Category: 3. Business

Oct 10 (Reuters) – S&P Global upgraded Egypt’s rating by a notch on Friday, citing ongoing reforms that have led to a sharp rebound in GDP growth, while Fitch highlighted the country’s fairly high growth potential and strong support from partners in its affirmation.

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S&P said the strategic importance of Egypt has been highlighted and elevated by the conflict in Gaza, and has been part of the reason that Gulf Cooperation Council members and other countries continue to extend financial support to Egypt.

“We consider the risk from an escalation of tensions with Israel has increased only moderately over recent months, and energy collaboration continues to progress,” Fitch added in its statement.

Alongside the IMF programme, the commitment to a market-determined exchange rate, should continue to support Egypt’s GDP growth prospects and fiscal consolidation efforts over fiscal years 2025-2028, S&P said.

According to S&P, the reforms undertaken over the past 18 months by the authorities, including the liberalization of the foreign exchange regime, have led to the sharp rebound in Egypt’s GDP growth, boosted tourism and inward remittances.

S&P and Fitch maintained their outlook for Egypt at ‘stable’.

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By Frances Yue

Major cryptocurrencies tumbled on Friday

Crypto bulls were buzzing with anticipation that several major Wall Street firms would win approval this week to launch exchange-traded funds investing in solana, a lesser-known cryptocurrency. But a partial U.S. government shutdown has thrown the process into limbo.

The Securities and Exchange Commission faced a Friday deadline to make a decision on the applications of ETFs investing in solana (SOLUSD), which were filed by firms such as Fidelity, Franklin Templeton (BEN) and Invesco (IVZ). If the agency approves the ETFs, it would mark another milestone as digital assets become increasingly mainstream.

Solana is the sixth-largest cryptocurrency by market capitalization and is often seen as a strong competitor to ether (ETHUSD), as both blockchains underpinning them allow people to build decentralized applications.

Investors had widely anticipated the SEC to greenlight the solana ETFs. However, the partial U.S. government shutdown, which began on Oct. 1, has thrown the timeline into uncertainty, as the SEC will not review and approve ETF applications during the lapse.

A SEC spokesperson told MarketWatch that the agency is currently operating in accordance with its plan for functioning during a government shutdown. That plan says the SEC will maintain a narrow set of essential functions during the shutdown, while many normal functions will be halted.

“The SEC will not review and approve applications for registration by entities (e.g., investment advisers, broker-dealers, transfer agents, nationally recognized statistical rating organizations, investment companies and municipal advisors) or with respect to new financial products,” it said.

This will likely lead to delays on crypto ETF decisions, according to Roxanna Islam, head of sector and industry research at VettaFi.

“Once the government reopens, I’m not sure [about the timeline]. I guess we don’t really have that clarity of when, in what order and how fast they will approve [the ETFs],” she said. “I think it will still be this year. I don’t think it’s going to be delayed that long, but we’re going to have to wait until the government reopens to see any progress in this.”

While the SEC approved an ETF investing directly solana in July, the fund was issued by a lesser-known firm and structured in a different way. The SEC has also approved a few ETFs that invest in multiple different crypto assets, including solana.

But it’s not just about solana. The SEC also faces deadlines later this month to make decisions on applications of ETFs tied to XRP (XRPUSD), dogecoin (DOGEUSD) and cardano (ADAUSD). Those cryptocurrencies are the fifth-, eighth- and 10th-largest tokens by market cap, respectively, according to CryptoMarketCap.

Once the government resumes functioning, it’s highly likely that those applications will be approved, Islam and several other market participants told MarketWatch.

In particular, the applications may benefit from a rule change that the SEC approved in September, which enabled the New York Stock Exchange (ICE), Nasdaq (NDAQ) and Cboe Global Markets (CBOE) exchanges to adopt generic listing standards for new crypto ETFs and other spot commodity exchange-traded products, without case-by-case SEC rulemaking reviews.

The change could result in much less friction in listing new spot crypto ETFs, and cut the maximum time for a new ETF from filing to launch to 75 days, from 240 days or longer, according to Adrian Fritz, global head of research at crypto asset manager 21Shares.

However, the delay in SEC approval because of the government shutdown may potentially weigh on the demand facing such products, VettaFi’s Islam said.

“You might get tired of waiting for it,” she noted. “I mean, people have been waiting almost eight months, and then you see delays – it’s just getting delayed further and further.”

That could potentially lead to some downward price action, which nevertheless may reverse once the ETFs are approved, she added.

Solana fell 7.9% to around $183.27 on Friday afternoon, according to FactSet data. XRP tumbled 19.53% to $2.03, while dogecoin plunged 12.35% to $0.22. In comparison, bitcoin declined 4.1% Friday afternoon to around $111,917.

Fritz, on the other hand, argued that exchange-traded products tied to smaller cryptocurrencies have seen significant inflows in Europe and Canada in the past few weeks, in anticipation of the potential ETF approvals in the U.S.

The delay due to the shutdown, he noted, may “make the buildup even better.”

-Frances Yue

This content was created by MarketWatch, which is operated by Dow Jones & Co. MarketWatch is published independently from Dow Jones Newswires and The Wall Street Journal.

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  10-10-25 1810ET
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Introduction

The global issue of illicit drug use has worsened, with 292 million users in 2022, a 20% increase over the past decade []. Cannabis is the most widely used illicit drug (228 million), followed by opioids (60 million), cocaine (23 million), and others []. Illicit drug users face various psychological and physiological problems, including mental disorders, cognitive deficits, cardiovascular dysfunction, and blood-borne infections. The social burden is also high, due to links with crime, violence, and sexual abuse []. Treatment is urgently needed, but globally, only about 10% of users receive treatment, a decline since 2015 [].

Traditional face-to-face psychosocial treatments remain important for illicit drug users but often fail to meet the needs of most patients due to time, location, and social stigma []. The COVID-19 pandemic accelerated the development of telehealth [] and pushed digital interventions from early simple interactions to more complex forms []. Modern digital interventions can provide multiple interaction methods via smart devices, such as apps, websites, email, text messages, video, audio, and computer programs. They overcome the limitations of traditional treatments and are valued for their flexibility and cost-effectiveness [-], better meeting personalized needs and improving treatment engagement []. Meta-analyses show that digital interventions are effective across different populations of illicit drug users [-].

However, dropout rates are particularly prominent in digital interventions [-]. Meta-analyses indicate that about one-third of individuals with substance use disorders fail to complete treatment [] and only 48% of early dropouts seek help again [], significantly increasing the risk of adverse outcomes [,]. Methodologically, the relatively high dropout rate limits the completeness of research findings, affecting the validity of results and the interpretation of treatment effects []. To improve the accuracy, this study clearly distinguishes three key concepts: engagement refers to behavioral involvement during use []; adherence reflects the alignment between actual behavior and intervention expectations []; while the dropout rate in this study is strictly defined as participants leaving, being lost to follow-up, or stopping participation before the outcome assessment for any reason. This conceptual clarification both distinguishes commonly confused terms and provides a methodological basis for enhancing the effectiveness of digital interventions, with important clinical implications.

Although the dropout rate is an important outcome indicator of intervention efficacy [], few studies have examined dropout rates among illicit drug users in digital interventions. A meta-analysis published in 2017 was the first to evaluate internet-based interventions in reducing illicit substance use after treatment and follow-up, but dropout rate was not the focus []. Moreover, existing research lacks systematic examination of clinical factors and intervention design, as well as dynamic assessment of dropout patterns at different time points [-], directly limiting the optimization of targeted intervention strategies.

Based on current research, this study aims to address the gap in dropout rate research in digital interventions. The study compared average dropout rates between the digital intervention and control groups to assess treatment retention under different experimental conditions. It also analyzed how variables at posttreatment and the longest follow-up time points affected dropout rates in the intervention group to support personalized intervention design for different research stages. These findings are important for advancing academic research and expanding clinical applications [].

Methods

Protocol Registration

This study strictly adheres to the guidelines of the Cochrane Handbook for Interventions [] and is reported according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines [] (the complete PRISMA checklist is available in ). The research protocol has been registered in the PROSPERO system: CRD42024534389.

Search Strategy

To comprehensively and systematically collect relevant literature, this study searched five major databases up to August 27, 2025, including Web of Science, PubMed, PsycINFO, Embase, and the Cochrane Controlled Trials Register. The search strategy combined controlled vocabulary (eg, MeSH terms) and free-text keywords using Boolean operators (“AND” and “OR”). The main search terms included the following: (“digital intervention” OR “internet intervention” OR “e-health” OR “m-health”) AND (“drug abuse” OR “substance use disorder” OR “illicit drugs”) AND (“psychotherapy” OR “psychoeducation” OR “psychodynamic”) AND (“randomized controlled trial” OR “single blind procedure” OR “random sample”). The complete search strategy for each database is provided in .

Inclusion and Exclusion Criteria

Inclusion criteria were as follows: (1) Individuals aged 18 years and above with illicit drug use behavior. Illicit drugs refer to controlled substances used for nonmedical or nonscientific purposes, including but not limited to cannabis, cocaine, amphetamines, and opioids []. (2) Digital psychosocial intervention is the primary treatment. Operationally defined as structured psychological intervention primarily delivered through digital platforms, including mobile applications, web-based programs, or digital communication tools, with or without minimal human support. (3) The article must report sample size and dropout rates. (4) Randomized controlled trials. Exclusion criteria were as follows: (1) treatment involving only face-to-face therapy. (2) mixed samples with insufficient proportion of illicit drug users (less than 80%) or without independent subgroup data (eg, alcohol and tobacco users). (3) non-English studies. (4) unpublished reports, study protocols, meta-analyses, reviews, doctoral theses, or other gray literature.

To ensure the accuracy of literature screening, a dual-screening process was adopted. First, two researchers independently screened the titles and abstracts of retrieved literature to exclude those clearly not meeting inclusion criteria. Subsequently, the full texts of the literature were reviewed for further evaluation. Finally, manual searches were conducted on the reference lists of included studies and related reviews to identify additional studies meeting inclusion criteria. Any disagreements were resolved through discussion.

Select Variables and Data Extraction

Outcome Variable

This study uses the dropout rate from randomized controlled trials as the primary outcome measure. Considering that the influencing factors at different treatment stages may vary [,-], the dropout rate data of the intervention and control groups at the end of treatment and at the longest follow-up time were extracted separately.

Moderator Variables

Previous studies have explored the factors influencing dropout among illegal drug users [], but due to differences in confounding variable control methods and insufficient understanding of the complexity of predictive factors, the results have been inconsistent []. Withdrawal from treatment is a dynamic process, and its mechanisms involve complex interactions of multiple factors []. It is difficult to fully explain the complexity of single-variable analysis []. Therefore, this study refers to previous research [] and selects multidimensional variables (): (1) Demographic characteristics of participants: most studies emphasize the role of patient-related variables in predicting dropout [,], and investigating individual differences (such as age, gender, race, digital literacy, etc.) is crucial for developing treatment interventions for specific populations [16]. (2) Baseline clinical characteristics of participants, including the type of illegal drug use, medication patterns, frequency of use, duration of use, and comorbid conditions. Different drugs may have differentiated effects on dropout rates due to their unique pharmacological mechanisms and withdrawal characteristics []. Additionally, the presence of comorbid mental disorders may exacerbate the likelihood of treatment interruption [], which also needs to be considered. (3) Therapist characteristics: the therapeutic orientation and experience level of therapists may be related to patient adherence []. Compared to busy clinic staff, full-time therapists are more likely to invest time and effort to retain and reengage patients who have discontinued treatment [32]. (4) Treatment characteristics: referring to the framework proposed by Derubeis et al [], which focuses on all factors that improve treatment and particularly on the relationship between treatment factors and outcomes. For example, this study extracted personalized feedback, real-time interaction, and therapeutic alliance. The optimization of these modifiable operational variables can directly enhance intervention effectiveness and improve patient treatment adherence [].

Data Extraction

Two researchers independently extracted data using a predesigned data extraction form. Disagreements were resolved through discussion or consultation with a third researcher. This data extraction form has been piloted in some studies and adjusted according to the recommendations and structured framework of the GRADE manual. For articles that met the inclusion criteria but lacked important data, we contacted the corresponding author via email, and studies that could not provide sufficient data to calculate effect sizes were excluded.

Quality Assessment

To assess the bias risk of the included studies, two researchers independently scored each study in five aspects using the revised Cochrane Risk of Bias tool ROB 2.0 []: randomization process, deviations from intended interventions, missing outcome data, measurement of the outcome, and selection of the reported result. Any disagreements were resolved through discussion.

Statistical Analysis and Software

We used Comprehensive Meta-Analysis software (CMA 4.0) to synthesize dropout rates across studies []. For each trial, dropout counts and total sample sizes were extracted separately for the intervention and control groups, from which group-specific dropout proportions were calculated. To stabilize variances and account for the bounded nature of proportions, these proportions were transformed into logit event rates with corresponding standard errors, which served as the primary effect size metric. Pooled estimates were calculated separately for intervention and control groups and subsequently back-transformed into raw proportions and expressed as percentages for interpretability, an approach that has been widely applied in meta-analyses of proportion-type outcomes []. Subsequently, between-study heterogeneity was examined using the Q statistic [,] and quantified with the I² statistic []. Given the significant heterogeneity among included studies in outcome measures and moderators [,], all analyses were conducted under a random-effects model []. Publication bias was assessed using funnel plots, Egger’s, Duval and Tweedie’s trim and fill, and Classic fail-safe N tests [], while sensitivity analyses were conducted to evaluate the robustness of the results. To explore potential influencing factors, meta-regression and subgroup analyses were further employed to examine the association between moderators in the intervention group and dropout rate.

Results

Characteristics of the Included Studies

After screening relevant articles based on predefined inclusion and exclusion criteria, a total of 41 studies were finally included (see ), involving 9693 participants with an age range of 19 to 50 years. The selection characteristics of the included studies are shown in . The studies included 82 intervention groups, with a total of 48 dropout rate data points, including 18 posttreatment dropout rates and 30 follow-up dropout rates, showing different data results between the two measurement points.

Risk of Bias Assessment

The risk of bias in the included studies was assessed using the Cochrane Risk of Bias tool (ROB 2.0). Detailed results and percentage plots are presented in . The results showed that approximately 90% of the included studies had a low risk in terms of the randomization process (D1), measurement of the outcome (D4), and selection of the reported result (D5). Approximately 55% of the included studies had some concerns about deviations from intended intervention (D2). About 50% of the studies had a high risk of missing outcome data (D3), which is a key focus of our research.

Meta-Analysis Results

Posttreatment

An analysis of 18 studies was conducted using a random-effects model. The main effect results () showed that the mean dropout rate in the intervention group was 22% (95% CI 0.13‐0.36), lower than that in the control group of 26% (95% CI 0.16‐0.39) []. However, heterogeneity testing indicated high variability among the studies (Q=396.18, df=17, P<.001; I²=96%). Further analysis revealed that the variance of the true effect size reached 2.02 (logit units) with a standard deviation of 1.42 (logit units).

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Meta-regression and subgroup analysis revealed that this extreme variability was primarily due to four variables among three categories (): (1) Participant demographic characteristics: The proportion with employment rate showed a weak positive correlation with dropout rate (OR 1.04, 95% CI 1.00‐1.07; P=.03). (2) Participant clinical characteristics: Participants with baseline clinical diagnoses showed a significant positive correlation with dropout rate (odds ratio [OR] 1.03, 95% CI 1.01‐1.06; P=.01). The dropout rate for those using cocaine as the baseline primary medication (OR 1.96, 95% CI 0.31‐12.57; P=.48) was significantly higher than that for those using cannabis and opioid medications. (3) Intervention characteristics: Intervention frequency showed a significant negative correlation with dropout rate (OR 0.77, 95% CI 0.60‐0.99; P=.04). The other 27 factors showed no significant correlation with dropout rate.

The funnel plot showed some studies beyond the expected range (), suggesting the presence of studies with extreme dropout rates. Combined with Egger’s test results (P<.001), this further confirmed the presence of publication bias. After trimming the 5 missing studies on the right side, the effect size was adjusted from 22% to 33%, still not crossing the clinical threshold. Further leave-one-out analysis showed that 366 unpublished studies would need to be included to make the current result statistically insignificant. Overall, the results indicate that despite publication bias, the adjusted effect size did not exceed the clinical threshold and the leave-one-out number was high, supporting the stability of the study conclusions. Sensitivity analysis also showed () that removing any single study would not change the overall trend.

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The Longest Follow-Up

Follow-up analysis of the intervention group was based on 30 studies, with an average dropout rate of 28.2% (95% CI 0.19‐0.39) (), while the rate in the control group was 27.8% (95% CI 0.20‐0.37). However, heterogeneity testing again indicated high variability among the studies (Q=1293.13, df=29, P=.000, I²=98%). Further analysis revealed that the variance of the true effect size reached 1.79 (logit units) with a standard deviation of 1.34 (logit units).

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Meta-regression analysis and subgroup analysis () revealed that this extreme variability is primarily due to 4 variables among three types of characteristics: (1) participant characteristics: dropout rate showed a negative correlation with single status (OR 0.95, 95% CI 0.91‐0.99; P=.01); (2) clinical characteristics: significantly positive correlation with baseline medication frequency (OR 1.18, 95% CI 1.05‐1.32; P=.004); (3) intervention characteristics: participants recruited via website showed a positive correlation with dropout rate (OR 5.74, 95% CI 1.85‐17.76; P=.002), while participants recruited via campus showed a negative correlation with dropout rate (OR 0.28, 95% CI 0.12‐0.66; P=.003); The association between the degree of digitalization and dropout rates varied depending on whether studies with unreported digitalization status (not reported [NR] group) were included. When all studies, including the NR group, were analyzed, the overall model reached statistical significance (Q=28.13, df=2, P<.001), with the NR group showing a strongly significant negative effect (OR 0.16, 95% CI 0.06‐0.41; P<.001). However, when the NR group was excluded and only studies explicitly reporting “fully digital” or “partially digital” were considered, the results were not statistically significant (Q=0.24, P=.62). The other 32 factors showed no significant correlation with dropout rate.

The funnel plot showed some studies beyond the expected range (see ). Combined with Egger test results (P=.023), publication bias was further confirmed. After trimming the six missing studies on the right side, the effect size changed from 28% to 37% after correction, without crossing the clinical threshold. Further leave-one-out sensitivity analysis showed that 1244 unpublished studies would need to be included to make the current results statistically insignificant, supporting the stability of the research conclusion. Meanwhile, sensitivity analysis (see ) indicated that the results of this study were robust and not dependent on individual studies.

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Discussion

Principal Findings

This meta-analysis systematically evaluated the treatment retention effect of digital psychosocial interventions among adult illicit drug users. The pooled dropout rate was 22%, slightly lower than the approximately 30% reported for face-to-face psychosocial interventions [], suggesting potential advantages of digital formats for treatment retention. Nevertheless, the substantial heterogeneity across studies limits the generalizability of these findings. Dropout rates also varied across settings and populations. For instance, adults with co-occurring severe mental disorders and substance use had an average dropout of 27% [], whereas clinical samples of opioid users showed rates as high as 41% []. Beyond dropout, adherence constitutes another key indicator of engagement, with evidence showing that participants completed, on average, 60% of digital intervention modules, and only about half finished the full program []. Taken together, these results underscore the importance of considering both dropout and adherence when evaluating intervention effectiveness. Building on this, our moderator analyses further revealed complex interactive effects. To ensure clarity, we retained the classification system established during data extraction, presenting results separately across four major categories of characteristics as well as between short-term and longest intervention stages.

At the posttreatment stage, dropout was significantly influenced by participants’ demographic, intervention, and clinical characteristics. Regarding demographics, unemployment did not predict dropout, whereas higher employment was unexpectedly associated with greater attrition. This suggests that unstable or high-intensity work may interfere with regular participation. In addition, the short-term income from employment may reduce some patients’ motivation for treatment, especially when symptoms temporarily improve, leading them to discontinue prematurely due to “feeling better” []. For intervention characteristics, intervention frequency showed a negative correlation with dropout, indicating that more frequent contact may help consolidate behavior change, strengthen the therapeutic alliance, and enhance commitment [-]. Future studies should explore the optimal intervention frequency under different conditions [], balancing treatment intensity with patient burden [].

The results of baseline clinical characteristics indicated that both baseline clinical diagnosis and baseline cocaine use were significantly positively associated with dropout rates. Specifically, patients with a clear baseline diagnosis were at greater risk of dropout due to challenges such as dependency, withdrawal symptoms, and impaired cognitive or emotional functioning []. For this population, the integration of adjunctive pharmacological or behavioral therapies is recommended to reduce dropout []. Furthermore, consistent with previous findings [], participants with baseline cocaine use were more likely to discontinue treatment. Cocaine use disorder is often closely linked to impulsive behavior and diminished adherence []. These substance-specific risks highlight the importance of developing differentiated intervention strategies tailored to distinct types of substance use in future research []. Nevertheless, the small sample size of drug-use subgroups (k≤5) remains a limitation, which could be addressed through multi-institutional collaborations to expand subgroup samples.

During the longest follow-up, dropout was significantly influenced by demographic, clinical, and intervention characteristics. In demographics, a higher proportion of single participants was linked to lower dropout. This may be related to reduced drug exposure in family environments [-]. In addition, single participants with low social support were more likely to continue seeking health information online. Future research could involve non–drug-using significant others in monitoring the intervention process and integrate peer support modules []. In clinical characteristics, participants with higher baseline drug use frequency faced markedly greater dropout risk. This finding is consistent with recent studies []. For this high-risk group, we recommend the implementation of multistage intensive intervention programs [], together with the development of immediate-response modules (eg, crisis management tools, real-time consultation functions) to reduce early dropout [].

In terms of intervention characteristics, participants recruited through websites exhibited higher dropout rates, whereas those recruited from campus showed lower dropout rates. This may be explained by the lack of intensive treatment services typically provided in clinical settings, as well as the relative stability of campus environments [,]. Based on this finding, we recommend adopting a mixed online–offline recruitment strategy []. In addition, intervention content should be optimized for online recruits [], including simplifying operational procedures, providing regular reminders, and offering personalized feedback. The study also analyzed the association between the degree of digitalization and dropout rates. During data processing, studies that did not report their digitalization status (23/30, 77%) were categorized separately as a “Not reported” group for analysis rather than being directly excluded. The analysis revealed a significant association: compared to the nonsignificant negative correlation between fully digital interventions and dropout rates, interventions with unreported digitalization status showed a significant negative correlation, while non-fully digital interventions demonstrated a significant positive correlation with dropout rates. However, the reliability of these subgroup comparisons is constrained by the prevalent issue of poorly reported data. When we excluded the “Not reported” studies and repeated the analysis, no significant differences were found between fully digital and partially digital interventions. This suggests that the initial findings were likely confounded by nonrandom reporting bias rather than reflecting true effects, making definitive evaluation difficult. Therefore, these results primarily highlight the urgent need for future research to standardize the reporting of specific digital intervention details in order to more reliably explore the role of digitalization degree and human support in improving retention rates [].

Research Significance

This study systematically evaluated the dropout rate and its predictive factors among adult illicit drug users in digital psychological interventions, thereby addressing a critical research gap in the field. Unlike previous studies that primarily focused on demographic characteristics, this analysis incorporated multidimensional predictive variables—including clinical features, therapist-related factors, and intervention characteristics—to establish a more systematic theoretical framework. The identification of eight key predictive factors provides valuable insights for personalized interventions, guiding the development of tailored digital tools for patients at high risk of dropout. Optimization strategies derived from this evidence are expected to substantially reduce dropout rates and enhance intervention effectiveness [].

Limitations and Future Research

This study has several limitations. First, few of the included trials provided detailed information on software quality or reasons for dropout, which limited our ability to assess the reasons why participants stopped treatment []. Future studies could combine machine learning methods to predict dropout risk [] and use participant-centered questionnaires to collect data on perceived barriers. Previous research [-] emphasized common reasons for dropout, including technical difficulties, lack of engagement, and perceived ineffectiveness of the intervention. Collaboration with software engineers may help optimize the digital experience and reduce technical-related attrition []. Additionally, methodological improvements, such as combining intention-to-treat analysis with run-in phase dropout screening [,], may provide more refined methods for managing early dropout.

Second, most of the digital interventions included in the studies adopted limited forms, such as videos, virtual characters, or text messages, and lacked interactive features. Incorporating gamification elements may enhance user engagement [], especially when personalized to individual preferences [,]. Emerging evidence suggests that well-designed therapeutic video games can improve cognitive and mental health outcomes [], even inducing neurobiological changes, including alterations in white matter microstructure [-].

Finally, many studies did not clearly report key methodological details, such as the degree of digitalization or level of human support. Although we conducted analyses including and excluding the “Not reported” category, the lack of such information led to inconsistent findings, preventing definitive conclusions regarding the impact of digitalization on dropout rates. Future studies should standardize reporting of intervention details, including digitalization and human support, to better understand active components and optimize strategies [,]. Another limitation is the high heterogeneity in the meta-analysis (I²>90%), which may reduce robustness. Despite sensitivity and moderator analyses, some variability remained unexplained, suggesting pooled effects may not apply equally across interventions, populations, or outcomes. Future research should adopt rigorous methodologies, including detailed reporting, preregistration, data sharing, and large-scale RCTs. Individual participant data meta-analyses can further clarify subgroup effects and sources of heterogeneity, improving generalizability [].

Conclusion

In summary, this meta-analysis systematically examined dropout rates and their predictive factors in digital psychosocial interventions for adult illicit drug users, aiming to provide a comprehensive picture of the research landscape in this field. The results indicate that both short-term and long-term adherence to interventions are characterized by considerable complexity. In the short term, dropout rates were primarily associated with employment status, baseline clinical diagnoses, baseline primary substance use, and intervention frequency. Over longer follow-up periods, marital status, baseline drug use frequency, and recruitment source emerged as key predictors. These findings suggest the need for further investigation into factors that contradict common assumptions or remain insufficiently reported in the literature, as well as greater standardization in the design, measurement, and reporting of randomized controlled trials to improve research quality. Moreover, more attention should be given to tailoring interventions for specific populations, particularly through the design of intervention functions and modules. Continued exploration in these areas will contribute to better supporting patients’ long-term recovery.

Bitcoin trades near $121.6K, testing major resistance at $126K. Analysts watch for a breakout or correction as futures open interest stays high.

Bitcoin is trading near $121,600 after reaching a new all-time high above $126,000 earlier this week. The asset has slowed and is now moving sideways just below a major diagonal resistance that has capped the price for several months. Analysts are watching to see whether Bitcoin can break through or if it will be rejected once again.

Meanwhile, the market is holding near a key level while open interest remains high in the futures market.

Bitcoin Struggles Near Long-Term Resistance

Bitcoin is facing resistance at a trendline that has held firm since earlier cycle tops. The latest weekly candle shows another rejection near $126,000, followed by a pullback of around 5.5%. Earlier this year, similar rejections led to drops of nearly 10% and close to 30%.

The difference this time is the size of the pullbacks. Each rejection has been smaller than the last. Rekt Capital pointed out that “this trendline looks to be a weakening point of rejection,” suggesting sellers may be losing strength at this level.

On the lower timeframes, Bitcoin is holding just above $121,000. This zone is marked by a previous support level and lines up with a Fibonacci retracement near $119,550. Daan Crypto Trades noted that this is the area to watch.

$BTC Not here to overcomplicate things.

We saw a sharp impulse up and an all time high sweep.

Currently price is consolidating beneath this diagonal resistance. If that’s broken, I am expecting this to move to new highs pretty quickly.

Key area to hold below is ~$120K. If… pic.twitter.com/MK7gYzINWJ

— Daan Crypto Trades (@DaanCrypto) October 10, 2025

The asset is currently consolidating in a tight range. Volume has slowed, which often happens before a larger move. The trend remains bullish overall. A confirmed higher low would support another leg higher, especially if resistance breaks.

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Bearish Scenario Still in Play

While short-term sentiment remains positive, some market participants are still cautious. Analyst Ali Martinez shared a scenario where Bitcoin gets rejected at $124,000 and begins a correction toward $96,000. If that level fails, the next major support is around $70,000.

The projected path shows a slow recovery beginning in late 2026, with Bitcoin climbing back toward $90,000. This scenario reflects a broader retracement, not a trend reversal, and suggests long-term structure could still remain intact.

Futures Market Remains Heated

Data from Glassnode shows that futures open interest is still elevated. This means that many traders are positioned on both the long and short sides. Sharp moves in either direction have triggered liquidations. Glassnode said the market is undergoing “a leverage reset” as volatility flushes out excess positions.

With Bitcoin sitting just below resistance and leverage still high, traders are watching closely. A move above the current range could open the way toward $130,000. If resistance holds, another pullback could extend the current consolidation.

An experimental gene therapy from Sarepta Therapeutics increased levels of the gene missing in an ultra-rare form of muscular dystrophy, according to data the company presented Friday.

The company has said it plans to file for approval in the disease, known as limb-girdle muscular dystrophy (LGMD) 2E. That would make it the first approved treatment in LGMD, a broad collection of highly rare diseases that can deprive patients of the ability to walk and in some cases shorten life. But it is likely to face a significant uphill battle. 

The LGMD 2E therapy relies on the same gene-ferrying virus that Sarepta uses in its other treatments, including its approved gene therapy for Duchenne muscular dystrophy, Elevidys, and experimental gene therapies for several other LGMD subtypes. 

Tech giant Nvidia is the world’s leading artificial-intelligence chipmaker, but the company’s success has also put it in the crossfire of trade tensions.

The Santa Clara, California-based company, which is approaching a market capitalization of $5 trillion, has seen rapid growth due to its chips, which are predominantly used to power massive data centers used by other tech firms, like OpenAI, the creator of popular AI chatbot ChatGPT.

But Nvidia’s leading technology has been used as a negotiating tool in President Donald Trump’s trade spat with China, which was kickstarted by Trump’s sweeping tariffs in April and has escalated over rare earth mineral disputes.

It’s further complicated Nvidia’s relationship with China, where it was doing roughly 25% of its graphics processing unit sales, estimates Gil Luria, head of technology research at D.A. Davidson. Nvidia’s popularity has also embroiled the company in a steep controversy for potentially allowing China to skirt around export restrictions as trade tensions continue.

“Nvidia has gotten caught in the middle of two very important things: a trade dispute between China and the United States … but more importantly, AI has become a matter of national security,” Luria said.

Nvidia CEO Jensen Huang has argued that restricting sales of American AI chips will ultimately enable Chinese developers to create their own alternatives.

Huang, 62, was born in Taiwan, and at age 9 was sent by his parents to live in Tacoma, Washington. In 1993, the Oregon State and Stanford University grad co-founded Nvidia, which started as a graphics-based processing company.

Huang — who is worth $167 billion, according to the Bloomberg Billionaires Index — has been treated as a rockstar in Taiwan for his success in the AI chips race, and previously worked as a microprocessor designer at now-competitor AMD.

“It’s really unusual to have somebody who can go from starting what was at the time a very small tech startup and throw it to the extraordinary level of success that Nvidia has grown to,” John Villasenor, a nonresident senior fellow at Brookings Institution and professor at the University of California, Los Angeles, said of Huang.

Nvidia powers the data centers that support AI technology and has been the go-to provider of those chips.

Nvidia essentially created the architecture for anyone who develops AI, leading to a surge in demand for its technology, according to Arun Sundararajan, a professor of technology, operations and statistics at NYU Stern School of Business.

The company said in September that it would invest up to $100 billion in OpenAI and provide it with data center chips as soon as late 2026.

Nvidia is competing with AMD for deals with partners like OpenAI, which said Monday it would use 6 gigawatts of AMD chips to power OpenAI’s data centers.

“The competition has undeniably arrived. Customers will choose the best technology stack for running the world’s most popular commercial applications and open-source models. We’ll continue to work to earn the trust and support of mainstream developers everywhere,” an Nvidia spokesperson said in a statement shared with CNN.

In recent years, the US government has sought to restrict Chinese access to American technology to slow Beijing’s progress on AI, thus allowing the United States to take the lead. Trump continued the trend in April, when he restricted China’s access to chips, including Nvidia’s H20, as part of his trade war.

Such restrictions on the sale of chips offended China, Luria said, and ultimately led to Beijing limiting the purchase of chips to their companies.

But the White House recently reversed their position.

“You want to sell the Chinese enough that their developers get addicted to the American technology stack,” Commerce Secretary Howard Lutnick said in July.

Trump in August greenlit sales of chips to China in an agreement with US chipmakers. Nvidia and AMD, Trump said, would give 15% of revenue from China sales to the US in exchange for export licenses. That includes giving China access to Nvidia’s H20 chips, which were released in 2024 to maintain access to the Chinese market following strict export controls.

But Beijing seemed unimpressed and trade tensions have only escalated since the start of a tit-for-tat trade war in April.

China has since increased import restrictions on US chips, including Nvidia’s processors. Trump said Friday on Truth Social that he would impose a 100% tariff on China “over and above any Tariff they are currently paying” beginning November 1 over export controls on rare earth minerals.

“Where this all gets resolved is unclear,” Luria said, because China believes “that stopping the sale of Nvidia chips into China creates some leverage on the US in the negotiation.”

Commerce Department officials are investigating whether Nvidia’s customer, Singapore-based Megaspeed, is helping China sidestep export restrictions for access to Nvidia’s tech, according to a report from the New York Times. CNN has not independently verified the Times’ reporting.

Nvidia did not respond to CNN’s request for comment.

And Nvidia’s H20 chips are widely believed to have contributed to DeepSeek, an advanced Chinese AI model that shook Silicon Valley upon its release earlier this year, raising concerns that China was further ahead on AI than previously understood.

China could also gain access to the chips on the black market, since another country could buy Nvidia’s chips and resell them to China, Sundararajan said.

“The bigger issue is if we push harder to restrict global access to Nvidia’s products, can that be counterproductive? Because it forces these countries to speed up their own pace of innovation,” Sundararajan said.

A novel artificial intelligence (AI) tool could improve the accuracy and objectivity of alopecia areata severity and treatment efficacy, according to the authors of a proof-of-concept case report published in JAAD Case Reports.¹

The current standard for quantifying hair loss in alopecia areata is the Severity of Alopecia Tool (SALT) score, the authors explained. But in real-world clinical practice, the SALT score is rarely used due to its time-consuming nature and, notably, high interrater variability.² Trichoscopy is another tool that allows for more detailed visualization of the hair and scalp to differentiate alopecia areata and monitor treatment progress, but it has limited availability in clinical settings because it requires specialized equipment and examiner expertise.

The new approach detailed in the care report leverages an AI-powered assessment tool to track and manage alopecia areata.¹ The system automatically calculates the area of alopecia (AI-Area) to derive an AI-SALT score.

“This case demonstrates a proof-of-concept for tracking and managing [alopecia areata] using an AI-based assessment tool,” the authors wrote. “For this case of [alopecia areata], AI is used to calculate accurate areas of alopecia, and trichoscopy was used to detect early follicular regrowth, providing a faster, more comprehensive, and objective evaluation of disease progression and treatment response.”

In the case of a 47-year-old male receiving intralesional triamcinolone acetonide for a solitary alopecia areata patch, the AI tool provided a more accurate and sensitive assessment than traditional manual methods. Specifically, the AI-SALT Score captured incremental progress between early visits that the Manual-SALT Score failed to detect.

“Using the AI tool to assess this patient’s alopecia, demonstrates fast, easy-to-use AI-powered imaging via smartphone to precisely and objectively quantify alopecia for clinical evaluation,” the authors wrote. “Furthermore, percentage change of alopecia area provides more accurate and sensitive assessment than the manual SALT system demonstrated by the AI-SALT scores’ finer decimal-level precision.”

This enhanced sensitivity has a direct impact on patient adherence and overall management value. At the 8-week mark, the patient was considering discontinuing treatment due to a lack of noticeable improvement based on subjective observation. However, the AI tool’s objective data, which demonstrated clear progress, alongside trichoscopic images showing hair regrowth, motivated the patient to continue therapy. This demonstrates the tool’s ability to ensure sustained adherence when therapy is working and improve outcomes.

A crucial aspect of the case is that it shows the AI tool’s capacity to generate objective, standardized documentation, the authors noted. Insurers routinely require precise evidence of disease severity, including accurate SALT scores, to approve high-cost treatment coverage. By offering fast, easy-to-use, and highly precise quantification, this AI-based system can provide the verifiable metrics needed to satisfy coverage requirements, potentially reducing administrative burden and accelerating patient access to care.

“Today, AI tools are increasingly being used to assess skin disorders, and we are beginning to integrate these tools into our daily clinical practice,” the authors explained. “In this case, the integration of AI enabled more accurate monitoring and patient engagement, suggesting potential to improve care through personalized and informed management of [alopecia areata].”

The tool does have limitations, they noted, especially in cases of androgenetic alopecia, which does not have distinct patches of hair thinning. However, combining AI assessment with trichoscopy to quantify individual hairs or follicular units does enhance assessment, they explained. The AI-SALT score’s utility should also be validated in more severe or widespread alopecia and beyond single-patch cases, as well as in diverse populations.

“Here, we present a proof-of-concept for integrating AI tools in clinical settings to assess and monitor AA, demonstrating the potential to deliver precise and objective data that inform treatment decisions and patient engagement,” the authors concluded. “However, this is a proof-of-concept used on one patient, and further validation is necessary to ensure broader applicability.”

References

1. Chan E, Ramsay K, Tyli R, et al. AI-based alopecia assessment: a proof of concept for enhancing accuracy and objectivity in hair loss measurement. JAAD Case Rep. Published online October 7, 2025. doi:10.1016/j.jdcr.2025.09.023.

2. King BA, Senna MM, Ohyama M, et al. Defining severity in alopecia areata: current perspectives and a multidimensional framework. Dermatol Ther. 2022;12(4):825-834. doi:10.1007/s13555-022-00711-3

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The US has moved to cancel what would have been the largest solar project in North America, as the Trump administration expands its attack on the embattled renewable energy industry.

Late on Thursday the Bureau of Land Management scrapped approval for Esmeralda 7, a 6.2 gigawatt project that could have powered nearly 2mn homes. It had begun the permitting process under the Biden administration.

The high-profile Nevada solar project backed by NextEra Energy, the largest renewable energy company in the US, is the latest to become a casualty of the Trump administration. The American president has called renewable energy projects a “scam”.

The Esmeralda 7 project consisted of seven solar farms and battery systems and was backed by power developers including Arevia Power, ConnectGen and Invenergy. It would have covered about 62,300 acres of federal lands in the Nevada desert north-west of Las Vegas.

Since January, Doug Burgum’s Department of the Interior has accelerated permitting for fossil fuel projects while tightening restrictions on solar and wind initiatives.

Large offshore wind projects have already been drawn into the administration’s crosshairs. In April Burgum ordered Equinor to halt construction activities on its 810 megawatt Empire offshore wind farm and issued a stop work order on Ørsted’s Revolution Wind.

While both projects were eventually allowed to proceed, industry backers say the uncertainty undermines US energy needs and investor confidence.

The crackdown on renewables comes as the country faces soaring power demand due to the proliferation of data centres to fuel the rise of artificial intelligence as well as the electrification of vehicles and home appliances.

NV Energy, the state’s largest utility, projects that power demand will be 34 per cent higher in 2035 compared with 2022.

“We remain deeply concerned that this administration continues to flout the law to the detriment of consumers, the grid and America’s economic competitiveness,” said Ben Norris, vice-president of regulatory affairs for the Solar Energy Industries Association.

“We need more power on the grid, fast, and the solar and storage industry is ready to provide it, but we need the administration to get serious about truly achieving American energy dominance.” 

The Department of the Interior did not confirm that the project had been cancelled, but said it and the project developers had “agreed to change” approach and that they would have the option to “submit individual project proposals to . . . more effectively analyse potential impacts”.

NextEra said it “remain[s] committed to pursuing our project’s comprehensive environmental analysis by working closely with the Bureau of Land Management”.

Invenergy declined to comment.