Internet Interventions-The Application of Information Technology in Mental and Behavioural Health最新文献

Background

Purpose

Methods

Results

Discussion

Background

Objective

Methods

Results

Conclusions

Objectives

The study aims to conduct a systematic literature review and meta-analysis to assess the effects of digital nature and actual nature on stress reduction.

Methods

In August 2023, Web of Science, Scopus, ProQuest, PubMed, and EBSCOhost databases were used, and ten articles were in the analysis, with a total sample size of 886 participants. Studies within- or between-subjects design conducted in either a randomized controlled trial or a quasi-experimental design were included. No restriction was put on the year of publication or geographical region. Conference papers and dissertations were also included whereas, book chapters were excluded. Participants included those who were exposed to at least one form of digital nature exposure, such as static images, videos, 360° pictures, and 360° videos. The risk of bias determined through Review Manager 5.4 was used to assess the quality of the studies. STATA software package version 16 was used for visual analysis of funnel plots. For the assessment of potential publication bias, Egger's test was implemented.

Results

Digital natural environments had the same level of stress recovery compared to actual environmental exposures with the same intervention content (SMD = −0.01; 95% CI: −0.15, 0.12). Subgroup analyses and meta-regression indicated that subjective or physiological stress measures, level of immersion, and data extraction method were not associated with pooled effect stress recovery. All subgroups showed comparable stress levels in both conditions. In addition, all included studies had different levels of risk of bias (low, moderate, and high).

Conclusions

The present study concludes that previous research has generally shown that stress levels are reduced in both digital and actual natural environments. The results of the meta-analysis support this conclusion with no significant differences between the two modes of stress recovery through nature viewing.

Background

AI-powered Digital Therapeutics (DTx) hold potential for enhancing stress prevention by promoting the scalability of P5 Medicine, which may offer users coping skills and improved self-management of mental wellbeing. However, adoption rates remain low, often due to insufficient user and stakeholder involvement during the design phases.

Objective

This study explores the human-centered design potentials of SHIVA, a DTx integrating virtual reality and AI with the SelfHelp+ intervention, aiming to understand stakeholder views and expectations that could influence its adoption.

Methods

Using the SHIVA example, we detail design opportunities involving AI techniques for stress prevention across modeling, personalization, monitoring, and simulation dimensions. Workshops with 12 stakeholders—including target users, digital health designers, and mental health experts—addressed four key adoption aspects through peer interviews: AI data processing, wearable device roles, deployment scenarios, and the model's transparency, explainability, and accuracy.

Results

Stakeholders perceived AI-based data processing as beneficial for personalized treatment in a secure, privacy-preserving environment. While wearables were deemed essential, concerns about compulsory use and VR headset costs were noted. Initial human facilitation was favored to enhance engagement and prevent dropouts. Transparency, explainability, and accuracy were highlighted as crucial for the stress detection model.

Conclusion

Stakeholders recognized AI-driven opportunities as crucial for SHIVA's adoption, facilitating personalized solutions tailored to user needs. Nonetheless, challenges persist in developing a transparent, explainable, and accurate stress detection model to ensure user engagement, adherence, and trust.

Introduction

Methods

Results

Discussion

Objective

Predicting who will not benefit enough from Internet-Based Cognitive Behavioral (ICBT) Therapy early on can assist in better allocation of limited mental health care resources. Repeated measures of symptoms during treatment is the strongest predictor of outcome, and we want to investigate if methods that explicitly account for time-dependency are superior to methods that do not, with data from (a) only two pre-treatment timepoints and (b) the pre-treatment timepoints and three timepoints during initial treatment.

Methods

We use 1) commonly used time-independent methods (i.e., Linear Regression and Random Forest models) and 2) time-dependent methods (i.e., multilevel model regression, mixed-effects random forest, and a Long Short-Term Memory model) to predict symptoms during treatment, including the final outcome. This is done with symptom scores from 6436 ICBT patients from regular care, using robust multiple imputation and nested cross-validation methods.

Results

The models had a 14 %–12 % root mean squared error (RMSE) in predicting the post-treatment outcome, corresponding to a balanced accuracy of 67–74 %. Time-dependent models did not have higher accuracies. Using data for the initial treatment period (b) instead of only from before treatment (a) increased prediction results by 1.3 % percentage points (12 % to 10.7 %) RMSE and 6 % percentage points BACC (69 % to 75 %).

Conclusion

Training prediction models on only symptom scores of the first few weeks is a promising avenue for symptom predictions in treatment, regardless of which model is used. Further research is necessary to better understand the interaction between model complexity, dataset length and width, and the prediction tasks at hand.

Background

Objective

Methods

Results

Conclusions

Background

Over the past two decades, the development of internet-based treatments for adolescents with anxiety and depressive disorders has advanced rapidly. To date, adolescents' preferences and perceived barriers for internet-based treatment remain largely unknown, especially in clinical samples. Therefore, this study explored the preferences and perceived barriers of adolescents with anxiety or depression regarding internet-based treatment.

Methods

This qualitative study included 21 adolescent patients with anxiety or depressive disorder, and varied levels of experience with internet-based treatment. Two focus groups (N₁ = 5, N₂ = 6) and semi-structured interviews (N = 10) were conducted, recorded, transcribed, and analyzed using a reflexive thematic analysis approach.

Results

The thematic analysis yielded five main themes, and 12 subthemes. The main themes were: independence, accessibility, content, therapist contact, and appearance. Adolescents highlighted self-direction as a benefit of internet-based treatment, and motivational challenges as a drawback. They found internet-based interventions convenient and particularly fitting for implementation during waiting periods before formal treatment. Guided interventions were preferred over mere self-help. Furthermore, adolescents stressed the importance of a clear, organized design, and recommended accessibility on both mobile phones and computers.

Conclusion

Findings provide a clear overview of the needs and preferences of adolescents with anxiety or depressive disorder regarding internet-based treatment. To address their diverse needs, internet-based interventions should be tailorable, should incorporate therapist guidance, and should already be available during the treatment waiting period. Results of this study can guide the development and implementation of new internet-based interventions, and may thereby help to further optimize their uptake among adolescent patients.

Grief is highly prevalent in adolescents, however, there have been no studies investigating internet delivered cognitive behaviour therapy for grief in adolescents (ICBT-G-A). In this paper, the co-design of an unguided ICBT-G-A intervention is described, and a protocol outlined for a pilot randomised controlled trial of the intervention. Participants will be randomised to the intervention (delivered via eight modules over a four-week period) or a four-week waitlist control. Intervention participants will complete a follow-up assessment at one-month post-intervention (eight weeks from the pre-intervention assessment). The intervention outcomes assessed at pre-intervention, post-intervention and follow-up include wellbeing and symptoms of anxiety, depression, post-traumatic stress, and prolonged grief. User feedback on experiences and acceptability of the intervention will be sought and feasibility assessed via programmatic data on recruitment and attrition.

Background

Pelvic girdle pain, low back pain, and pelvic floor dysfunction can affect women's mobility, quality of life, and well-being during pregnancy and the postpartum period. Digital interventions for treating perinatal depression and lifestyle changes have been studied. Research on digital physiotherapy for musculoskeletal issues related to pregnancy and the postpartum period is sparse.

Methods

This qualitative study involved in-depth, semi-structured interviews with 19 participants, of whom six were pregnant and 13 had given birth. Participants were recruited from a private clinic in Sweden through convenience sampling and had received digital physiotherapy prior to the interviews. An interview guide with questions exploring participants' experiences of digital physiotherapy, including its impact on musculoskeletal issues and daily life, and their motivation for seeking digital healthcare was used. Data were analyzed using a qualitative content analysis with an inductive approach.

Results

The analysis resulted in two main categories: Finding a new way into physiotherapy treatment and Personalized progress through tailored physiotherapy. These main categories encompassed four generic categories: Convenience and dissatisfaction motivators for digital physiotherapy, A dual experience – appreciated but not always comprehensive, Being involved in the rehabilitation process, and Perceived physical and mental improvements after digital physiotherapy.

Conclusion

Digital physiotherapy was well-accepted and perceived as beneficial for managing musculoskeletal symptoms during pregnancy and after childbirth. High accessibility and flexibility were considered advantages. However, inability to undergo a physical assessment was a challenge. Digital physiotherapy may be recommended as a complement to usual care, particularly for women with limited access to a physiotherapist specialized in women's health. Future studies exploring digital physiotherapy's efficacy for musculoskeletal issues during pregnancy and after childbirth are highly recommended.