Original Paper
Abstract
Background: Evidence-based digital therapeutics represent a new treatment modality in mental health, potentially providing cost-efficient, accessible means of augmenting existing treatments for chronic mental illnesses. CT-155/BI 3972080 is a prescription digital therapeutic under development as an adjunct to standard of care treatments for patients 18 years of age and older with experiential negative symptoms (ENS) of schizophrenia. Individual components of CT-155/BI 3972080 are designed based on the underlying principles of face-to-face treatment. A positive therapeutic alliance between patients and health care providers is linked with improved clinical outcomes in mental health. Likewise, establishing a similar therapeutic alliance with a digital therapeutic (ie, digital working alliance [DWA]) may be important for engagement and treatment effectiveness of this modality.
Objective: This study aimed to investigate the establishment and maintenance of a DWA between a beta version of CT-155/BI 3972080 (CT-155 beta) and adults with ENS of schizophrenia.
Methods: Two multicenter, exploratory, single-arm studies (study 1: CT-155-C-001 and study 2: CT-155-C-002) enrolled adults with schizophrenia and ENS receiving stable antipsychotic medication (≥12 weeks). Participants had access to CT-155 beta and were presented with daily in-app activities during a 3-week orientation phase that included lessons designed to facilitate building of a DWA. In study 2, the 3-week orientation phase was followed by an abbreviated active 4-week phase. Digital literacy at baseline was evaluated using the Mobile Device Proficiency Questionnaire (MDPQ). The mobile Agnew Relationship Measure (mARM) was used to assess DWA establishment after 3 weeks in both studies, and after 7 weeks in study 2 to assess DWA maintenance. Participant safety, digital literacy, and correlations between negative symptom severity and DWA were assessed in both studies.
Results: Of the enrolled participants, 94% (46/49) and 86% (43/50) completed studies 1 and 2, respectively. Most were male (study 1: 71%, 35/49; study 2: 80%, 40/50). The baseline digital literacy assessed through MDPQ score was comparable in both studies (study 1: mean 30.56, SD 8.06; study 2: mean 28.69, SD 8.31) indicating proficiency in mobile device use. After 3 weeks, mARM scores (study 1: mean 5.16, SD 0.8; study 2: mean 5.36, SD 1.06) indicated that a positive DWA was established in both studies. In study 2, the positive DWA established at week 3 was maintained at week 7 (mARM: mean 5.48, SD 0.97). There were no adverse events (AEs) in study 1, and 3 nonserious and nontreatment-related AEs in study 2.
Conclusions: A positive DWA was established between participants and CT-155 beta within 3 weeks. The second 7-week study showed maintenance of the DWA to the end of the study. Results support the establishment and maintenance of a DWA between adults with ENS of schizophrenia and a beta version of CT-155/BI 3972080, a prescription digital therapeutic under development to target these symptoms.
Trial Registration: Clinicaltrials.gov NCT05486312; https://clinicaltrials.gov/study/NCT05486312
doi:10.2196/64959
Keywords
Introduction
The therapeutic alliance was defined by Bordin and colleagues in 1979 as a measure of the quality of the relationship between a patient and health care provider, and its pivotal influence on clinical outcomes is well documented and supported [
- ]. The following three main components constitute the therapeutic alliance: (1) the patient-provider bond, (2) a collaborative approach to assignment of therapeutic tasks, and (3) agreement on therapeutic goals [ ]. In face-to-face therapy, the quality of this working alliance predicts clinical outcomes independently of the type of implemented psychotherapy or assessed outcome measures [ - ]. Several studies suggest that there is an association between a positive therapeutic alliance and improved outcomes in people with mental illness, including schizophrenia. For example, there is evidence that maintaining a positive alliance is associated with adherence to schizophrenia medication [ , ], that a positive alliance predicts improved overall psychotic symptomatic outcomes [ , ], and that strong alliances are associated with better treatment engagement and improvements in schizophrenia symptoms [ ].Schizophrenia is a chronic debilitating psychiatric disorder that occurs in approximately 1 in every 300 people worldwide [
]. Symptoms generally emerge in late adolescence or early adulthood and are categorized as positive, negative, or cognitive [ , ]. Positive symptoms, such as delusions, hallucinations and disorganized thinking, represent an excess or distortion of normal functions, and tend to relapse and remit. Negative symptoms involve reductions or absences in normal emotional and social functioning. Cognitive symptoms affect memory, attention and executive function. Both negative and cognitive symptoms being chronic in nature contribute substantially to the significant emotional and socioeconomic burden associated with schizophrenia [ - ]. Experiential negative symptoms (ENS) are a key subset of negative symptoms characterized by deficits in pleasure (anhedonia), motivation (avolition), and social interest (asociality), and have a profound impact on functional outcomes [ , ].The American Psychiatric Association practice guidelines for the treatment of people with schizophrenia highlight the benefits of evidence-based psychosocial treatments, social skills training and psychoeducation as adjunct therapies to medication [
]. To date there is no treatment approved by the US Food and Drug Administration (FDA) for negative symptoms. Despite promising evidence for psychological approaches including cognitive behavioral therapy and cognitive remediation in treating negative symptoms [ - ], people with schizophrenia face significant barriers to engaging in psychosocial therapy. These include symptom-related barriers, stigma and social isolation, and insufficient health care resources [ - ], all of which may contribute to a low uptake of psychosocial therapy in people with schizophrenia [ - ].Digital therapeutics (DTx) have the potential to bridge this gap by broadening access to adjunctive psychosocial therapies and facilitating greater frequency of exposure in a way that is flexible and convenient for those receiving treatment [
, ]. DTx are defined by the International Organization for Standardization as any evidence-based “health software intended to treat or alleviate a disease, disorder, condition, or that has a demonstrable positive therapeutic impact on a patient’s health” [ ]. For DTx intended as treatments for vulnerable populations such as those with serious mental illness, it is particularly important that they are regulated, rigorously tested, and found to have proven effectiveness and strong safety profiles. Recent studies evaluating novel DTx in the form of smartphone apps have shown this modality to be acceptable and feasible to implement in individuals with chronic psychiatric disorders such as schizophrenia [ - ]. While there is evidence to suggest that the therapeutic alliance can be established and maintained in remote online settings of psychotherapy [ , , ] it is not fully understood if individuals with severe mental illness, such as schizophrenia, can establish and maintain a therapeutic alliance with a DTx [ - ]. Importantly, while recent studies in people with schizophrenia evaluated the feasibility of DTx they did not assess the establishment of a therapeutic alliance [ , , ].We conducted 2 independent studies to assess the establishment and maintenance of a digital working alliance (DWA) between adults with ENS of schizophrenia and a beta version of CT-155/BI 3972080 (hereafter referred to as CT-155 beta), a novel software accessible on mobile phones under development to treat ENS of schizophrenia adjunctive to standard of care. In December 2023, CT-155/BI 3972080 was granted breakthrough device designation by the FDA [
]. In the studies reported here, we assessed whether it is feasible to establish and maintain a positive DWA between people living with ENS of schizophrenia and CT-155 beta, and if the strength of the DWA correlates or differs according to severity of negative symptoms, age, or race.Methods
Study Design
Two independent single-arm, multicenter, exploratory studies (study 1: CT-155-C-001 and study 2: CT-155-C-002/NCT05486312) were conducted across 10 sites in the United States. Patient informed consent was obtained at an initial in-person screening visit, during which site personnel assisted with the download and setup of CT-155 beta onto participants’ personal smartphone devices. Eligibility was confirmed and CT-155 beta was activated at an in-person baseline visit on Day 1. Both studies comprised a screening period, a treatment period, and a follow-up period (
). The treatment period comprised a 3-week orientation phase in study 1 and study 2, with an additional 4-week active phase in study 2 (further details in Intervention section). Both studies concluded with a follow-up period during which a remote teleconference follow-up visit occurred ( ).
Participants
Eligible participants were 18-64 years old (study 1) and ≥18 years old (study 2) with a primary diagnosis of schizophrenia for at least 1 year as per the ICD-10 (International Classification of Diseases, Tenth Revision; study 1) [
], and the DSM-5 (Diagnostic and Statistical Manual of Mental Disorders [Fifth Edition]; studies 1 and 2) [ ], with moderate-to-severe ENS (defined as a score of ≤30 on the Motivation and Pleasure–Self Report [MAP-SR]) [ ], and receiving a stable dose of a maximum of 2 different antipsychotic medications for at least 12 weeks before screening. Exclusion criteria included a DSM-5 or ICD-10 diagnosis other than schizophrenia, or a substance or alcohol use disorder, excluding caffeine and nicotine. People receiving clozapine, haloperidol, or who had received psychotherapy within 12 weeks of screening were also excluded. People with active prominent positive symptoms that, in the opinion of the investigator, would preclude effective engagement in treatment for negative symptoms were also excluded. The complete list of eligibility criteria for both studies is included in Table S1 in .Intervention
CT-155/BI 3972080 is a novel software accessible on mobile devices, designed to treat the ENS of schizophrenia and under development for use adjunctive to standard of care treatments for schizophrenia. As such CT-155/BI 3972080 is currently FDA regulated as an investigational device, more specifically, a software as a medical device [
]. The development of the software was informed by an iterative patient-centered design process, which consisted of 2 phases during which feedback was captured from 4 peer support specialists with lived experiences of schizophrenia, and 15 patient panel participants with a diagnosis of schizophrenia (refer to Supplementary Methods in for further details).CT-155 beta has 3 core phases. First, the 3-week orientation phase in each study was designed based on the core features of in-person therapeutic alliance to encourage daily adherence to lessons and activities by providing schizophrenia-specific psychoeducation and introducing core therapeutic skills (
) [ ]. An empathic style of communication informed by patient feedback, provision of schizophrenia-specific psychoeducation, and introducing core therapeutic skills are patient-centric features incorporated into CT-155. Like therapists getting to know their patient, this phase focuses on DWA establishment through asking questions and rapport building, followed by collaborative treatment approach and goal definition. These patient-centric features that support DWA are subsequently integrated into all therapeutic components to optimize the experience by infusing an interactive, empathic, knowledgeable, and personally meaningful nature to the in-app experience that builds confidence for achieving treatment goals and is critical to treatment outcome. Second, study 2 included an active phase, during which participants receive access to CT-155 beta for an additional 4 weeks after the 3-week orientation phase ( ), designed to reinforce orientation phase components and introduce adaptive goal setting [ , ]. The active phase comprises evidence-based digitized therapeutic techniques leveraging the underlying principles of face-to-face treatment [ - ]. Examples of the therapeutic techniques used include adaptive goal setting [ , ] and social skills training [ ]. Adaptive goal setting is focused on delivering interventions known to target negative symptoms in important functional areas (ie, social items, work and school items, and recreation items). Finally, during a brief follow-up phase, participants from both studies complete qualitative exit interviews to help understand the user experience and inform further CT-155/BI 3972080 development.
Protocol Deviations
In study 1, 12 major protocol deviations occurred with 10 participants (Table S2 in
). Deviations included assessment performed outside of protocol window (n=6), CT-155 beta was uninstalled (n=2), failed inclusion criteria (n=2), and visit occurred outside of protocol window (n=1). One participant with failed inclusion deviation was deemed ineligible and terminated the study early. One participant had 3 major deviations (uninstalled CT-155 beta before week 4, schizophrenia diagnosis confirmed <1 year before enrollment, and antipsychotic added <12 weeks before screening) was included in the intent-to-treat-population but excluded from the per protocol population. In study 2, 1 major protocol deviation occurred with 1 participant (visit outside protocol window; Table S2 in ).Assessments
Patient Digital Literacy
The digital literacy of participants at baseline was assessed using the self-administered Mobile Device Proficiency Questionnaire (MDPQ). While developed for assessing mobile proficiency in older adults, the MDPQ has also been used in health studies to assess proficiency in individuals with subjective cognitive complaints and those with cirrhosis [
- ]. The MDPQ consists of 46 items rated on a 5-point Likert scale and assesses seven aspects of digital literacy: (1) mobile device basics, (2) communication, (3) data and file storage, (4) internet, (5) entertainment, (6) privacy, and (7) troubleshooting and software management [ ]. Participants were asked to rate their ability to perform specific digital tasks on a scale of 1 (never tried it) to 5 (very easily performed). Subscale scores were obtained by averaging responses within each subscale and overall scores were calculated as the sum of mean subscale scores. The overall scores fall in a range of 8 (low) to 40 (great ability to perform a number of tasks with a mobile device). Internal reliability of the MDPQ was demonstrated with a Cronbach α score of 0.975.Experiential Negative Symptom Severity Assessment
Participants completed the MAP-SR at screening. The MAP-SR is a 15-item validated self-administered tool that assesses the motivation and pleasure domain of negative symptoms in people with psychotic disorders and scores are rated on a 5-point Likert scale, where low scores reflect greater severity [
]. In addition, the Clinical Assessment Interview for Negative Symptoms–Motivation and Pleasure (CAINS-MAP) assessment was conducted at baseline in both studies, and at week 7 in study 2, to evaluate ENS symptom severity. The CAINS-MAP is a validated 13-item clinician-administered interview-based assessment with items scored on a 4-point scale, with lower scores indicating lower negative symptom severity [ ].Digital Working Alliance Assessment
The DWA was assessed using the self-administered 25-item mobile Agnew Relationship Measure (mARM) questionnaire. The mARM is a measure used to evaluate the DWA of mobile health interventions across 5 therapeutic alliance domains: bonding, partnership, confidence, openness, and initiative [
]. It provides an alliance measure with good content and face validity suitable for assessing alliance with DTx [ ]. The mARM includes five subscales to represent the dimensions of the DWA: (1) the bond subscale focuses on the perceived acceptance, support, friendliness, and understanding the patient develops with the app, (2) the partnership subscale measures the extent of the perceived collaboration of the patient with the therapeutic tasks, (3) the confidence subscale focuses on patient optimism and their belief in the competency of the app, (4) the openness subscale assesses the perceived freedom to disclose personal concerns without fear of judgment or embarrassment, and (5) the client initiative subscale focuses on the client willingness to take responsibility for their treatment progress.The mARM was administered at week 1, week 2, and week 3 in study 1 to assess the establishment of the DWA during the orientation phase. In study 2, it was administered at week 3 to assess DWA establishment and again at week 7 to evaluate the maintenance of DWA during the abbreviated active phase. The overall mARM score is calculated at each time point as the mean of all subscale scores and is interpreted along a Likert scale ranging from 1 (strongly disagree) to 7 (strongly agree), with 4 equating to “neutral,” and scores higher than the neutral midpoint suggesting a positive DWA [
- ]. The potential association between various factors including age, race, and ENS severity, and the DWA was also assessed.Safety
Safety was assessed by recording adverse events (AEs), serious AEs, adverse device effects (ADEs), serious ADEs, and discontinuations from the study due to AEs, from signing of informed consent through to the end of study period.
Sample Size and Statistical Analyses
Both studies were exploratory in nature and sample size and analyses were selected for optimal assessment of DWA establishment and maintenance between schizophrenia patients and CT-155 beta. Data presented are from the intent-to-treat population, defined as all participants who were enrolled in each study to receive treatment with CT-155 beta. Descriptive statistics were evaluated for all study variables. To determine if DWA was related to ENS, age, or engagement, correlation analyses between mARM scores and baseline CAINS-MAP, age at screening, and session completion were performed using a Spearman rank-order correlation analysis and correlation coefficients were calculated together with the 95% 2-sided confidence intervals. To investigate effects of race, variance of the mean overall mARM score between Black or African American and participants of all other races were compared using an F test. To assess whether digital literacy was related to ENS and engagement, correlation analyses between baseline MDPQ scores and CAINS-MAP and session completion scores were also performed using a Spearman rank-order correlation analysis. Engagement was assessed using the number of sessions completed during the study whereby a session was defined as any interaction within CT-155 beta for >60 seconds.
Ethical Considerations
The study protocols and study materials were reviewed by the Western Copernicus Group independent institutional review board (approval numbers: 20215315 [study 1] and 20220609 [study 2]). The studies were conducted in compliance with the respective clinical study protocols, in accordance with the principles of the Declaration of Helsinki [
], the International Council for Harmonisation of Good Clinical Practice [ ], and applicable US regulatory requirements. Patient informed consent was obtained from all participants. Participants were deidentified and assigned a unique identifier to protect personal data and privacy throughout both studies. Throughout both studies, a study team maintained compliance with applicable laws and accepted security standards, including the Health Insurance Portability and Accountability Act of 1996, the U.S. National Institute of Standards and Technology Framework, and the Service Organization Control Type 2 controls. The compliance program was designed to (1) ensure the security, confidentiality, integrity, and availability of the assets and other sensitive information that was collected, used, and maintained; (2) protect against any anticipated threats or hazards to the security, integrity, or availability of such information; (3) maintain information security controls that were appropriate to Click Therapeutics size, scope, and business; (4) maintain safeguards and controls to protect information from loss, theft, destruction, unauthorized manipulation, disclosure, or unavailability. Participants were offered up to US $465 and US $735 for partaking in study 1 and study 2, respectively. Incentive values reflect fair market value; variance in incentive values is reflective of the differing observation periods between the 2 studies.Results
Patient Demographics and Baseline Characteristics
In study 1, a total of 49 participants were enrolled and 46 (94%) completed the study. In study 2, a total of 50 participants were enrolled and 43 (86%) completed the study. Participant disposition is presented in
. The median age of enrolled participants was 46 (range 18-64) years in study 1 and 53.5 (range 23-64) years in study 2. Of those enrolled, most were male (study 1: 35/49, 71%; study 2: 40/50, 80%), and more than half were Black or African American (study 1: 27/49, 55%; study 2: 29/50, 58%). The majority never attended college (study 1: 31/49, 63%; study 2: 32/50, 64%) and most had a self-reported annual income of <US $25,000 (study 1: 46/49, 94%; study 2: 47/50, 94%; ). The median MAP-SR score at screening was 25.0 (IQR 17.0-28.0) in study 1 and 15.0 (IQR 7.0-23.0) in study 2. At baseline, the median overall CAINS-MAP score was 2.4 (IQR 2.0-2.9) in study 1 and 2.6 (IQR 1.6-2.9) in study 2. Baseline demographics and characteristics of study 2 participants are also being reported in another manuscript reporting separate outcomes.
Baseline characteristics | Total enrolled in study 1 (n=49) | Total enrolled in study 2 (n=50) | |||
Sex, male, n (%) | 35 (71) | 40 (80) | |||
Race, n (%) | |||||
Black or African American | 27 (55) | 29 (58) | |||
White | 21 (43) | 15 (30) | |||
Asian | 1 (2) | 3 (6) | |||
American Indian or Alaska Native | 0 (0) | 1 (2) | |||
Other | 0 (0) | 2 (4) | |||
Ethnicity, n (%) | |||||
Hispanic or Latino | 13 (27) | 12 (24) | |||
Not Hispanic or Latino | 36 (74) | 38 (76) | |||
Education, n (%) | |||||
Less than high school | 10 (20) | 7 (14) | |||
High school | 21 (43) | 25 (50) | |||
Some college | 10 (20) | 12 (24) | |||
College degree | 8 (16) | 6 (12) | |||
Annual income (US $), n (%) | |||||
<25,000 | 46 (94) | 47 (94) | |||
25,000-49,999 | 3 (6) | 3 (6) | |||
Screening assessments | |||||
MAP-SRa, median (IQR) | 25.0 (17.0-28.0) | 15.0 (7.0-23.0) | |||
Baseline assessments | |||||
MDPQb, median (IQR) | 32.0 (23.2-39.1) | 30.1 (21.4-35.5) | |||
CAINS-MAPc overall score, median (IQR) | 2.4 (2.0-2.9) | 2.6 (1.6-2.9) |
aMAP-SR: Motivation and Pleasure scale–Self Report.
bMDPQ: Mobile Device Proficiency Questionnaire.
cCAINS-MAP: Clinical Assessment Interview for Negative Symptoms Motivation and Pleasure subscale.
The median overall MDPQ score was comparable in both studies at baseline (study 1: 32.01, IQR 23.20-39.07; study 2: 30.10, IQR 21.36-35.46), indicating proficiency in the use of mobile devices before accessing CT-155 beta. A summary of MDPQ overall and subscale scores is presented in
. Overall, MDPQ subscales scores revealed proficiency across most operations from the basic task of “turning the device on and off” and communication tasks, such as “open emails” to the more advanced tasks such as those related to privacy “Setting up a password to lock/unlock the device,” and tasks related to the troubleshooting and software management, such as “Restarting the device when it is frozen, or not working right.” However, participants in both studies were slightly less proficient with data and file storage tasks, such as “Transfer information (files such as music, pictures, documents) from portable device to computer” ( ). There was no correlation between MDPQ and baseline CAINS-MAP in study 1 (ρ=0.01, 95% CI –0.31 to 0.34) and study 2 (ρ=–0.01, 95% CI –0.29 to 0.28). Furthermore, there was no correlation between MDPQ and degree of participant engagement with CT-155 beta (number of completed sessions) in study 1 (ρ=0.00, P=.99) and study 2 (ρ=–0.18, P=.22).MDPQa scores | Study 1, median (IQR) (n=42) | Study 2, median (IQR) (n=49) | |||
Overall | 32.01 (23.20-39.07) | 30.10 (21.36-35.46) | |||
Subscale scores | |||||
Mobile device basics | 4.61 (4.00-5.00) | 4.44 (3.67-4.89) | |||
Communication | 3.94 (2.33-5.00) | 3.67 (2.56-4.56) | |||
Data and file storage | 3.00 (1.67-5.00) | 3.00 (1.00-4.00) | |||
Internet | 4.25 (3.38-5.00) | 3.88 (2.50-4.88) | |||
Calendar | 4.00 (2.67-5.00) | 4.00 (2.33-4.67) | |||
Entertainment | 4.70 (3.40-5.00) | 4.20 (3.40-5.00) | |||
Privacy | 4.00 (3.00-5.00) | 4.00 (2.50-5.00) | |||
Troubleshooting and software management | 4.00 (2.75-5.00) | 4.00 (2.60-4.80) |
aMDPQ: Mobile Device Proficiency Questionnaire.
Digital Working Alliance Summary
The mean overall mARM score in study 1 was 5.15 (SD 0.74) indicating establishment of a positive DWA by week 1 that did not significantly change by week 3, 5.16 (SD 0.77). Similarly, in study 2, participants also formed a positive DWA by week 3, with a mean overall mARM score of 5.36 (SD 1.06;
). Furthermore, the positive DWA in study 2 was maintained through the subsequent 4-week treatment period with mean overall mARM score of 5.48 (SD 0.97) at week 7 ( ). The individual mARM subscale scores (Bond, Partnership, Confidence, Openness, and Client initiative) remained consistent and above “neutral” (score of 4) at the end of the DWA and core-skills-building phase at week 3 in both studies demonstrating that participants successfully formed a positive DWA with CT-155 beta across all subscale categories ( ). In study 2, no significant difference in mARM subscale scores were seen from weeks 3 to 7 (refer to for subscale scores). Overall, at the individual level, positive correlations (ρ=0.27-0.84) were observed between subscale scores at week 3 and those at week 7 (Table S3 in ).mARMa scores | DWAb formation (week 3) | DWAb maintenance (week 7) | |||||
Study 1, mean (SD) (n=40) | Study 2, mean (SD) (n=45) | Study 2, mean (SD) (n=44) | |||||
Overall | 5.16 (0.77) | 5.36 (1.06) | 5.48 (0.97) | ||||
Subscale scores | |||||||
Bond | 5.67 (1.03) | 5.63 (1.31) | 5.82 (1.19) | ||||
Partnership | 4.83 (0.72) | 5.67 (1.32) | 5.85 (1.18) | ||||
Confidence | 5.49 (1.04) | 5.68 (1.27) | 5.62 (1.23) | ||||
Openness | 4.94 (1.06) | 4.83 (1.25) | 5.16 (1.04) | ||||
Client initiative | 4.56 (0.75) | 4.60 (0.99) | 4.64 (0.93) |
amARM: mobile Agnew Relationship Measure.
bDWA: digital working alliance.
Correlations Between DWA and ENS Severity, Engagement, Age, and Race
In both studies, the strength of DWA at week 3, as assessed by the mean overall mARM score, was similar in participants with severe and moderate ENS (
). In study 1, this finding was consistent at each week across the 3-week study. Accordingly, there was no significant correlation between mARM overall scores at week 3 and the baseline CAINS-MAP total score in study 1 (ρ=–0.16, P=.36) and study 2 (ρ=–0.13, P=.41; Table S4 in ).mARMa scores | Study 1b | Study 2 | |||||||||||||
Week 1 | Week 2 | Week 3 | Week 3 | ||||||||||||
Moderate (n=29), mean (SD) | Severe (n=10), mean (SD) | Moderate (n=30), mean (SD) | Severe (n=10), mean (SD) | Moderate (n=30), mean (SD) | Severe (n=10), mean (SD) | Moderate (n=40), mean (SD) | Severe (n=4), mean (SD) | ||||||||
Overall | 5.21 (0.75) | 4.96 (0.73) | 5.19 (0.82) | 5.05 (0.82) | 5.21 (0.79) | 5.00 (0.73) | 4.81 (0.94) | 4.99 (0.70) | |||||||
Subscale scores | |||||||||||||||
Bond | 5.61 (0.95) | 5.14 (1.10) | 5.72 (1.16) | 5.44 (0.82) | 5.76 (1.02) | 5.40 (1.05) | 5.59 (1.37) | 6.00 (0.95) | |||||||
Partnership | 4.90 (0.82) | 4.84 (0.67) | 4.80 (0.94) | 4.94 (0.61) | 4.83 (0.78) | 4.84 (0.55) | 4.56 (0.93) | 4.83 (1.40) | |||||||
Confidence | 5.65 (0.95) | 5.29 (0.96) | 5.55 (0.92) | 5.34 (1.06) | 5.52 (1.06) | 5.40 (1.01) | 4.63 (1.01) | 4.50 (0.62) | |||||||
Openness | 4.97 (1.06) | 4.65 (1.23) | 5.03 (1.10) | 4.85 (1.13) | 5.05 (1.13) | 4.60 (0.77) | 4.21 (1.10) | 4.19 (0.90) | |||||||
Client initiative | 4.57 (0.88) | 4.65 (0.83) | 4.57 (0.75) | 4.38 (0.78) | 4.60 (0.62) | 4.43 (1.07) | 4.96 (1.16) | 5.60 (1.46) |
amARM: mobile Agnew Relationship Measure.
bTo minimize participant burden, mARM was assessed at weeks 1 and 2 in study 1 only. Severity of negative symptoms based on CAINS-MAP score: moderate <29 points; severe ≥29 points.
Strength of DWA correlated positively with the number of completed sessions by participants at week 3 in study 2 (ρ=0.34, P=.02), but was not significantly correlated in study 1 (ρ=0.22, P=.19;
). Neither age (study 1, ρ=0.31, P=.05; study 2, ρ=0.15, P=.32) nor race significantly correlated with overall mARM scores (study 1, F test=1.1928, P=.72; study 2, F test=1.1615, P=.75; Table S4 in ). In this study population, there was no clear relationship between digital literacy and DWA scores. Assessed by quartile MDPQ scores, mean mARM scores in the highest and lowest quartiles were study 1: 4.9 (SD 0.9) and 5.7 (SD 0.4) and study 2: 5.8 (SD 0.9) and 4.8 (SD 1.2), respectively (Table S5 in ).
Safety Data Across Two Studies
No ADEs, AEs, or serious AEs were reported during study 1. In study 2, three AEs were reported by 2 participants, none of which were considered severe or related to CT-155 beta use, nor led to study discontinuation. AEs reported were a sinus infection (n=1), arthralgia (n=1), and a rash (n=1). The sinus infection and arthralgia resolved after 6 days; the rash remained present at end of follow-up.
Discussion
Our findings from 2 independent single-arm multicenter exploratory studies show that a DWA can be established and maintained between people with schizophrenia and a smartphone-based digital therapeutic, namely the abbreviated beta version of CT-155/BI 3972080, a prescription digital therapeutic to target ENS adjunctive to standard of care treatments for schizophrenia. During the orientation phase in study 1, participants established a positive DWA with CT-155 beta. This was replicated in a separate and independent cohort of participants at the end of an equivalent orientation phase in study 2. Furthermore, the positive DWA was maintained during the additional 4-week active phase in study 2, as demonstrated by an equivalent positive mARM score at week 7.
While a strong therapeutic alliance is linked to successful clinical outcomes for in-person therapy, the relationship between DWA, engagement, and outcomes requires further investigation [
- ]. The concept of a therapeutic alliance with DTx is gaining traction in line with the rapid expansion of digital mental health research and implementation; however, the role and relevance of the bond, a core feature of the in-person therapeutic alliance, is somewhat unclear for DTx [ ] and not investigated in sufficient detail. Our results add to a growing body of evidence demonstrating that a therapeutic alliance can be established between people with serious mental illnesses and DTx. For example, in recent studies evaluating the benefits of conversational agents (or chatbots) that deliver cognitive behavioral therapy for symptoms of depression, anxiety, and substance use disorder, participants reported high acceptability and affective bond formation [ - ]. Another investigation of a digital health intervention for people with early psychosis, based on cognitive behavioral therapy principles and delivered through a smartphone app, demonstrated that engagement was significantly and positively associated with therapeutic alliance, as assessed by mARM [ ]. However, measures to assess bond formation varied with a short-revised version of the Working Alliance Inventory and a Working Alliance Questionnaire (based on the Working Alliance Inventory) being used, making comparisons across studies difficult.Although there are several lines of evidence demonstrating that a therapeutic alliance is possible for people with schizophrenia and is associated with improved outcomes, there are some factors related to the disorder itself, such as negative symptoms, lack of insight and self-stigma, that can adversely impact the therapeutic alliance between patient and therapist [
- ]. One could therefore speculate that patients who formed and valued a therapeutic alliance with a DTx may accept this additional therapeutic modality for the treatment of schizophrenia. Notably, in our studies, there was no correlation between the severity of negative symptoms at baseline and the establishment or maintenance of a DWA, indicating that even the participants with the most severe symptoms could form and maintain an alliance with CT-155 beta. In line with previous reports examining associations between age and therapeutic alliance we also did not observe any correlation between age and the DWA strength [ , , , ]. Similarly, we did not observe any correlation between race and strength of DWA. While some reports emphasize the importance of ethnic matching between patients and clinicians in improving therapeutic alliance [ , ], the lack of correlation with race here highlights the adaptable nature of a DTx modality to fit the needs of different communities.Although not specifically assessed, the user-centered design process that guided the development of CT-155/BI 3972080 could have improved overall participant engagement and effectiveness. A recent systematic review showed that making end-users central to the product development process leads to enhanced cultural sensitivity, increased acceptance and engagement [
- ]. The review also provided some key recommendations for coproduction, including stakeholder involvement at all stages of development, which was implemented into the development of CT-155/BI 3972080. Considering the 3 pillars of the therapeutic alliance involve enhancing the patient-provider bond, shared goals and adopting a collaborative approach in the therapeutic approach, the patient-centered development process may have contributed to the establishment of a positive DWA achieved here.The participants in both studies demonstrated a reasonable level of mobile device proficiency. This finding is aligned with recent data from the United States showing that 70%-80% among people with severe mental illness own a smartphone [
- ]. However, smartphone ownership does not necessarily equate with digital literacy [ ]. In this study we confirmed that participating people with schizophrenia owning a smartphone showed digital literacy similar to that from the general population [ ], but greater than that reported for people with serious mental illness [ ]. Indeed, a study in the United Kingdom showed that 42.2% of people with serious mental illness had no foundation digital skills [ ], while electronic health literacy has been described as either moderate or low in 2 independent cohorts of adults with schizophrenia spectrum disorders [ ]. However, one should consider that the younger generation diagnosed with severe mental illness and schizophrenia are now digital natives and past research may not reflect current trends.Limitations of these studies include their small sample size, single-arm design, and exploratory nature. While the sample size was limited, the demographics of participants were reflective of the general schizophrenia population including a higher incidence in males [
, ], the majority being Black or African American [ ] and a level of education no greater than high school [ ]. In addition, almost all (94%) participants in this study reported an annual income under US $25,000, which likely reflects low employment rates in people with schizophrenia [ ]. Furthermore, while all participants met inclusion criteria, there was a difference in median MAP-SR score at screening between study 1 and 2. However, as no correlation between ENS severity and DWA was observed in both studies, the difference in baseline MAP-SR scores could be linked to broader population compared in 2 studies. While only a few out of protocol window deviations were recorded, they may impact the DWA assessments over time through asking questions and rapport-building. Preliminary data from these studies support the further evaluation of CT-155 in an ongoing large phase III trial (CONVOKE; NCT05838625). Although the mARM assessment possesses good face and content validity [ ], further validation of this assessment for the measurement of DWA in severe chronic mental illness is essential to inform further optimization of this assessment for use in clinical trials [ ]. In addition, establishment and assessment of therapeutic alliance between participants and digital devices or apps is relatively new. There is a need to use standardized and validated scales with psychometric validation to assess DWA in future studies which will also make it feasible to compare data and outcomes. Finally, it should also be noted that the eligibility criteria for the studies mandated that participants had a smartphone, an email address, and regular access to the internet through mobile data plan, Wi-Fi or both. Accordingly, the association of digital literacy measured by MDPQ with DWA and engagement should be interpreted in the context of this study population. The study inclusion criteria for participants to own their own mobile device may have biased toward a more digital proficient study population and therefore not reflective of patients who do not own a mobile device and who potentially may have a lower device proficiency. As such, future studies are warranted to understand the impact of lower digital proficiency on adherence to a digital therapeutic and on establishing DWA. While recent evidence suggests that rates of smartphone ownership among adults with severe mental illness (including schizophrenia) are comparable to the general adult US population [ , ], it is encouraging to see high levels of mobile device proficiency in people living with schizophrenia and owning smartphones.The results from these 2 studies confirm that a positive DWA can be formed within a 3-week period and this DWA was maintained for the entire 7-week duration of study 2, demonstrating that a therapeutic alliance can be formed with a DTx in people living with schizophrenia. Importantly, the formation of a positive DWA was not limited by severe ENS, supporting its potential benefits for those experiencing severe symptoms. Furthermore, people with schizophrenia who participated in these studies demonstrate good digital literacy and acceptance of CT-155 beta for the treatment of ENS. The development of a DTx with proven acceptability, safety, and effectiveness profiles as an adjunctive treatment for ENS of schizophrenia would represent a promising, accessible, scalable, and affordable treatment option for clinical and patient communities for whom the lack of effective treatments for ENS has presented significant challenges. These preliminary data support further evaluation of the potential importance of the patient-CT-155 DWA to therapeutic effectiveness in an ongoing large phase III trial (CONVOKE; NCT05838625). With the expansion of mobile device ownership, DTx offers a potential patient-centered, evidence-based therapeutic support 24 hours a day, 7 days a week. In addition, incorporated into standard of care, evidence based DTx may act as clinician extenders, increasing the breadth and depth of care provided. Understanding how best to engage and optimally implement DTx with people living with schizophrenia to have the greatest reach and impact is of great importance and may shape future clinical research. In addition, understanding who may benefit from a DTx, how to best introduce these tools to patients as part of the clinical workflow, and how to support continued engagement, are important future steps.
Acknowledgments
The authors would like to acknowledge and thank all study participants for contributing their time and effort to take part in these studies. The authors would also like to acknowledge Alankar Gupta and Eehwa Ung (employees of Click Therapeutics at the time of the study), as well as Uma Vaidyanathan (employee of Boehringer Ingelheim Pharmaceuticals, Inc., at the time of the study) for their respective contributions to the study. Editorial support in the form of initial preparation of the manuscript with input from all authors, and collation and incorporation of author feedback to develop subsequent drafts, assembling tables and figures, copyediting, and referencing was provided by Lieve Desbonnet, PhD, and Jonnie Plumb, PhD, of Avalere Health Global Limited, and was funded by Boehringer Ingelheim International GmbH. This work was funded by Boehringer Ingelheim (study identification numbers: study 1, CT-155-C-001; study 2, CT-155-C-002, ClinicalTrials.gov identifier: NCT05486312).
Data Availability
To ensure independent interpretation of clinical study results and enable authors to fulfill their role and obligations under the ICMJE (International Committee of Medical Journal Editors) criteria, Boehringer Ingelheim grants all external authors access to clinical study data pertinent to the development of the publication. In adherence with the Boehringer Ingelheim Policy on Transparency and Publication of Clinical Study Data, scientific and medical researchers can request access to clinical study data when it becomes available on Vivli - Center for Global Clinical Research Data [
], and earliest after publication of the primary manuscript in a peer-reviewed journal, regulatory activities are complete, and other criteria are met. Please refer to “Medical & Clinical Trials | Clinical Research | MyStudyWindow” [ ] for further information.Authors' Contributions
CS and CDC contributed equally as co-first authors. SEL and AP contributed equally as co-last authors. CDC, CS, MP, TC, and SEL contributed to the conceptualization of the study. CS, MP, OB, TC, and SEL were involved in the development of methodology. CS contributed to the software development. CS and OB contributed to data validation. AP, BDH, OB, and SEL were involved in formal analysis. AP, OB, and SEL contributed to study resourcing, and AP, BDH, CS, MP, OB, and SEL contributed to visualization of the work submitted for publication. OB contributed to data curation. AP, CS, CDC, and SEL supervised research activity and outputs. All authors contributed to the manuscript development and reviewed the final manuscript.
Conflicts of Interest
CT-155 beta was co-developed by Boehringer Ingelheim and Click Therapeutics Inc. This work was funded by Boehringer Ingelheim. CS, MP, OB and SEL are employees of Click Therapeutics, Inc. CDC is an employee of Boehringer Ingelheim International GmbH. TC was an employee of Click Therapeutics at the time of the study. AP and BDH are employees of Boehringer Ingelheim Pharmaceuticals, Inc; BDH was an employee of Click Therapeutics at the time of the study. AP is also affiliated with King’s College London, London, UK and the University of Washington, Seattle, WA, USA. Boehringer Ingelheim authors were involved in the study design, analysis and interpretation of the results, and the decision to submit.
Supplementary methods and tables
DOCX File , 50 KBReferences
- Bordin E. The generalizability of the psychoanalytic concept of the working alliance. Psychotherapy: Theory, Research & Practice. 1979;16(3):252-260. [CrossRef]
- Martin DJ, Garske JP, Davis MK. Relation of the therapeutic alliance with outcome and other variables: a meta-analytic review. J Consult Clin Psychol. 2000;68(3):438-450. [Medline]
- Ardito RB, Rabellino D. Therapeutic alliance and outcome of psychotherapy: historical excursus, measurements, and prospects for research. Front Psychol. 2011;2:270. [FREE Full text] [CrossRef] [Medline]
- Flückiger C, Del Re AC, Wampold BE, Horvath AO. The alliance in adult psychotherapy: a meta-analytic synthesis. Psychotherapy (Chic). 2018;55(4):316-340. [FREE Full text] [CrossRef] [Medline]
- Flückiger C, Del Re AC, Wampold BE, Symonds D, Horvath AO. How central is the alliance in psychotherapy? A multilevel longitudinal meta-analysis. J Couns Psychol. 2012;59(1):10-17. [CrossRef] [Medline]
- Baier AL, Kline AC, Feeny NC. Therapeutic alliance as a mediator of change: a systematic review and evaluation of research. Clin Psychol Rev. 2020;82:101921. [CrossRef] [Medline]
- Chang JG, Roh D, Kim C. Association between therapeutic alliance and adherence in outpatient schizophrenia patients. Clin Psychopharmacol Neurosci. 2019;17(2):273-278. [FREE Full text] [CrossRef] [Medline]
- McCabe R, Bullenkamp J, Hansson L, Lauber C, Martinez-Leal R, Rössler W, et al. The therapeutic relationship and adherence to antipsychotic medication in schizophrenia. PLoS One. 2012;7(4):e36080. [FREE Full text] [CrossRef] [Medline]
- Shattock L, Berry K, Degnan A, Edge D. Therapeutic alliance in psychological therapy for people with schizophrenia and related psychoses: a systematic review. Clin Psychol Psychother. 2018;25(1):e60-e85. [CrossRef] [Medline]
- Priebe S, Richardson M, Cooney M, Adedeji O, McCabe R. Does the therapeutic relationship predict outcomes of psychiatric treatment in patients with psychosis? A systematic review. Psychother Psychosom. 2011;80(2):70-77. [FREE Full text] [CrossRef] [Medline]
- Browne J, Wright AC, Berry K, Mueser KT, Cather C, Penn DL, et al. The alliance-outcome relationship in individual psychosocial treatment for schizophrenia and early psychosis: a meta-analysis. Schizophr Res. 2021;231:154-163. [CrossRef] [Medline]
- Schizophrenia. World Health Organization; 2022. URL: https://www.who.int/news-room/fact-sheets/detail/schizophrenia [accessed 2023-11-29]
- American Psychiatric Association (APA). Diagnostic and Statistical Manual of Mental Disorders fifth edition (DSM-5). Washington, D.C. American Psychiatric Association Publishing; 2013:1-140.
- Kahn RS, Sommer IE, Murray RM, Meyer-Lindenberg A, Weinberger DR, Cannon TD, et al. Schizophrenia. Nat Rev Dis Primers. 2015;1:15067. [CrossRef] [Medline]
- Kotzeva A, Mittal D, Desai S, Judge D, Samanta K. Socioeconomic burden of schizophrenia: a targeted literature review of types of costs and associated drivers across 10 countries. J Med Econ. 2023;26(1):70-83. [FREE Full text] [CrossRef] [Medline]
- Kadakia A, Catillon M, Fan Q, Williams GR, Marden JR, Anderson A, et al. The economic burden of schizophrenia in the United States. J Clin Psychiatry. 2022;83(6):22m14458. [FREE Full text] [CrossRef] [Medline]
- Solmi M, Seitidis G, Mavridis D, Correll CU, Dragioti E, Guimond S, et al. Incidence, prevalence, and global burden of schizophrenia - data, with critical appraisal, from the Global Burden of Disease (GBD) 2019. Mol Psychiatry. 2023;28(12):5319-5327. [CrossRef] [Medline]
- Kamil SH, Velligan DI. Caregivers of individuals with schizophrenia: who are they and what are their challenges? Curr Opin Psychiatry. 2019;32(3):157-163. [CrossRef] [Medline]
- Correll CU, Schooler NR. Negative symptoms in schizophrenia: a review and clinical guide for recognition, assessment, and treatment. Neuropsychiatr Dis Treat. 2020;16:519-534. [FREE Full text] [CrossRef] [Medline]
- Liemburg EJ, Enriquez-Geppert S, Wardenaar KJ, Bruggeman R, Aleman A, PROGR-S investigators, et al. GROUP investigators. Expressive deficits and amotivation as mediators of the associations between cognitive problems and functional outcomes: results from two independent cohorts. Schizophr Res. 2020;218:283-291. [CrossRef] [Medline]
- American Psychiatric Association (APA). Practice Guidelines for the Treatment of Patients With Schizophrenia - Third Edition. Washington, D.C. American Psychiatric Association Publishing; 2021.
- Cella M, Roberts S, Pillny M, Riehle M, O'Donoghue B, Lyne J, et al. Psychosocial and behavioural interventions for the negative symptoms of schizophrenia: a systematic review of efficacy meta-analyses. Br J Psychiatry. 2023;223(1):321-331. [FREE Full text] [CrossRef] [Medline]
- Barlati S, Nibbio G, Vita A. Evidence-based psychosocial interventions in schizophrenia: a critical review. Curr Opin Psychiatry. 2024;37(3):131-139. [FREE Full text] [CrossRef] [Medline]
- Riehle M, Böhl MC, Pillny M, Lincoln TM. Efficacy of psychological treatments for patients with schizophrenia and relevant negative symptoms: a meta-analysis. Clin Psychol Eur. 2020;2(3):e2899. [FREE Full text] [CrossRef] [Medline]
- Prytys M, Garety PA, Jolley S, Onwumere J, Craig T. Implementing the NICE guideline for schizophrenia recommendations for psychological therapies: a qualitative analysis of the attitudes of CMHT staff. Clin Psychol Psychother. 2011;18(1):48-59. [CrossRef] [Medline]
- Health Quality Ontario. Cognitive behavioural therapy for psychosis: a health technology assessment. Ont Health Technol Assess Ser. 2018;18(5):1-141. [FREE Full text] [Medline]
- Haddock G, Eisner E, Boone C, Davies G, Coogan C, Barrowclough C. An investigation of the implementation of NICE-recommended CBT interventions for people with schizophrenia. J Ment Health. 2014;23(4):162-165. [CrossRef] [Medline]
- Kopelovich SL, Strachan E, Sivec H, Kreider V. Stepped care as an implementation and service delivery model for cognitive behavioral therapy for psychosis. Community Ment Health J. 2019;55(5):755-767. [CrossRef] [Medline]
- Burgess-Barr S, Nicholas E, Venus B, Singh N, Nethercott A, Taylor G, et al. International rates of receipt of psychological therapy for psychosis and schizophrenia: systematic review and meta-analysis. Int J Ment Health Syst. 2023;17(1):8. [FREE Full text] [CrossRef] [Medline]
- Medalia A, Erlich MD, Soumet-Leman C, Saperstein AM. Translating cognitive behavioral interventions from bench to bedside: the feasibility and acceptability of cognitive remediation in research as compared to clinical settings. Schizophr Res. 2019;203:49-54. [FREE Full text] [CrossRef] [Medline]
- Chung JY. Digital therapeutics and clinical pharmacology. Transl Clin Pharmacol. 2019;27(1):6-11. [FREE Full text] [CrossRef] [Medline]
- Achtyes ED, Ben-Zeev D, Luo Z, Mayle H, Burke B, Rotondi AJ, et al. Off-hours use of a smartphone intervention to extend support for individuals with schizophrenia spectrum disorders recently discharged from a psychiatric hospital. Schizophr Res. 2019;206:200-208. [CrossRef] [Medline]
- ISO/TR 11147:2023 Health informatics - personalized digital health - digital therapeutics health software systems. International Organization for Standardization; 2023. URL: https://www.iso.org/standard/83767.html [accessed 2024-01-01]
- Granholm E, Holden J, Dwyer K, Mikhael T, Link P, Depp C. Mobile-assisted cognitive behavioral therapy for negative symptoms: open single-arm trial with schizophrenia patients. JMIR Ment Health. 2020;7(12):e24406. [FREE Full text] [CrossRef] [Medline]
- Ben-Zeev D, Brenner CJ, Begale M, Duffecy J, Mohr DC, Mueser KT. Feasibility, acceptability, and preliminary efficacy of a smartphone intervention for schizophrenia. Schizophr Bull. 2014;40(6):1244-1253. [FREE Full text] [CrossRef] [Medline]
- Fulford D, Gard DE, Mueser KT, Mote J, Gill K, Leung L, et al. Preliminary outcomes of an ecological momentary intervention for social functioning in schizophrenia: pre-post study of the Motivation and Skills Support app. JMIR Ment Health. 2021;8(6):e27475. [FREE Full text] [CrossRef] [Medline]
- Schlosser DA, Campellone TR, Truong B, Etter K, Vergani S, Komaiko K, et al. Efficacy of PRIME, a mobile app intervention designed to improve motivation in young people with schizophrenia. Schizophr Bull. 2018;44(5):1010-1020. [CrossRef] [Medline]
- Eichenberg C, Aranyi G, Rach P, Winter L. Therapeutic alliance in psychotherapy across online and face-to-face settings: a quantitative analysis. Internet Interv. 2022;29:100556. [FREE Full text] [CrossRef] [Medline]
- Sagui-Henson SJ, Welcome Chamberlain CE, Smith BJ, Li EJ, Castro Sweet C, Altman M. Understanding components of therapeutic alliance and well-being from use of a global digital mental health benefit during the COVID-19 pandemic: longitudinal observational study. J Technol Behav Sci. 2022;7(4):439-450. [FREE Full text] [CrossRef] [Medline]
- Tong F, Lederman R, D'Alfonso S, Berry K, Bucci S. Digital therapeutic alliance with fully automated mental health smartphone apps: a narrative review. Front Psychiatry. 2022;13:819623. [FREE Full text] [CrossRef] [Medline]
- Tremain H, McEnery C, Fletcher K, Murray G. The therapeutic alliance in digital mental health interventions for serious mental illnesses: narrative review. JMIR Ment Health. 2020;7(8):e17204. [FREE Full text] [CrossRef] [Medline]
- Borghouts J, Eikey E, Mark G, De Leon C, Schueller SM, Schneider M, et al. Barriers to and facilitators of user engagement with digital mental health interventions: systematic review. J Med Internet Res. 2021;23(3):e24387. [FREE Full text] [CrossRef] [Medline]
- Müssener U. Digital encounters: human interactions in mHealth behavior change interventions. Digit Health. 2021;7:20552076211029776. [FREE Full text] [CrossRef] [Medline]
- Bucci S, Berry N, Ainsworth J, Berry K, Edge D, Eisner E, et al. Effects of Actissist, a digital health intervention for early psychosis: a randomized clinical trial. Psychiatry Res. 2024;339:116025. [FREE Full text] [CrossRef] [Medline]
- CT-155: Prescription digital therapeutic; US FDA Breakthrough Therapy Designation. Boehringer Ingelheim. URL: https://www.boehringer-ingelheim.com/science/human-pharma/clinical-pipeline/cns/digital-therapeutic [accessed 2024-05-13]
- The ICD-10 classification of mental and behavioural disorders: clinical descriptions and diagnostic guidelines. World Health Organization; 1992. URL: https://www.who.int/publications/i/item/9241544228 [accessed 2023-12-22]
- Llerena K, Park SG, McCarthy JM, Couture SM, Bennett ME, Blanchard JJ. The Motivation and Pleasure Scale-Self-Report (MAP-SR): reliability and validity of a self-report measure of negative symptoms. Compr Psychiatry. 2013;54(5):568-574. [FREE Full text] [CrossRef] [Medline]
- Software as a Medical Device (SaMD). U.S. Food and Drug Administration; 2018. URL: https://www.fda.gov/medical-devices/digital-health-center-excellence/software-medical-device-samd [accessed 2024-07-08]
- Jacob J, Stankovic M, Spuerck I, Shokraneh F. Goal setting with young people for anxiety and depression: what works for whom in therapeutic relationships? A literature review and insight analysis. BMC Psychol. 2022;10(1):171. [FREE Full text] [CrossRef] [Medline]
- Tryon GS, Birch SE, Verkuilen J. Meta-analyses of the relation of goal consensus and collaboration to psychotherapy outcome. Psychotherapy (Chic). 2018;55(4):372-383. [CrossRef] [Medline]
- Avasthi A, Sahoo S, Grover S. Clinical practice guidelines for cognitive behavioral therapy for psychotic disorders. Indian J Psychiatry. 2020;62(Suppl 2):S251-S262. [FREE Full text] [CrossRef] [Medline]
- Sivec HJ, Montesano VL. Cognitive behavioral therapy for psychosis in clinical practice. Psychotherapy (Chic). 2012;49(2):258-270. [CrossRef] [Medline]
- Morrison AK. Cognitive behavior therapy for people with schizophrenia. Psychiatry (Edgmont). 2009;6(12):32-39. [FREE Full text] [Medline]
- Tai S, Turkington D. The evolution of cognitive behavior therapy for schizophrenia: current practice and recent developments. Schizophr Bull. 2009;35(5):865-873. [FREE Full text] [CrossRef] [Medline]
- Roque NA, Boot WR. A new tool for assessing mobile device proficiency in older adults: the Mobile Device Proficiency Questionnaire. J Appl Gerontol. 2018;37(2):131-156. [FREE Full text] [CrossRef] [Medline]
- Shimokihara S, Ikeda Y, Matsuda F, Tabira T. Association of mobile device proficiency and subjective cognitive complaints with financial management ability among community-dwelling older adults: a population-based cross-sectional study. Aging Clin Exp Res. 2024;36(1):44. [FREE Full text] [CrossRef] [Medline]
- Ismond KP, Eslamparast T, Farhat K, Stickland M, Spence JC, Bailey RJ, et al. Assessing patient proficiency with internet-connected technology and their preferences for E-health in cirrhosis. J Med Syst. 2021;45(7):72. [FREE Full text] [CrossRef] [Medline]
- Kring AM, Gur RE, Blanchard JJ, Horan WP, Reise SP. The Clinical Assessment Interview for Negative Symptoms (CAINS): final development and validation. Am J Psychiatry. 2013;170(2):165-172. [CrossRef] [Medline]
- Berry K, Salter A, Morris R, James S, Bucci S. Assessing therapeutic alliance in the context of mHealth interventions for mental health problems: development of the mobile Agnew Relationship Measure (mARM) questionnaire. J Med Internet Res. 2018;20(4):e90. [FREE Full text] [CrossRef] [Medline]
- Agnew-Davies R, Stiles WB, Hardy GE, Barkham M, Shapiro DA. Alliance structure assessed by the Agnew Relationship Measure (ARM). Br J Clin Psychol. 1998;37(2):155-172. [CrossRef] [Medline]
- Clarke J, Proudfoot J, Whitton A, Birch M, Boyd MR, Parker G, et al. Therapeutic alliance with a fully automated mobile phone and web-based intervention: secondary analysis of a randomized controlled trial. JMIR Ment Health. 2016;3(1):e10. [FREE Full text] [CrossRef] [Medline]
- WMA Declaration of Helsinki – ethical principles for medical research involving human participants. World Medical Association; 2022. URL: https://www.wma.net/policies-post/wma-declaration-of-helsinki-ethical-principles-for-medical-research-involving-human-subjects/ [accessed 2023-12-22]
- ICH-E6 good clinical practice. International Council for Harmonization of Technical Requirements for Pharmaceuticals for Human Use; 2021. URL: https://www.ich.org/page/efficacy-guidelines#6-2 [accessed 2023-12-22]
- Prochaska JJ, Vogel EA, Chieng A, Kendra M, Baiocchi M, Pajarito S, et al. A therapeutic relational agent for reducing problematic substance use (Woebot): development and usability study. J Med Internet Res. 2021;23(3):e24850. [FREE Full text] [CrossRef] [Medline]
- Prochaska JJ, Vogel EA, Chieng A, Baiocchi M, Maglalang DD, Pajarito S, et al. A randomized controlled trial of a therapeutic relational agent for reducing substance misuse during the COVID-19 pandemic. Drug Alcohol Depend. 2021;227:108986. [FREE Full text] [CrossRef] [Medline]
- Darcy A, Daniels J, Salinger D, Wicks P, Robinson A. Evidence of human-level bonds established with a digital conversational agent: cross-sectional, retrospective observational study. JMIR Form Res. 2021;5(5):e27868. [FREE Full text] [CrossRef] [Medline]
- He Y, Yang L, Zhu X, Wu B, Zhang S, Qian C, et al. Mental health chatbot for young adults with depressive symptoms during the COVID-19 pandemic: single-blind, three-arm randomized controlled trial. J Med Internet Res. 2022;24(11):e40719. [FREE Full text] [CrossRef] [Medline]
- Hassan L, Eisner E, Berry K, Emsley R, Ainsworth J, Lewis S, et al. User engagement in a randomised controlled trial for a digital health intervention for early psychosis (Actissist 2.0 trial). Psychiatry Res. 2023;329:115536. [FREE Full text] [CrossRef] [Medline]
- Lysaker PH, Davis LW, Buck KD, Outcalt S, Ringer JM. Negative symptoms and poor insight as predictors of the similarity between client and therapist ratings of therapeutic alliance in cognitive behavior therapy for patients with schizophrenia. J Nerv Ment Dis. 2011;199(3):191-195. [CrossRef] [Medline]
- Jung E, Wiesjahn M, Lincoln TM. Negative, not positive symptoms predict the early therapeutic alliance in cognitive behavioral therapy for psychosis. Psychother Res. 2014;24(2):171-183. [CrossRef] [Medline]
- Kvrgic S, Cavelti M, Beck EM, Rüsch N, Vauth R. Therapeutic alliance in schizophrenia: the role of recovery orientation, self-stigma, and insight. Psychiatry Res. 2013;209(1):15-20. [CrossRef] [Medline]
- Goldberg SB, Baldwin SA, Riordan KM, Torous J, Dahl CJ, Davidson RJ, et al. Alliance with an unguided smartphone app: validation of the digital working alliance inventory. Assessment. 2022;29(6):1331-1345. [FREE Full text] [CrossRef] [Medline]
- Chao PJ, Steffen JJ, Heiby EM. The effects of working alliance and client-clinician ethnic match on recovery status. Community Ment Health J. 2012;48(1):91-97. [CrossRef] [Medline]
- Ziguras S, Klimidis S, Lewis J, Stuart G. Ethnic matching of clients and clinicians and use of mental health services by ethnic minority clients. Psychiatr Serv. 2003;54(4):535-541. [CrossRef] [Medline]
- Berry N, Machin M, Ainsworth J, Berry K, Edge D, Haddock G, et al. Developing a theory-informed smartphone app for early psychosis: learning points from a multidisciplinary collaboration. Front Psychiatry. 2020;11:602861. [FREE Full text] [CrossRef] [Medline]
- Brotherdale R, Berry K, Branitsky A, Bucci S. Co-producing digital mental health interventions: a systematic review. Digit Health. 2024;10:20552076241239172. [FREE Full text] [CrossRef] [Medline]
- Chauhan A, Walton M, Manias E, Walpola RL, Seale H, Latanik M, et al. The safety of health care for ethnic minority patients: a systematic review. Int J Equity Health. 2020;19(1):118. [FREE Full text] [CrossRef] [Medline]
- Luther L, Buck BE, Fischer MA, Johnson-Kwochka AV, Coffin G, Salyers MP. Examining potential barriers to mHealth implementation and engagement in schizophrenia: phone ownership and symptom severity. J Technol Behav Sci. 2022;7(1):13-22. [FREE Full text] [CrossRef] [Medline]
- Torous J, Wisniewski H, Liu G, Keshavan M. Mental health mobile phone app usage, concerns, and benefits among psychiatric outpatients: comparative survey study. JMIR Ment Health. 2018;5(4):e11715. [FREE Full text] [CrossRef] [Medline]
- Young AS, Cohen AN, Niv N, Nowlin-Finch N, Oberman RS, Olmos-Ochoa TT, et al. Mobile phone and smartphone use by people with serious mental illness. Psychiatr Serv. 2020;71(3):280-283. [FREE Full text] [CrossRef] [Medline]
- Kozelka E, Acquilano SC, Al-Abdulmunem M, Guarino S, Elwyn G, Drake RE, et al. Documenting the digital divide: identifying barriers to digital mental health access among people with serious mental illness in community settings. SSM Ment Health. 2023;4:100241. [CrossRef]
- Mamedova S, Pawlowski E. Stats in brief: a description of U.S. adults who are not digitally literate. National Center for Education Statistics; 2018. URL: https://nces.ed.gov/pubs2018/2018161.pdf [accessed 2024-07-08]
- Spanakis P, Wadman R, Walker L, Heron P, Mathers A, Baker J, et al. Measuring the digital divide among people with severe mental ill health using the essential digital skills framework. Perspect Public Health. 2024;144(1):21-30. [CrossRef] [Medline]
- Athanasopoulou C, Välimäki M, Koutra K, Löttyniemi E, Bertsias A, Basta M, et al. Internet use, eHealth literacy and attitudes toward computer/internet among people with schizophrenia spectrum disorders: a cross-sectional study in two distant European regions. BMC Med Inform Decis Mak. 2017;17(1):136. [FREE Full text] [CrossRef] [Medline]
- He H, Liu Q, Li N, Guo L, Gao F, Bai L, et al. Trends in the incidence and DALYs of schizophrenia at the global, regional and national levels: results from the Global Burden of Disease Study 2017. Epidemiol Psychiatr Sci. 2020;29:e91. [FREE Full text] [CrossRef] [Medline]
- Li X, Zhou W, Yi Z. A glimpse of gender differences in schizophrenia. Gen Psychiatr. 2022;35(4):e100823. [FREE Full text] [CrossRef] [Medline]
- Olbert CM, Nagendra A, Buck B. Meta-analysis of Black vs. White racial disparity in schizophrenia diagnosis in the United States: do structured assessments attenuate racial disparities? J Abnorm Psychol. 2018;127(1):104-115. [CrossRef] [Medline]
- Holm M, Taipale H, Tanskanen A, Tiihonen J, Mitterdorfer-Rutz E. Employment among people with schizophrenia or bipolar disorder: a population-based study using nationwide registers. Acta Psychiatr Scand. 2021;143(1):61-71. [FREE Full text] [CrossRef] [Medline]
- D'Alfonso S, Lederman R, Bucci S, Berry K. The digital therapeutic alliance and human-computer interaction. JMIR Ment Health. 2020;7(12):e21895. [FREE Full text] [CrossRef] [Medline]
- A global clinical research data sharing platform. Vivli. URL: https://vivli.org/ [accessed 2024-12-08]
- Data and document sharing. Boehringer Ingelheim. URL: https://www.mystudywindow.com/msw/datasharing [accessed 2024-12-08]
Abbreviations
ADE: adverse device effect |
AE: adverse event |
CAINS-MAP: Clinical Assessment Interview for Negative Symptoms Motivation and Pleasure subscale |
DSM-5: Diagnostic and Statistical Manual of Mental Disorders (Fifth Edition) |
DTx: digital therapeutics |
DWA: digital working alliance |
ENS: experiential negative symptoms |
FDA: US Food and Drug Administration |
ICD-10: International Classification of Diseases, Tenth Revision |
MAP-SR: Motivation and Pleasure scale – Self Report |
mARM: mobile Agnew Relationship Measure |
MDPQ: Mobile Device Proficiency Questionnaire |
Edited by J Torous; submitted 31.07.24; peer-reviewed by L Parri, P Harvey; comments to author 29.08.24; revised version received 24.10.24; accepted 01.11.24; published 27.01.25.
Copyright©Cassandra Snipes, Cornelia Dorner‑Ciossek, Brendan D Hare, Olya Besedina, Tim Campellone, Mariya Petrova, Shaheen E Lakhan, Abhishek Pratap. Originally published in JMIR Mental Health (https://mental.jmir.org), 27.01.2025.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Mental Health, is properly cited. The complete bibliographic information, a link to the original publication on https://mental.jmir.org/, as well as this copyright and license information must be included.