Digital phenotyping in depression diagnostics: Integrating psychiatric and engineering perspectives.

Multiple digital data sources can capture moment-to-moment information to advance a robust understanding of opioid use disorder (OUD) behavior, ultimately creating a digital phenotype for each patient. This information can lead to individualized interventions to improve treatment for OUD. The aim is to examine patient engagement with multiple digital phenotyping methods among patients receiving buprenorphine medication for OUD. The study enrolled 65 patients receiving buprenorphine for OUD between June 2020 and January 2021 from 4 addiction medicine programs in an integrated health care delivery system in Northern California. Ecological momentary assessment (EMA), sensor data, and social media data were collected by smartphone, smartwatch, and social media platforms over a 12-week period. Primary engagement outcomes were meeting measures of minimum phone carry (≥8 hours per day) and watch wear (≥18 hours per day) criteria, EMA response rates, social media consent rate, and data sparsity. Descriptive analyses, bivariate, and trend tests were performed. The participants' average age was 37 years, 47% of them were female, and 71% of them were White. On average, participants met phone carrying criteria on 94% of study days, met watch wearing criteria on 74% of days, and wore the watch to sleep on 77% of days. The mean EMA response rate was 70%, declining from 83% to 56% from week 1 to week 12. Among participants with social media accounts, 88% of them consented to providing data; of them, 55% of Facebook, 54% of Instagram, and 57% of Twitter participants provided data. The amount of social media data available varied widely across participants. No differences by age, sex, race, or ethnicity were observed for any outcomes. To our knowledge, this is the first study to capture these 3 digital data sources in this clinical population. Our findings demonstrate that patients receiving buprenorphine treatment for OUD had generally high engagement with multiple digital phenotyping data sources, but this was more limited for the social media data. RR2-10.3389/fpsyt.2022.871916.

IntroductionPhysical activity (PA) is crucial for older adults’ well-being and mitigating health risks. Encouraging active lifestyles requires a deeper understanding of the factors influencing PA, which conventional approaches often overlook by assuming stability in these determinants over time. However, individual-level determinants fluctuate over time in real-world settings. Digital phenotyping (DP), employing data from personal digital devices, enables continuous, real-time quantification of behaviour in natural settings. This approach offers ecological and dynamic assessments into factors shaping individual PA patterns within their real-world context. This paper presents a study protocol for the DP of PA behaviour among community-dwelling older adults aged 65 years and above.Methods and analysisThis 2-week multidimensional assessment combines supervised (self-reported questionnaires, clinical assessments) and unsupervised methods (continuous wearable monitoring and ecological momentary assessment (EMA)). Participants will wear a Garmin Vivosmart V.5 watch, capturing 24/7 data on PA intensity, step count and heart rate. EMA will deliver randomised prompts four times a day via the Smartphone Ecological Momentary Assessment3 application, collecting real-time self-reports on physical and mental health, motivation, efficacy and contextual factors. All measurements align with the Behaviour Change Wheel framework, assessing capability, opportunity and motivation. Machine learning will analyse data, employing unsupervised learning (eg, hierarchical clustering) to identify PA behaviour patterns and supervised learning (eg, recurrent neural networks) to predict behavioural influences. Temporal patterns in PA and EMA responses will be explored for intraday and interday variability, with follow-up durations optimised through random sliding window analysis, with statistical significance evaluated in RStudio at a threshold of 0.05.Ethics and disseminationThe study has been approved by the ethical committee of Hasselt University (B1152023000011). The findings will be presented at scientific conferences and published in a peer-reviewed journal.Trial registration numberNCT06094374.

Mood and anxiety disorders are highly prevalent and comorbid worldwide, with variability in symptom severity that fluctuates over time. Digital phenotyping, a growing field that aims to characterize clinical, cognitive and behavioral features via personal digital devices, enables continuous quantification of symptom severity in the real world, and in real-time. In this study, N=114 individuals with a mood or anxiety disorder (MA) or healthy controls (HC) were enrolled and completed 30-days of ecological momentary assessments (EMA) of symptom severity. Novel real-world measures of anxiety, distress and depression were developed based on the established Mood and Anxiety Symptom Questionnaire (MASQ). The full MASQ was also completed in the laboratory (in-lab). Additional EMA measures related to extrinsic and intrinsic motivation, and passive activity data were also collected over the same 30-days. Mixed-effects models adjusting for time and individual tested the association between real-world symptom severity EMA and the corresponding full MASQ sub-scores. A graph theory neural network model (DEPNA) was applied to all data to estimate symptom interactions. There was overall good adherence over 30-days (MA=69.5%, HC=71.2% completion), with no group difference (t(58)=0.874, p=0.386). Real-world measures of anxiety/distress/depression were associated with their corresponding MASQ measure within the MA group (t's > 2.33, p's < 0.024). Physical activity (steps) was negatively associated with real-world distress and depression (IRRs > 0.93, p's ≤ 0.05). Both intrinsic and extrinsic motivation were negatively associated with real-world distress/depression (IRR's > 0.82, p's < 0.001). DEPNA revealed that both extrinsic and intrinsic motivation significantly influenced other symptom severity measures to a greater extent in the MA group compared to the HC group (extrinsic/intrinsic motivation: t(46) = 2.62, p < 0.02, q FDR < 0.05, Cohen's d = 0.76; t(46) = 2.69, p < 0.01, q FDR < 0.05, Cohen's d = 0.78 respectively), and that intrinsic motivation significantly influenced steps (t(46) = 3.24, p < 0.003, q FDR < 0.05, Cohen's d = 0.94). Novel real-world measures of anxiety, distress and depression significantly related to their corresponding established in-lab measures of these symptom domains in individuals with mood and anxiety disorders. Novel, exploratory measures of extrinsic and intrinsic motivation also significantly related to real-world mood and anxiety symptoms and had the greatest influencing degree on patients' overall symptom profile. This suggests that measures of cognitive constructs related to drive and activity may be useful in characterizing phenotypes in the real-world.

Are student nurses ready for new technologies in mental health? Mixed-methods study

Digital phenotyping - the moment-by-moment quantification of individual-level human behavior using data from personal digital devices - offers a novel approach to continuous, passive monitoring of psychiatric patients. Changes in behavioral digital phenotypes may serve as early warning signs of relapse or clinical deterioration, creating an opportunity for timely preventive intervention. A systematic search of PubMed, PsycINFO, and IEEE Xplore was conducted for studies published up to January 2026. We included prospective and retrospective observational studies using passively collected smartphone or wearable data to predict relapse or clinical deterioration in individuals with diagnosed psychiatric disorders, with reported quantitative model performance metrics. Study quality was assessed using the modified Newcastle-Ottawa Scale [16] and the PROBAST [25, 26] tool. Data were synthesized narratively in accordance with the SWiM guideline. Fifty-two studies encompassing 4,814 participants met inclusion criteria. Disorders studied included schizophrenia spectrum disorders (35%), bipolar disorder (27%), and major depressive disorder (23%). Key predictive features included alterations in sleep patterns (83% of studies), physical activity (83%), GPS-derived mobility (75%), and social communication frequency (65%). Machine learning models reported AUC values ranging from 0.70 to 0.88 for predicting relapse one to four weeks in advance, although the majority of these estimates were derived from internal validation and are likely to overestimate real-world performance. Multi-modal data integration and individual-level modeling consistently outperformed single-modality and population-level approaches. High risk of bias was identified in 75% of studies, primarily attributable to inadequate analytic methodology and reliance on internal validation. Passive digital phenotyping demonstrates significant promise for predicting psychiatric relapse across diagnostic categories, with moderate-to-good predictive discrimination (AUC 0.70-0.88) achievable up to four weeks prior to confirmed relapse. However, substantial methodological limitations - including reliance on internal validation, heterogeneous outcome definitions, and limited demographic diversity - must be addressed. Standardized outcome definitions, prospective external validation in diverse cohorts, and closed-loop intervention trials are required before widespread clinical implementation can be responsibly pursued.

Sleep disorders, a critical issue in global health, affect millions worldwide.Disorders ranging from insomnia to sleep apnea profoundly impact individual well-being and societal productivity [1].While traditional diagnostic and therapeutic methods like polysomnography and cognitive-behavioral therapy for insomnia are effective, they are also labor-intensive, less patient-centered, and expensive.The emergence of digital phenotyping, using data from personal digital devices such as smartphones and wearables, heralds a promising new direction in sleep medicine [2].Digital phenotyping offers several advantages over traditional methods.It allows continuous, active, and passive data collection in a patient's natural environment, capturing a nuanced and comprehensive image of daily sleep patterns.These insights illuminate the interplay between sleep, lifestyle, behavior, health, and overall well-being [2].Digital phenotyping is also cost-effective, negating the need for expensive equipment or hospitalization, facilitating early identification of high-risk individuals for testing, and reducing unnecessary healthcare expenditure.Recent studies have validated the use of digital phenotyping in sleep medicine, revealing that sleep patterns derived from smartphones or wearable devices closely correlate with actigraphy, a noninvasive method for monitoring rest/activity cycles [3,4].Techniques introduced to measure aspects such as sleep stages and sleep apnea events using only smartphone data demonstrate that digital phenotyping may facilitate screening for sleep disorders [5].Additionally, conditions like mood disorders, closely linked to sleep-wake rhythms, can be assessed or predicted based on digital

Digital phenotyping is the real-time collection of individual-level active and passive data from users in naturalistic and free-living settings via personal digital devices, such as mobile phones and wearable devices. Given the novelty of research in this field, there is heterogeneity in the clinical use cases, types of data collected, modes of data collection, data analysis methods, and outcomes measured. The primary aim of this scoping review was to map the published research on digital phenotyping and to outline study characteristics, data collection and analysis methods, machine learning approaches, and future implications. We utilized an a priori approach for the literature search and data extraction and charting process, guided by the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-analyses Extension for Scoping Reviews). We identified relevant studies published in 2020, 2021, and 2022 on PubMed and Google Scholar using search terms related to digital phenotyping. The titles, abstracts, and keywords were screened during the first stage of the screening process, and the second stage involved screening the full texts of the shortlisted articles. We extracted and charted the descriptive characteristics of the final studies, which were countries of origin, study design, clinical areas, active and/or passive data collected, modes of data collection, data analysis approaches, and limitations. A total of 454 articles on PubMed and Google Scholar were identified through search terms associated with digital phenotyping, and 46 articles were deemed eligible for inclusion in this scoping review. Most studies evaluated wearable data and originated from North America. The most dominant study design was observational, followed by randomized trials, and most studies focused on psychiatric disorders, mental health disorders, and neurological diseases. A total of 7 studies used machine learning approaches for data analysis, with random forest, logistic regression, and support vector machines being the most common. Our review provides foundational as well as application-oriented approaches toward digital phenotyping in health. Future work should focus on more prospective, longitudinal studies that include larger data sets from diverse populations, address privacy and ethical concerns around data collection from consumer technologies, and build "digital phenotypes" to personalize digital health interventions and treatment plans.

Background: Stress is a complex phenomenon that may have a negative influence on health and well-being; consequently, it plays a pivotal role in mental health. Although the incidence of mental disorders has been continuously rising, development of prevention and treatment methods has been rather slow. Through the ubiquitous presence of smartphones and wearable devices, people can monitor stress parameters in everyday life. However, the reliability and validity of such monitoring are still unsatisfactory.Methods: The aim of this trial is to find a relationship between psychological stress and saliva cortisol levels on the one hand and physiological parameters measured by smartphones in combination with a commercially available wearable device on the other. Participants include cohorts of individuals with and without a psychiatric disorder. The study is conducted in two settings: one naturalistic and one a controlled laboratory environment, combining ecological momentary assessment (EMA) and digital phenotyping (DP). EMA is used for the assessment of challenging and stressful situations coincidentally happening during a whole observation week. DP is used during a controlled stress situation with the Trier Social Stress Test (TSST) as a standardized psychobiological paradigm. Initially, participants undergo a complete psychological screening and profiling using a standardized psychometric test battery. EMA uses a smartphone application, and the participants keep a diary about their daily routine, activities, well-being, sleep, and difficult and stressful situations they may encounter. DP is conducted through wearable devices able to continuously monitor physiological parameters (i.e., heart rate, heart rate variability, skin conductivity, temperature, movement and acceleration). Additionally, saliva cortisol samples are repeatedly taken. The TSST is conducted with continuous measurement of the same parameters measured during the EMA.Discussion: We aim to identify valid and reliable digital biomarkers for stress and stress reactions. Furthermore, we expect to find a way of early detection of psychological stress in order to evolve new opportunities for interventions reducing stress. That may allow us to find new ways of treating and preventing mental disorders.Trial Registration: The competing ethics committee of the Canton of Zurich, Switzerland, approved the study protocol V05.1 May 28, 2019 [BASEC: 2019-00814]; the trial was registered at ClinicalTrials.gov [NCT04100213] on September 19, 2019.

Youth from racial and ethnic minoritized groups have experienced an increase in suicidal thoughts and behaviors (STBs) in recent years. Mobile health technology (mHealth) and digital phenotyping hold promise as means to measure STBs and related risk factors in these groups. Such tools are more likely to be successful when designed with input from the youth and caregivers who will use the technology. This study aimed to refine a digital phenotyping smartphone application, GeoMood, customized to measure STBs and relevant risk factors, such as family conflict and experiences of discrimination. The app was designed to collect passive data from smartphones (e.g., location, phone usage), as well as short-response survey data via ecological momentary assessments (EMAs) to further understand digital phenotypes of STBs. We conducted semi-structured qualitative interviews with five youths of color and five caregivers to obtain feedback and refine the smartphone application, GeoMood. The ultimate goal of the interviews was to assess the app's potential acceptability from the two sets of users for whom the app was developed. Both youth and caregivers reviewed the youth version, which differs from the caregiver version content by the inclusion of items addressing suicidal behavior. Interviews were analyzed using a qualitative manifest analytic approach. Youth found the app to be an acceptable tool for measuring STBs. Caregivers were concerned about assessing self-injury explicitly. Youth and caregiver feedback confirms openness by participating youth to using mHealth tools for measurement of STBs, but caregivers experience hesitation with the direct questions of such tools. Feedback was useful in refining the mobile tool and suggests multimodal assessment (text and emoji prompts) may appeal to users. Results from this study may improve the acceptability of future apps designed to measure and address disparities among particularly vulnerable groups of youth.

Digital phenotyping—the moment-by-moment quantification of human phenotypes in situ using data related to activity, behavior, and communications, from personal digital devices, such as smart phones and wearables—has been gaining interest. Personalized health information captured within free-living settings using such technologies may better enable the application of patient-generated health data (PGHD) to provide patient-centered care. The primary objective of this scoping review is to characterize the application of digital phenotyping and digitally captured active and passive PGHD for outcome measurement in surgical care. Secondarily, we synthesize the body of evidence to define specific areas for further work. We performed a systematic search of four bibliographic databases using terms related to “digital phenotyping and PGHD,” “outcome measurement,” and “surgical care” with no date limits. We registered the study (Open Science Framework), followed strict inclusion/exclusion criteria, performed screening, extraction, and synthesis of results in line with the PRISMA Extension for Scoping Reviews. A total of 224 studies were included. Published studies have accelerated in the last 5 years, originating in 29 countries (mostly from the USA, n = 74, 33%), featuring original prospective work (n = 149, 66%). Studies spanned 14 specialties, most commonly orthopedic surgery (n = 129, 58%), and had a postoperative focus (n = 210, 94%). Most of the work involved research-grade wearables (n = 130, 58%), prioritizing the capture of activity (n = 165, 74%) and biometric data (n = 100, 45%), with a view to providing a tracking/monitoring function (n = 115, 51%) for the management of surgical patients. Opportunities exist for further work across surgical specialties involving smartphones, communications data, comparison with patient-reported outcome measures (PROMs), applications focusing on prediction of outcomes, monitoring, risk profiling, shared decision making, and surgical optimization. The rapidly evolving state of the art in digital phenotyping and capture of PGHD offers exciting prospects for outcome measurement in surgical care pending further work and consideration related to clinical care, technology, and implementation.

Products and services based on digital wellbeing technologies typically include mobile device apps as well as browser-based apps to a lesser extent, and can include telephony-based services, text-based chatbots and voice activated chatbots. Many of these digital products and services are simultaneously available across many channels in order to maximize availability for users. Digital wellbeing technologies offer useful methods for real time data capture of the interactions of users with the products and services. We can design what data are recorded, how and where it may be stored, and crucially, how it can be analyzed to reveal individual or collective usage patterns. Digital phenotyping is the term given to the capturing and use of user log data from health and wellbeing technologies used in apps and cloud-based services. Digital phenotyping was originally proposed to correlate a person’s mental state by using their metadata and even sensor data on their smartphone. In some cases, the data is physiological, for example pulse or movement-related, and it is collected automatically. In other cases, the data is actually metadata, for example, when a call is made and the call duration rather than the content of the call. Oftentimes, as would be expected from a personal device located on the body of the user, rich data pertaining to geo-location, social media use and interaction is gathered. Health and wellbeing-related, scientifically validated assessment scales may also generate digital phenotype data. Another form of digital phenotype data is Ecological Momentary Assessment (EMA), which originally made use of paper-diary techniques to enable people to record their observations or answers to specific questions and combined the ecological validity with the rigorous measurement techniques of psychometric research. EMA secures data about both behavioural and intrapsychic aspects of individuals' daily activities, and it obtains reports about the experience as it occurs, thereby minimizing the effects of reliance on memory and reconstruction which can often be impaired by hindsight bias or recall bias. The use of digital phenotyping data and its analysis using machine learning and artificial intelligence is important since many national public health organizations are exploring how to use digital technologies such as health apps and cloud-based services for the self-management of diseases and thus logging user interactions allows for greater insight into user needs and provides ideas for improving these digital interventions, for example through enhanced personalization. Public health services benefit since the data can be automatically and hence cost-effectively collected. Such data may facilitate new ways for digital epidemiological analyses and provide data to inform health policies. If the public health organizations promote health apps and digital phenotyping analysis using machine learning and artificial intelligence is taken up by these organizations, then there is clear need for guidelines on the ethical application of these ‘democratized’ algorithms and techniques. My keynote talk begins by reviewing the evolution of the use of technology to support peoples’ health and wellbeing, from telecare and telehealth through to personalised healthcare, the growth of the idea of ‘quantified self’ and ultimately, self-managed care. I then discuss the growing use of commercially available digital devices and software for selfcare, and the explosion in the data arising from their use in society. The opportunities for the application of machine learning to the data, including EMA data are explored and the implications are discussed, across such areas as big data for research study design, ethics, the ‘servitization’ of machine learning, bias, surveillance, and health and wellbeing services. In order to illustrate my work, I will draw upon case studies from digital health and wellbeing, including maternal mental health, crisis helplines and apps for people living with dementia.

BackgroundWe present a digital phenotyping protocol designed to continuously and objectively measure behavioral, physiological, and contextual data during pregnancy and the postpartum period using passive sensing from Garmin smartwatches and smartphones, along with active ecological momentary assessments (EMAs). This novel protocol uniquely adapts to the unpredictable timing of childbirth, spanning from the third trimester through 6 weeks post partum, to accurately capture critical temporal changes and maternal-infant outcomes. By providing high-frequency real-time data, this methodology offers comprehensive insights into pregnancy-related behaviors and physiological processes, overcoming the limitations of traditional retrospective self-report methods.ObjectiveWe aim to develop a protocol for longitudinal data collection supporting digital phenotyping that is optimized for pregnancy and the postpartum period. This protocol leverages the pregnant population’s heightened interest in health and tracking. It aims to minimize the burden on the participants, increase retention, and assess the value of wearables compared to smartphones to determine the appropriate data collection methods.MethodsData will be collected from 30 nulliparous participants from the start of the third trimester through 6 weeks post partum. This protocol uses 3 distinct 1-time surveys, alongside daily and weekly EMAs, to capture real-time maternal experience data. Passive maternal data—such as activity, vitals, sleep, and location—are collected via smartphones and Garmin smartwatches. Participants are expected to log data about the newborn after delivery through the mobile app Huckleberry. This protocol was developed in collaboration with the Northeastern University Sath Laboratory, which focuses on digital phenotyping and longitudinal data collection, and the Tufts Medical Center’s obstetrics and gynecology department, which has expertise in working with the pregnant population.ResultsThis study was funded in August 2024. Data collection is projected to run from October 2025 to July 2026. As of September 2025, the study has been approved, and recruitment and data collection are to begin. The results are expected to be published by August 2026. We plan to assess the retention rates, survey and EMA completion rates, wear time of the smartwatch without intervention, and data volume logged in the Huckleberry app. In addition, we will perform digital phenotyping to determine whether the data collected during pregnancy can be used to predict breastfeeding outcomes, delivery outcomes, and maternal-infant well-being.ConclusionsThis protocol integrates the use of digital phenotyping in pregnancy and postpartum research, providing a novel method for capturing real-time indicators of maternal well-being. It will determine the expected rates of data completion and appropriate sample size using a power analysis for a more extensive future study. By integrating smartphone and wearable sensor data, this protocol has the potential to transform the way maternal health clinical interventions are designed and implemented in the future.International Registered Report Identifier (IRRID)PRR1-10.2196/77175

BackgroundThe growing concern on adolescent mental health calls for proactive early detection and intervention strategies. There is a recognition of the link between digital phenotypes and mental health, drawing attention to their potential use. However, the process of collecting digital phenotype data presents challenges despite its promising prospects.ObjectiveThis study aims to develop and validate system concepts for collecting adolescent digital phenotypes that effectively manage inherent challenges in the process.MethodsIn a formative investigation (N=34), we observed adolescent self-recording behaviors and conducted interviews to develop design goals. These goals were then translated into system concepts, which included planners resembling interfaces, simplified data input with tags, visual reports on behaviors and moods, and supportive ecological momentary assessment (EMA) prompts. A proof-of-concept study was conducted over 2 weeks (n=16), using tools that simulated the concepts to record daily activities and complete EMA surveys. The effectiveness of the system was evaluated through semistructured interviews, supplemented by an analysis of the frequency of records and responses.ResultsThe interview findings revealed overall satisfaction with the system concepts, emphasizing strong support for self-recording. Participants consistently maintained daily records throughout the study period, with no missing data. They particularly valued the recording procedures that aligned well with their self-recording goal of time management, facilitated by the interface design and simplified recording procedures. Visualizations during recording and subsequent report viewing further enhanced engagement by identifying missing data and encouraging deeper self-reflection. The average EMA compliance reached 72%, attributed to a design that faithfully reflected adolescents’ lives, with surveys scheduled at convenient times and supportive messages tailored to their daily routines. The high compliance rates observed and positive feedback from participants underscore the potential of our approach in addressing the challenges of collecting digital phenotypes among adolescents.ConclusionsIntegrating observations of adolescents’ recording behavior into the design process proved to be beneficial for developing an effective and highly compliant digital phenotype collection system.

BackgroundSmartphone-based digital phenotyping uses built-in sensors and usage patterns to passively capture behavioral and environmental data relevant to health and has been applied extensively in mental health and chronic disease contexts.ObjectiveThis review synthesizes studies that use smartphone-based digital phenotyping, defined as approaches that rely exclusively on onboard smartphone sensors to characterize specific health conditions. To our knowledge, this work provides the most comprehensive cross-condition synthesis of smartphone-based digital phenotyping to date, spanning mental health, physical health, and substance use disorders (SUDs), and highlighting common practices, gaps, and opportunities for future research.MethodsWe conducted a scoping review of English-language, peer-reviewed papers published between 2012 and 2025 in Google Scholar, IEEE Xplore, ACM Digital Library, and PubMed using terms such as "mobile sensing" and "digital phenotyping." Eligible papers used onboard smartphone sensors to assess health and went beyond self-report. Studies that did not rely on smartphone auxiliary sensing modalities or digital phenotyping were excluded.ResultsWe performed a descriptive synthesis of study characteristics, sensors, and health domains. Of 111 papers identified, 65 met inclusion criteria. Most studies were observational and relied on passive sensing. Sample sizes ranged from fewer than 10 to over 18,000 participants, with a median of 52 (IQR=26‐126). Mental health conditions were most frequently examined, including depression (n=16), bipolar disorder (n=11), stress or anxiety (n=10), and schizophrenia (n=8). Less commonly studied conditions included SUDs (n=7), Parkinson disease (n=4), and sleep apnea (n=2). Sensor streams varied widely and included diverse passive smartphone data sources capturing mobility, communication, device usage, environmental context, and user interaction patterns. Ground-truth measurements most commonly relied on validated clinical scales (eg, Patient Health Questionnaire-9, Young Mania Rating Scale [YMRS], and Pittsburgh Sleep Quality Index; n=41), followed by ecological momentary assessments (n=18), clinician-confirmed diagnoses (n=9), and physiological measures such as polysomnography (n=3). Across studies, recurring methodological limitations included incomplete or inconsistent sensor descriptions, limited reporting of data quality (eg, sampling rates and missingness), and heterogeneous validation practices. These issues limit comparability and reproducibility and underscore the need for clearer reporting standards and greater data availability.ConclusionsThis scoping review provides the first comprehensive synthesis of smartphone-only digital phenotyping studies spanning mental health, physical health, and SUDs. Unlike prior reviews, this work maps behavioral associations derived exclusively from smartphone sensors across a broad range of health domains. The primary contribution of this review lies in its consolidation of behavioral associations observed across studies, enabling researchers to correlate new findings to the existing evidence base and identify opportunities for replication, extension, or clinical translation. Collectively, these findings highlight both the promise of smartphone-based digital phenotyping in real-world settings and the need for improved standardization to support translation into clinical and public health applications.

Digital phenotyping is a novel approach to refer to moment-by-moment quantification of the individual-level social, physical, cognitive, emotional and behavioral phenotype in situ, using data from personal digital devices. This concept, understood as a tool to retrieve data on the users' state, including information about their own perception on their health, is unveiling as a powerful instrument to better understand patients with mental disorders for scientific and clinical goals, but also to provide personalized coaching and support that may facilitate disease detection, monitoring and treatment. In this paper, we review previous works on digital phenotyping for mental health care, through a framework that facilitates the description of relevant studies. We propose a general architecture for digital phenotyping platforms from the main key functionalities that have been identified and analyze the main barriers and needs to be overcome to exploit data in a patient-centric way. In particular, there is a requirement for extensive user validation, as existing studies are still very preliminary and, as a consequence, it is also key to explore the integration of digital phenotyping mhealth solutions as cornerstone tools for integrated care delivery.