Connected Care: Challenges and Potential of Digital Interventions in Substance Use Disorder - a Narrative Review

Abstract

Introduction

mHealth interventions have demonstrated positive outcomes across various health domains and represent promising tools for supporting the treatment of Substance Use Disorders (SUD). However, gaps remain regarding user and clinician engagement, factors associated with positive outcomes, and the need for cultural adaptation.

Objective

To analyze recent literature on the effectiveness of mHealth interventions in the treatment of SUD, identifying interaction patterns and functionalities that promote adherence in diverse contexts.

Methods

A narrative review was conducted based on PRISMA 2020 guidelines, searching PUBMED, SCOPUS, EMBASE and WEB OF SCIENCE for English-language articles published between 2014 and 2024. The review focused on barriers, facilitators, and the effectiveness of implementing digital interventions (mHealth) in the management of SUD.

Results

Of the 593 articles identified, 34 were included. Despite considerable heterogeneity in evaluated outcomes, digital interventions demonstrated positive impacts on smoking cessation, contingency management for opioids, and reduction in alcohol and cannabis use. Notably, response variability was observed according to cultural and generational differences, severity of use, substance type, and digital literacy. Additional challenges include long-term adherence and a scarcity of studies in low- and middle-income countries.

Conclusion

Digital interventions show tangible clinical benefits across multiple substances, with usability and connected care driving sustained engagement. The use of mHealth, particularly as a complementary and combined approach in treating individuals with SUD, is associated with clinical and accessibility benefits. However, further research is required to evaluate its applicability across diverse populations and contexts.

Introduction

In the DSM-5, Substance Use Disorders (SUD) unify abuse and dependence in a continuous diagnosis by substance, characterized by 11 cognitive, behavioral, and physiological criteria at 12 months, including craving, and categorized as mild (2–3), moderate (4–5), or severe (≥6 criteria).1,2 From an epidemiological and clinical point of view, SUD constitute an important cause of disability, psychiatric comorbidities, and premature mortality, with an estimated global prevalence of 2.2%.3,4 However, this burden is unevenly distributed among sociodemographic strata: men, adolescents and young adults, socioeconomically vulnerable populations, and racial, ethnic, and sexual minorities concentrate higher prevalence, severity, or adverse outcomes.4 At the same time, some of these groups, especially young adults and Hispanic/Latino patients, face important barriers to clinical identification and access to specialized treatment.5

According to the United Nations Office on Drugs and Crime’s (UNODC) World Drug Report 2024, approximately 64 million people worldwide suffer from SUD, however, only one in 11 receives treatment, with one in 18 among women and one in seven among men6. In Brazil, the consumption of alcohol, tobacco, and other psychoactive substances has prevalence rates that require continuous attention from public health policies7. In low- and middle-income countries (LMICS), including Brazil, high prevalence rates of psychoactive substance use intersect with severe structural barriers, reinforcing the clinical imperative of accessible and sustainable public health strategies.8

Standard treatment for SUD combines pharmacotherapy, psychosocial interventions, and longitudinal monitoring, but its reach is limited by frequent operational barriers such as costs, geographic isolation, rigid schedules, and social stigma.9 In this context, digital health, especially mobile health (mHealth), emerges as a key strategy to expand the reach of interventions and strengthen the health system10. With more than 318,000 health applications available and exponential daily growth, this technology offers unprecedented scalability11. This potential makes digital interventions a strategic means to address the global burden of mental illness, reaching populations with historical barriers to access to traditional care.12

Despite the potential, critical engagement challenges persist, with approximately 90% of mental health apps being uninstalled within 10 days13. Early abandonment stems from a combination of factors, such as poor user experience (UX) and technical failures, generic and non-personalized architectures, concerns about data privacy and security, digital literacy barriers, and the absence of reliable human support.12,14 Emerging evidence suggests that siloed digital tools often underperform, whereas connected and integrated, relationship-centered models of care that connect digital platforms directly to health professionals, peers, or care coordinators substantially strengthen retention and therapeutic efficacy.15,16

Understanding the patterns of mHealth use in the treatment of individuals with SUD, as well as the characteristics of interaction and functionality tailored to the specific needs of this target population across different cultures and socioeconomic and educational levels, can provide deeper insights into challenges and solutions to improve adherence to these tools and elucidate their overall contribution to treatment.17 An evident gap is studies in LMICs with a shortage of SUD-focused interventions,18 in addition to previous reviews having mapped feasibility and effectiveness of digital interventions broadly, but rarely focused on connected care, usability factors, and integration with clinical support, which leaves open which components support adherence in real-world conditions.19,20 This narrative review goes beyond the previous literature by integrating clinical outcomes, user engagement, and connected care in SUD, assessing mHealth effectiveness, the functionalities that underpin adherence, and strategies for real-world implementation.

Methods

This narrative review followed the 2020 Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement to ensure transparent data search, selection, and reporting.21 The guiding question of this review was: What is the effectiveness, and what are the determinants of implementation (barriers and facilitators) of digital interventions (mHealth) in addressing SUD?

Eligibility Criteria

The selection was restricted to English-language articles published between 2014 and 2024 that involved adult samples diagnosed with SUD. Studies were eligible for inclusion if they met the following predetermined criteria: (1) involved adult populations (aged ≥ 18 years) with a diagnosis or harmful pattern of substance use disorder (alcohol, cannabis, tobacco/nicotine, opioids, stimulants, or multiple substances); (2) evaluated digital or mobile health (mHealth) interventions delivered via smartphones, applications, or text-based platforms, either as standalone tools or combined with clinical care; and (3) reported clinical efficacy, retention, usability, acceptability, or implementation determinants. Eligible study designs comprised randomized controlled trials (RCTs), non-randomized controlled studies, longitudinal cohorts, mixed-methods designs, qualitative investigations, and systematic reviews/meta-analyses. Qualitative and mixed-methods studies were purposefully included to identify in-depth barriers and facilitators of user engagement, while meta-analyses provided benchmark clinical estimates. Studies were excluded if they focused exclusively on telemedicine (synchronous video consultations) without dedicated mHealth tools, machine learning predictive models without an interactive intervention component, protocols without reported data, or non-peer-reviewed literature. The search was restricted to peer-reviewed articles published in English between 2014 and 2024.

Search Strategy

The literature search was conducted across four major biomedical and scientific databases: PubMed, Scopus, Embase, and Web of Science. To ensure methodological robustness, the strategy was specifically focused on peer-reviewed indexed literature; unindexed gray literature and preprints were excluded. The search strategy combined Medical Subject Headings (MeSH) and free-text descriptors, such as “substance-related disorders”, “mobile applications”, “mHealth”, “smartphone apps”, and their relevant synonyms, using Boolean operators (AND, OR, NOT) as detailed in Table 1.

Table 1.

Study Search Strategy in the Database

Database	Search Strategy
PUBMED	(((Substance-Related Disorders[MeSH Terms]) OR (“Substance-Related Disorders”[Title/Abstract] OR “Substance Related Disorder”[Title/Abstract] OR “Disorder, Substance Related”[Title/Abstract] OR “Disorders, Substance Related”[Title/Abstract] OR “Related Disorder, Substance”[Title/Abstract] OR “Related Disorders, Substance”[Title/Abstract] OR “Drug Use Disorders”[Title/Abstract] OR “Drug Use Disorder”[Title/Abstract] OR “Disorder, Drug Use”[Title/Abstract] OR “Organic Mental Disorders, Substance-Induced”[Title/Abstract] OR “Organic Mental Disorders, Substance Induced”[Title/Abstract] OR “Substance Abuse”[Title/Abstract] OR “Abuse, Substance”[Title/Abstract] OR “Substance Abuses”[Title/Abstract] OR “Substance Dependence”[Title/Abstract] OR “Dependence, Substance”[Title/Abstract] OR “Substance Addiction”[Title/Abstract] OR “Addiction, Substance”[Title/Abstract] OR “Chemical Dependence”[Title/Abstract] OR “Chemical Dependences”[Title/Abstract] OR “Dependence, Chemical”[Title/Abstract] OR “Dependences, Chemical”[Title/Abstract] OR “Drug Dependence”[Title/Abstract] OR “Dependence, Drug”[Title/Abstract] OR “Drug Addiction”[Title/Abstract] OR “Addiction, Drug”[Title/Abstract] OR “Prescription Drug Abuse”[Title/Abstract] OR “Abuse, Prescription Drug”[Title/Abstract] OR “Drug Abuse, Prescription”[Title/Abstract] OR “Substance Use”[Title/Abstract] OR “Substance Uses”[Title/Abstract] OR “Use, Substance”[Title/Abstract] OR “Drug Abuse”[Title/Abstract] OR “Abuse, Drug”[Title/Abstract] OR “Drug Habituation”[Title/Abstract] OR “Habituation, Drug”[Title/Abstract] OR “Substance Use Disorders”[Title/Abstract] OR “Disorder, Substance Use”[Title/Abstract] OR “Substance Use Disorder”[Title/Abstract])) AND ((Mobile Applications[MeSH Terms]) OR (“Mobile Applications”[Title/Abstract] OR “Application, Mobile”[Title/Abstract] OR “Applications, Mobile”[Title/Abstract] OR “Mobile Application”[Title/Abstract] OR “Mobile Apps”[Title/Abstract] OR “App, Mobile”[Title/Abstract] OR “Apps, Mobile”[Title/Abstract] OR “Mobile App”[Title/Abstract] OR “Portable Software Apps”[Title/Abstract] OR “App, Portable Software”[Title/Abstract] OR “Portable Software App”[Title/Abstract] OR “Software App, Portable”[Title/Abstract] OR “Portable Software Applications”[Title/Abstract] OR “Application, Portable Software”[Title/Abstract] OR “Portable Software Application”[Title/Abstract] OR “Software Application, Portable”[Title/Abstract] OR “Smartphone Apps”[Title/Abstract] OR “App, Smartphone”[Title/Abstract] OR “Apps, Smartphone”[Title/Abstract] OR “Smartphone App”[Title/Abstract] OR “Portable Electronic Apps”[Title/Abstract] OR “App, Portable Electronic”[Title/Abstract] OR “Electronic App, Portable”[Title/Abstract] OR “Portable Electronic App”[Title/Abstract] OR “Portable Electronic Applications”[Title/Abstract] OR “Application, Portable Electronic”[Title/Abstract] OR “Electronic Application, Portable”[Title/Abstract] OR “Portable Electronic Application”[Title/Abstract]))) NOT ((((Telemedicine[MeSH Terms]) OR (Telemedicine[Title/Abstract])) OR ((Machine Learning[MeSH Terms]) OR (“Machine Learning”[Title/Abstract]))) OR ((Artificial Intelligence[MeSH Terms]) OR (“Artificial Intelligence”[Title/Abstract])))
WEB OF SCIENCE	“Substance-Related Disorders” (Topic) and “Mobile Applications” (Topic) and Preprint Citation Index (Exclude – Database) NOT Telemedicine (Topic) or “Machine Learning” (Topic) or “Artificial Intelligence” (Topic) and Preprint Citation Index (Exclude – Database) LINK PERMANENTE: https://www.webofscience.com/wos/alldb/summary/d37bd1ff-13e6-43b9-add1-7e7a7a08339b-95cbae63/relevance/1
SCOPUS	((TITLE-ABS-KEY (“Substance-Related Disorders”) AND TITLE-ABS-KEY (“Mobile Applications”))) AND NOT ((TITLE-ABS-KEY (telemedicine) OR TITLE-ABS-KEY (“Machine Learning”) OR TITLE-ABS-KEY (“Artificial Intelligence”)))
EMBASE	“drug dependence”/syn AND “mobile application”/syn NOT (“telemedicine”/syn OR “machine learning”/syn OR “artificial intelligence”/syn) AND ([embase]/lim NOT ([embase]/lim AND [medline]/lim) OR ([medline]/lim NOT ([embase]/lim AND [medline]/lim) NOT ([embase classic]/lim AND [medline]/lim))) AND [2018–2023]/py

Study Selection

Two reviewers independently screened the titles, abstracts, and, subsequently, the full texts of the selected articles. Potentially relevant reports were obtained in full text and evaluated independently. Discrepancies were resolved by consensus or through adjudication by a third senior reviewer.

Data Extraction and Synthesis

Data from each selected article were independently extracted by two reviewers using a standardized data extraction form that included the following variables: author(s), publication year, study objective, sample characteristics (sample size, target substance, clinical setting), study design, specific mHealth intervention/features, primary clinical outcomes, and implementation barriers/facilitators. Due to the marked methodological and clinical heterogeneity among the included designs, interventions, and assessed metrics, a narrative and thematic synthesis was performed. Findings were systematically grouped and synthesized into substance-specific clinical categories (alcohol, cannabis, tobacco, opioids/stimulants, and general/cross-substance SUD care) alongside cross-cutting implementation themes encompassing usability, retention, and connected clinical integration.

Results

The database search yielded 593 articles. After duplicate removal and screening, 34 studies were included (Figure 1). Table 2 summarizes the methodological and geographical characteristics, as well as the assessment instruments, of all included studies. Clinical outcomes and intervention efficacy, stratified by substance category, are presented below.

Figure 1.

PRISMA 2020 Flow Diagram of Study Selection. Flowchart detailing the identification, screening, eligibility, and inclusion process of studies evaluating mobile health (mHealth) interventions for substance use disorders across PubMed, Scopus, Embase, and Web of Science.

Table 2.

Characteristics of the Included Studies

Category, Author & Year	Objective	N	Design & Setting	Main Findings	Ref.
Alcohol
Farren et al, 2022	Efficacy of UCD app	111 (AUD)	RCT; Ireland	Reduced heavy drinking days.	[22]
Bertholet et al, 2017	Alcooquizz app acceptability	130 (Unhealthy use)	Pilot; CH/CA	−27% weekly consumption.	[23]
Barrio et al, 2017	SIDEAL app usability	24 (AUD)	Pilot; Spain	Heavy drinking days fell from 25 to 5.8.	[24]
Sharma et al, 2023	Relapse prevention (Quest)	30 (SDA)	Pilot; India	High usability (5.8/7).	[25]
Hammond et al, 2021	DynamiCare feasibility	61 (AUD)	RCT; USA	Higher retention at 90 days.	[26]
Manning et al, 2021	SWiPE app feasibility	1309 (Risk use)	Pilot; Australia	Reduced weekly consumption.	[27]
Malte et al, 2021	Step Away app usability	55 (Veterans)	Cohort; USA	Acceptable usability (SUS ~70).	[28]
Tait et al, 2019	Daybreak program efficacy	793 (Risk/AUD)	Quasi-RCT; Australia	Reduced alcohol-related risk.	[29]
Gonzalez et al, 2015	LBMI-A vs Bibliotherapy	54 (AUD)	Pilot; USA	Increased days of abstinence.	[30]
Leightley et al, 2022	Drinks: Ration App	123 (Veterans)	RCT; UK	Reduced weekly units (−28.2).	[31]
Lang et al, 2024	Post-relapse motivation	10 (AUD)	Mixed-methods; DE	Motivation remained stable	[32]
Garnett et al, 2020	Drink Less efficacy	5562 (Risk use)	RCT Protocol; UK	Large-scale trial design.	[33]
Cannabis
Albertella et al, 2019	APTT app evaluation	111 (Users)	Pilot; Australia	Reduced dependence (SDS).	[34]
Vederhus et al, 2020	App vs Traditional care	250 (Users)	Comparative; Norway	App reached more women (46%).	[35]
Tobacco
Masaki et al, 2020	CASC system with CO checker	584 (Nicotine)	RCT; Japan	Higher continuous abstinence.	[36]
Webb et al, 2020	Quit Genius therapy vs VBA	556 (Smokers)	RCT; UK	Superior to brief advice.	[37]
Zeng et al, 2016	ACT app adherence	84 (Smokers)	Pilot; USA	Adherence predicted cessation.	[38]
Mistik et al, 2022	Beat Smoking app	63 (Smokers)	RCT; Turkey	Positive initial effect.	[39]
Hébert et al, 2020	Smart-T2 JITAI	81 (Smokers)	RCT; USA	Similar to in-person care.	[40]
Opioids & Stimulants
Marino et al, 2024	WEconnect CM for MOUD	600 (Opioids)	Observational; USA	Higher treatment retention.	[41]
Hallgren et al, 2023	CM for Methamphetamine	10 (Meth)	Pilot; USA	Feasible and usable.	[42]
Zhang et al, 2022	DTx for Methamphetamine	100 (MUD)	Experimental; China	Reduced craving.	[43]
Hodges et al, 2022	HOPE app for MOUD	25 (Opioids)	Pilot; USA	Acceptable retention.	[44]
Schulte et al, 2016	Recovery app perspectives	94 (Heroin)	Focus groups; Multi	Key for recovery; cultural barriers identified.	[45]
Siefried et al, 2024	S-Check app effect	10 (Meth)	RCT; Australia	Increased help-seeking.	[46]
Cross-Substance and SUD Care
Stocker et al, 2024	DIDE app adherence	24 (Any SUD)	Prospective; France	High engagement after-hours.	[47]
Aharonovich et al, 2017	HealthCall-S for HIV/SUD	47 (HIV/SUD)	RCT; USA	Reduced drug/alcohol use.	[48]
Paquette et al, 2021	LETS ACT app integration	56 (Various)	Pilot; USA	Useful therapeutic aid.	[49]
Conway et al, 2024	Resonance Breathing (eRPB)	30 (SUD)	Pilot; USA	Helped manage stress/craving.	[50]
Ashford et al, 2020	Sober Grid social network	1273 (Users)	Analytics; USA	Connections predict sobriety.	[51]
Roos et al, 2024	Mindful Journey app	34 (SUD)	RCT Protocol; USA	Feasibility and protocol focus.	[52]
Han et al, 2018	EMA app feasibility	75 (Heroin/ATS)	Pilot; China	Low acceptability (privacy concerns).	[53]
Serre et al, 2023	Craving-Manager app	274 (Expected)	RCT Protocol; France	Relapse predictor management.	[54]
Paquette et al, 2023	Efficacy of BA with vs without App	243 (SUD)	Three-arm RCT; USA	BA + App improved engagement and clinical outcomes.	[55]

Applicability of mHealth for Alcohol Reduction and Cessation

The validation of tools such as the QUEST application indicates that responsive, user-centered design tailored to specific populations, such as military veterans, enhances engagement and achieves acceptance rates exceeding 65%.25 Within this supportive framework, the use of digital peer-support communities may sustain recovery and reduce mean alcohol consumption from 37.1 to 17.5 drinks per week (p <0.001). Platforms such as Sober Grid (n = 1,273) appear to function as emotional coping resources during periods of vulnerability, although frequent check-ins may be associated with shorter durations of sobriety.51 Similarly, brief interventions based on goal setting and personalized feedback, delivered via the Step Away application, have demonstrated short-term effectiveness, reducing the prevalence of binge drinking from 95.4% to 64.3% (p <0.001) and mean daily alcohol consumption from 6.5 to 1.9 units (p <0.001).28 Tools targeting specific populations, such as military personnel, further support the feasibility of remote monitoring in reducing excessive alcohol use.31 Finally, robust evidence of effectiveness has been observed with the Drink Less application, which demonstrated significant reductions in weekly alcohol consumption (intervention: −28.2 units; p =0.003) and AUDIT scores (intervention: −3.9 points; p =0.003).33

Applicability of mHealth in the Reduction and Cessation of Cannabis Use

One study demonstrated a significant reduction in the severity of dependence, as measured by the Severity of Dependence Scale (SDS), and in cannabis-related problems, assessed using the Cannabis Use Problems Identification Test (CUPIT). These findings were accompanied by a decrease in frequency of use from 17.5 to 11.8 days per month (p <0.001). The results support the feasibility and acceptability of the APTT application as an engagement tool for adults who avoid formal treatment.34 Another study reported a higher preference among women for the HAP application (46%) compared to conventional treatment (26%; p <0.001). Among app users who had never previously sought formal help, women represented the majority (52%), suggesting that digital tools may reduce gender-related barriers to access.35

Applicability of mHealth in Smoking Cessation

Adherence to mHealth interventions has been associated with instructional design quality and engagement with specific app features, including gamification elements and automated behavioral support.38 Clinical trials of the Quit Genius application37 reported abstinence rates of up to 44.5% during the first month. Although some effects were not sustained at 6 months, the intervention group maintained significantly higher abstinence rates through week 52 (52.3% vs 41.5%) compared with usual care.37 Furthermore, engagement with theory-based content within these applications has been identified as a key factor in promoting smoking cessation and reducing tobacco consumption.38

Applicability of mHealth in the Monitoring and Management of Opioid, Methamphetamine, and Heroin Use

Digital interventions have demonstrated effectiveness in supporting recovery and enhancing treatment retention. Contingency management (CM), integrated into the WEconnect application, has shown promising results in reducing opioid use and improving therapeutic engagement.41 In supporting individuals who use methamphetamine, a specific digital therapeutic demonstrated high adherence rates, with 88.5% of users remaining active after 8 weeks and 61.5% after 24 weeks.43 Regarding perceived care, use of the HOPE application was associated with a significant increase in abstinence self-efficacy scores (p =0.02).44 Additionally, the S-Check application was found to be effective in nearly tripling the rate of seeking specialized professional help (45.5% vs 23.5%; p =0.04).46 Finally, analyses of both patient and provider perspectives highlight that smartphone-based support is a valuable tool for maintaining sobriety, although it requires context-specific cultural adaptations.45

Applicability of mHealth in the Care of Substance Use Disorders

Evidence indicates that mobile applications can support patients between face-to-face consultations, extending beyond the traditional boundaries of care delivery. mHealth tools provide continuous support outside clinical settings, fostering greater autonomy in self-management of the condition.47 The feasibility of such integration is reinforced by the high acceptability of the HealthCall-S system, with reports indicating that 95% of participants used the tool daily as a complementary source of support in their routine.48 Studies further highlight that personalization enables just-in-time interventions grounded in cognitive-behavioral therapy (CBT) and mindfulness, optimizing craving management and relapse prevention.49,52 Consistent with these findings, high acceptability has also been observed in behavioral support applications, with users averaging 8 minutes per session and accumulating approximately 113 minutes of active use.55 Regarding user experience, digital tools for substance use monitoring have achieved an 81.8/100 score on the System Usability Scale (SUS), indicating excellent usability and improved communication with healthcare providers.47 Conversely, applications lacking appropriate cultural and design adaptations for the target population tend to have lower acceptance, regardless of their clinical efficacy.45,53

Discussion

The findings of this review are consistent with national studies,56,57 indicating that the integration of mHealth resources within healthcare networks extends beyond technological innovation and constitutes a strategic response to the limited availability of in-person treatment services observed in Brazil. This is particularly relevant for public health management, as it offers a pathway for resource optimization and reduction of operational costs, enabling support to reach users in real time and beyond traditional clinical hours, an approach whose scalability has been globally recognized by the World Health Organization (WHO).58 In this context, connected care emerges as an adjunctive therapeutic tool to democratize access, address infrastructure gaps, and strengthen long-term adherence in high-demand settings. The assessment of its applicability in the care of individuals with substance use disorders presented in this review suggests good acceptability among both patients and clinicians, with promising outcomes, particularly in reducing patterns of substance use. However, further investigation is warranted to understand better variability in responses across cultural and generational differences, severity of use, substance type, and levels of digital literacy.

In the late 2000s, the first mHealth interventions targeting substance use disorders (SUD) began to emerge; however, it was not until 2020 that a large systematic review was published to evaluate primary consumption outcomes, concluding that the evidence for efficacy was not yet considered robust.59 mHealth interventions, including mobile applications, SMS-based approaches, and wearable devices, have shown promise for the follow-up, monitoring, and continuous support of individuals with SUD, particularly outside traditional clinical settings.19 These solutions enable real-time monitoring, fostering continuous support and user autonomy,60 and facilitating the identification of high-risk situations and the delivery of just-in-time interventions.61 However, to enhance the uptake and effectiveness of mHealth interventions, it is essential to incorporate personalized approaches and ensure integration with healthcare professionals.17

Regarding the use of digital interventions for cannabis users, a systematic review with meta-analysis highlights their potential in reducing consumption, while raising important questions about the optimal dose of exposure, both in terms of duration and frequency of the intervention, for achieving greater effectiveness.62,63 A randomized clinical trial demonstrated that a digital intervention was more effective than non-interactive educational information about cannabis in reducing the quantity of cannabis consumed over a 3-month period.64

Smartphone app-based interventions are recognized as a key component of tobacco control, offering evidence-based solutions to persistent challenges such as low retention and insufficient support in traditional programs.65 Evidence suggests that these applications constitute a promising alternative to conventional smoking cessation approaches by delivering real-time, continuous, and cost-effective support, with the potential to expand reach and enhance adherence.66,67 The versatility of digital tools further enables the modernization of established behavioral strategies, including positive reinforcement. However, replicating the effectiveness of contingency management, which has been largely studied in high-income countries, requires careful consideration of cultural and socioeconomic adaptations, a critical factor for successful implementation in low- and middle-income settings such as Brazil.68 With respect to alcohol consumption, digital interventions have demonstrated potential to reduce alcohol use at the population level. They may be widely implemented as a strategy to mitigate alcohol-attributable harms.69

The analysis of this review’s findings indicates that, despite the diversity of studies demonstrating the potential of mHealth in SUD care, particularly as complementary and combined approaches, critical methodological limitations and knowledge gaps persist regarding its applicability in specific populations. These include vulnerable groups, individuals with limited access to technology, and those with psychiatric comorbidities, underscoring the need for more inclusive and methodologically robust research designs.

It is important to highlight that, although a structured search with dual review was conducted, the analysis of the findings is subject to the interpretation bias inherent in narrative reviews. Language and time-period restrictions constitute potential limitations, as does the possibility of publication bias. Furthermore, substantial heterogeneity across study designs, sample sizes, intervention modalities, and outcome measures necessitates caution in interpretation. The primary implication of these limitations is the need for caution when generalizing the findings, which apply primarily to adult populations in controlled clinical settings, predominantly in high-income countries, and may not be directly transferable to populations with distinct characteristics.

Digital interventions demonstrate tangible clinical benefits across substance categories, including reductions in alcohol and cannabis consumption, enhanced smoking cessation rates, and improved retention in opioid contingency management programs. Usability, behavioral personalization, and integration with clinical workflows emerge as primary determinants of sustained engagement. Overall, the use of mHealth, particularly as a complementary and combined approach in treating individuals with SUD, is associated with clinical and accessibility benefits. However, further research is required to precisely evaluate its applicability across diverse populations and contexts.

Funding Statement

The Article Processing Charges associated with the publication of this research was supported by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) (AUXPE — Process 88881.859210/2023-01).

Data Sharing Statement

Data sharing is not applicable to this article as no new datasets were generated or analyzed during the current study (narrative review based on public databases).

Ethics Approval and Consent to Participate

Not applicable, as this study is a literature review of published studies and did not involve primary data collection with human participants or animals.

Author Contributions

All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

Disclosure

The authors report no conflicts of interest in this work.