Technology and psychotherapeutic interventions: Bibliometric analysis of the past four decades

Digital interventions targeting excessive substance use and substance use disorders: a comprehensive and systematic scoping review and bibliometric analysis

Addictive substances are prevalent world-wide, and their use presents a substantial and persistent public health problem. A wide range of digital interventions to decrease use and negative consequences thereof have been explored, differing in approach, theoretical grounding, use of specific technologies, and more. The current study was designed to comprehensively map the recent (2015-2022) extant literature in a systematic manner, and to identify neglected and emerging knowledge gaps. Four major databases (Medline, Web of Science Core Collection, and PsychInfo) were searched using database-specific search strategies, combining terms related to clinical presentation (alcohol, tobacco or other drug use), technology and aim. After deduplication, the remaining n=13,917 unique studies published were manually screened in two stages, leaving a final n=3,056 studies, the abstracts of which were subjected to a tailored coding scheme. Findings revealed an accelerating rate of publications in this field, with randomized trials being the most common study type. Several meta-analyses on the topic have now been published, revealing promising and robust effects. Digital interventions are being offered on numerous levels, from targeted prevention to specialized clinics. Detailed coding was at times made difficult by inconsistent use of specific terms, which has important implications for future meta-analyses. Moreover, we identify several gaps in the extant literature - few health economic assessments, unclear descriptions of interventions, weak meta-analytic support for some type of interventions, and limited research on many target groups, settings and new interventions like video calls, chatbots and artificial intelligence - that we argue are important to address in future research.

Internet interventions and therapies for addressing the negative impact of digital overuse: a focus on digital free tourism and economic sustainability

BACKGROUND

The excessive use of information technologies (IT) and online digital devices are causing symptoms of burnout, anxiety, stress and dependency that affect the physical and mental health of our society, extending to leisure time and work relationships. Digital free tourism (DFT) is a phenomenon that emerges as a solution to technostress and pathologies derived from digital hyperconnection. The objective of this research is to advance the knowledge of new structures of motivational factors that can understand the decision of a tourist to make a DFT trip. To this end, it is investigated whether family and social engagement and health and relaxation have a positive impact on the behavioral intention of the potential tourist and whether this influences sustainability due to the importance of DFT in the new economic framework.

METHODS

With a quantitative approach, the methodology used consisted of an online questionnaire among potential travelers. IBM SPSS Statistics 22.0 statistical software was used to evaluate the data obtained and confirm the relationships of the model and the research hypotheses.

RESULTS

The results of the questionnaire assessed the contribution of each construct to the tourist's behavioral intention and the tourist's decision to make the decision to undertake a DFT experience.

CONCLUSIONS

DFT can be a driver of economic sustainability and health therapy in tourism in the digital age. This study aims to expand the lines of research on DFT and determine the complex factors that can lead a tourist to participate in the DFT experience. The results obtained can help managers of companies in the sector to offer more efficient and sustainable services that contribute to the health and wellbeing of tourists as a differentiating factor.

Prognostic Risk Factors in Randomized Clinical Trials of Face-to-Face and Internet-Based Psychotherapy for Depression: A Systematic Review and Meta-Regression Analysis

Importance

Variables such as severe symptoms, comorbidity, and sociodemographic characteristics (eg, low educational attainment or unemployment) are associated with a poorer prognosis in adults treated for depressive symptoms. The exclusion of patients with a poor prognosis from RCTs is negatively associated with the generalizability of research findings.

Objective

To compare the prognostic risk factors (PRFs) in patient samples of RCTs of face-to-face therapy (FTF) and internet-based therapy (IBT) for depression.

Data Sources

PsycINFO, Cochrane CENTRAL, and reference lists of published meta-analyses were searched from January 1, 2000, to December 31, 2021.

Study Selection

RCTs that compared FTF (individual or group therapy) and IBT (guided or self-guided interventions) against a control (waitlist or treatment as usual) in adults with symptoms of depression were included.

Data Extraction and Synthesis

Data were extracted by 2 independent observers. The Cochrane revised risk-of-bias tool was used to assess the risk of bias. The study was preregistered with OSF Registries and followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guideline.

Main Outcomes and Measures

The primary outcome was the standardized mean difference (Hedges g effect size) in depressive symptoms at treatment termination (assessed with standard patient self-report questionnaires), with a positive standardized mean difference indicating larger improvements in the intervention compared with those in the control group. Meta-regression analyses were adjusted for the type of control group. Three preregistered and 2 exploratory sensitivity analyses were conducted. A prognostic risk index (PROG) was created that calculated the sum of 12 predefined individual indicators, with scores ranging from 0 to 12 and higher scores indicating that a sample comprised patients with poorer prognoses.

Results

This systematic review and meta-regression analysis identified 105 eligible RCTs that comprised 18 363 patients. In total, 48 studies (46%) examined FTF, and 57 studies (54%) examined IBT. The PROG was significantly higher in the RCTs of FTF than in the RCTs of IBT (FTF: mean [SD], 3.55 [1.75]; median [IQR], 3.5 [2.0-4.5]; IBT: mean [SD], 2.27 [1.66]; median [IQR], 2.0 [1.0-3.5]; z = -3.68, P < .001; Hedges g = 0.75; 95% CI, 0.36-1.15). A random-effects meta-regression analysis found no association of the PROG with the effect size. Sensitivity analyses with outliers excluded and accounting for risk of bias or small-study effects yielded mixed results on the association between the PROG and effect size.

Conclusions and Relevance

The findings of this systematic review and meta-regression analysis suggest that samples of RCTs of FTF vs IBT differ with regard to PRFs. These findings have implications for the generalizability of the current evidence on IBT for depression. More RCTs of internet-based interventions with clinically representative samples are needed, and the reporting of PRFs must be improved.

Systematic review of machine learning utilization within outpatient psychodynamic psychotherapy research

Introduction

Although outpatient psychodynamic psychotherapy is effective, there has been no improvement in treatment success in recent years. One way to improve psychodynamic treatment could be the use of machine learning to design treatments tailored to the individual patient's needs. In the context of psychotherapy, machine learning refers mainly to various statistical methods, which aim to predict outcomes (e.g., drop-out) of future patients as accurately as possible. We therefore searched various literature for all studies using machine learning in outpatient psychodynamic psychotherapy research to identify current trends and objectives.

Methods

For this systematic review, we applied the Preferred Reporting Items for systematic Reviews and Meta-Analyses Guidelines.

Results

In total, we found four studies that used machine learning in outpatient psychodynamic psychotherapy research. Three of these studies were published between 2019 and 2021.

Discussion

We conclude that machine learning has only recently made its way into outpatient psychodynamic psychotherapy research and researchers might not yet be aware of its possible uses. Therefore, we have listed a variety of perspectives on how machine learning could be used to increase treatment success of psychodynamic psychotherapies. In doing so, we hope to give new impetus to outpatient psychodynamic psychotherapy research on how to use machine learning to address previously unsolved problems.

Online ACT intervention for fibromyalgia: An exploratory study of feasibility and preliminary effectiveness with smartphone-delivered experiential sampling assessment

INTRODUCTION

Acceptance and commitment therapy (ACT) is an effective treatment for chronic pain conditions. ACT seeks to produce clinical change by enhancing Psychological Flexibility (PF). This exploratory (feasibility and preliminary effectiveness) study presents a pilot application of an online ACT group intervention for fibromyalgia (FM) with an extensive Experiential Sampling (ES) assessment of outcome and process variables via smartphone.

METHOD

5-weekly ACT online group sessions were applied to 9 female FM patients. Questionnaire-based assessments of several clinical outcomes and PF processes were conducted pre- and post-intervention, and at 6-month follow-up. Extensive (6 weeks pre- and 6 weeks post-intervention) smartphone-delivered ES was implemented to gather process and outcome data in the patients' usual contexts. Clinically significant change was evaluated both at the group level and individually.

RESULTS

This treatment format appears to be feasible and acceptable to participants, with good adoption and completion rates (75 %) and excellent rates of treatment completion and clinical adherence (100 %). Participants showed significant reductions in affective pain, distress and biopsychosocial impact of FM both post-intervention and at 6-month follow-up (as measured with questionnaires), as well as significant improvements in satisfaction with actions and emotional discomfort (as measured through ES). Multilevel regression analyses indicated that PF-related processes assessed through ES had a significant impact on clinical outcomes and predicted the impact of FM at the 6-month follow-up.

CONCLUSIONS

A brief online group ACT intervention for FM was both feasible and acceptable to participants. Also, there was preliminary evidence of effectiveness in enhancing pain-related PF and producing clinical benefits in FM.

Editorial Introduction to Technological Approaches for the Treatment of Mental Health in Youth

According to the World Health Organization (WHO), 10–20% of adolescents (10–19 years old) worldwide suffer from mental health conditions, with 50% starting at the age of 14 (World Health Organization 2020) [...]