As a result, usage data (analytics) can provide a proxy for evaluating the engagement of a solution. However, there is paucity of guidance on how usage data or analytics should be used to assess and evaluate digital mental health interventions. Objective This review aimed to examine how usage data are collected and analyzed in evaluations of mental health mobile apps for transition-aged youth (15-29 years). Methods A scoping review was conducted using the Arksey and O’Malley framework. A systematic search was conducted on 5 journal databases using keywords related to usage and engagement, mental health apps, and evaluation. A total of 1784 papers from 2008 to 2019 were identified and screened to ensure that they included analytics and evaluated a mental health app for transition-aged youth. After full-text screening, 49 papers were included in the analysis. Results Of the 49 papers included in the analysis, 40 unique digital mental health innovations were evaluated, and about 80% (39/49) of the papers were published over the past 6 years. About 80% involved a randomized controlled trial and evaluated apps with information delivery features. There were heterogeneous findings in the concept that analytics was ascribed to, with the top 3 being engagement, adherence, and acceptability. There was also a significant spread in the number of metrics collected by each study, with 35% (17/49) of the papers collecting only 1 metric and 29% (14/49) collecting 4 or more analytic metrics. The number of modules completed, the session duration, and the number of log ins were the most common usage metrics collected. Conclusions This review of current literature identified significant variability and heterogeneity in using analytics to evaluate digital mental health interventions for transition-aged youth. The large proportion of publications from the last 6 years suggests that user analytics is increasingly being integrated into the evaluation of these apps. Numerous gaps related to selecting appropriate and relevant metrics and defining successful or high levels of engagement have been identified for future exploration. Although long-term use or adoption is an important precursor to realizing the expected benefits of an app, few studies have examined this issue. Researchers would benefit from clarification and guidance on how to measure and analyze app usage in terms of evaluating digital mental health interventions for transition-aged youth. Given the established role of adoption in the success of health information technologies, understanding how to abstract and analyze user adoption for consumer digital mental health apps is also an emerging priority.

scholarly journals Manipulating efficacy expectancies in an ambulatory smartphone-based digital placebo mental health intervention: a randomized controlled trial (Preprint)

2020 ◽  
Author(s):  
Esther Stalujanis ◽  
Joel Neufeld ◽  
Martina Glaus Stalder ◽  
Angelo Belardi ◽  
Gunther Meinlschmidt
Keyword(s):  
Mental Health ◽  
Controlled Trial ◽  
Health Intervention ◽  
Health Interventions ◽  
Mental Health Intervention ◽  
Placebo Effects ◽  
Intention To Treat ◽  
Randomized Controlled ◽  
Mental Health Interventions ◽  
Efficacy Expectancies

BACKGROUND Smartphone-based mental health interventions provide new ways to treat mental disorders. There is certain evidence on the efficacy of such interventions. Placebo effects represent a substantial element of the mechanisms of action of face-to-face mental health interventions. OBJECTIVE We manipulated efficacy expectancies and investigated whether time trajectories of efficacy expectancies differed between conditions across a smartphone-based digital placebo mental health intervention. METHODS We conducted a randomized, controlled, single-blinded superiority trial with a multi-arm parallel design. Participants underwent a smartphone-based digital placebo mental health intervention for 20 consecutive days. We induced prospective efficacy expectancies by manipulating initial instructions on the purpose of the intervention and retrospective efficacy expectancies by manipulating feedback on the success of the intervention at days 1, 4, 7, 10, and 13. 132 healthy participants were randomized to four conditions: prospective expectancy only (n=33), retrospective expectancy only (n=33), combined expectancy (n=34), or control (n=32). Changes in efficacy expectancies were assessed with the Credibility Expectancy Questionnaire, at the introductory session and on intervention days 1, 7, 14, and 20. We performed our analyses for the intention-to-treat sample using a random effects model, with intervention day as time variable and condition as two factors: prospective expectancy (yes vs. no), and retrospective expectancy (yes vs.no), allowed to vary over participant and intervention day. RESULTS Credibility (b = -1.63, 95%confidence interval (CI) [-2.37, -0.89], P < 0.001) and expectancy (b = -0.77, 95%CI [-1.49, -0.05], P = 0.04) decreased across intervention days. For credibility and expectancy, we found significant three-way interactions intervention day*prospective expectancy*retrospective expectancy (b = 2.05, 95%CI [0.60, 3.50], P < 0.01 resp. b = 1.55, 95%CI [0.14, 2.95] P = 0.03). Efficacy expectancies decreased least in the combined expectancy and in the control condition, most in the prospective expectancy only and the retrospective expectancy only condition. CONCLUSIONS This is the first study investigating the induction of efficacy expectancies across a placebo smartphone-based mental health intervention. Efficacy expectancies decreased throughout intervention days and differed between conditions. Our findings may pave the way for diminishing and exploiting digital placebo effects and help to improve treatment efficacy of digital mental health interventions. CLINICALTRIAL ClinicalTrials.gov Identifier: NCT02365220. Registered February 18, 2015.

scholarly journals Effectiveness of Mental Health Intervention for Suicide Prevention

2021 ◽  
Vol 2 (4) ◽  
Author(s):  
Nimesh G. Desai ◽  
Vishi Sachdeva ◽  
Aishwarya John ◽  
Kumar Gourav ◽  
Gibson O. Anugwom
Keyword(s):  
Mental Health ◽  
Family Members ◽  
Health Intervention ◽  
Health Interventions ◽  
P Value ◽  
Mental Health Intervention ◽  
Suicide Behavior ◽  
Significant Difference ◽  
Before And After ◽  
Mental Health Interventions

Background: Various mental health interventions like priority in-patient care, ECT, psychotherapies, pharmacotherapies, etc. have been tried throughout the world to decrease the morbidity and mortality associated with suicide behavior. Aims: To establish the effectiveness of mental health intervention for preventing suicide, for those at risk and to understand the perception of patients and family members about the usefulness of interventions for preventing suicide. Material and methods: The patients admitted to the Psychiatry Intensive Care Unit (PICU) at the Institute of Human Behavior and Allied Sciences (IHBAS), in view of suicide behavior, during the 12 months period from 1st July 2018 to 30th June 2019 were included in the study. A target population of 88 patients was taken up for cross-sectional follow-up assessment. They were assessed for suicide behavior for the period of 12 months prior to and subsequent to the hospitalization. Results: A statistically significant difference was found in both, the number of patients attempting suicide before and after hospitalization (N = 88, Chi-square = .2, p-value < 0.04), as well as in the mean number of attempts before and after hospitalization (N = 88, p-value <0.01). Also, three fourth of patients/family members were completely satisfied with the care provided while the remaining one-fourth were only partially satisfied. Conclusion: This study has established not only the usefulness of timely mental health intervention for the prevention of suicide as perceived by family or patients but also provides statistical evidence for the effectiveness of such mental health interventions.

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