Research on digital mental health care in groups with serious mental illness (SMI), including schizophrenia spectrum disorders (SSDs), has grown slowly compared to other mental health conditions such as anxiety and depression [1,2]. Historically, this has been due to concerns about the ability to afford and use technology (ie, device ownership, access to the internet, and cellular data plans), understand the limitations of the technology (ie, privacy, crisis planning, and digital literacy), and make effective gains from digitally delivered content.

Recent research shows that cell phone and smartphone ownership and internet access are high in populations with SMI with and without psychosis [3,4] and have shown to be an adequate means of service delivery over the COVID-19 pandemic. Leveraging personal technologies (ie, laptops, tablets, smartphones, and wearable technologies) has also been framed as a critical measure to address access to specialized mental health care in remote areas and populations identified as marginalized, as access to smartphones and the internet is improving in these communities [5-7]. In addition, it has the potential to address barriers to stigma, time, and cost that limit the frequency and duration of available mental health care [8,9]. Given the potential to overcome such significant barriers, experts predict the use of personal technologies to deliver or assist with mental health care to continue to grow and hope to capitalize on the burst of enthusiasm brought about by the COVID-19 pandemic [10-12].

Although technologically delivered and assisted care is steadily gaining credibility and acceptability among mental health care professionals, there remain questions about the efficacy, safety and potential limitations of technologically delivered care in SSDs [13]. In fact, research has found that out of the publicly available technology advertised for mental health care, only 3% have scientific evidence supporting their use [14]. In schizophrenia and psychosis, a commercial review found that only 6 publicly available apps out of 700 were supported by scientific evidence, and many contained stigmatizing themes and misinformation [15].

To this end, it is critical to identify evidence-based digital mental health tools available for use on personal technologies by individuals with SSDs and evaluate the state of research in the field and readiness for implementation. Previous reviews in this area have tended to focus on 1 technical modality (eg, mobile apps [16] and SMS text messaging [17]) or 1 aspect of recovery (eg, relapse prevention [18]) but have yet to examine the field collectively across technological platforms, treatment targets, and therapeutic approaches to provide a high-level snapshot of the extant literature.

This study is a scoping review examining the use of personal technologies to deliver digital mental health interventions in the treatment of SSDs. The overarching aim was to examine the state of the existing evidence supporting digital health tools for individuals with SSDs. Specifically, we identified gaps in the literature and explored the next steps for future research as the field moves toward implementation. To this end, we examined studies across the research stage, from development papers to implementation studies, using various types of commonly owned personal technologies that may be used to support clinical outcomes and recovery in populations experiencing SSDs. Personal technologies were chosen as the target technology for this review, as it can be argued that this may be the most efficient way to increase access to specialized information and care for individuals who cannot access traditional care consistently. It is also plausible that personal technologies may be the most accessible and cost-effective technological aid to traditional care.

Given the complex and broad nature of our research question, our results are presented in alignment with the scoping review methodology, focusing on the state of research and identification of key concepts and gaps [19,20]. Our methods are structured according to PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) principles endorsed by the Cochrane Collaboration [21] and the scoping review reporting framework [22].

The included studies were published in English, examined the use of digital health tools in populations with SSDs, and published between 2010 and 2023 (given the evolution of technology during this period). Population, Intervention, Comparison, and Outcome principles guided the formulation of the inclusion criteria using the categories population, intervention, and outcome of interest (control type was not applicable as we included a range of study types; refer to Textbox 1 for details). Studies were excluded if no full text was available (eg, editorials and conference presentations), if not published in English, if examined in a population with health challenges other than SSDs (eg, depression, substance use, and physical health conditions), if they did not use technology to deliver an intervention, or if they did not examine clinical targets related to SSDs. We also did not include studies that examined the pivot to virtual care (eg, phone, video, or other communication technology-based care) during the COVID-19 pandemic and virtually delivered care more broadly, as the only technological component was videoconferencing or telephone delivery of treatment as usual (TAU). The included studies sought to enhance the delivery of TAU (ie, technologically supported TAU) or deliver adjunct interventions (ie, interventions not included in TAU).

A total of 3 core databases were searched: PsycINFO, Embase, and MEDLINE. In addition, Cochrane and PROSPERO databases were searched for existing reviews and protocols. References of resulting included studies were hand searched.

Search strategies used for general databases were as follows:

Search strategies used for databases powered by OVID were as follows: schizophrenia spectrum.mp. OR psychotic disorder.mp. OR exp psychosis/AND (sms or short messag* service* or texting or text messag*).mp. OR (mobile apps* or smartphone app* or telehealth) OR (eHealth or mHealth or mobile health or internet intervention or web-based treatment or web-based intervention or wearabl*).mp.

Studies resulting from the initial search were exported into a reference manager (EndNote; Clarivate) [23], where the initial deduplication occurred, and then transferred into a web-based review management system, where the system again identified duplicates (Covidence; Veritas Health Innovation) [24]. This platform also allowed for the independent review of each study for inclusion. Authors (JD, MI, LT, AZ, and TA) conducted the review using the title, abstract, and full text. Review conflicts were reviewed by the first author (JD) and team and, if needed, by the last authors (SK and GF). At least 2 reviewers reviewed each publication.

Both qualitative and quantitative data were extracted from the included studies. First, methodological information was collected, including research design and publication type, sample size, diagnostic group, length of study, and outcome measures. Second, intervention information was collected on the digital intervention target, the type of technology used, the evidence-based approach used, and the length of the intervention. Third, study outcome data were extracted, including qualitative or descriptive and quantitative data regarding primary outcomes. Both qualitative and quantitative data are reported descriptively.

The reported results are descriptive, in line with scoping review methods, and provide a high-level overview of key findings. Extracted data are charted according to the type of technology used, followed by subcategories regarding the stage of research (eg, development, pilot feasibility and efficacy, and effectiveness and implementation trials), themes, and issues uncovered through data synthesis. Of note, we distinguish between blended care and multitechnological interventions. Blended care is defined as integrating technology into traditionally delivered care (ie, in-person care) [25], whereas we define multitechnology interventions as using >1 technological platform and may be integrated with traditional or virtual care. Furthermore, given the large number of included studies, findings report on primary outcomes. A more detailed review would be required to investigate secondary and tertiary findings. Primary outcomes pertaining to feasibility use highly varied measures; therefore, we provided a high-level description of outcomes.

Our search yielded 999 studies, of which, through careful review, 92 (9.21%) studies were included (refer to Figure 1 for details of the search exclusion), investigating approximately 50 unique interventions. It is difficult to know the exact number of unique interventions as this is not always clearly reported. Included studies were published in a range of countries, predominately in North America (40/92, 43%), Europe (33/92, 36%), Australia (13/92, 14%), and East Asia (7/92, 7%). Studies were published from 2010 to 2023, with the majority published between 2018 and 2020 (50/92, 54%; Figure 2).

We primarily examined results through the intersecting lens of technology type and stage of research (ie, study design and aim). Among the 92 included studies, 11 (12%) focused on web or internet-based interventions, 25 (27%) examined smartphone apps, 16 (17%) investigated SMS text messaging, and 40 (43%) used multiple types of technology to deliver the interventions. No included studies used wearable technology to deliver an intervention. Studies included in this review that used videoconferencing or phone calls did so in combination with other types of technologies and are thus counted under multitechnology interventions.

Regarding the stage of the included research, of 92 studies, there are 5 (5%) qualitative studies investigating development and patient experiences, 32 (35%) pilot trials (26/32, 81% open trials and 6/32, 19% randomized control trials [RCTs]), 13 (14%) protocol papers, 23 (25%) conducted RCTs, 16 (17%) secondary analyses (both quantitative and qualitative), 3 (3%) nonrandomized trials, and no implementation trials. Given the number of secondary analyses, it is important to note the number of unique technologies being studied to reflect a potentially more accurate picture of the spread of digital mental health that is leveraging personal technologies in this population. There are 50 unique technologies studied within the 92 included studies. Several unique technologies (19/50, 38%) are investigated in >1 publication, with several published protocols suggesting ongoing investigations using these interventions. Contextually, it is important to note that all included trials recruited from mental health care clinics and institutions providing traditional care, and as such, all interventions included in this study can be considered blended or integrated to varying degrees with traditional care.

Factors of feasibility in included studies vary, such as measures of acceptability, usability, technology engagement, and user feedback. The vast majority of included studies report high levels of acceptability and usability. Technology and user feedback are mixed and reveal several high-level themes. First, technology engagement tends to decrease over time regardless of technology type. Second, many studies report self-reflection and therapeutic rapport as positive features of using these technologies. Third, various types of prompts were used with varying success to enhance engagement.

Across all technologies and all study designs, 60% (55/92) of the studies did not report on any safety indices at all. The most common indicator (31/92, 34%) of safety used was reporting serious adverse events (SAE) and adverse events (AE). Most studies reported that no SAEs or AEs were related to the technological intervention, and if there were any present, they provided a short description of the SAE or AE. Furthermore, the vast majority of the studies (64/92, 70%) did not comment on the privacy measures taken by the study team or built into the technology used to deliver the intervention.

There were 16 RCTs and 1 pilot RCT examining the efficacy of the technological intervention as its primary outcome; see Table 1). Regarding methodology, most RCTs use TAU (10/17, 58%) as their comparison group, with others using varying active comparisons, including phone calls, films, sham apps, and other therapies. RCTs were most commonly reported using single-blind designs (7/17, 41%); however, 2 (12%) were double blinded, and 8 (47%) did not use blinding procedures for their assessors or participants. Approximately 52% (9/17) of the trials showed a positive change (ie, improvement in target) during the course of the intervention in favor of the technology, with another showing positive change in 1 primary outcome but not both [26] and another showing no overall change but a change in the positive symptom subscale scores [27]. Compared to the control, 47% (8/17) of the RCTs show significant group differences in their primary outcome, with the same 2 studies reporting significant group differences in part of their primary outcomes [26,27]. Only 4 studies reported effect sizes, with 3 being interpretable as medium to high and 1 as low [28-31]. The type of effect sizes reported were inconsistent across studies and thus were interpreted as low, moderate, and high based on suggested interpretation guidelines for each type of effect size to aid in comparison. A more detailed examination of efficacy, which includes all study types with primary efficacy outcomes, can be found in the sections below divided by technology type.

Detailed results are outlined in the subsequent sections and focus on the stage of research and notable results. Key gaps and future directions are explored in the Discussion section. A summary of the included studies can be found in Multimedia Appendix 1 [26-120].

The primary aim of this review was to provide a high-level overview of the use of personal technologies in research on digital mental health interventions for individuals with SSDs. Publication patterns show rapid growth in the area over the past 5 to 7 years, highlighting increasing interest in this area [10-12]. The lower number of publications reported for 2021 to 2023 is likely partly attributable to a combination of the large number of protocols published in 2020, as many of these trials may still be underway, and research challenges related to the COVID-19 pandemic. Personal technologies leveraged most often included the use of smartphone apps or an app in combination with other technology, such as a clinician web portal.

Our review confirms that personal technologies have already garnered acceptance among individuals with SSDs, as evidenced by consistently high ratings of acceptability, usability, and satisfaction. Some studies even report that augmenting care with technology improved engagement in traditional in-person or remote care [44,101,106]. In included studies which report participant feedback, participants typically report that these technologies facilitate self-reflection and understanding, better communication with their clinical teams, improved access to evidence-based resources, and support in times of need. Findings related to feasibility presented in this review are similar to findings reported elsewhere for groups with SMI [121].

Despite the high levels of acceptance and interest, sustained engagement in technological interventions is an ongoing concern [122] and is not well understood. Measures of engagement are highly inconsistent across studies, and include module completion, response rates, skills and homework completion, and number of posts, making it almost impossible to understand a cohesive story regarding engagement with digital tools in groups with SSDs. While simple metrics such as screen clicks or the number of days or hours used are commonly reported, the field lacks an understanding of clinically meaningful engagement for this metric [123]. Beyond access metrics, there is no consistent measure of technology use. The challenge of heterogeneity in engagement metrics is already well known [124], but a lack of progress in agreement around these common metrics hinders future advances in the field [125]. Furthermore, likely in part owing to a lack of definition in this area, means of addressing technology disengagement are also poorly understood. A review examining the use of persuasive system design (design features designed to address or enhance engagement) in technologies designed for use in depression and anxiety reported that persuasive system design efforts did not systematically lead to improved engagement but are associated with improved efficacy [126].

Moreover, some ongoing challenges may impact the feasibility of using such technologies in practice, including understanding the link between privacy protections embedded in these technologies and meaningful engagement. Privacy concerns are seldom outlined in the included publications, which may result from the dearth of available guidelines, policies, and reporting standards. Privacy concerns are often cited by health care providers as a key barrier to the use of technology in practice [127], and as such, to encourage technological adoption, more data on privacy features needs to be made available. There is also a dearth of information regarding patient safety and urgent or crisis resources and support. In this review, most of the included studies (55/92, 60%) did not report on safety at all, which is well above the proportions reported across the field of digital mental health (35%) [128]. The most common safety indicators reported in this review and across the field [128] were SAEs and other AEs, which are often mandatory reporting standards for ethics boards and do not provide nuanced information regarding technology use and patient well-being.

Furthermore, there are promising early results regarding efficacy, but overall findings are mixed. Pilot studies tend to show more optimistic outcomes across a range of mental health outcomes; however, once studies reach the RCT phase, findings become more mixed. Similar descriptions are found in other areas of SMI, such as bipolar disorder [129]. In areas such as depression and anxiety, where more research on efficacy has been conducted, clearer conclusions about effectiveness can be drawn [130]. In the included RCTs, it does not appear that study procedures, such as blinding, type of technology, or sample size, systematically impact outcomes, as no obvious patterns have emerged between study type or methodology. However, with few studies featuring an active control group, any assessment of efficacy is still preliminary. Future studies should consider the active intervention when planning a control group and the purpose of the technology (eg, if testing an evidence-based treatment that is typically delivered in person and not included in TAU, then TAU would not be an active control group, such as an in-person service delivering the evidence-based treatment would better represent an active control). However, there are some interventions that have garnered significant support and exhibit positive early results [33,53,60,84,86,113].

Of note, all included studies used samples of individuals currently in treatment and thus were all offered adjunctly to traditional clinician-delivered care. However, few studies described how the technology was blended or integrated into the care structure. As such, the field provides little direction on the role of technology and its integration into existing care models. Furthermore, there were few clinical integration and effectiveness studies, underscoring the next frontier for this research in SSDs. It is important to understand how external factors, including primary treatment methods, may impact engagement, efficacy, and effectiveness. For example, the included studies that described the role of technology within the clinical context described technology-forward (ie, self-guided and clinician-supported) and clinician-forward (ie, clinician-delivered and technology-supported) interventions. Clinician-forward interventions, in our small sample of studies that described clinical integration, seemed to demonstrate better engagement [96] and better outcomes [33,84,96] than technology-forward interventions, depending on the target and treatment approach [29].

Other reviews reported mixed findings in relation to blended (technology-supported) approaches. One review and meta-analysis of digital mental health interventions across disorders suggested that technology-supported interventions outperform (Hedges g=0.16; P<.001) traditional TAU (solely clinician-delivered) [131]. However, this review did not report on the more nuanced technological differences that we highlight in this review regarding whether the intervention was delivered by the technology (ie, technology-forward) or supported clinician-delivered care (ie, clinician-forward). Another review and meta-analysis suggested that technology engagement was associated with efficacy regardless of intervention type (ie, guided or unguided) [132], suggesting that engagement may be the underlying mechanism for effectiveness in these trials rather than the level of clinician involvement. Therefore, more research is needed to define the role of technology within clinical spaces to understand the implementation and best practices related to technology-assisted care.

Future research in this promising area should focus on the identified key gaps to move the field toward implementation readiness. First, studies should clarify the technology’s intended role, intervention targets, and functionality. This is important for understanding implementation into existing clinical structures and interpreting study outcomes. Second, given the significant barriers of safety and privacy to clinical adoption, a more detailed exploration of safety and privacy indices is critical. Finally, a better understanding of barriers to participating in and disengagement from digital interventions is crucial for understanding its usefulness and scope in clinical settings.

There are several limitations to this review. First, as a scoping review, we could not provide a detailed review of each study but rather a high-level examination of the findings in the field to date. Therefore, data have been synthesized and presented in accordance with outlined scoping review guidelines, and, as such, the methodological quality of each study was not rigorously reviewed. Second, we limited our search to using personal technologies to deliver or support specialized care for individuals with SSDs. Other technologies are being used in SSD treatment that this review did not include, such as virtual reality, avatar therapy, cognitive remediation, brain stimulation, and more. Third, we restricted this review to outcomes directly related to general recovery (ie, core symptoms and functioning) and treatment engagement and did not focus on specific domains such as cognitive deficits. Fourth, other interventions exist for comorbid conditions, such as substance use and smoking cessation [133], physical activity [134], and resources for family and social support [135]. Finally, we only included technologies that were used to deliver interventions; we did not include studies that were used to improve the understanding of SSDs, such as observational methods (eg, technology-assisted EMA or health services–based interventions, including those that are embedded in electronic medical records for medication algorithms, symptom monitoring, or other purposes). Future reviews may consider focusing on these additional areas of digital mental health to understand how other technologies may support the recovery of individuals with SSD diagnoses.

Overall, using personal technologies to deliver specialized care requires more careful consideration before advocating for broad implementation, highlighting the same challenges as other psychotherapeutic intervention research. Namely, the prolonged time between investigation and implementation is due to varied findings and a lack of cohesive targets and direction. Despite these challenges, as evidenced by this review, there is great promise for leveraging personal technology in mental health care to help provide pieces of the holistic care necessary for recovery in SSDs. Multifaceted mental health conditions such as SSDs are highly heterogeneous and may require multifaceted and flexible interventions. Although the exact nature of meaningful technological support is still being discovered, the studies in this review overwhelmingly demonstrate how flexibly personal technologies can support recovery from medication adherence to potentially delivering complex psychosocial interventions such as CBT. Furthermore, studies clearly demonstrated the feasibility of personal technologies to extend access to specialized information beyond traditional care and into everyday life. Therefore, these technologies are well suited to be integrated into existing specialty care structures for individuals with SSD diagnoses.