Behavior change techniques in mobile health interventions promoting recovery from substances: A synthesis of reviews and meta-analyses

Abstract

Objective

This synthesis of reviews and meta-analyses delves into the landscape of behavior change techniques (BCTs) employed in digital interventions designed to help individuals abstain from or reduce consumption of substances (including alcohol, tobacco, and illicit drugs). This review considers the “black box” problem in mHealth programs by using Michie et al.’s BCT taxonomy to describe BCTs that have been used in intervention literature and to explore potential active ingredients that may contribute to intervention effectiveness.

Methods

We synthesize findings from 49 systematic reviews and meta-analyses. While individual studies often express inconclusiveness for the effectiveness of specific BCTs, this review uncovers promising avenues for future research. Our analysis focuses on mobile health (mHealth) just-in-time adaptive interventions (JITAIs), with a specific emphasis on substance use reduction.

Results

Eleven BCTs were studied extensively in these reviews, including self-monitoring of behavior, feedback on behavior, goal setting, social support, prompts/cues, and behavior substitution. Our synthesis of evidence points to prompts/cues as particularly promising and highlights a handful of BCTs that demand further investigation, including self-monitoring, goal setting, and feedback on behavior.

Conclusions

This review identifies specific limitations in each step of review formulation and provides nuanced suggestions to enhance the efficacy of future research endeavors.

Keywords

substance use alcohol tobacco illicit drug mHealth mobile health JITAI BCT behavior change techniques

Introduction

Although addiction is treatable, individuals in recovery typically experience multiple lapse and relapse episodes. According to the National Institute on Drug Abuse, 40 to 60% of patients with substance use disorders (SUDs) relapse after treatment.¹ Relapse can be dangerous or even deadly for some drugs such as opioids and alcohol, as persons in recovery can more easily overdose due to lack of tolerance following a period of abstinence.¹

The most effective behavioral interventions for relapse prevention include cognitive behavioral therapy for addiction and co-occurring mental health disorders, contingency management, and mindfulness coaching.² The standard treatment for SUDs involve medication management (when available) and monthly individual counseling, usually with a 3- or 6-month follow up.^3,4 Although the standard treatment methods are effective, individuals with SUD face many barriers to in-person treatment, and access to counseling may not be readily available in the moment when an individual is facing strong urges to use; recent estimates suggest that only 10% of individuals with an SUD are receiving treatment.^2,5 Barriers include perceived stigma, fear of punishment, lack of social support, lack of trust in providers, transportation difficulties, and lack of available services.⁶ Substance use interventions can also be applicable for the large proportion of individuals who struggle from SUD but do not have a formal diagnosis, or whose symptoms are subclinical.^5,7 Thus, for the remainder of this review, we will refer to “substance use involvement” or “substance use recovery,” instead of “substance use disorder” to be inclusive of the more general population affected by substance use problems.

Just-in-time adaptive interventions (JITAIs) are a promising mobile health (mHealth) methodology for complementing or replacing in-person behavioral therapy that is either out-of-reach or undesired, as JITAIs focus on providing an individual with tailored support, at the right time and right level.⁸ Generally, just-in-time components of mHealth interventions are delivered through program-initiated push notifications or text messages in the moment when an individual is likely to need immediate support. In some cases a program could apply “just-in-time” pull components (i.e., participant-initiated) if some type of support is immediately available to an individual that is seeking help in a time of need (e.g., pressing a “support” button in an app or texting an alert line).⁸ JITAIs have been used to target behaviors such as reducing sedentary behavior, increasing physical activity, and preventing dietary lapses, in addition to smoking and substance abuse.^8–11 These interventions may be particularly helpful for relapse prevention because JITAIs are uniquely positioned to target predictors of inhibitory self-control to support abstinence when cravings are strong.⁸

JITAIs, which are relatively new, are becoming increasingly popular and well-studied. The number of studies indexed in Google Scholar using search terms “‘JITAI’ AND ‘mHealth’” have steadily increased from 74 published in 2019 to 275 published in 2025. However, most of them use many treatment components simultaneously (e.g., web- or app-based lessons together with standardized push messages and a social interaction component). The use of simultaneous intervention components is natural and mirrors most health promotion programs, whether delivered in person or via remote methods. Nevertheless, the use of multiple components and behavioral strategies simultaneously, particularly when delivered using a novel modality, produces a “black box” problem wherein it is difficult to evaluate which strategies are driving the behavior change.¹² As the popularity of JITAIs continues to grow, it is critical to understand the differential efficacy and effectiveness of individual mobile intervention components.

Michie et al.’s Behavior Change Technique (BCT) taxonomy is a set of 93 BCTs that enables researchers to identify specific components and applications in interventions as “active ingredients” of an intervention.¹³ The application of BCTs in non-mHealth interventions targeting substance use have been found effective in several studies.¹⁴ Incorporating specific BCTs, such as problem-solving, goal setting of behavior, self-monitoring of behavior, and biofeedback, have led to reductions in substance use in traditional interventions, supporting the utility of this taxonomy in identifying active ingredients of interventions.^15,16 The identification of individual BCTs within interventions can allow for future disentanglement of treatment component effectiveness and lead to the development of more efficient and effective interventions.

To fill this gap in the literature, this synthesis summarizes existing systematic reviews and meta-analyses that have sought to examine the role of BCTs in digital behavior change interventions for substance use recovery.

Methods

As part of a larger systematic review and meta-analysis, we searched PubMed, Web of Science, Scopus, and Embase using MeSH and Boolean search terms to identify eHealth and mHealth studies with at least one just-in-time (JIT) component targeting a reduction in substance use behaviors or dietary behaviors such as calorie restriction. The full search terms applied are located in Appendix A. This review of reviews represents a sub-analysis of the review articles identified with this search; these articles were omitted from the larger systematic review and meta-analysis project. Here, we examine systematic reviews and meta-analyses published between 2009 and 2024 returned using our search terms, focusing on the reviews of substance use (i.e., reviews about dietary behaviors were excluded for the purposes of this review paper).

As part of the larger study, two reviewers screened the titles and abstracts of each paper identified using search terms; all systematic reviews and meta-analyses were moved to a separate collection to be used for this synthesis of reviews. These review papers were then fully reviewed by two reviewers, and those that fit inclusion criteria were included in this paper. Inclusion criteria for this review included: must be identified as a systematic review, meta-analysis, or review of more than one quantitative study that contained at least one JITAI component; must target outcomes related to substance use (i.e., alcohol, tobacco or nicotine, or other illicit drugs); describes at least one clearly identifiable BCT from Michie et al.’s BCT taxonomy in its content either directly referenced within the text, or implied in the text and identified by our team. For inclusion in this synthesis, reviews were required to focus on interventions in which all primary studies incorporated a JITAI component, although specific implementation and features of the JITAI varied across studies.

In some cases, BCTs were directly named in the review papers, and these were recorded; however, for other reviews, BCTs were described in the text, and our team identified which BCT was being referenced based on operational definitions outlined in Michie et al. (2013). For example, in several studies, “self-monitoring of behavior” was not directly named, but “self-reporting” was described and BCT [2.3] was identified. For example, interventions that required participants to record or track their substance use behaviors were coded as self-monitoring of behavior [2.3]; interventions that provided participants with personalized information about their behavior were coded with feedback on behavior [2.2]; interventions that required participants to establish goals or targets for their behavior changes, either long-term or short-term were coded as goal setting (behavior) [1.1]. For some BCT categories, particularly social support [3.1-3.3], reviews often evaluated the concept at a broader level, rather than distinguishing between specific subtypes. In this case, we also synthesized findings, grouping its use under the broader “social support” category. Once reviews were identified for inclusion, each review was evaluated for which BCTs were included, whether the unique effects of each BCT were able to be isolated given the study design, which BCTs were found effective, and any other notable conclusions.

For each included review, we extracted which BCTs were present, whether their independent effects could be isolated, and any reported conclusions regarding effectiveness. Effectiveness classifications were based on the conclusions reported by the authors of each review. In this synthesis, a BCT was considered “effective” if the review authors reported that it was associated with positive behavioral outcomes. This synthesis reflects the determinations made by the review authors, rather than applying a new effectiveness criterion. Some reviews did not explicitly evaluate or declare the effectiveness of the individual BCTs, often focusing on the effectiveness of the intervention instead. For these reviews, effectiveness was coded as “N/A,” but they were still included, as they provide information on which BCTs were implemented in digital interventions, even if their independent effectiveness was not determined.

Extracted data was coded manually using a structured Excel spreadsheet using a predefined framework If a review reported that the unique effect of a BCT was evaluated but not found effective, it was coded with 0; if a BCT was evaluated and found effective, it was coded with 1; if a BCT was evaluated but results were mixed between studies, it was coded with 2. As noted previously, if the unique effect of the BCT could not be evaluated, results were coded as “N/A”. “Mixed results” are defined as reviews that included at least one study that found the BCT effective for decreasing the likelihood of substance use involvement, while other studies in the same review found that BCT to have null effect on substance use involvement.

“Positive behavior change” or “positive clinical outcomes” refers to the reduction of substance use involvement, including but not limited to smoking and alcohol use reduction. These outcomes were used to inform effectiveness classifications in this synthesis.

Results

Summary of search results

Of the N = 12,045 articles retrieved in the initial search, we identified 152 systematic reviews and meta-analyses which could facilitate analysis of differential effectiveness of BCTs across included digital interventions. After removing duplicates as well as those that did not contain an mHealth or JIT component, 118 systematic reviews and meta-analyses remained. Of these reviews, n = 49 included substance use outcomes as detailed in the PRISMA flow chart in Figure 1. Intervention modalities covered in reviews included smartphone apps, reviewed in 21 reviews; text messaging, in 31 reviews; websites or web-based interventions, in 21 reviews; wearable technologies, in 3 reviews; and video games, in 3 reviews.

Figure 1.

PRISMA flow diagram of study selection process.

Behavior change techniques identified

Eleven primary BCTs were found in the 49 reviews that studied substance use, summarized in Table 1 (BCT names are italicized and numbers are identified in brackets). The eleven most widely used and studied BCTs in mobile health interventions include self-monitoring of behavior [2.3] (40 reviews^17–55), feedback on behavior [2.2] (34 reviews^{17,18,20,21,23,26–34,36–38,40,41,44–51,53,56–61}), goal setting (behavior) [1.1] (23 reviews^{17,18,23,26,28–32,35,40,45–47,49–54,61}), and types of social support [3.1, 3.2, 3.3] (13 reviews^{17,23,26,29,31,32,35,52–55,58,62}). Other commonly used BCTs include problem solving [1.2] (7 reviews^{18,23,26,32,44,50,55}), action planning [1.4] (6 reviews^{18,23,32,50,53,55}), valued self-identity [13.4] (14 reviews^{21,24,25,27,31,37,42,43,57,58,60,62–64}), prompts/cues [7.1] (11 reviews^{18,26,29,30,33,34,37,48,49,58,60}), information about antecedents [4.2] (5 reviews^{32,37,41,50,58}), behavior substitution [8.2] (2 reviews^32,53), and reward (outcome) [10.10] (1 review⁵³). Reviews finding that no BCTs were found effective are listed as “none,” while reviews that did not isolate the effect of the BCT, those that did not declare effectiveness of the BCT, or those finding mixed effects are listed as “N/A” in Table 1.

Table 1.

Summary of review articles extracted and BCTs assessed.

Citation	Substance(s) targeted	BCTs included in studies reviewed	BCTs declared effective	Number of studies included	Overall conclusions
Zhao et al., (2016)¹⁷	Alcohol	Self-monitoring of behavior [2.3], feedback on behavior [2.2], goal setting (behavior) [1.1], social support [3.1-3]	self-monitoring of behavior [2.3]	23	Evidence suggests that app-based interventions show promise, but more RCTs are needed to confirm effectiveness
Pugatch et al., (2018)⁵⁶	Smoking	self-monitoring of behavior [2.3], feedback on behavior [2.2]	N/A	3	Tunneling may improve engagement and knowledge, but more research is required to support that claim
Webb et al., (2010)¹⁸	Alcohol, Smoking	problem solving [1.2], self-monitoring of behavior [2.3], goal setting (behavior) [1.1], action planning [1.4], feedback on behavior [2.2], prompts/cues [7.1]	goal setting (behavior) [1.1], action planning [1.4], prompts/cues [7.1], self-monitoring of behavior [2.3]	85	Findings support investing in more intensive, theory-based interventions incorporating multiple BCTs
Orr et al., (2015)⁶³	Smoking	valued self-identity [13.4], reward (outcome) [10.10]	N/A	38	SMS interventions may support behavior change, though results are mixed and require further validation
Berrouiguet et al., (2016)¹⁹	Alcohol	self-monitoring of behavior [2.3]	self-monitoring of behavior [2.3]	36	Text messaging may enhance engagement and mental health outcomes
Donker et al., (2013)²⁰	Alcohol, Smoking, Illicit Drugs	self-monitoring of behavior [2.3], feedback on behavior [2.2]	self-monitoring of behavior [2.3]	8	Evidence-based mental health apps show promise in reducing symptoms, though further validation is needed
Brown et al., (2013)²¹	Tobacco	valued self-identity [13.4], self-monitoring of behavior [2.3], feedback on behavior [2.2]	feedback on behavior [2.2]	8	There is a need for affordable, personalized, and age-appropriate tobacco cessation interventions.
Ybarra et al., (2016)²²	Smoking	self-monitoring of behavior [2.3]	none	5	Text messaging interventions increase smoking cessation rates, though effect variability requires further study.
McCrabb et al., (2018)²³	Alcohol, Smoking	goal setting (behavior) [1.1], action planning [1.4], feedback on behavior [2.2], self-monitoring of behavior [2.3], social support [3.1-3], problem solving [1.2]	feedback on behavior [2.2], action planning [1.4]	45	Internet-based smoking cessation interventions are effective
Mehta et al., (2010)⁶²	Smoking	valued self-identity [13.4], social support [3.1-3]	valued self-identity [13.4]	10	Internet interventions reduce smoking, with valued self-identity identified as a key predictor.
Spohr et al., (2015)²⁴	Smoking	self-monitoring of behavior [2.3], valued self-identity [13.4]	self-monitoring of behavior [2.3]	13	Text messaging interventions significantly improve smoking cessation outcomes.
Fjeldsoe et al., (2009)²⁵	Alcohol, Smoking	self-monitoring of behavior [2.3], valued self-identity [13.4]	N/A	14	SMS interventions show positive short-term effects, though long-term efficacy remains unclear.
Palmer et al., (2018)²⁶	Alcohol, Smoking	self-monitoring of behavior [2.3], feedback on behavior [2.2], problem solving [1.4], social support [3.1-3], prompts/cues [7.1], goal setting (behavior) [1.1]	N/A	72	SMS-based smoking cessation support increases quit rates.
Oosterveen et al., (2017)²⁷	Alcohol, Smoking	valued self-identity [13.4], self- monitoring of outcomes of behavior [2.4], feedback on behavior [2.2]	N/A	45	eHealth SNAPO interventions show short-term efficacy, with limited evidence on long-term outcomes.
Hutton et al., (2020)²⁸	Alcohol	feedback on behavior [2.2], self-monitoring of behavior [2.3], goal setting (behavior) [1.1]	N/A	18	mHealth interventions provide an accessible and cost-effective method for youth health promotion.
Tofighi et al., (2017)²⁹	Alcohol, Illicit Drugs	self-monitoring of behavior [2.3], prompts/cues [7.1], social support 3.1-3], goal setting (behavior) [1.1], feedback on behavior [2.2]	N/A	11	Text messaging interventions are feasible and acceptable for substance use treatment.
Quanbeck et al., (2014)³⁰	Alcohol	goal setting (behavior) 1.1], feedback on behavior [2.2], prompts/cues [7.1], self-monitoring of behavior [2.3]	N/A	14	mHealth systems vary widely in complexity, from simple reminders to comprehensive recovery tools.
Dick et al., (2019)⁵⁷	Illicit Drugs	feedback on behavior [2.2], valued self-identity [13.4]	feedback on behavior [2.2]	8	Digital interventions may modestly reduce harm from substance misuse.
Kong et al., (2014)⁵⁸	Smoking	information about antecedents [4.2], feedback on behavior [2.2], social support [3.1-3], goal setting (behavior) [1.1], prompts/cues [7.1], valued self-identity [13.4]	N/A	22	Connected interventions show positive effects on SUD recovery and high acceptability.
Scott-Sheldon et al., (2016)³¹	Smoking	feedback on behavior [2.2], goal setting (behavior) [1.1], valued self-identity [13.4], self-monitoring of behavior [2.3], social support	N/A	20	Text messaging interventions are highly effective for smoking cessation and scalable at low cost.
Nesvag et al., (2018)⁶⁵	Alcohol, Cannabis	feedback on behavior [2.2], social-support [3.1-3], self-monitoring of behavior [2.3]	N/A	43	Digital interventions are feasible but show inconsistent effectiveness in SUD recovery outcomes.
Kaner et al., (2017)³²	Alcohol	feedback on behavior [2.2], goal setting (behavior) [1.1], self-monitoring of behavior [2.3], action planning [1.4], information about antecedents [4.2], social support [3.1-3], problem solving [1.2], behavior substitution [8.2]	behavior substitution [8.2], problem solving [1.2],	57	Digital interventions moderately reduce alcohol consumption in the short term.
Getty et al., (2019)⁵⁹	Alcohol, Tobacco	feedback on behavior [2.2]	N/A	6	Mobile-delivered contingency management shows potential for enhancing SUD treatment.
Staiger et al., (2020)³³	Alcohol, Tobacco, Illicit Drugs	feedback on behavior [2.2], self-monitoring of behavior [2.3], action planning [1.4], information about antecedents [4.2],	none	20	App-based interventions should be compared to equivalent non-app formats to determine relative efficacy.
O’rourke et al., (2016)³⁴	Alcohol	feedback on behavior [2.2], prompts/cues [7.1], self-monitoring of behavior [2.3]	feedback on behavior [2.2], prompts/cues [7.1]	11	Technology-based interventions offer flexibility that is particularly appealing to young adults.
Ashford et al., (2019)³⁵	Alcohol, Illicit Drugs	goal setting (behavior) [1.1], self-monitoring of behavior [2.3], social support [3.1-3]	self-monitoring of behavior [2.3]	22	Digital recovery services show potential but currently lack strong evidence of effectiveness.
Gonzalez et al., (2021)³⁶	Alcohol, Smoking	self-monitoring of behavior [2.3], feedback on behavior [2.2]	N/A	23	Cultural tailoring in mHealth is increasing, though mechanisms remain poorly understood.
Vodopivec-Jamsek et al., (2012)³⁷	Alcohol, Tobacco	self-monitoring of behavior [2.3], information about antecedents [4.2], valued self-identity [13.4], feedback on behavior [2.2], prompts/cues [7.1]	none	4	Mobile messaging improves preventive health behaviors, though effects vary.
Giroux et al., (2017)³⁸	Alcohol, Illicit Drugs	self-monitoring of behavior [2.3], feedback on behavior [2.2]	N/A	18	Online psychological interventions largely reflect traditional self-administered approaches.
Holmes et al., (2018)³⁹	Alcohol, Smoking	self-monitoring of behavior [2.3]	N/A	6	Digital tools may improve access to mental health care, though evidence remains limited.
Hoeppner et al., (2017)⁴⁰	Alcohol	self-monitoring of behavior [2.3], goal setting (behavior) [1.1], feedback on behavior [2.2]	self-monitoring of behavior [2.3], feedback on behavior [2.2]	266	Smartphone apps for alcohol use show potential public health benefits.
Carreiro et al., (2020)⁴¹	Alcohol	self-monitoring of behavior [2.3], feedback on behavior [2.2], information about antecedents [4.2]	feedback on behavior [2.2]	32	Digital interventions are associated with improved SUD recovery outcomes.
Sawares et al., (2017)⁴²	Alcohol	valued self-identity [13.4], self-monitoring of behavior [2.3]	N/A	10	User engagement is critical for achieving behavior change in digital interventions.
Head et al., (2013)⁶⁴	Smoking	valued self-identity [13.4]	N/A	19	Personalized interventions are significantly associated with intervention effectiveness
Hall et al., (2015)⁶⁰	Smoking	feedback on behavior [2.2], prompts/cues [7.1], valued self-identity [13.4]	none	15	Evidence on optimal intervention characteristics is limited; further research is needed.
Humphreys et al., (2021)⁴⁴	Alcohol	problem solving [1.2], feedback on behavior [2.2], self-monitoring of outcomes of behavior [2.2]	problem solving [1.2], feedback on behavior [2.2], self-monitoring of outcomes of behavior [2.2]	45	Identifying common BCTs and controlling studies for quality and effectiveness can inform development of transdiagnostic interventions.
Champion et al., (2019)⁴⁵	Alcohol, Smoking	self-monitoring of behavior [2.3], goal setting (behavior) [1.1], self-monitoring of behavior [2.3], feedback on behavior [2.2]	N/A	22	eHealth school-based interventions show small but positive effects on health behaviors.
Hou et al., (2014)⁴⁶	Alcohol, Tobacco	feedback on behavior [2.2], goal setting (behavior) [1.1], and self-monitoring of behavior [2.3]	feedback on behavior [2.2], self-monitoring of behavior [2.3]	38	Web-based interventions are generally effective in improving targeted health outcomes.
Tong et al., (2021)⁴⁷	Alcohol, Smoking	feedback on behavior [2.2], self-monitoring of behavior [2.3], goal setting (behavior) [1.1]	self-monitoring of behavior [2.3]	39	Personalized mobile interventions show early promise but remain underdeveloped.
Kohl et al., (2013)⁶¹	Alcohol, Smoking	feedback on behavior [2.2], goal setting (behavior) [1.1]	N/A	41	Digital interventions often produce small and unsustained behavior change effects.
Gold et al., (2021)⁴⁸	Alcohol, Smoking	feedback on behavior [2.2], self-monitoring of behavior [2.3], prompts/cues [7.1]	N/A	92	Digital interventions yield small positive effects, potentially limited by adherence issues.
Piette et al., (2017)⁴⁹	Smoking	feedback on behavior [2.2], prompts/cues [7.1], self-monitoring of behavior [2.3], goal setting (behavior) [1.1]	N/A	N/A	Interactive voice response interventions improve cardiovascular risk factors and disease management.
Furness et al., (2020)⁵⁰	Alcohol, Smoking	goal setting (behavior) [1.1], self-monitoring of behavior [2.3], information about antecedents [4.2], problem solving [1.2], action planning [1.4], feedback on behavior [2.2]	N/A	24	eHealth interventions benefit cancer patients, though delivery mode effects are unclear.
Milne-Ives et al., (2020)⁵¹	Alcohol, Illicit Drugs	goal setting (behavior) [1.1], feedback on behavior [2.2], self-monitoring of behavior [2.3]	N/A	52	Greater evaluation of individual and combined BCTs is needed to improve app effectiveness.
Akinosun et al., (2021)⁵²	Alcohol, Smoking	social support [3.1-3], goal setting (behavior) [1.1]	social support [3.1-3]	25	Digital interventions show limited impact on some cardiovascular risk factors.
Armanasco et al., (2017)⁴³	Alcohol, Smoking	self-monitoring of behavior [2.3], valued self-identity [13.4], reward(outcome) [10.10], goal setting (behavior) [1.1]	N/A	35	Further behavior-specific and quantitative research is recommended.
Edwards et al., (2016)⁵³	Alcohol, Smoking	goal setting (behavior) [1.1], self-monitoring of behavior [2.3], feedback on behavior [2.2], reward (outcome) [10.10], social support [3.1-3], action planning [1.4]	N/A	55	Stronger collaboration across disciplines is needed to maximize intervention impact.
Morrison et al., (2012)⁵⁴	Smoking	social support [3.1-3]	N/A	52	More research is needed on user-initiated and self-management features.
Young et al., (2018)⁵⁵	Alcohol, Smoking	self-monitoring of behavior [2.3], social support [3.1-3], goal setting (behavior) [1.1], action planning [1.4], problem solving [1.2]	goal setting (behavior) [1.1]	7	Online lifestyle interventions may improve depressive symptoms when targeting behavior change.

Review findings

Six reviews^{17,18,20,24,40,44} were able to isolate the effects of self-monitoring of behavior [2.3], and all six concluded that this BCT was found effective for positive clinical outcomes, including smoking reduction and reduction of alcohol consumption. Three additional reviews showed mixed results after being unable to fully isolate the effects of [2.3] among their included studies, and thus could not render strong conclusions.^19,35,56 Additionally, three reviews^22,33,37 found [2.3] ineffective. Only four reviews^34,40,44,47 were able to isolate the positive effects of feedback on behavior [2.2], finding the BCT effective for reducing consumption of alcohol and tobacco use, and three additional reviews^23,41,57 showed mixed results. Two reviews^33,37 found that [2.2] was ineffective. After self-monitoring and feedback, the next most studied BCT in these reviews was goal setting for behavior [1.1],^{17,18,23,26,28–32,35,40,43,45–47,49–53,55,58,61} though only one review⁵⁵ was able to isolate the effects of the BCT, concluding that this BCT was found effective in reducing alcohol use and smoking. One additional review¹⁸ found mixed results. Social support [3.1-3] was also commonly studied and evaluated in 13 reviews,^{17,23,26,29,31,32,35,52–55,58,62} though none reported clear evidence that these BCTs were effective to promote substance use reduction.

Another common BCT was prompts/cues [7.1], studied in 11^{18,26,29,30,33,34,37,48,49,58,60} reviews, with one review³⁴ finding prompts/cues [7.1] effective for a reduction in alcohol use and another review¹⁸ finding mixed results. One review found the BCT ineffective.³⁷ Valued self-identity [13.4], was evaluated in 14 reviews,^{21,24,25,27,31,37,42,43,57,58,60,62–64} with only one review⁶² finding the BCT effective for promoting smoking reduction and one review finding the BCT ineffective for alcohol and tobacco cessation.³⁷ While information about antecedents [4.2] was studied in five reviews,^{32,37,41,50,58} it was not found individually effective in any reviews, but it was found ineffective in two reviews.^33,37 Another BCT included in the reviews was problem solving [1.2], used in seven reviews,^{18,23,26,32,44,50,55} with two^32,44 of the reviews that included it found that it was effective, though without differentiating effectiveness between behaviors. Action planning [1.4], used in six reviews,^{18,23,32,50,53,55} was found to have mixed results in two reviews,^18,23 however, it was not reported as individually effective in any review. It was reported as ineffective in one review.³³ Behavior substitution [8.2] was used in two reviews,^32,53 and was found effective for reducing alcohol consumption in 50% of the reviews in which it was included. Reward on outcome [10.10] was studied in three reviews^43,53,63 however, none were able to isolate the effect of the BCT. The total number of reviews including and evaluating each BCT is shown in Table 2.

Table 2.

Total number of reviews in which each BCT was evaluated, found effective, or results were mixed.

BCT	Number of reviews reported	Number of reviews where unique effect of BCT was evaluated	Number of reviews where BCT was found effective	Number of reviews where BCT had mixed results	Number of reviews where BCT was found ineffective
Self-Monitoring of behavior	40	12	6	3	3
Feedback on behavior	34	9	4	3	2
Goal Setting (behavior)	23	2	1	1	0
Social Support	13	0	0	0	0
Prompts/cues	11	3	1	1	1
Valued Self-Identity	14	2	1	0	1
Information About Antecedents	5	2	0	0	2
Problem Solving	7	2	2	0	0
Action Planning	6	3	0	2	1
Reward (outcome)	1	0	0	0	0
Behavior Substitution	2	1	1	0	0

Summary of results

While feedback on behavior [2.2], self-monitoring of behavior [2.3], and goal setting (behavior) [1.1] were evaluated in the most reviews, prompts/cues [7.1], and behavior substitution [8.2] were effective in the largest proportions of the reviews that included them. Problem solving [1.2] and action planning [1.4] had the next largest proportions, while self-monitoring of behavior [2.3], feedback on behavior [2.2], goal setting (behavior) [1.1], and valued self-identity [13.4] had the lowest non-zero proportions for effectiveness.

Discussion

Overall, most of the systematic reviews and meta-analyses included in this synthesis yielded mixed or inconclusive results and declared that further research is needed to better characterize BCT effectiveness for preventing substance use in JITAIs. Most of the reviews in this paper yielded mixed results for the effects of individual BCTs on positive behavior change, due to variations between results in individual studies, where only some studies within each review were able to isolate the effects of the BCTs. This limited the ability to determine conclusive results for the effects of BCTs; however, it helps clarify where evidence is beginning to emerge and highlights what steps need to be taken in the future to reach more conclusive results.

Despite these mixed findings, this synthesis provides a clearer picture of how BCTs are currently being implemented and evaluated in digital substance use interventions. While several techniques, such as feedback on behavior [2.2], self-monitoring of behavior [2.3], and goal setting (behavior) [1.1] were evaluated in the most reviews, prompts/cues [7.1] and behavior substitution [8.2] appeared most promising. These finding suggest that some BCTs may deserve more attention in future intervention design and evaluation. Ybarra et al. suggests that the next steps for smoking cessation, in particular, is to determine if the just-in-time components, like the text-messages themselves, are sufficient on their own or if other BCT components, such as setting a quit day and preparing, need to be paired with them.²² In addition, they suggest that future studies should compare text messaging interventions to other controls that do not require an in-person component, such as telephone quit lines, which would provide context for the relative impact of mHealth interventions.²² Spohr et al. provides a different approach, suggesting that prioritizing the development of adaptively tailored programming for text message interventions in smoking cessation, especially with the incorporation of ecological momentary assessment (EMA) would be beneficial to mHealth research.²⁴ EMA, especially when used in conjunction with behavioral interventions, allows real-time assessment of cue-induced craving, facilitating proactive measures to prevent relapse. The reviews also acknowledge potential biases in their studies, but generally, most reviews declare a low risk of bias. Additionally, many studies within the reviews utilized randomized controlled trials, which allow for a more systematic approach to analysis that can be more easily replicated.

A major limitation of our synthesis was the inability to conduct substance-specific analysis. Of the 49 studies included in this synthesis, 36 focused on alcohol use as the primary health behavior, while several others grouped multiple substances, such as alcohol use and smoking use, together. Because of this, isolated analysis on specific substances was infeasible. The individual reviews included in our synthesis also had some limitations, such as including a small number of studies or a small number of participants in each individual study. Many reviews focused on the effects of the delivery method (e.g., the app itself) rather than the effect of the BCTs used, which is not applicable to our review. Incomplete reporting of BCTs was a considerable confounding factor across many such reviews and is not easily addressable without systematic reconsideration in reporting of systematic reviews and meta-analyses and highlights the need for interventionists to attempt to isolate the effects of key BCTs whenever possible. Other reviews evaluated which BCTs were used most often without considering the effectiveness of those BCTs. Another common limitation among the systematic reviews was that the studies analyzed were too heterogenous with respect to target outcomes for a meta-analysis to be conducted. Many studies focused on the effectiveness of the intervention, rather than the effectiveness of the BCT, while still acknowledging the use of BCTs. For example, Scott-Sheldon et al.³¹ and Palmer et al.²⁶ found that text messaging interventions were correlated with increased smoking cessation rates, indicating that such JITAIs show promise for improving substance use outcomes. While these findings demonstrate the potential effectiveness of digital interventions, they do not isolate the effects of individual BCTs contributing to these outcomes. Additionally, since these conclusions were drawn across reviews with various study designs, samples, and outcome measures, some variability in effectiveness findings is expected and reflects the nature of such a synthesis. A few reviews acknowledge the fact that there was a wide variability in definitions and in inclusion criteria, so it was difficult to accurately compare across studies. Moving forward, it will be helpful for future interventions to adopt similar reporting measures and outcomes to promote comparability and enable more definitive conclusions to be conferred.

One major limitation of our study was that it was difficult to compare the usage of BCTs across multiple systematic reviews and meta-analyses because there was a lack of uniformity. Some reviews did not state explicitly which BCTs were used, and instead, they were described in the methods. In these cases, trained coders certified from the BCT taxonomy retroactively coded which BCT was being described, provided sufficient description to meet criteria. While multiple steps were taken to ensure as much accuracy as possible in these codings, there is likely error present. In the future, it would be beneficial for studies to specifically identify which BCTs are used in their interventions, perhaps as a supplementary table or file, to better facilitate systematic comparison and move the field forward. We recommend that researchers consider factorial designs or other innovative trial designs to pinpoint the active ingredients of interventions.

Conclusion

While few definitive conclusions could be made from the review of 49 systematic reviews and meta-analyses focusing on substance use, there are promising results that clearly set up the path for future research of the use of BCTs in JITAIs for substance abuse cessation and relapse prevention. Although there is evidence suggesting that social support, feedback on behavior, and self-monitoring of outcomes of behavior are effective for relapse prevention, these results are mixed within reviews, so further research should evaluate the context in which these BCTs are most effective. Notably, this synthesis sheds light on less commonly studied BCTs such as prompts/cues [7.1] and behavior substitution [8.2], which are promising avenues for further exploration. Beyond echoing the call for more investigation, this synthesis serves as a roadmap, pinpointing specific limitations in each step in the formulation of a systematic review/meta-analysis and providing suggestions on how to improve upon them for future research. We recognize the complexities in drawing conclusions from the existing literature and advocate for a more refined approach in future research. Rather than offering a single definitive answer, this synthesis clarifies which BCTs are most frequently used across digital substance use interventions and which appear most consistently associated with positive outcomes among prior reviews. By doing so, we provide a reference point for future intervention development and highlight areas where more rigorous research is needed to better understand the active ingredients of digital substance use interventions.

Footnotes

ORCID iDs

Nithya P. Gurumurthy

Lex Hurley

Nisha Gottfredson O’Shea

Consent to participate

There are no human participants in this article and informed consent is not required.

Author Contributions

Conceptualization: NPG (lead), NGO (equal), LH (supporting), BTN (supporting), Data curation: NPG (lead), NGO (supporting), LH (supporting), BTN (supporting), Formal analysis: NPG, Investigation: NPG, Methodology: NGO (lead), BTN (equal), LH (equal), NPG (supporting), Project administration: NPG (lead), NGO (supporting), LH (supporting), BTN (supporting), Supervision: NGO (lead), LH (supporting), BTN (supporting), Visualization: NPG (lead), LH (supporting), NGO (supporting), BTN (supporting), Writing – original draft: NPG (lead), Writing – review & editing: NPG (lead), NGO (supporting), LH (supporting), BTN (supporting).

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Appendix

References

Abuse NI on D . Treatment and Recovery. National Institute on Drug Abuse (NIDA). [Internet]. -- [cited 2024 Jan 25]. Available from. https://nida.nih.gov/publications/drugs-brains-behavior-science-addiction/treatment-recovery

Witkiewitz

Marlatt

. Therapist’s Guide to Evidence-Based Relapse Prevention. Elsevier, 2011, p. 394.

Carroll

Kiluk

. Cognitive Behavioral Interventions for Alcohol and Drug Use Disorders: Through the Stage Model and Back Again. Psychol Addict Behav J Soc Psychol Addict Behav 2017; 31(8): 847–861. https://doi.org/10.1037/adb0000311

McHugh

Hearon

Otto

. Cognitive-Behavioral Therapy for Substance Use Disorders. Psychiatr Clin North Am 2010; 33(3): 511–525, PubMed PMID: 20599130; PubMed Central PMCID: PMC2897895. https://doi.org/10.1016/j.psc.2010.04.012

Sahker

Pro

Poudyal

, et al. Evaluating the substance use disorder treatment gap in the United States, 2016-2019: A population health observational study. Am J Addict 2024; 33(1): 36–47. https://doi.org/10.1111/ajad.13465

Farhoudian

Razaghi

Hooshyari

, et al. Barriers and Facilitators to Substance Use Disorder Treatment: An Overview of Systematic Reviews. Subst Abuse Res Treat 2022; 16: 11782218221118462, PubMed PMID: 36062252. https://doi.org/10.1177/11782218221118462

Williams

Fletcher

Frost

, et al. Comparison of Substance Use Disorder Diagnosis Rates From Electronic Health Record Data With Substance Use Disorder Prevalence Rates Reported in Surveys Across Sociodemographic Groups in the Veterans Health Administration. JAMA Netw Open 2022; 5(6): e2219651, PubMed PMID: 35771574; PubMed Central PMCID: PMC9247731. https://doi.org/10.1001/jamanetworkopen.2022.19651

Nahum-Shani

Smith

Spring

, et al. Just-in-Time Adaptive Interventions (JITAIs) in Mobile Health: Key Components and Design Principles for Ongoing Health Behavior Support. Ann Behav Med 2018; 52(6): 446–462. https://doi.org/10.1007/s12160-016-9830-8

Hardeman

Houghton

Lane

, et al. A systematic review of just-in-time adaptive interventions (JITAIs) to promote physical activity. Int J Behav Nutr Phys Act 2019; 16(1): 31. https://doi.org/10.1186/s12966-019-0792-7

10.

Forman

Goldstein

Crochiere

, et al. Randomized controlled trial of OnTrack, a just-in-time adaptive intervention designed to enhance weight loss. Transl Behav Med 2019; 9(6): 989–1001. https://doi.org/10.1093/tbm/ibz137

11.

Thomas

Bond

. Behavioral response to a just-in-time adaptive intervention (JITAI) to reduce sedentary behavior in obese adults: Implications for JITAI optimization. Health Psychol Off J Div Health Psychol Am Psychol Assoc 2015; 34S(0): 1261–1267, PubMed PMID: 26651467; PubMed Central PMCID: PMC4681309. https://doi.org/10.1037/hea0000304

12.

Danaher

Brendryen

Seeley

, et al. From black box to toolbox: Outlining device functionality, engagement activities, and the pervasive information architecture of mHealth interventions. Internet Interv 2015; 2(1): 91–101, PubMed PMID: 25750862; PubMed Central PMCID: PMC4346786. https://doi.org/10.1016/j.invent.2015.01.002

13.

Michie

Richardson

Johnston

, et al. The behavior change technique taxonomy (v1) of 93 hierarchically clustered techniques: building an international consensus for the reporting of behavior change interventions. Ann Behav Med Publ Soc Behav Med 2013; 46(1): 81–95. https://doi.org/10.1007/s12160-013-9486-6

14.

Klamert

Craike

Bedi

, et al. Behaviour change techniques in physical activity-focused interventions for young people at risk of problematic substance use: A systematic review and meta-analysis. Early Interv Psychiatry 2023; 17(12): 1139–1153. https://doi.org/10.1111/eip.13467

15.

Thal

Maunz

Quested

, et al. Behavior change techniques in physical activity interventions for adults with substance use disorders: A systematic review. Psychol Addict Behav 2023; 37(3): 416–433. https://doi.org/10.1037/adb0000842

16.

Hartmann Boyce

Chepkin

, et al. Nicotine replacement therapy versus control for smoking cessation - Hartmann Boyce, J - 2018 | Cochrane Library. [Internet]. [cited 2025 Mar 20]. Available from. https://www.cochranelibrary.com/cdsr/doi/10.1002/14651858.CD000146.pub5/full

17.

Zhao

Freeman

. Can Mobile Phone Apps Influence People’s Health Behavior Change? An Evidence Review. J Med Internet Res 2016; 18(11): e5692. https://doi.org/10.2196/jmir.5692

18.

Webb

Joseph

Yardley

, et al. Using the Internet to Promote Health Behavior Change: A Systematic Review and Meta-analysis of the Impact of Theoretical Basis, Use of Behavior Change Techniques, and Mode of Delivery on Efficacy. J Med Internet Res 2010; 12(1): e1376. https://doi.org/10.2196/jmir.1376

19.

Berrouiguet

Baca-García

Brandt

, et al. Fundamentals for Future Mobile-Health (mHealth): A Systematic Review of Mobile Phone and Web-Based Text Messaging in Mental Health. J Med Internet Res 2016; 18(6): e135, PubMed PMID: 27287668; PubMed Central PMCID: PMC4920962. https://doi.org/10.2196/jmir.5066

20.

Donker

Petrie

Proudfoot

, et al. Smartphones for Smarter Delivery of Mental Health Programs: A Systematic Review. J Med Internet Res 2013; 15(11): e2791. https://doi.org/10.2196/jmir.2791

21.

Brown

. A Review of the Evidence on Technology-Based Interventions for the Treatment of Tobacco Dependence in College Health. Worldviews Evid Based Nurs 2013; 10(3): 150–162. https://doi.org/10.1111/wvn.12000

22.

Ybarra

Jiang

Free

, et al. Participant-level meta-analysis of mobile phone-based interventions for smoking cessation across different countries. Prev Med 2016; 89: 90–97. https://doi.org/10.1016/j.ypmed.2016.05.002

23.

McCrabb

Baker

Attia

, et al. Internet-Based Programs Incorporating Behavior Change Techniques Are Associated With Increased Smoking Cessation in the General Population: A Systematic Review and Meta-analysis. Ann Behav Med 2019; 53(2): 180–195. https://doi.org/10.1093/abm/kay026

24.

Spohr

Nandy

Gandhiraj

, et al. Efficacy of SMS Text Message Interventions for Smoking Cessation: A Meta-Analysis. J Subst Abuse Treat 2015; 56: 1–10. https://doi.org/10.1016/j.jsat.2015.01.011

25.

Fjeldsoe

Marshall

Miller

. Behavior Change Interventions Delivered by Mobile Telephone Short-Message Service. Am J Prev Med 2009; 36(2): 165–173. https://doi.org/10.1016/j.amepre.2008.09.040

26.

Palmer

Sutherland

Barnard

, et al. The effectiveness of smoking cessation, physical activity/diet and alcohol reduction interventions delivered by mobile phones for the prevention of non-communicable diseases: A systematic review of randomised controlled trials. PLOS ONE 2018; 13(1): e0189801. https://doi.org/10.1371/journal.pone.0189801

27.

Oosterveen

Tzelepis

Ashton

, et al. A systematic review of eHealth behavioral interventions targeting smoking, nutrition, alcohol, physical activity and/or obesity for young adults. Prev Med 2017; 99: 197–206. https://doi.org/10.1016/j.ypmed.2017.01.009

28.

Hutton

Prichard

Whitehead

, et al. mHealth Interventions to Reduce Alcohol Use in Young People: A Systematic Review of the Literature. Compr Child Adolesc Nurs 2020; 43(3): 171–202. https://doi.org/10.1080/24694193.2019.1616008

29.

Tofighi

Nicholson

McNeely

, et al. Mobile phone messaging for illicit drug and alcohol dependence: A systematic review of the literature. Drug Alcohol Rev 2017; 36(4): 477–491. https://doi.org/10.1111/dar.12535

30.

Quanbeck

Chih

Isham

, et al. Mobile Delivery of Treatment for Alcohol Use Disorders. Alcohol Res Curr Rev 2014; 36(1): 111–122, PubMed PMID: 26259005; PubMed Central PMCID: PMC4432850.

31.

Scott-Sheldon

LAJ

Lantini

Jennings

, et al. Text Messaging-Based Interventions for Smoking Cessation: A Systematic Review and Meta-Analysis. JMIR MHealth UHealth 2016; 4(2): e5436. https://doi.org/10.2196/mhealth.5436

32.

Kaner

Beyer

Garnett

, et al. Personalised digital interventions for reducing hazardous and harmful alcohol consumption in community dwelling populations. Cochrane Database Syst Rev 2017; 9(9): CD011479. https://doi.org/10.1002/14651858.CD011479.pub2

33.

Staiger

O’Donnell

Liknaitzky

, et al. Mobile Apps to Reduce Tobacco, Alcohol, and Illicit Drug Use: Systematic Review of the First Decade. J Med Internet Res 2020; 22(11): e17156. https://doi.org/10.2196/17156

34.

O⿿Rourke

Humphris

Baldacchino

. Electronic communication based interventions for hazardous young drinkers: A systematic review. Neurosci Biobehav Rev 2016; 68: 880–890. https://doi.org/10.1016/j.neubiorev.2016.07.021

35.

Ashford

Bergman

Kelly

, et al. Systematic review: Digital recovery support services used to support substance use disorder recovery. Hum Behav Emerg Technol 2020; 2(1): 18–32. https://doi.org/10.1002/hbe2.148

36.

Gonzalez

Early

Gordon-Dseagu

, et al. Promoting Culturally Tailored mHealth: A Scoping Review of Mobile Health Interventions in Latinx Communities. J Immigr Minor Health 2021; 23(5): 1065–1077, PubMed PMID: 33988789; PubMed Central PMCID: PMC8120499. https://doi.org/10.1007/s10903-021-01209-4

37.

Vodopivec-Jamsek

de Jongh

Gurol-Urganci

, et al. Mobile phone messaging for preventive health care. Cochrane Database of Systematic Reviews 2012; 12: CD007457. https://doi.org/10.1002/14651858.CD007457.pub2 Accessed 02 June 2026.

38.

Giroux

Goulet

Mercier

, et al. Online and Mobile Interventions for Problem Gambling, Alcohol, and Drugs: A Systematic Review. Front Psychol [Internet]. 2017;8. 954. https://doi.org/10.3389/fpsyg.2017.00954, [cited 2024 Jan 18] Available from. https://www.frontiersin.org/articles/10.3389/fpsyg.2017.00954

39.

Holmes

van Agteren

Dorstyn

. A systematic review of technology-assisted interventions for co-morbid depression and substance use. J Telemed Telecare 2019; 25(3): 131–141. https://doi.org/10.1177/1357633X17748991

40.

Hoeppner

Schick

Kelly

, et al. There is an app for that - Or is there? A content analysis of publicly available smartphone apps for managing alcohol use. J Subst Abuse Treat 2017; 82: 67–73. https://doi.org/10.1016/j.jsat.2017.09.006

41.

Carreiro

Newcomb

Leach

, et al. Current reporting of usability and impact of mHealth interventions for substance use disorder: A systematic review. Drug Alcohol Depend 2020; 215: 108201. https://doi.org/10.1016/j.drugalcdep.2020.108201

42.

Sawares

Shen

Xue

, et al. The Impact of Mobile Apps on Alcohol Use Disorder: A Systematic Review Protocol. JMIR Res Protoc 2017; 6(4): e6975. https://doi.org/10.2196/resprot.6975

43.

Armanasco

Miller

Fjeldsoe

, et al. Preventive Health Behavior Change Text Message Interventions: A Meta-analysis. Am J Prev Med 2017; 52(3): 391–402. https://doi.org/10.1016/j.amepre.2016.10.042

44.

Humphreys

Evans

Makin

, et al. Identification of Behavior Change Techniques From Successful Web-Based Interventions Targeting Alcohol Consumption, Binge Eating, and Gambling: Systematic Review. J Med Internet Res 2021; 23(2): e22694. https://doi.org/10.2196/22694

45.

Champion

Parmenter

McGowan

, et al. Effectiveness of school-based eHealth interventions to prevent multiple lifestyle risk behaviours among adolescents: a systematic review and meta-analysis. Lancet Digit Health 2019; 1(5): e206–e221. https://doi.org/10.1016/S2589-7500(19)30088-3

46.

Hou

Charlery

SAR

Roberson

. Systematic literature review of Internet interventions across health behaviors. Health Psychol Behav Med 2014; 2(1): 455–481. https://doi.org/10.1080/21642850.2014.895368

47.

Tong

Quiroz

Kocaballi

, et al. Personalized mobile technologies for lifestyle behavior change: A systematic review, meta-analysis, and meta-regression. Prev Med 2021; 148: 106532. https://doi.org/10.1016/j.ypmed.2021.106532

48.

Gold

Yau

Rigby

, et al. Effectiveness of Digital Interventions for Reducing Behavioral Risks of Cardiovascular Disease in Nonclinical Adult Populations: Systematic Review of Reviews. J Med Internet Res 2021; 23(5): e19688. https://doi.org/10.2196/19688

49.

Piette

List

Rana

, et al. Mobile Health Devices as Tools for Worldwide Cardiovascular Risk Reduction and Disease Management. Circulation 2015; 132(21): 2012–2027. https://doi.org/10.1161/CIRCULATIONAHA.114.008723

50.

Furness

Sarkies

Huggins

, et al. Impact of the Method of Delivering Electronic Health Behavior Change Interventions in Survivors of Cancer on Engagement, Health Behaviors, and Health Outcomes: Systematic Review and Meta-Analysis. J Med Internet Res 2020; 22(6): e16112. https://doi.org/10.2196/16112

51.

Milne-Ives

Lam

Cock

, et al. Mobile Apps for Health Behavior Change in Physical Activity, Diet, Drug and Alcohol Use, and Mental Health: Systematic Review. JMIR MHealth UHealth 2020; 8(3): e17046. https://doi.org/10.2196/17046

52.

Akinosun

Polson

Diaz-Skeete

, et al. Digital Technology Interventions for Risk Factor Modification in Patients With Cardiovascular Disease: Systematic Review and Meta-analysis. JMIR MHealth UHealth 2021; 9(3): e21061, PubMed PMID: 33656444; PubMed Central PMCID: PMC7970167. https://doi.org/10.2196/21061

53.

Edwards

Lumsden

Rivas

, et al. Gamification for health promotion: systematic review of behaviour change techniques in smartphone apps. BMJ Open 2016; 6(10): e012447. https://doi.org/10.1136/bmjopen-2016-012447

54.

Morrison

Yardley

Powell

, et al. What Design Features Are Used in Effective e-Health Interventions? A Review Using Techniques from Critical Interpretive Synthesis. Telemed E-Health 2012; 18(2): 137–144. https://doi.org/10.1089/tmj.2011.0062

55.

Young

Trapani

Dawson

, et al. Efficacy of online lifestyle interventions targeting lifestyle behaviour change in depressed populations: A systematic review. Aust N Z J Psychiatry 2018; 52(9): 834–846. https://doi.org/10.1177/0004867418788659

56.

Pugatch

Grenen

Surla

, et al. Information Architecture of Web-Based Interventions to Improve Health Outcomes: Systematic Review. J Med Internet Res 2018; 20(3): e7867. https://doi.org/10.2196/jmir.7867

57.

Dick

Whelan

Davoren

, et al. A systematic review of the effectiveness of digital interventions for illicit substance misuse harm reduction in third-level students. BMC Public Health 2019; 19(1): 1244. https://doi.org/10.1186/s12889-019-7583-6

58.

Kong

Ells

Camenga

, et al. Text messaging-based smoking cessation intervention: A narrative review. Addict Behav 2014; 39(5): 907–917. https://doi.org/10.1016/j.addbeh.2013.11.024

59.

Getty

Morande

Lynskey

, et al. Mobile telephone-delivered contingency management interventions promoting behaviour change in individuals with substance use disorders: a meta-analysis. Addiction 2019; 114(11): 1915–1925. https://doi.org/10.1111/add.14725

60.

Hall

Cole-Lewis

Bernhardt

. Mobile Text Messaging for Health: A Systematic Review of Reviews. Annu Rev Public Health 2015; 36(1): 393–415. https://doi.org/10.1146/annurev-publhealth-031914-122855

61.

Kohl

Crutzen

Vries

. Online Prevention Aimed at Lifestyle Behaviors: A Systematic Review of Reviews. J Med Internet Res 2013; 15(7): e2665. https://doi.org/10.2196/jmir.2665

62.

Mehta

Sharma

. Internet and Cell Phone Based Smoking Cessation Programs among Adolescents. Acta Didact Napoc 2010; 3(4): 11–24.

63.

Orr

King

. Mobile phone SMS messages can enhance healthy behaviour: a meta-analysis of randomised controlled trials. Health Psychol Rev 2015; 9(4): 397–416. https://doi.org/10.1080/17437199.2015.1022847

64.

Head

Noar

Iannarino

, et al. Efficacy of text messaging-based interventions for health promotion: A meta-analysis. Soc Sci Med 2013; 97: 41–48. https://doi.org/10.1016/j.socscimed.2013.08.003

65.

Nesvåg

McKay

. Feasibility and Effects of Digital Interventions to Support People in Recovery From Substance Use Disorders: Systematic Review. J Med Internet Res 2018; 20(8): e9873. https://doi.org/10.2196/jmir.9873