Personal Digital Devices Research Articles

MODERN PERSPECTIVES OF APPLYING THE CONCEPT OF ZERO TRUST IN BUILDING A CORPORATE INFORMATION SECURITY POLICY

Modern businesses have undergone significant changes as a result of digital advances and the recent COVID-19 pandemic. In particular, there has been an increase in the number of employees working remotely, using personal digital devices alongside corporate devices, and the enterprise itself moving business processes to the cloud or using hybrid environments that combine both cloud and on-premises services. Taken together, this leads to increased interaction between devices and services over open networks, creating new risks of cyber-attack. It is this situation that has led to the relevance and direction of this research. The paper analyzes the current state of effectiveness of the application of enterprise information security policy, in particular, identifies the main limitations associated with the difficulty, and sometimes impossibility, to control the behavioral aspects of enterprise employees to comply with the basic provisions of security policy and general information security. The basic principles of the Zero Trust conceptual approach are analyzed and the main advantages of its application in the formation of the security policy as a strategic approach to ensuring the information security of the enterprise in the conditions of dynamic growth of new threats and transformation of modern business are determined. At the same time, it is established that one of the key components of the Zero Trust architecture is the access control system. As a result, forming the prospects of applying the concept of Zero Trust in the construction and implementation of the information security policy, the necessity of conducting an accompanying study of the effectiveness of modern mechanisms of identification/authentication of access subjects was determined.

Personal digital technology use among practicing Registered Nurses and Registered Practical Nurses.

Many clinicians are using their personal digital devices (such as smartphones) while at work for personal and professional purposes. The purpose of this research was to understand how Ontario nurses used their own digital devices within the workplace. Reported here are the findings from the on-line questionnaire of a mixed methods design. Participants (N = 169) had a mean age of 41years, were mostly female, and with an average of 15.2years of nursing experience. Most (73%) used their own device within the workplace for pragmatic reasons (telling time), patient care (accessing information, drug management, and administration), and communication among the healthcare team. This research offers emerging insight into how personally owned devices are being integrated into healthcare practices and highlighted tensions among workplace efficiency and enhanced team communication. This research supports the development of guidelines for personal device use within healthcare settings.

Mobile apps for oral healthcare: recommendations for navigating uncharted terrain.

The past two years have prompted significant changes with regards to how healthcare is both taught and delivered. There has been a shift towards remote healthcare interventions where appropriate. As we return towards pre-pandemic practice, we must recognise that the healthcare environment has permanently changed. It is vital that safeguards and 'pandemic proofing' are built into healthcare provision.The prevalence of personal digital devices continues to increase, along with internet coverage. This technological revolution has also brought with it a plethora of free or cheap online platforms that allow individuals with limited IT skills to make mobile applications (apps). These factors create a perfect environment for considering mobile apps as viable, widespread healthcare interventions. There is also great potential for these to contribute to tackling inequality, reducing barriers and enhancing healthcare access. However, their use must be carefully considered.This article discusses the myriad of considerations pertaining to both developing and recommending mobile apps for healthcare. Examples of contemporaneous examples will be used, in conjunction with an app developed by the authors to demonstrate the process of creating bespoke resources.

Development and acceptability of a mobile health application integrated with the electronic heath record for treatment of chronic insomnia disorder.

To describe the development and feasibility of a cognitive behavioral therapy for insomnia (CBT-I) program delivered via personal digital devices and fully integrated with the electronic health record (EHR). A multidisciplinary team of clinicians and members of our Center for Digital Health collaborated to develop a Chronic Insomnia Interactive Care Plan (ChI-ICP), an application that provides personalized and just in time education and promotes self-management using CBT-I concepts, and is activated from and fully integrated into the EHR. Following development, we evaluated patient engagement and workflows, assessed changes to provider workload, and examined outcomes on measures of insomnia during a pilot deployment of the application. A total of 222 patients were enrolled and 179 engaged with the plan during the 3-month pilot program. Enrolled patients generated an average of 3.9 ± 2.3 In Basket messages, most being automated notifications related to noncompletion of assigned tasks, while only a few were related to patients requesting additional training or help with insomnia. Sleep efficiency improved from baseline until the completion of the program from 74.5% ± 16.7% to 87.6% ± 10.8% (P = .001), and the Insomnia Severity Index improved from 14.9 ± 5.22 to 11.6 ± 4.80 (P = .006). In this pilot implementation of an integrated ChI-ICP, patient engagement was favorable, workflows and workload were not significantly burdensome for the care teams, and initial evaluation of efficacy was favorable. This provides evidence for an application that is a scalable method to assist patients with chronic insomnia and future work should assess its efficacy in controlled trials. Morgenthaler TI, Kolla BP, Anderson SE, etal. Development and acceptability of a mobile health application integrated with the electronic heath record for treatment of chronic insomnia disorder. J Clin Sleep Med. 2022;18(12):2785-2792.

Digital Phenotyping in Health Using Machine Learning Approaches: Scoping Review

Background 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. Objective 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. Methods 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. Results 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. Conclusions 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.

A novel framework to estimate cognitive impairment via finger interaction with digital devices.

Measuring cognitive function is essential for characterizing brain health and tracking cognitive decline in Alzheimer’s Disease and other neurodegenerative conditions. Current tools to accurately evaluate cognitive impairment typically rely on a battery of questionnaires administered during clinical visits which is essential for the acquisition of repeated measurements in longitudinal studies. Previous studies have shown that the remote data collection of passively monitored daily interaction with personal digital devices can measure motor signs in the early stages of synucleinopathies, as well as facilitate longitudinal patient assessment in the real-world scenario with high patient compliance. This was achieved by the automatic discovery of patterns in the time series of keystroke dynamics, i.e. the time required to press and release keys, by machine learning algorithms. In this work, our hypothesis is that the typing patterns generated from user-device interaction may reflect relevant features of the effects of cognitive impairment caused by neurodegeneration. We use machine learning algorithms to estimate cognitive performance through the analysis of keystroke dynamic patterns that were extracted from mechanical and touchscreen keyboard use in a dataset of cognitively normal (n = 39, 51% male) and cognitively impaired subjects (n = 38, 60% male). These algorithms are trained and evaluated using a novel framework that integrates items from multiple neuropsychological and clinical scales into cognitive subdomains to generate a more holistic representation of multifaceted clinical signs. In our results, we see that these models based on typing input achieve moderate correlations with verbal memory, non-verbal memory and executive function subdomains [Spearman’s ρ between 0.54 (P < 0.001) and 0.42 (P < 0.001)] and a weak correlation with language/verbal skills [Spearman’s ρ 0.30 (P < 0.05)]. In addition, we observe a moderate correlation between our typing-based approach and the Total Montreal Cognitive Assessment score [Spearman’s ρ 0.48 (P < 0.001)]. Finally, we show that these machine learning models can perform better by using our subdomain framework that integrates the information from multiple neuropsychological scales as opposed to using the individual items that make up these scales. Our results support our hypothesis that typing patterns are able to reflect the effects of neurodegeneration in mild cognitive impairment and Alzheimer’s disease and that this new subdomain framework both helps the development of machine learning models and improves their interpretability.

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

Depression is a serious medical condition and is a leading cause of disability worldwide. Current depression diagnostics and assessment has significant limitations due to heterogeneity of clinical presentations, lack of objective assessments, and assessments that rely on patients' perceptions, memory, and recall. Digital phenotyping (DP), especially assessments conducted using mobile health technologies, has the potential to greatly improve accuracy of depression diagnostics by generating objectively measurable endophenotypes. DP includes two primary sources of digital data generated using ecological momentary assessments (EMA), assessments conducted in real-time, in subjects' natural environment. This includes active EMA, data that require active input by the subject, and passive EMA or passive sensing, data passively and automatically collected from subjects' personal digital devices. The raw data is then analyzed using machine learning algorithms to identify behavioral patterns that correlate with patients' clinical status. Preliminary investigations have also shown that linguistic and behavioral clues from social media data and data extracted from the electronic medical records can be used to predict depression status. These other sources of data and recent advances in telepsychiatry can further enhance DP of the depressed patients. Success of DP endeavors depends on critical contributions from both psychiatric and engineering disciplines. The current review integrates important perspectives from both disciplines and discusses parameters for successful interdisciplinary collaborations. A clinically-relevant model for incorporating DP in clinical setting is presented. This model, based on investigations conducted by our group, delineates development of a depression prediction system and its integration in clinical setting to enhance depression diagnostics and inform the clinical decision making process. Benefits, challenges, and opportunities pertaining to clinical integration of DP of depression diagnostics are discussed from interdisciplinary perspectives.

Remote Patient Monitoring in Diabetes: How to Acquire, Manage, and Use All of the Data.

The ability of patients and health care providers to use various forms of technology for general health has significantly increased in the past several years with the expansion of telehealth, digital applications, personal digital devices, smartphones, and other internet-connected platforms and devices. For individuals with diabetes, this also includes connected blood glucose meters, continuous glucose monitoring devices, and insulin delivery systems. In this article, the authors outline several steps to facilitate the acquisition, management, and meaningful use of digital diabetes data that can enable successful implementation of both diabetes technology and telehealth services in primary care clinics.

Digital Approaches to Automated and Machine Learning Assessments of Hearing: Scoping Review.

BackgroundHearing loss affects 1 in 5 people worldwide and is estimated to affect 1 in 4 by 2050. Treatment relies on the accurate diagnosis of hearing loss; however, this first step is out of reach for >80% of those affected. Increasingly automated approaches are being developed for self-administered digital hearing assessments without the direct involvement of professionals.ObjectiveThis study aims to provide an overview of digital approaches in automated and machine learning assessments of hearing using pure-tone audiometry and to focus on the aspects related to accuracy, reliability, and time efficiency. This review is an extension of a 2013 systematic review.MethodsA search across the electronic databases of PubMed, IEEE, and Web of Science was conducted to identify relevant reports from the peer-reviewed literature. Key information about each report’s scope and details was collected to assess the commonalities among the approaches.ResultsA total of 56 reports from 2012 to June 2021 were included. From this selection, 27 unique automated approaches were identified. Machine learning approaches require fewer trials than conventional threshold-seeking approaches, and personal digital devices make assessments more affordable and accessible. Validity can be enhanced using digital technologies for quality surveillance, including noise monitoring and detecting inconclusive results.ConclusionsIn the past 10 years, an increasing number of automated approaches have reported similar accuracy, reliability, and time efficiency as manual hearing assessments. New developments, including machine learning approaches, offer features, versatility, and cost-effectiveness beyond manual audiometry. Used within identified limitations, automated assessments using digital devices can support task-shifting, self-care, telehealth, and clinical care pathways.

Heart Watch Study: protocol for a pragmatic randomised controlled trial

IntroductionPersonal digital devices that provide health information, such as the Apple Watch, have developed an increasing array of cardiopulmonary tracking features which have received regulatory clearance and are directly marketed...

Creating the ‘virtual’ witness: the limits of empathy

ABSTRACT This article analyses two interactive exhibits – The National Holocaust Centre and Museum’s ‘The Forever Project’ and the Shoah Foundation’s ‘New Dimensions in Technology’ – which have created digital versions of Holocaust survivors who will respond to visitors’ questions, with the intention of creating meaningful ‘interactions’ with these survivors long after the ‘biological’ witnesses have passed away. It argues that the formal qualities of these ‘virtual witnesses’ have cognitive and emotional associations that inhibit viewer empathy: first, since the digital avatar is a form normally associated with games and play, participants may consciously or subconsciously ‘test’ the limits of this interactive technology; second, people use vocal commands to interact with the avatar, much like ‘virtual assistants’ found on personal digital devices; and third, their ‘uncanny valley’ appearance is affectively disturbing. It concludes by querying as to the use of these and other technologies for memorial exhibits.

Physical activity, patient-reported symptoms, and clinical events: Insights into postprocedural recovery from personal digital devices

BackgroundPersonal digital devices may offer insights into patient recovery and an approach for remote monitoring after procedures.ObjectiveTo examine associations between activity measured using personal digital devices, patient-reported outcome measures (PROMs), and clinical events among patients after catheter ablation for atrial fibrillation (AF) or bariatric surgery.MethodsWe aggregated personal digital device, PROM, and electronic health record data in a study conducted at 2 health systems. We used Fitbit devices for step count assessments, KardiaMobile for cardiac rhythm assessments, and PROMs for pain and palpitations over 5 weeks.ResultsAmong 59 patients, 30 underwent AF ablation and 29 bariatric surgery. Thirty-six patients (63%) reported pain. There was no difference in median [interquartile range] daily steps between patients with and those without pain (4419 [3286–7041] vs 3498 [2609–5888]; P = .23). Among AF ablation patients, 21 (70%) reported palpitations. Median daily steps were lower among those with palpitations than among those without (4668 [3021–6116] vs 8040 [6853–10,394]; P = .03). When accounting for within-subject correlation, recordings of AF were associated with a significant mean decrease in median daily steps (–351; 95% confidence interval –524 to –177; P <.01). Patients who received a new antiarrhythmic drug prescription had AF recorded in a median of 5 [5–5] of 5 total weeks, whereas patients who did not receive a new antiarrhythmic recorded AF in a median of 1 [0–3] week (P = .02).ConclusionPersonal digital device and PROM data can provide insight into postprocedural recovery outside of usual clinical settings and may inform follow-up and clinical decision-making. (ClinicalTrials.gov Identifier: NCT03436082)

Building information modeling indoor path planning: A lightweight approach for complex BIM building

AbstractThe increased growth of building complexity confronts the limited device resources, especially for lightweight personal digital devices. The research of handling these complex buildings with limited resources has become a pivotal research topic. This paper explores the task of lightweight indoor path planning in complex building information modeling (BIM) buildings. Both the environment modeling and path searching are important addressed through indoor path planning with a lightweight web‐based approach. A lightweight framework is designed in which the complex indoor path planning of a whole BIM building can be simplified into many local path searching of the building's interior spaces and all the online computation is decreased within no more than two simpler ones. In addition, an automatically indoor environment modeling method is proposed to partition the BIM building into many local interior spaces, constructing their spatial relationship and local grid map in a multilayered network. In the end, a heuristics optimized A star algorithm is implemented to speed up the shortest local path searching for the case of the path ends in a narrow corner. The final experiment shows that the online computational cost can be reduced greatly and dynamically to an average of 2 s when searching for the shortest path in a complex BIM building.

Blue light-induced retinal damage: a brief review and a proposal for examining the hypothetical causal link between person digital device use and retinal injury

Background: There is growing concern that the increased use of personal digital devices, which emit a high proportion of their light in the blue wavelengths, may have harmful effects on the retina. Extensive historical as well as current research demonstrates that exposure to high energy visible light (blue light) can damage the retina under certain circumstances. There are, however, no studies that directly address whether blue light at the intensities emitted by digital devices can potentially cause such harm. The present review aimed to examine whether blue light exposure from computers, tablets, and cell phones can, when used habitually over a prolonged period of time, be harmful to the retinal.&#x0D; Methods: A search of the literature on blue light-induced retinal damage was performed using a number of scientific search engines, including BioOne Complete™, Google Scholar™, Paperity™, PubMed™, and ScienceOpen™. Studies most significant for addressing the question of possible harmful effects of blue light emitted by personal digital devices were selected from this search and reviewed.&#x0D; Results: The data from the selected studies were summarized and their limitations in addressing the question of whether the blue light from personal digital devices is capable of producing retinal damage were addressed. Based on these limitations, a practical experimental protocol for collecting the additional data needed was proposed. Data from pilot experiments are presented that indicate the practicality of this approach.&#x0D; Conclusions: The currently available data on the effects of blue light on the retina are not sufficient to refute the hypothesis that the use of personal digital devices could, over a lifetime, produce retinal damage. Additional studies, such as those proposed in this article, are needed to resolve this issue.

Medical students' awareness of Personal Digital Assistant Devices' impact on their health.

Aims:1. To evaluate medical students’ awareness of personal digital assistant devices impacts on their overall health. 2. To estimate medical students’ hours on digital devices. 3. To determine the most common effect of personal digital assistant devices on medical student health.Settings and Method:This descriptive cross-sectional study was conducted at the college of medicine of a in Riyadh, Saudi Arabia. The participants were medical students, and a questionnaire consists of 3 sections was used. A sample of 289 medical students participated in the study. Kruskal–Wallis test was used to analyze the relationship between the knowledge score and the year of study.Statistical Analysis used and Results:The research included medical students whose ages ranged from 19 to 25 years with a median (IQR) of 22.00 (21.00–23.00). Approximately, 56% of the participants were males, and the majority of the students were from year 3. The average hours spent daily while using PDA for studying was 5 hours with SD 2.7. 167 (58%) of the students reported that studying using PDAs has affected their life. The most experienced effects reported was dry eyes and vision problems 53 (54%).Conclusions:The current study revealed that the majority of medical students have adequate knowledge of PDAs’ effects on their health despite their continuous use. More attention should be paid to reduce the effects on their health by raising campaigns to increase the awareness of all the students and the community.

Digital Phenotyping and Patient-Generated Health Data for Outcome Measurement in Surgical Care: A Scoping Review.

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.

Nurses' Use of Personal Smartphone Technology in the Workplace: Scoping Review.

BackgroundThere has been an increase in the technological infrastructures of many health care organizations to support the practice of health care providers. However, many nurses are using their personal digital devices, such as smartphones, while at work for personal and professional purposes. Despite the proliferation of smartphone use in the health care setting, there is limited research on the clinical use of these devices by nurses. It is unclear as to what extent and for what reasons nurses are using their personal smartphones to support their practice.ObjectiveThis review aimed to understand the current breadth of research on nurses’ personal smartphone use in the workplace and to identify implications for research, practice, and education.MethodsA scoping review using Arksey and O’Malley’s methodological framework was conducted, and the following databases were used in the literature search: CINAHL, PubMed, ProQuest Dissertations and Theses, Embase, MEDLINE, Nursing and Allied Health Database, Scopus, Web of Science, and Cochrane Reviews. Search terms used were Nurs* AND (personal digital technology OR smartphone OR cellphone OR mobile phone OR cellular phone). Inclusion criteria included research focused on nurses’ use of their own digital technologies, reported in English, and published between January 2010 and January 2020. Exclusion criteria were if the device or app was implemented for research purposes, if it was provided by the organization, if it focused on infection control, and if it was focused on nursing students or nursing education.ResultsA total of 22 out of 2606 articles met the inclusion criteria. Two main themes from the thematic analyses included personal smartphone use for patient care and implications of personal smartphone use. Nurses used their smartphones to locate information about medications, procedures, diagnoses, and laboratory tests. Downloaded apps were used by nurses to locate patient care–related information. Nurses reported improved communication among health team members and used their personal devices to communicate patient information via text messaging, calling, and picture and video functions. Nurses expressed insight into personal smartphone use and challenges related to distraction, information privacy, organizational policies, and patient perception.ConclusionsNurses view personal smartphones as an efficient method to gather patient care information and to communicate with the health care team. This review highlights knowledge gaps regarding nurses’ personal device use and information safety, patient care outcomes, and communication practices. This scoping review facilitates critical reflection on patient care practices within the digital context. We infer that nurses’ use of their personal devices to communicate among the health care team may demonstrate a technological “work-around” meant to reconcile health system demands for cost-efficiency with efforts to provide quality patient care. The current breadth of research is focused on acute care, with little research focus in other practices settings. Research initiatives are needed to explore personal device use across the continuum of health care settings.

The influence of technology on academic distraction: A review

The detrimental effects of academic distraction have become increasingly problematic in recent years due to student media multitasking. In recent years, educators have gradually transitioned many classes to the online learning environment, effectively reducing teacher supervision. This has been greatly exacerbated with the advent of coronavirus disease 2019 and subsequent social distancing guidelines. In an effort to help students and educators better understand the implications of technology on academic distraction and academic performance, this literature review discusses the effects of three forms on technology on student distraction: laptops, smartphones, and social media use, particularly the social media platform Facebook. While the effects of student laptops on academic distraction are somewhat ambivalent, the debate is largely moot in the current educational environment. The detrimental effects of student smartphone and social media use on academic distraction are more conspicuous, especially with the pervasiveness of personal digital devices. Potential self‐regulatory and environmental interventions are discussed. [ABSTRACT FROM AUTHOR] Copyright of Human Behavior & Emerging Technologies is the property of John Wiley & Sons, Inc. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Appropriate prescribing of dental prophylaxis in patients with orthopedic implants.

Total joint arthroplasty frequently is performed in the United States to treat degenerative hip and knee arthritis. Despite the routine nature of this procedure, recommendations about dental prophylaxis in patients with prosthetic joints have varied considerably over the past decade. In 2016, the American Academy of Orthopaedic Surgeons (AAOS) published appropriate use criteria for dental prophylaxis in patients with orthopedic implants. Despite these recommendations, discrepancies in prescribing practices persist, and controversy surrounding this topic remains prevalent in the medical literature. This article reviews the AAOS appropriate use criteria and discusses the ongoing controversy about the use of dental prophylaxis in patients with orthopedic implants.

Digital Phenotyping Using Multimodal Data

Digital phenotyping involves the quantification of in situ phenotypes using personal digital devices and holds the potential to dramatically reshape how serious mental illnesses (SMI) assessment is conducted. Despite promise, few, if any, digital phenotyping efforts for SMI have garnered the support necessary for clinical implementation. In this paper, we highlight how digital phenotyping efforts can be improved by integrating data from multiple channels (i.e., “multimodal” data integration). We begin by arguing that “multimodal” integration is critical for digital phenotyping of many, possibly most, SMI symptoms. Next, we consider computational approaches that can accommodate multimodal data. We conclude by considering next steps for multimodal data for research and clinical applications. We punctuate this paper with examples of multimodal digital phenotyping using natural language processing (NLP) to measure speech disorganization in psychosis.