Perceptions and experiences of patients, caregivers, and clinicians on using digital phenotyping measures for outcome prediction in first episode of psychosis (FEP): A qualitative study

Perceptions and experiences of patients, caregivers, and clinicians on using digital phenotyping measures for outcome prediction in first episode of psychosis (FEP): A qualitative study | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 17 September 2025 V1 Latest version Share on Perceptions and experiences of patients, caregivers, and clinicians on using digital phenotyping measures for outcome prediction in first episode of psychosis (FEP): A qualitative study Authors : Lu Yang 0009-0009-1625-6784 , Kate Ross , Katie Stewart , Mackenzie Palmer , Qiang Ye , David Olson , Sabina Abidi , Jason Morrison , Philip Tibbo , and JianLi Wang [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.175811421.13653509/v1 180 views 124 downloads Contents Abstract Acknowledgements Ethical approval and informed consent statements Data availability statement Abstract Introduction Results Discussion Conclusions Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Background : Predicting outcomes in first-episode psychosis (FEP) is critical for tailoring interventions. Digital phenotyping, the real-time collection of high-resolution data through mobile and wearable devices, has emerged as a promising method to improve mental health assessment and prognostic prediction. Objective : To explore the experiences and perspectives of patients, caregivers, and clinicians on using digital phenotyping for outcome prediction in FEP. Method : This qualitative study is part of a mixed-methods longitudinal project. Patients with FEP (aged 12–35) were recruited from the Early Psychosis Intervention Nova Scotia (EPINS) program (N = 40) and completed a six-day digital phenotyping assessment involving ecological momentary assessment (EMA) via smartphone and passive monitoring using wearable devices. Semi-structured interviews were then conducted with a random sample of 19 patients, 6 caregivers, and 6 clinicians and analyzed thematically following Braun and Clarke’s six-step framework. Results : Three themes emerged: (1) experiences and attitudes; (2) perceived value; and (3) barriers and concerns. Patients and caregivers generally reported positive experiences, highlighting benefits such as increased self-awareness and motivation for physical activities. Barriers included practical burden, psychological risks, technical and accessibility challenges, prediction validity, and data privacy. Conclusion : Digital phenotyping is generally acceptable and feasible in FEP, but concerns need to be considered in future applications. Perceptions and experiences of patients, caregivers, and clinicians on using digital phenotyping measures for outcome prediction in first episode of psychosis (FEP): A qualitative study Lu Yang, MD, PhD 1 , Kate Ross, BSc 1 , Katie Stewart, BSc 1 , Mackenzie Palmer, BSc 1 , Qiang Ye, PhD 2 , David Olson, MD, PhD 3,4 , Sabina Abidi, MD, FRCPC 3,4 , Jason M Morrison, MD, MSc, FRCPC 3,4 , Philip G Tibbo, MD, FRCPC 3,4 , JianLi Wang, PhD 1,3 * 1 Department of Community Health and Epidemiology, Faculty of Medicine, Dalhousie University. Canada. 2 Faculty of Computer Science, Dalhousie University. Canada. 3 Department of Psychiatry, Faculty of Medicine, Dalhousie University. Canada. 4 Early Psychosis Intervention Nova Scotia, Nova Scotia Health. Canada. Corresponding author: JianLi Wang Email: [email protected] Mailing address: Centre for Clinical Research, Room 402 5790 University Ave, Halifax, Nova Scotia Canada B3H 1V7 Phone number: 902-473-6684 Acknowledgements We would like to acknowledge Rachel Church and Laura Carnegy at the NSEPP, Nova Scotia Health, for their assistance in recruiting participants. We gratefully acknowledge the contribution of all study participants. The Authors declare that there is no conflict of interest. This work was supported by a joint research award from Mental Health Research Canada and the Institute for Advancements in Mental Health, a Canadian Institutes of Health Research (CIHR) Canada Research Chair award (CRC-2020-00169) to JLW, and a Mitacs Accelerate internship (IT39940) to LY. Ethical approval and informed consent statements The Health Research Ethics Board of Nova Scotia Health approved this study. All patient participants provided written informed consent, and caregiver and clinician participants provided recorded verbal consent. Data availability statement The qualitative data generated and analyzed in this study is not publicly available due to concerns regarding participant confidentiality. Limited de-identified data may be made available from the corresponding author on reasonable request and with appropriate ethical approvals. Abstract Background : Predicting outcomes in first-episode psychosis (FEP) is critical for tailoring interventions. Digital phenotyping, the real-time collection of high-resolution data through mobile and wearable devices, has emerged as a promising method to improve mental health assessment and prognostic prediction. Objective : To explore the experiences and perspectives of patients, caregivers, and clinicians on using digital phenotyping for outcome prediction in FEP. Method : This qualitative study is part of a mixed-methods longitudinal project. Patients with FEP (aged 12–35) were recruited from the Early Psychosis Intervention Nova Scotia (EPINS) program (N = 40) and completed a six-day digital phenotyping assessment involving ecological momentary assessment (EMA) via smartphone and passive monitoring using wearable devices. Semi-structured interviews were then conducted with a random sample of 19 patients, 6 caregivers, and 6 clinicians and analyzed thematically following Braun and Clarke’s six-step framework. Results : Three themes emerged: (1) experiences and attitudes; (2) perceived value; and (3) barriers and concerns. Patients and caregivers generally reported positive experiences, highlighting benefits such as increased self-awareness and motivation for physical activities. Barriers included practical burden, psychological risks, technical and accessibility challenges, prediction validity, and data privacy. Conclusion : Digital phenotyping is generally acceptable and feasible in FEP, but concerns need to be considered in future applications. Keywords: psychotic disorders; digital technology; qualitative research Introduction Psychotic disorders, including schizophrenia, affect 3-4% of the population and are among the leading causes of disability worldwide (GBD 2019 Mental Disorders Collaborators, 2022; Moreno-Küstner, Martín, & Pastor, 2018). The outcomes of first-episode psychosis (FEP) vary considerably, and even patients who achieve remission often experience persistent functional impairments or face high risk of relapses (Catalan et al., 2021; Wold et al., 2024). Predicting outcomes is highly valuable for tailoring pharmacological and psychological interventions. However, accurate prediction remains challenging. Demographic, socioeconomic, behavioral, and clinical factors interact in complex ways that influence prognosis (Wimberley et al., 2016). Traditional psychiatric assessments rely heavily on self-report, which is vulnerable to recall and reporting bias. This limitation can be significant in individuals with psychosis when poor illness insight is a factor (Farina, Assaf, Corbera, & Chen, 2022; Jongs et al., 2022). Digital phenotyping, which refers to the use of mobile devices (e.g. smartphones, wearable devices) for data collection in everyday life, has been widely recognized as a promising approach for psychopathological evaluation (Oudin et al., 2023). It enables the capture of real-time and high-resolution data in real-world environments (Lee et al., 2023). By combining active (e.g., ecological momentary assessment [EMA] surveys) and passive (e.g., accelerometry, geolocation) data, digital phenotyping can depict the temporal dynamics of both psychological and behavior changes, addressing key limitations of traditional interview-based assessments (Andorko et al., 2019; Colombo et al., 2019). Emerging evidence suggests that digital phenotyping provides a valid approach for measuring negative symptoms in psychotic disorders, suggesting its potential to contribute to prognosis prediction (Cohen et al., 2021; Narkhede et al., 2022). However, few studies were conducted in patients with FEP. Additionally, despite the increasing interest in the use of digital phenotyping in psychiatric research, we have limited knowledge about the perceptions of patients, caregivers and clinicians on their experience and utility of digital phenotyping data; these insights are important as they have implications for research approaches and their use in clinical practice. Existing studies have been largely focused on patients’ perspectives. While participants generally reported positive attitudes and acceptance toward EMA surveys and other phone-based self-reporting tools, they also identified barriers to engagement and adherence, including technical difficulties, interference with daily activities, and privacy concerns (Steare et al., 2021; Trelfa et al., 2025). Increased self-awareness was consistently recognized as a perceived benefit, with patients reporting that EMA-based symptom tracking helped overcome memory deficits and enhanced self-reflection (Moitra, Gaudiano, Davis, & Ben-Zeev, 2017). Two studies using wearable Fitbit devices found that patients generally expressed positive views on passive data collection, particularly recognizing the value of reflecting their sleep data (Griffiths et al., 2021; Meyer et al., 2018). Increased motivation for physical activity associated with device use was also reported; however, about half of the patients experienced physical discomfort from wearing the device (Meyer et al., 2018). Despite these findings, few studies systematically integrated perspectives of multiple stakeholder groups. This lack of triangulation limits understanding of the feasibility and acceptability of digital phenotyping in psychosis care and the identification of areas for improvement. As part of a longitudinal study using digital phenotyping measures among FEP patients, we conducted qualitative interviews with patients, their caregivers and clinicians. The objective of this paper was to: 1) describe experiences and attitudes toward the use of digital phenotyping measures involving EMA surveys and wearable devices; and 2) examine potential ethical, privacy, and confidentiality issues associated with this data-driven approach in clinical decision-making. Study design This qualitative study was part of a 6-month mixed-methods longitudinal study conducted at the Nova Scotia Early Psychosis Program (NSEPP). NSEPP is an outpatient program (with one adult and one adolescent site) providing comprehensive psychiatric specialty services to patients with FEP aged 12-35 years residing in Halifax Regional Municipality. Patients receiving services at NSEPP who had consented to research contact were invited to participate. Eligibility required agreement to complete smartphone-based EMA survey and to use a wearable device for six days (n = 40). Exclusion criteria were a diagnosed learning disability, or a history of head trauma or neurological disease. At baseline, demographic and clinical information was collected using a structured questionnaire. A six-day digital data collection was then initiated using a wearable device (Fitbit Charge 6). Each participant was provided with a Fitbit and instructed to wear it continuously on their non-dominant wrist for six days to capture real-time physical data. These data were synchronized with the Fitbit server and include measures of activity (e.g., step count, distance traveled, exercise level), sleep (e.g., sleep stages, sleep duration), and cardiovascular function (e.g., heart rate, heart rate variability). Active data were collected via EMA surveys, delivered six times daily through text messages links to web-based questionnaires programmed in REDCap (Harris et al., 2019). Morning and evening surveys were sent at 9:00 a.m. and 9:00 p.m., with four additional surveys distributed at approximately two-hour intervals throughout the day. The EMA surveys consisted of self-rated questions assessing general mental health status, screening for psychotic symptoms (yes/no), and inquiring about activities such as sleep, substance use, and social engagement (yes/no or multiple-choice options). A technician conducted a briefing session with each participant on Fitbit app installation, synchronization and EMA practice. The technician also provided ongoing technical support as needed. After the six-day digital phenotyping assessment, we invited the patient participants, their caregivers and NSEPP clinicians for one-on-one qualitative interviews. The Health Research Ethics Board of Nova Scotia Health approved this study. All patient participants provided written informed consent, and caregiver and clinician participants provided recorded verbal consent. Patient and caregiver participants We interviewed a random sample of 19 patients selected from our longitudinal study cohort (n = 40) to reach theme saturation. Caregivers were identified by the patients and were invited to participate. A total of six caregivers were interviewed, including one who supported an adolescent patient. Patients were interviewed in person; caregivers were interviewed separately either via telephone or in person at NSEPP. Clinician participants We interviewed six NSEPP clinicians (a psychiatrist, a psychologist, a social worker, a nurse and an occupational therapist) in-person or through an online meeting platform. Five of the clinicians are from the adult program and one clinician is from the adolescent program. Data collection The team members developed semi-structured interview questionnaires for patients, caregivers and clinicians respectively (See Supplemental Material, Table S1 ). The questions were designed regarding (1) participants’ attitudes and views towards the use of mobile and wearable devices for data collection; (2) the use of digital data for outcome prediction; (3) the potential ethics, privacy and confidentiality issues involved in the data-driven approach in clinical decision making. Interviews were conducted from September 2024 to April 2025. An interviewer guide with prompting questions was developed to facilitate the interviewing process and to address the concerns and questions from the participants. All the interviews were recorded and transcribed verbatim for analysis. The interviewer collected sociodemographic and clinical data from patients using a structured questionnaire during in-person interviews. Demographic data of caregivers were collected before their qualitative interviews, while data of clinicians was obtained through the NSEPP information system. Data analysis The analysis of qualitative data was based on thematic theory and following Braun and Clarke’s six-step framework (Braun & Clarke, 2006). First, each transcript was reviewed by the co-authors (LY, MP, KR, and KS). Initial codes for each response of participants were generated independently by the analyzers. Then consistency of the initial codes was then compared, and final codes were generated to create a code book. After annotating each code, themes were generated with definitions based on the codes. In the final stages, the themes were reviewed, discussed, and further modified by the co-authors. Results Participants The demographic and clinical characteristics of the 19 enrolled patients are shown in Table 1 . The mean age of the patient sample was 25.4±4.8 years old (range 17–33); most were male (63.2%), White (78.9%), single (89.5%), had at least high school education or equivalent (73.7%), and identified their mental health status as good to excellent (73.7%). At the interview stage, 21.0% were employed, and the median treatment duration in NSEPP was 34 months. Among the six caregivers, one (16.7%) was aged 18–34 years, two (33.3%) were aged 35–49 years, and three (50.0%) were aged 50–64 years. All were female. Five were parents of the patient and one was a spouse. Five (83.3%) identified themselves as White, and one (16.7%) identified as Asian. Among the six clinicians, there was one physician, one psychologist, two nurses, one social worker, and one occupational therapist. Five were female, and all identified themselves as White. Four clinicians had five or more years of experience working with patients with FEP. Their background knowledge and experience of digital phenotyping varied and were noted following their quotes. Three main themes emerged from all participants during the thematic analysis ( Table 2 ): (1) experiences and attitudes toward digital phenotyping; (2) perceived value of digital phenotyping; and (3) perceived barriers and concerns. Themes #2 and #3 also include sub-themes. These themes with illustrative quotes are discussed below. Experiences and attitudes toward digital phenotyping Most patients reported positive experiences with the study, describing it as “ pretty good ” , “ super fun ” , or “ beneficial ”. One patient who withdrew from the study due to psychological distress described their overall experience as neutral: “ Um, I’m not really positive or negative about it, it just kind of was an event that happened ” (P17, Male). Most patients expressed interest in collecting data through wearable and mobile devices, which they described as “ cool ” , “ fun ” or “ innovative ”. However, a few patients expressed resistance to using digital tools: “ I’m kind of old school…I prefer to have minimal technology ” (P3, Male). Engagement and compliance varied, with some patients acknowledging occasionally missing entries of EMA survey due to forgetfulness or interference with daily activities or removing the Fitbit device because of physical discomfort. All caregivers expressed positive attitudes toward study participation. Most had little awareness of patients’ adherence, except for one caregiver of an adolescent who reported occasional missed EMA entries and removal of wearable devices. Clinicians, however, showed a mix of optimism and caution. Most of the clinicians expressed a positive attitude, but few remained cautious and doubtful. For example, one clinician described the concept of digital phenotyping as “ interesting ” but still expressed doubts: “ I don’t honestly know enough about the research to say whether this is gonna significantly contribute anything new to the literature that’s worth the expense of it ” (CL4, no prior experience with digital phenotyping, adult program). Participants expressed mixed views on the clinical and predictive value of digital phenotyping (key perspectives and illustrative quotes in Table 3 ). Although some patients emphasized its ability to capture accurate, real-time data compared to traditional clinical interviews, several questioned its predictive validity given the reliance on one-time and short-term data collection. Caregivers expressed either supportive or uncertain views, with one describing it as irrelevant to clinical practice. Clinicians highlighted advantages of digital phenotyping for both data collection and clinical practice, including real-time monitoring, consistent data capture, and the potential to enhance engagement and rapport. However, some of them expressed uncertainty or skepticism about its contribution: “ if you said, it would give us more objective data about who’s not inactive…I wonder is it going to be that much better than getting reports from family members, right? ” (CL4). Clinicians also raised doubts about the robustness of predictions, expressing concerns on the short-term data collection, potential biases in EMA self-reported data, and the use of unvalidated biological measures. Beyond clinical applications, most patients also identified personal health benefits, including increased motivation for physical and outdoor activities associated with Fitbit use and enhanced self-awareness promoted by EMA surveys. Caregivers also observed physical benefits; however, clinicians did not discuss this theme during the interviews. Participants identified several barriers to engagement with digital phenotyping (key perspectives and illustrative quotes in Table 4 ). Practical challenges, especially excessive frequency of the EMA, were the most frequently reported among patients. Clinicians also expressed concerns about the feasibility of six consecutive days of EMA, with one suggesting a longer data collection period with less frequent surveys. Some patients found wearing the Fitbit disruptive due to physical discomfort or overstimulation from reminders. One caregiver also observed minor physical discomfort associated with wearing the device, while most expressed no concerns. Limited accessibility was consistently mentioned by patients and clinicians. Two patients reported limited access to smartphones during school or work time, which hindered them from EMA. A clinician from the adolescent program emphasized that school restrictions and financial constraints often limited adolescents’ access to smartphones. Many clinicians also emphasized the importance of ensuring equal access to wearable devices. Other less commonly mentioned practical barriers by patients included usability challenges with EMA (e.g., confusing wording or difficulties with interference) and technical issues related to device synchronization. Psychological and emotional barriers were raised primarily by patients and clinicians. While most patients found the impact manageable, some reported distress when answering sensitive EMA questions (e.g., delusional thoughts or hallucinations), and one withdrew due to repeated reminders of past trauma: “ It was a constant reminder of all the things that had happened in the past, so it kept bringing up emotional trauma for me over and over ” (P17, Male). Some patients emphasized that their emotional responses might vary with clinical states. Clinicians were especially concerned about potential exacerbation of paranoia or distress and suggested readiness assessment and support plans. Caregivers did not report psychological concerns, except one who emphasized readiness for individuals with obsessive thoughts about body shape or weight before using wearable devices with exercise-monitoring functions. Interestingly, privacy and confidentiality concerns were limited among patients and caregivers. Only a few patients perceived smartphone-based data collection as intrusive or worried about unauthorized data access. Caregivers generally reported no concerns. However, clinicians emphasized the need for secure data storage and transmission, although most of them acknowledged limited expertise for suggesting specific strategies. In contrast to the enthusiasm expressed by patients and caregivers about reviewing data and predicted results, clinicians were generally cautious. They strongly emphasized ethical concerns related to communicating and applying predictive results, warning that deterministic or poorly contextualized results could be harmful. As one clinician articulated: “ With patients that have an idea of a certain outcome, it actually dramatically affects their recovery ” (prior research experience with digital phenotyping, adult program, CL1). To mitigate these risks, clinicians recommended that prediction feedback be delivered by clinician teams, with contextual framing and actionable recommendations to support recovery and preserve hope. Discussion To our knowledge, this is the first study to explore the experiences and perspectives of all three stakeholder groups (patients, caregivers, and clinicians) on using digital phenotyping in early psychosis. Participants generally expressed positive attitudes with digital phenotyping, though engagement varied depending on patient’s characteristics, and potentially on clinical stability. Reported barriers included the frequency of EMA surveys, emotional distress triggered by EMA questions, discomfort from wearing Fitbit, technical issues, and limited accessibility to digital tools. Enhanced emotional awareness and increased motivation for physical activity were identified by patients and caregivers as key contributors to engagement. Participants expressed mixed views on the clinical and predictive value of digital phenotyping and both patients and clinicians expressed doubts about the validity of prediction results derived from digital data. Related concerns included circumstantial nature of data collection, potential biases in self-reported EMA data, and the use of unvalidated biological measures. The patients in our study consistently identified psychological and physical health benefits from engaging with digital phenotyping, which is consistent with previous qualitative studies, suggesting that intrinsic motivators were significant facilitators of engagement (Griffiths et al., 2021; Trelfa et al., 2025). Despite the recognition of personal health benefits, participants expressed mixed views on the clinical and predictive value of digital phenotyping. Building stakeholders’ understanding and consensus on its clinical integration will be crucial for successful implementation. Our findings suggested substantial potential to improve stakeholders’ confidence, knowledge and involvement through targeted education and ongoing communication. Although the timely analysis and response of collected digital data might also enhance engagement (Meyer et al., 2018), clinicians were cautious about delivering digital data and predictive results. On the one hand, such outcomes may cause bias in treatment decisions and lead to an imbalanced allocation of resources. Although digital phenotyping shows promise in shedding light on symptoms, prediction of psychosis remains challenging (Yang et al., 2025). There is also ongoing debate regarding the selection of interventions for at-risk populations and their cost-effectiveness (Aceituno, Vera, Prina, & McCrone, 2019). On the other hand, the negative prediction results may contribute to self-stigma and deprivation of hope among patients and caregivers (Cleary, Hunt, Escott, & Walter, 2010). A qualitative study in FEP found that attitudes toward transparent communication about illness varied depending on the clinical stage and psychological readiness (Huurman et al., 2023). Although patients and caregivers expressed enthusiasm in reviewing their data and prediction results, structured implementation strategies should be developed to ensure the effectiveness of risk communication and ethical integration. The excessive frequency of EMA surveys emerged as a major barrier. In previous EMA studies on individuals with psychotic disorders, the EMA typically lasted for 6-7 days with a daily frequency of 8-10 times (Michel et al., 2023; Mote & Fulford, 2020; Orth et al., 2022). The acceptability of EMA was assessed mainly through quantitative data. A meta-analysis (N = 39) found an average EMA completion rate of 67.15%, with no significant predictors of adherence identified (Bell et al., 2024). Most EMA studies provided monetary incentives for survey completion, which might be a significant facilitator of engagement compared to our study. Findings from a small number of qualitative studies suggested that patients generally perceived EMA as a positive experience, but also complained about the conflicts with daily activities, repetitive questions, and excessive reminders (Bouws et al., 2025; Moitra et al., 2017). These findings are consistent with our results, in which patients reported distress from the repetitive and frequent responding and difficulty in maintaining full compliance. To reduce survey burden, both patients and clinicians recommended a longer data collection period with a lower prompting frequency. Moitra et al. (Moitra et al., 2017) implemented an EMA protocol with 4 prompts per day over a one-month period. The time-response effect analysis showed that response rates declined only by week four compared to earlier weeks, suggesting stable compliance and potential feasibility for a longer assessment period. A study on individuals with mood disorders also found that administering EMA questionnaires five times daily over a two-week period did not appear to affect overall compliance (van Genugten et al., 2020). Clinicians in our study emphasized that short-term EMA protocols may have limited capacity to capture a comprehensive picture of a patient’s experience. This is consistent with previous evidence indicating that the timing of monitoring may significantly influence the sensitivity to detecting responses to stimuli (Castañeda-Babarro, Arbillaga-Etxarri, Gutiérrez-Santamaría, & Coca, 2020). Further research is warranted to identify an optimal EMA protocol that achieves the balance between feasibility and the validity of data. One major concern raised by clinicians is that the use of digital phenotyping devices may potentially exclude lower-income or marginalized populations. A previous survey (N = 215) found that 95% of patients with psychosis owned a smartphone, while 10–15% owned a fitness tracker (Eisner, Berry, & Bucci, 2023). We provided Fitbit devices for patient participants and all those contacted for participation owned a smartphone. However, limited accessibility to smartphones remains a barrier particularly among adolescents due to the school restrictions on phone use. Our finding emphasized the importance of adapting digital phenotyping procedures in individuals with school- or work-related restrictions to ensure EMA adherence. Other less reported practical barriers, such as discomfort from wearing the Fitbit, technical difficulties with the wearable device, and personal dislike of digital technologies, further suggested the need for a more tailored digital phenotyping approach oriented to individual needs, demographics, preferences, clinical status and digital literacy. Our data suggested that it is critical to carefully consider that potential psychological risk associated with applying digital phenotyping among psychiatric patients. Although all clinicians expressed such concerns, most patients reported that the psychological impact was manageable. Our findings are consistent with previous studies supporting the tolerability of digital phenotyping in patients with psychosis (Fowler et al., 2021; Raugh et al., 2021). Identifying predictors associated with psychological side effects is warranted to inform readiness assessment and tailored support plans. Limitations Limitations of this study include its single-site design. The proportion of patients younger than 19 years in our sample was lower than the typically reported proportion of 11–18% among individuals with FEP (Amminger et al., 2011). As a result, the perspective of adolescents was not well represented. Additionally, all interviews were conducted with English-speaking stakeholders, excluding the views of non-English-speaking individuals. Most participants were in a relatively stable clinical phase during the data collection period. Additional perspectives from individuals in the acute phase or experiencing severe symptoms are needed to inform more generalizable strategies. Conclusions In conclusion, most of the patients and caregivers expressed positive attitudes and experiences with digital phenotyping, indicating good acceptability and feasibility. Participants expressed mixed views on its clinical utility and raised concerns about predictive validity and ethical risks. Future digital phenotyping studies in individuals with mental disorders should consider several key barriers including EMA survey burden, usability and technical challenges, equity of access and protection of privacy and confidentiality. References Aceituno, D., Vera, N., Prina, A. M., & McCrone, P. (2019). 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JMIR Mhealth Uhealth, 13 , e56185. doi:10.2196/56185 Supplementary Material File (table 1.docx) Download 17.52 KB File (table 2.docx) Download 22.78 KB File (table 3.docx) Download 19.56 KB File (table 4.docx) Download 21.62 KB Information & Authors Information Version history V1 Version 1 17 September 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords digital technology psychotic disorders qualitative research Authors Affiliations Lu Yang 0009-0009-1625-6784 Dalhousie University Department of Community Health and Epidemiology View all articles by this author Kate Ross Dalhousie University Department of Community Health and Epidemiology View all articles by this author Katie Stewart Dalhousie University Department of Community Health and Epidemiology View all articles by this author Mackenzie Palmer Dalhousie University Department of Community Health and Epidemiology View all articles by this author Qiang Ye Dalhousie University Faculty of Computer Science View all articles by this author David Olson Dalhousie University Department of Psychiatry View all articles by this author Sabina Abidi Dalhousie University Department of Psychiatry View all articles by this author Jason Morrison Dalhousie University Department of Psychiatry View all articles by this author Philip Tibbo Dalhousie University Department of Psychiatry View all articles by this author JianLi Wang [email protected] Dalhousie University Department of Community Health and Epidemiology View all articles by this author Metrics & Citations Metrics Article Usage 180 views 124 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Lu Yang, Kate Ross, Katie Stewart, et al. Perceptions and experiences of patients, caregivers, and clinicians on using digital phenotyping measures for outcome prediction in first episode of psychosis (FEP): A qualitative study. Authorea . 17 September 2025. DOI: https://doi.org/10.22541/au.175811421.13653509/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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