Smartphone Apps and Digital Tools for Managing Schizophrenia and Psychotic Disorders: A Clinical Review

Abstract

There is increasing interest in smartphone digital mental health interventions (DMHIs) for managing schizophrenia. Currently, numerous applications (or, apps) that target different specific domains (e.g., medication adherence, social functioning, relapse prediction, and general mental well-being) have been developed and evaluated in research studies; however, few are commercially available to patients. The emergence of DMHIs has also highlighted the critical role of app engagement and the therapeutic alliance in effective treatments. This has driven the adoption of hybrid care models that integrate traditional psychotherapy with smartphone-based tools for symptom monitoring and intervention delivery, such as psychoeducation, cognitive-behavioral therapy for psychosis modules, and other targeted supports. This clinical review examines the current landscape of DMHIs for managing schizophrenia, including research evaluating outcomes related to effectiveness and implementation.

Schizophrenia is among the most disabling disorders in all of medicine (1). The costs to society are greater than those of nearly any other chronic health condition, and the burden to patients and family members is of the greatest magnitude (2). Existing treatments, most commonly antipsychotics, have demonstrated effectiveness, but benefits tend to be concentrated around positive symptoms (e.g., delusions, hallucinations) (3). These interventions less consistently affect cognitive, social, and functional impairments associated with the illness. Likewise, current diagnostic and monitoring techniques for schizophrenia remain limited, and today, it remains challenging to predict who will develop the condition or who will relapse. Therefore, new approaches to improving risk prediction and providing effective adjunctive interventions for all aspects of symptomology of schizophrenia are urgently needed.

Over the past two decades, digital health technologies, particularly through smartphone devices, have been explored as a potential new solution to improve the monitoring and treatment of schizophrenia (4). Unlike wearable devices and actigraphy, smartphones can also collect sensor data, but they can also offer surveys as well as offer more interactive features and the ability to deliver application (or, app)-based interventions (5). The potential of digital technology, especially smartphones, for managing schizophrenia rests on their ability to function in both diagnostic and therapeutic roles. Although such apps are not intended to replace traditional mental health services, they can facilitate information gathering to aid in diagnosis (e.g., symptom monitoring) and offer therapeutic skills on demand (e.g., cognitive-behavioral therapy). Research on digital technologies for clinical monitoring and intervention is rapidly evolving, and this literature is the subject of this clinical review. Here, we consider the accessibility of such technologies in this population before we examine the existing evidence examining the use of digital health for schizophrenia across six key domains (mood monitoring; medication adherence and therapeutic processes; relapse prediction; loneliness and social functioning; personal wellness; and symptom self-management).

ACCESS, INTEREST, AND ENGAGEMENT IN DIGITAL HEALTH TECHNOLOGY

Any consideration of the clinical use of smartphones in managing schizophrenia must consider the device ownership among patients and their interest in using health apps. Past research has extensively shown that people with schizophrenia own and use smartphones. Increases in smartphone ownership in this population have mirrored the increased penetration of smartphones in broader society, as reports from 2012 to 2014 suggested ownership rates of approximately 60% (6, 7), and newer research suggests rates of at least 80% or higher in urban areas (8–10). Nearly a decade ago, in 2016, a national survey study led by the National Alliance on Mental Illness in the United States highlighted many patient-led clinical-related use cases of smartphones, including playing music to block hallucinations, tracking medications, and monitoring symptoms (11). The 2023 U.K. study showed that 75% reported having used health-related apps (12), an increase from 30% in 2020 (9).

Although the data support high rates of access to and use of smartphones by people with schizophrenia, there has remained concern that technology use may induce paranoia (7). However, the evidence does not support this. Instead, multiple reviews suggest that app-based interventions are feasible and acceptable and do not induce paranoia (13–15). A 2024 study further demonstrated that social media usage was associated with a subsequent decrease of about 6% in paranoia (16). This research is critical, because it shows an instance of technology being beneficial. This does not mean that there are no adverse events; however, a recent systematic review found that although many studies fail to adequately report adverse events in digital health schizophrenia studies, rates are likely low (17). A 2025 systematic review and meta-analysis of adverse events in clinical trials of mental health apps further notes that the rate of clinical deterioration among participants in the smartphone app and control conditions is around 7% (18). The similarity between the groups across 171 trials that tested app interventions suggests that deterioration is not dependent solely on the app. Research also demonstrates that individuals with schizophrenia can accurately report symptoms through technology, further validating that self-report measures can be an accurate measure of clinical severity (19). Thus, although digital mental health interventions (DMHIs) should always be monitored for risks, current research does not suggest a high level of risk associated with these approaches.

Beyond safety considerations, smartphones offer users unique advantages through self-reporting capabilities in the form of ecological momentary assessments (EMAs) and passive sensor data collection in the form of real-time behavioral monitoring (20), which can illuminate relationships between environmental factors, physiological patterns such as sleep and step counts, and symptoms (21). Several studies have validated this methodology by establishing correlations between self-reported activities and passively collected mobility metrics (22–24). This combination of active reporting and passive monitoring distinguishes smartphone-based approaches from computerized models, allowing for new opportunities for personalized intervention and symptom management in the mental health field.

However, accessing apps for patients to use that are specifically related to managing schizophrenia is a challenge. Although there is ample research literature to support potential benefits, little of this work has been translated into accessible apps that patients can download from online app stores (e.g., Android’s Google Play store or Apple’s App Store). A 2022 review search of these online app stores found that, of 537 apps that returned in a search for schizophrenia and psychosis, only six apps were relevant to psychosis and were feasible to download or use outside of a study: Psychosis Intervention and Early Recovery (PIER); Schizophrenia Health Storylines; Schizophrenia; Schizophrenia Treatment—Remedies for Schizophrenia; Schizophrenia Disease; and Coping with Schizophrenia. Of these six apps, the average time since the app was last updated was 1,121 days (15), suggesting that developers may not routinely monitor and update content. Today, only three of the aforementioned apps continue to be available on the U.S. commercial marketplace: PIER, Schizophrenia, and Coping with Schizophrenia. This lack of available options is in stark contrast with examples of research studies on app interventions that have benefited individuals with schizophrenia spectrum disorders (25–27). Other approaches (e.g., MindApps.org) have utilized a database approach to identifying mental health–related apps that could have benefits for individuals with particular conditions (28, 29). Early research suggests the promise of DMHIs for schizophrenia spectrum disorders in at least six areas that are highlighted in this review (see Figure 1) : mood monitoring, medication adherence, relapse prediction, personal wellness, loneliness and social functioning, and symptom self-management. In the following sections, studies in each of these areas will be described.

FIGURE 1. .

Six approaches to utilize apps

MOOD MONITORING

Up to 30% of individuals with schizophrenia may also have comorbid depression (30, 31), which is associated with worse clinical outcomes (32). Fortunately, data collected from smartphones can potentially improve the prediction of changes in disorder-relevant states. For example, smartphone-based mood tracking, when combined with sensor data (e.g., sleep patterns or activity patterns), has been shown to predict future depressive symptoms a week in advance (33). Beyond predictive utility, individuals with schizophrenia spectrum disorders may benefit from self-monitoring clinically relevant states and/or sharing such reports with individuals who are involved in their care. One approach involves sending regular (i.e., daily or weekly) questionnaires to assess patients’ mental health–related symptoms, which patients can complete at any time. This implementation provides a comprehensive, real-time perspective of the patient’s life outside of clinical care (34, 35) and can be used as a point of clinical discussion. Studies examining this approach have demonstrated feasibility and acceptability to individuals with schizophrenia spectrum disorders, with one study noting that participants continued to remain engaged for 11.6 months and that 61% of the app users completed the daily and weekly surveys regularly (85%–100%) (34). In particular, the app was focused on documenting user experiences with EMA surveys, which were monitored by their physicians. Because this study was completed without any compensation to the patients or physicians, the intrinsic motivation for patients to remain engaged demonstrates that digital interventions can be a sustainable real-world model. At the same time, caution should still be practiced, because the study also noted that 8% of the app users developed delusions involving the use of the app and the research team (34). Furthermore, there is evidence that app-based monitoring could have specific benefits beyond traditional methods such as paper-based assessments. In one study, participants were offered a psychosocial intervention targeting multiple domains (e.g., medication adherence, physical health monitoring, functional mobility, diet management, and psychoeducation) and in two different formats: a stakeholder- and expert-reviewed manual versus a smartphone app. Researchers noted a significant preference for the mobile app, with all patients in the app group being active users of the app; on the other hand, only 68% of individuals in the manual group were actively using the manual (36).

Smartphone apps for mood monitoring and symptom tracking may also increase emotional self-awareness and assess recovery. A pioneering 2014 pilot study showed that using a self-management app incorporating surveys, psychoeducation, and medication adherence tracking was associated with a 2.9-point and a 4.1-point mean reduction in positive and general Positive and Negative Syndrome Scale symptom scores, respectively (37). Now, a decade later, mood tracking has been used as a digital placebo in intervention studies, selected to account for the nonspecific effects and expectations around app use. However, mood tracking has proven to be as beneficial in symptom reduction as apps designed to support self-management directly (25), suggesting that mood monitoring may have a large stand-alone clinical benefit.

MEDICATION ADHERENCE AND THERAPEUTIC PROCESSES

In conjunction with general mood monitoring, medication adherence remains a significant challenge in individuals with schizophrenia (38), and apps can be effective in promoting adherence. For instance, one study implemented video verification of medication ingestion and instructions for accurate dosage. Cases of nonadherence and other concerns were flagged for clinician intervention. More than 60% of the 235 participants had 80% or more medication adherence over 14 days (39). Similar approaches using incentives and monitoring supported by artificial intelligence (AI) demonstrated a medication adherence rate of approximately 95% for all app-using patients over 3 months, in comparison with a 64% medication adherence rate in the control group (40). In another study, researchers explored a digital medication system incorporating aripiprazole tablets with an ingestible event marker, a sensor that activates on interaction with stomach acid. The app also incorporates record keeping for users to input mood and manual medication tracking. On a web-based browser, caregivers and health care providers were given access to the data. From this, researchers found that participants expressed satisfaction with this framework (41). This approach was the first digital pill system cleared by the U.S. Food and Drug Administration (FDA) and a landmark for innovation in managing schizophrenia. However, in late 2024, the manufacturer removed the product from the marketplace.

In addition to remote monitoring and ingestible medication trackers, some apps provide personalized medication reminders and dosage scheduling (42–44). This method serves to remind patients of their antipsychotic medication regimens while also incorporating tailored user interactions with survey responses. In one app-augmented intervention, the authors demonstrated how setting up specific medication reminders and dosage schedules improved adherence scores significantly 12 weeks after the installation of the app (42). Still, there are other varied approaches to incorporating medication adherence monitoring. These approaches range from direct communication platforms to survey psychoeducational resources (43) and medication reminder systems to help track refills and clinic appointments (45). Although the comparative clinical efficacy between such apps has not been studied, evidence suggests that this approach is feasible and may offer benefits.

Recent and ongoing work has also aimed to extend the clinical focus to therapeutic aspects beyond medication management. For example, using smartphone apps to enhance the clinical encounter itself by targeting key therapeutic variables known to contribute to positive psychiatric treatment, including patient-clinician shared decision making and the therapeutic alliance (46–48). Other ongoing work uses smartphones to enhance emotional awareness (e.g., alexithymia) and emotional granularity among individuals with serious mental illnesses, including schizophrenia, to reduce suicide risk (49). In comparison with mood monitoring, many medication adherence apps had more direct, ongoing clinical monitoring and feedback throughout, whereas the self-monitoring mood apps were often used without direct clinical feedback. Thus, it is essential to acknowledge that the clinician’s involvement in apps may enhance the therapeutic value.

RELAPSE PREDICTION

Relapse prediction remains a challenge in managing schizophrenia. Smartphone sensor data may aid in relapse prediction by incorporating novel data related to behaviors such as sleep, step counts, and screen time. This approach assumes that distinct behavior changes (e.g., increased time spent at home [50]) occur in the period preceding relapse and that these changes can be detected by smartphone devices. The use of this approach, often called digital phenotyping, has been examined for its benefits in predicting relapse. In general, these approaches appear to be feasible, and the accuracy of predictions appears to be promising (51–55). For example, one app included a feature for intervening with elevated symptoms through early warning sign alerts, which were personalized with care coordinators on the basis of patient-specific triggers or changes (56). Another study demonstrated that social behavior, measured by means of a mobile app’s collection of phone call logs and call duration, was able to provide insight into the relationships between social factors and the occurrence of relapse (57). Although these studies show promise in using smartphone sensors and app survey data, there remains a need for replication and further studies. Many studies in this area are pilot studies, have small sample sizes, or use varying clinical definitions of relapse. As a result, these approaches are not highly generalizable, and specific examples of real-world implementation are scarce (58).

LONELINESS AND SOCIAL FUNCTIONING

Several interventions have focused specifically on addressing loneliness and social connections in this population as well. Loneliness was a strong predictor of functioning when patients with schizophrenia used EMAs to monitor symptoms such as daily and social experiences (59, 60). Research on digital interventions that target loneliness has shown encouraging engagement rates, with one study, which used recorded telehealth videos with therapists, noting that about 67% of the participants remained engaged with the mobile app 3 months postintervention (61). Another app implemented questionnaires for momentary loneliness and psychotic symptoms, which prompted suggestions for certain activities or behavioral changes for the individual. Between week 1 through week 3, researchers found that loneliness decreased over time, with participants stating that using the app felt as if someone was listening to them (62).

Regarding social functioning, DMHIs can be categorized according to a primary goal of either intervention or assessment. Several studies have used digital technologies in interventions to reduce loneliness or improve social functioning. One study reported reductions in social impairment through the utilization of smartphone EMAs and therapeutic video content. The videos provided opportunities for skill practice and offered support for social goals, where they broke up large goals to make them seem more attainable. This approach led to notable increases in positive social self-perceived appraisals and self-perceptions of their communication skills throughout the 8-week study period (63). Other studies used a hybrid therapeutic approach with app-augmented care, offering social functioning–related intervention strategies and homework on mobile apps (64, 65).

Still, more studies have examined the predictive value of smartphone-collected data in predicting variables relevant to loneliness and social functioning. One study examined passively collected audio recordings of participants’ social interactions, which effectively reduced bias common in self-reporting and clinical interviews in capturing social interaction frequency (66). Another study collected GPS data and found that mobility patterns correlated with social behavior in daily life in conjunction with two daily surveys (67). Other findings have demonstrated correlations between digital phenotyping data and several social functioning subdomains, such as interpersonal behavior and functional autonomy (68). Together, all of the aforementioned approaches represent an advancement in addressing loneliness and social challenges of schizophrenia, offering new pathways for intervention and support.

PERSONAL WELLNESS

Individuals with schizophrenia spectrum disorders have significantly shorter life expectancies than the general population (69). This increased morbidity is associated with differences in cardiovascular health that are often linked with diet, physical activity, smoking, and medication-related weight gain (70). Accordingly, wellness-focused digital interventions (targeting, e.g., physical activity, diet, sleep, or smoking) may have a significant impact on the outcomes of schizophrenia spectrum disorders (71, 72). There is an expanding literature base on this topic with many promising results.

Some digital interventions have focused on tracking health behaviors; one straightforward use case involves tracking exercise with a smartphone or wearable accelerometer data (73, 74). In one study, the participants’ perceived general medical health quality was assessed through surveys correlated with accelerometer data collected through the mobile app. By the end of the 4-week study, results showed high engagement rates, with participants utilizing the app on at least 85% of the days. Furthermore, there was a notable improvement in exercise motivation, with no participant reporting a decreased self-rating during the posttest evaluation (73). Another mobile wellness promotion method combined inspirational messages that promoted self-monitoring behaviors in conjunction with in-person health coaching sessions for young adults with serious mental illnesses. This integrated approach led to significant changes in baseline weight and decreased cardiovascular disease risk factors (75). These findings suggest the value of using mobile approaches for monitoring and promoting exercise adherence.

Sleep monitoring has also garnered significant attention in the field. Currently, there is evidence that abnormal sleep patterns and other sleep disturbances have been linked to psychotic experiences (76, 77). In one study, participants used an app to monitor sleep time and quality through the completion of a survey and the use of a wearable device that collects accelerometer data on the wrist to assess the validity and reliability of the reported data (78). Another app in development would synthesize data collected from wearable devices and bed sensors to track sleep durations (72). For example, one app implemented surveys that alerted clinicians when responses were clinically significant (78). Together, these aspects of sleep monitoring can help provide insights into sleep quality that may inform clinical interventions.

There has also been research showing that tobacco usage is disproportionately high among individuals with serious mental illnesses (79). However, smoking cessation apps developed for the general public have illustrated limited usability and effectiveness for people with serious mental illnesses (80). From a 2023 review, investigators noted that there are links between symptom severity and smoking; participants themselves saw a need and clinical benefit to track their habits alongside their mental health symptoms (81). As such, several apps to address smoking cessation within the community of serious mental illnesses have been developed (82–85). One such intervention demonstrated significantly higher engagement metrics and improved usability among the population with serious mental illnesses while achieving superior sustained abstinence rates compared with those of standard apps (84). The engagement levels were further explored in another study, which demonstrated higher app engagement in a smoking cessation app intended specifically for the population with serious mental illnesses, compared with an app that was intended to be a self-educational guide for individuals with general tobacco use disorder (86). Another innovative app used EMAs to track smoking behaviors and cessation attempts in real time, incorporated a social networking component to facilitate community building and peer support, and implemented a gamified mission-based structure that incentivized users to participate in health-promoting activities. Patients with serious mental illnesses found these interventions useful, and qualitative data favored the app’s approach to normalizing smoking relapse and encouraging self-efficacy in patients (85). Other incentives and interventions were also in the form of video recordings to confirm abstinence and were provided alongside a coach who was trained in cognitive-behavioral therapy (80).

SYMPTOM SELF-MANAGEMENT

There have also been several interventions designed to target self-management of schizophrenia symptoms, with some studies demonstrating feasible approaches or promising effectiveness of these novel interventions. In this context, self-management refers to the use of skills to reduce burdens and functional interference associated with schizophrenia symptoms. Many of these interventions combine momentary surveys with tailored responses based on user inputs (25, 87–89). These real-time, ecological, therapeutic interventions have shown promise for long-term condition management.

Several notable examples have demonstrated not only the feasibility of delivery but also the clinical effectiveness. One example uses brief momentary surveys to select brief interventions supporting self-management skills (37, 88, 90). This intervention has, across studies, demonstrated acceptability (37) among patients; clinical effectiveness equivalent to in-person, recovery-oriented services (88); and reduced costs relative to usual care (91). A similarly tailored intervention-based app that provides self-management support to young adults with early psychosis showed similar benefits in reducing symptoms and improving functioning in initial trials (25), although more recent work has not demonstrated benefits beyond a comparison clinical monitoring intervention (25). Several apps have enhanced traditional EMA and mobile health frameworks by incorporating peer support or other human support mechanisms to improve engagement and ensure the effective use of these tools. For example, in one app, daily questionnaires and quarterly mental health assessments were integrated with peer supporters who facilitated engagement with the app user while clinical staff monitored the survey responses for early relapse indicators. With a focus on feasibility, the researchers demonstrated that high data availability remained throughout the year-long study, suggesting that blended human support can help maximize the information gathered from DMHIs (89). Similarly, a qualitative study reported on an app that used peer specialists, who are trained individuals, to build rapport and promote self-management with users through direct messaging on the app (87). These dual-support approaches represent an advancement in intervention designs in their combinations of assessments with human connection outside of the clinical setting.

Beyond peer support interventions, another app was structured into four modules spanning symptom monitoring, problem solving, anxiety management, and goal setting to provide a holistic approach to self-management. Preliminary results reported significant improvements in users’ subjective perceptions of their well-being (92). Evidence further suggests that smartphones may be preferred by some over traditional models of clinic-based groups, with both groups reporting high satisfaction and similar clinical outcomes (88). Additional research on this app revealed that it helped improve significant moderate and severe depression symptoms (27).

A separate app utilizes feed, scheduling, and text-based functions, with its correlated study having no research attrition and was noted to be feasible with positive user feedback (93). This growing body of evidence suggests that diverse digital approaches to schizophrenia self-management can effectively complement traditional treatment modalities while enhancing accessibility and user engagement, unlike ever before.

DISCUSSION

Overall, there is significant potential in smartphone apps as a digital tool for patients with schizophrenia. Throughout our exploration of the literature, there has been evidence that implementing self-assessments, reminders, and psychoeducation tools can lead to clinical improvements or build emotional self-awareness, which can itself be clinically beneficial. Additionally, sensor collection such as sleep duration, social functioning, and GPS location has provided insight into behavioral anomalies and detecting worsening symptoms and even relapse. Thus, although clinical findings are preliminary, they show that smartphone apps are feasible and offer clinical potential for managing schizophrenia.

GAPS AND FUTURE DIRECTIONS

Although DMHIs show promise, it is important to acknowledge that challenges remain. One of the obstacles is the lack of real-world app options for users with schizophrenia (15). Several other limitations are specific to assessment approaches and digital phenotyping. Studies have identified low survey completion rates, use of various measures, and participants’ reluctance to engage because of privacy concerns; these limitations continue to limit the generalizability of these approaches (94). Furthermore, in terms of user surveys to monitor symptoms, there could be potential self-reporting biases where patients may significantly overestimate their scores in comparison with passerby judgments, as shown in a 2012 study (95). The regulatory landscape adds another layer of complexity. Currently, no schizophrenia-specific apps have received FDA clearance or approval for commercial distribution. Of note, there are ongoing efforts to seek FDA approval, such as the CT-155 study, which has reported emerging data that support digital interventions for managing negative symptoms in schizophrenia (96).

Both the FDA and the U.K. Medicines and Healthcare products Regulatory Agency are developing regulatory frameworks for DMHIs. Still, the absence of established guidelines creates additional liability concerns for clinicians who implement these technologies in standard care. To address these limitations, clinicians should provide comprehensive psychoeducation regarding the purpose and potential benefits and risks of digital assessment.

Another significant challenge associated with any DMHIs and monitoring tools concerns their extensive data collection practices. These apps may collect a comprehensive set of personal information—sometimes including geolocation, sensor metrics, and digital behavioral patterns—that can reveal sensitive mental health characteristics (97). This presents substantial risks for unauthorized exploitation, including targeted advertising, third-party commercialization, and potentially discriminatory automated decision-making processes, especially in commercially available apps (98). As DMHI use progresses, privacy and data security must be key components of DMHIs for both research and commercial purposes, so users have a clear and transparent understanding of how their information is collected and used. This can also mitigate patient concerns with data mistrust and increase app engagement.

Regarding intervention tools, there is still a limited number of peer-reviewed studies on DMHIs for psychosis and serious mental illness (13, 86), and in particular, fully powered, randomized, hybrid, or implementation trials. Many studies in this area focus on the feasibility and acceptability of these approaches. Even more limited than the extent of research is the availability of digital interventions for the general population. Although these tools are commercially available in different capacities, there is evidence that app engagement is lower when patients are given a standalone app. One study noted that only 4% of patients open and download a smartphone app when downloaded in real-world conditions (99). Furthermore, even if patients initially engage with the app, engagement with self-help apps declines over time (94). This has led to interest in the use of hybrid care models, which combine synchronous in-person or telehealth appointments with the use of asynchronous DMHIs, such as smartphone apps, wearable devices, or other digital tools, between sessions (100). Thus, future research should aim to investigate the best approaches to better integrate smartphone and wearable technologies into standard clinical treatments.

One expanding approach to integrating apps into care is to use a digital navigator—a human who can support digital literacy, aid in selecting apps, and facilitate app engagement between and during clinical sessions. The digital navigator role has been well studied for managing schizophrenia with several models (101, 102) and may partially address these limitations. Another evolving method has been combining AI into care. Preliminary research has noted that AI can significantly reduce the time required for clinicians to review data (103). At the same time, it is important to understand that although AI can improve clinical efficiency, its responses to mental health crises are currently limited, compared with the responses of human clinicians (104). Furthermore, data collection in DMHIs can be subject to interpretation or misinterpretation by AI tools. As with all technology, AI usage also faces data security and privacy challenges (105).

Given the increasing reliance on technology today in medicine, a lack of digital literacy can be a significant barrier to patient care and has been recognized as a social determinant of health (106). As such, poorer mental health outcomes can be indicators of lower socioeconomic status and poverty, which may exacerbate this digital isolation (107). Furthermore, patients with schizophrenia are shown to be more socially excluded, compared with their typical peers, because of a lack of knowledge, further emphasizing the need for increased education in digital literacy and exposure to digital tools to adjust to the status quo (108). To add, the impact of digital exclusion on patients with schizophrenia can be multifaceted. For instance, it can directly affect care by limiting the patient’s utility of telehealth or digital tools (109), or it can exacerbate isolation and loneliness (110). In one study, researchers found that although about 86% of their participants with bipolar disorder or psychosis spectrum disorders owned a smartphone, 42% of them lacked skills, such as using device settings and remembering passwords, to use their phones more fully (111). Thus, there is a need to implement programs and provide further resources to mitigate the digital divide. As such, a digital literacy program called Digital Outreach for Obtaining Resources and Skills has shown that daily functional impairment in patients with serious mental illness in local mental health centers improved after 8 weeks of program participation (112).

CONCLUSIONS

An increasing array of digital treatment options is available for managing schizophrenia that can facilitate improvements in daily functioning, symptom recognition, and overall quality of life. Evidence supports that these approaches are feasible and acceptable to people with schizophrenia. A wealth of pilot data also supports the preliminary efficacy of these approaches, but less is known about real-world engagement outside of studies and real-world clinical integration. However, clinicians today should be open to using apps in care and consider them as an expanding suite of adjunctive clinical tools that can support a broad range of clinical targets, ranging from medication adherence to social functioning.