Comparing Location Data From Smartphone and Dedicated Global Positioning System Devices: Implications for Epidemiologic Research.

In this study, we compared location data from a dedicated Global Positioning System (GPS) device with location data from smartphones. Data from the Interventions, Equity, and Action in Cities Team (INTERACT) Study, a study examining the impact of urban-form changes on health in 4 Canadian cities (Victoria, Vancouver, Saskatoon, and Montreal), were used. A total of 337 participants contributed data collected for about 6 months from the Ethica Data smartphone application (Ethica Data Inc., Toronto, Ontario, Canada) and the SenseDoc dedicated GPS (MobySens Technologies Inc., Montreal, Quebec, Canada) during the period 2017-2019. Participants recorded an average total of 14,781 Ethica locations (standard deviation, 19,353) and 197,167 SenseDoc locations (standard deviation, 111,868). Dynamic time warping and cross-correlation were used to examine the spatial and temporal similarity of GPS points. Four activity-space measures derived from the smartphone app and the dedicated GPS device were compared. Analysis showed that cross-correlations were above 0.8 at the 125-m resolution for the survey and day levels and increased as cell size increased. At the day or survey level, there were only small differences between the activity-space measures. Based on our findings, we recommend dedicated GPS devices for studies where the exposure and the outcome are both measured at high frequency and when the analysis will not be aggregate. When the exposure and outcome are measured or will be aggregated to the day level, the dedicated GPS device and the smartphone app provide similar results.

Guidelines for the development, performance evaluation and validation of new sleep technologies (DEVSleepTech guidelines) - a protocol for a Delphi consensus study.

New sleep technologies are being developed, refined and delivered at a fast pace. However, there are serious concerns about the validation and accuracy of new sleep-related technologies being made available, as many of them, especially consumer-sleep technologies, have not been tested in comparison with gold-standard methods or have been approved by health regulatory agencies. The importance of proper validation and performance evaluation of new sleep technologies has already been discussed in previous studies and some recommendations have already been published, but most of them do not employ standardized methodology and are not able to cover all aspects of new sleep technologies. The current protocol describes the methods of a Delphi consensus study to create guidelines for the development, performance evaluation and validation of new sleep devices and technologies. The resulting recommendations are not intended to be used as a quality assessment tool to evaluate individual articles, but rather to evaluate the overall procedures, studies and experiments performed to develop, evaluate performance and validate new technologies. We hope these guidelines can be helpful for researchers who work with new sleep technologies on the appraisal of their reliability and validation, for companies who are working on the development and refinement of new sleep technologies, and by regulatory agencies to evaluate new technologies that are looking for registration, approval or inclusion on health systems.

Digital phenotyping data and anomaly detection methods to assess changes in mood and anxiety symptoms across a transdiagnostic clinical sample.

INTRODUCTION: Clinical assessment of mood and anxiety change often relies on clinical assessment or self-reported scales. Using smartphone digital phenotyping data and resulting markers of behavior (e.g., sleep) to augment clinical symptom scores offers a scalable and potentially more valid method to understand changes in patients' state. This paper explores the potential of using a combination of active and passive sensors in the context of smartphone-based digital phenotyping to assess mood and anxiety changes in two distinct cohorts of patients to assess the preliminary reliability and validity of this digital phenotyping method. METHODS: Participants from two different cohorts, each n = 76, one with diagnoses of depression/anxiety and the other schizophrenia, utilized mindLAMP to collect active data (e.g., surveys on mood/anxiety), along with passive data consisting of smartphone digital phenotyping data (geolocation, accelerometer, and screen state) for at least 1 month. Using anomaly detection algorithms, we assessed if statistical anomalies in the combination of active and passive data could predict changes in mood/anxiety scores as measured via smartphone surveys. RESULTS: The anomaly detection model was reliably able to predict symptom change of 4 points or greater for depression as measured by the PHQ-9 and anxiety as measured for the GAD-8 for both patient populations, with an area under the ROC curve of 0.65 and 0.80 for each respectively. For both PHQ-9 and GAD-7, these AUCs were maintained when predicting significant symptom change at least 7 days in advance. Active data alone predicted around 52% and 75% of the symptom variability for the depression/anxiety and schizophrenia populations respectively. CONCLUSION: These results indicate the feasibility of anomaly detection for predicting symptom change in transdiagnostic cohorts. These results across different patient groups, different countries, and different sites (India and the US) suggest anomaly detection of smartphone digital phenotyping data may offer a reliable and valid approach to predicting symptom change. Future work should emphasize prospective application of these statistical methods.

Smartphone swabs as an emerging tool for toxicology testing: a proof-of-concept study in a nightclub.

OBJECTIVES: Smartphones have become everyday objects on which the accumulation of fingerprints is significant. In addition, a large proportion of the population regularly uses a smartphone, especially younger people. The objective of this study was to evaluate smartphones as a new matrix for toxico-epidemiology. METHODS: This study was conducted during two separate events (techno and trance) at an electronic music nightclub in Grenoble, France. Data on reported drug use and whether drugs were snorted directly from the surface of the smartphone were collected using an anonymous questionnaire completed voluntarily by drug users. Then, a dry swab was rubbed for 20 s on all sides of the smartphone. The extract was analyzed by liquid chromatography coupled to tandem mass spectrometry on a Xevo TQ-XS system (Waters). RESULTS: In total, 122 swabs from 122 drug users were collected. The three main drugs identified were MDMA (n=83), cocaine (n=59), and THC (n=51). Based on declarative data, sensitivity ranged from 73 to 97.2 % and specificity from 71.8 to 88.1 % for MDMA, cocaine, and THC. Other substances were identified such as cocaine adulterants, ketamine, amphetamine, LSD, methamphetamine, CBD, DMT, heroin, mescaline, and several NPS. Numerous medications were also identified, such as antidepressants, anxiolytics, hypnotics, and painkillers. Different use patterns were identified between the two events. CONCLUSIONS: This proof-of-concept study on 122 subjects shows that smartphone swab analysis could provide a useful and complementary tool for drug testing, especially for harm-reduction programs and toxico-epidemiolgy studies, with acceptable test performance, despite declarative data.

Telemonitoring in patients with asthma: a systematic review.

BACKGROUND: Monitoring and managing asthma using technology can help increase patient adherence and achieve better asthma control. This study aimed to evaluate the effectiveness of telemonitoring using smartphones and telephone communication compared to usual outpatient clinical evaluation in patients with asthma. DATA SOURCES: This systematic review was conducted in 2023. Databases PubMed, Scopus, Web of Science, and the Google Scholar search engine, were searched from 2013 to 2022. DATA SELECTION: The selected studies were randomized clinical trials that used telemonitoring in patients with asthma. The quality of the studies was evaluated using the JADAD scale. Data were collected using a data extraction form, and the findings were synthesized narratively. This systematic review was conducted following the PRISMA checklist. RESULTS: Initially, 4,147 articles were found, of which 14 were included in the study. The results showed that in some cases, telemonitoring using smartphones and telephone communication in patients with asthma is effective, while in other studies, its effectiveness was not observed. CONCLUSIONS: Telemonitoring using smartphones and telephone communication in patients with asthma can be considered an appropriate strategy to reduce the use of healthcare resources and improve quality of life. However, further studies are recommended to investigate the effectiveness of each of these technologies and their specific outcomes.

Viewing distance, font size and symptoms of eyestrain in non-presbyopic and presbyopic smartphone users.

PURPOSE: Smartphones are the most frequently used digital devices globally with ~6.80 billion users. Despite the ubiquitous use of smartphones, limited information is known on the preferred viewing distance and font size of smartphone users. This study investigated viewing distance, font size and symptoms of eyestrain in non-presbyopic and presbyopic smartphone users. METHODS: In this quantitative research study, viewing distance and font size were measured in a group of non-presbyopes (n = 107) and presbyopes (n = 53), whilst participants viewed a text message and a web page on their own smartphone. Subjects also responded to a verbal questionnaire related to the characteristics of their smartphone and the computer vision syndrome questionnaire to assess symptoms of eyestrain. Data were analysed using descriptive and inferential statistics. RESULTS: For the total sample, the mean viewing distance for a text message was 37.13 ± 8.82 cm (median 36.00 cm), and for a web page was 36.11 ± 7.98 cm (median 36.00 cm). Presbyopes had longer median viewing distances compared with non-presbyopes for a text message (41 cm vs. 34 cm, p < 0.001) and web page (40 cm vs. 34 cm, p < 0.001). The font size for non-presbyopes were <1.0 M whilst for presbyopes were >1.2 M. More than twice the percentage of non-presbyopes were classified with digital eyestrain (DES) compared with presbyopes. CONCLUSIONS: Non-presbyopes used shorter viewing distances, smaller font sizes and were more predisposed to DES than presbyopes. The viewing distances adopted by presbyopes were similar to the conventional near-working distance of 40 cm. Eye care practitioners should consider viewing distances when assessing near-visual functions and prescribing a near refractive correction, particularly in non-presbyopes. There should be greater awareness of the importance of adopting appropriate viewing distances when using smartphones.

Contextual Acceptance of COVID-19 Mitigation Mobile Apps in the United States: Mixed Methods Survey Study on Postpandemic Data Privacy.

BACKGROUND: The COVID-19 pandemic gave rise to countless user-facing mobile apps to help fight the pandemic ("COVID-19 mitigation apps"). These apps have been at the center of data privacy discussions because they collect, use, and even retain sensitive personal data from their users (eg, medical records and location data). The US government ended its COVID-19 emergency declaration in May 2023, marking a unique time to comprehensively investigate how data privacy impacted people's acceptance of various COVID-19 mitigation apps deployed throughout the pandemic. OBJECTIVE: This research aims to provide insights into health data privacy regarding COVID-19 mitigation apps and policy recommendations for future deployment of public health mobile apps through the lens of data privacy. This research explores people's contextual acceptance of different types of COVID-19 mitigation apps by applying the privacy framework of contextual integrity. Specifically, this research seeks to identify the factors that impact people's acceptance of data sharing and data retention practices in various social contexts. METHODS: A mixed methods web-based survey study was conducted by recruiting a simple US representative sample (N=674) on Prolific in February 2023. The survey includes a total of 60 vignette scenarios representing realistic social contexts that COVID-19 mitigation apps could be used. Each survey respondent answered questions about their acceptance of 10 randomly selected scenarios. Three contextual integrity parameters (attribute, recipient, and transmission principle) and respondents' basic demographics are controlled as independent variables. Regression analysis was performed to determine the factors impacting people's acceptance of initial data sharing and data retention practices via these apps. Qualitative data from the survey were analyzed to support the statistical results. RESULTS: Many contextual integrity parameter values, pairwise combinations of contextual integrity parameter values, and some demographic features of respondents have a significant impact on their acceptance of using COVID-19 mitigation apps in various social contexts. Respondents' acceptance of data retention practices diverged from their acceptance of initial data sharing practices in some scenarios. CONCLUSIONS: This study showed that people's acceptance of using various COVID-19 mitigation apps depends on specific social contexts, including the type of data (attribute), the recipients of the data (recipient), and the purpose of data use (transmission principle). Such acceptance may differ between the initial data sharing and data retention practices, even in the same context. Study findings generated rich implications for future pandemic mitigation apps and the broader public health mobile apps regarding data privacy and deployment considerations.

Transforming Digital Phenotyping Raw Data Into Actionable Biomarkers, Quality Metrics, and Data Visualizations Using Cortex Software Package: Tutorial.

As digital phenotyping, the capture of active and passive data from consumer devices such as smartphones, becomes more common, the need to properly process the data and derive replicable features from it has become paramount. Cortex is an open-source data processing pipeline for digital phenotyping data, optimized for use with the mindLAMP apps, which is used by nearly 100 research teams across the world. Cortex is designed to help teams (1) assess digital phenotyping data quality in real time, (2) derive replicable clinical features from the data, and (3) enable easy-to-share data visualizations. Cortex offers many options to work with digital phenotyping data, although some common approaches are likely of value to all teams using it. This paper highlights the reasoning, code, and example steps necessary to fully work with digital phenotyping data in a streamlined manner. Covering how to work with the data, assess its quality, derive features, and visualize findings, this paper is designed to offer the reader the knowledge and skills to apply toward analyzing any digital phenotyping data set. More specifically, the paper will teach the reader the ins and outs of the Cortex Python package. This includes background information on its interaction with the mindLAMP platform, some basic commands to learn what data can be pulled and how, and more advanced use of the package mixed with basic Python with the goal of creating a correlation matrix. After the tutorial, different use cases of Cortex are discussed, along with limitations. Toward highlighting clinical applications, this paper also provides 3 easy ways to implement examples of Cortex use in real-world settings. By understanding how to work with digital phenotyping data and providing ready-to-deploy code with Cortex, the paper aims to show how the new field of digital phenotyping can be both accessible to all and rigorous in methodology.

Daily links between objective smartphone use and sleep among adolescents.

INTRODUCTION: Concerns abound on how digital technology such as smartphone use may impair adolescent sleep. Although these linkages are supported in cross-sectional studies, research involving intensive longitudinal assessments and objective measures has called into question the robustness of associations. METHODS: In this study, a sample of ethnically diverse U.S. adolescents (N = 71; Mage = 16.49; 56% girls) wore Fitbit devices and submitted screenshots of their smartphone screen time, pickups, and notifications over a 14-day period in 2021. The Fitbits recorded nightly sleep quality and sleep onset. Adolescents also completed daily diaries reporting the previous night's sleep onset time and sleep quality. RESULTS: On days when adolescents engaged in greater nighttime screen time and, to some extent, pickups relative to their own average, they also had poorer sleep outcomes that night. Greater screen time was associated with later self-reported and Fitbit-recorded sleep onset and poorer self-reported sleep quality. Greater pickups was associated with later self-reported and Fitbit-recorded sleep onset. Smartphone use during the day did not relate to sleep outcomes, indicating the importance of distinguishing nighttime from daytime use. CONCLUSIONS: Parents and clinicians should help adolescents develop healthy digital skills to avoid exacerbating sleep problems that are known to occur during this developmental period.

Smartphone Assessment of the Sitting Heel-Rise Test.

The study presents a new approach for assessing plantarflexor muscles' function using a smartphone. The test involves performing repeated heel raises for 60 s while seated. The seated heel-rise test offers a simple method for assessing plantarflexor muscles' function in those with severe balance impairment who are unable to complete tests performed while standing. The study aimed to showcase how gyroscopic data from a smartphone placed on the lower limb can be used to assess the test. Eight participants performed the seated heel-rise test with each limb. Gyroscope and 2D video analysis data (60 Hz) of limb motion were used to determine the number of cycles, the average rise (T-rise), lowering (T-lower), and cycle (T-total) times. The number of cycles detected matched exactly when the gyroscope and kinematic data were compared. There was good time domain agreement between gyroscopic and video data (T-rise = 0.0005 s, T-lower = 0.0013 s, and T-total = 0.0017 s). The 95% CI limits of agreement were small (T-total -0.1118, 0.1127 s, T-lower -0.1152, 0.1179 s, and T-total -0.0763, 0.0797 s). Results indicate that a smartphone placed on the thigh can successfully assess the seated heel-rise test. The seated heel-rise test offers an attractive alternative to test plantarflexor muscles' functionality in those unable to perform tests in standing positions.

Multisensory Fusion for Unsupervised Spatiotemporal Speaker Diarization.

Speaker diarization consists of answering the question of "who spoke when" in audio recordings. In meeting scenarios, the task of labeling audio with the corresponding speaker identities can be further assisted by the exploitation of spatial features. This work proposes a framework designed to assess the effectiveness of combining speaker embeddings with Time Difference of Arrival (TDOA) values from available microphone sensor arrays in meetings. We extract speaker embeddings using two popular and robust pre-trained models, ECAPA-TDNN and X-vectors, and calculate the TDOA values via the Generalized Cross-Correlation (GCC) method with Phase Transform (PHAT) weighting. Although ECAPA-TDNN outperforms the Xvectors model, we utilize both speaker embedding models to explore the potential of employing a computationally lighter model when spatial information is exploited. Various techniques for combining the spatial-temporal information are examined in order to determine the best clustering method. The proposed framework is evaluated on two multichannel datasets: the AVLab Speaker Localization dataset and a multichannel dataset (SpeaD-M3C) enriched in the context of the present work with supplementary information from smartphone recordings. Our results strongly indicate that the integration of spatial information can significantly improve the performance of state-of-the-art deep learning diarization models, presenting a 2-3% reduction in DER compared to the baseline approach on the evaluated datasets.

Comparative Evaluation of the Performance of a Mobile Device Camera and a Full-Frame Mirrorless Camera in Close-Range Photogrammetry Applications.

The comparative evaluation of the performance of a mobile device camera and an affordable full-frame mirrorless camera in close-range photogrammetry applications involves assessing the capabilities of these two types of cameras in capturing images for 3D measurement purposes. In this study, experiments are conducted to compare the distortion levels, the accuracy performance, and the image quality of a mobile device camera against a full-frame mirrorless camera when used in close-range photogrammetry applications in various settings. Analytical methodologies and specialized digital tools are used to evaluate the results. In the end, generalized conclusions are drawn for using each technology in close-range photogrammetry applications.

OPTORER: A Dynamic Routing and Touring Service for Indoors and Outdoor Tours.

This paper introduces a new routing and touring service both for outdoor and indoor places of touristic and cultural interest designed to be used in the wider area of Attica, Greece. This service is the result of the work performed in OPTORER (OPTORER: OPtimal rouTing and explOration of touRistic and cultural arEas of interest within Attica given personalized adaptive preferences, promoted underlying purpose, and interactive experience), project, and it aspires to offer a range of innovative and thematic routes to several specified points of interest in the selected area of Attica, encouraging the combination of indoor and outdoor routes in a single tour. The aim is to optimize the user experience while promoting specific, user-centric features, with safety and social welfare being a priority for every designed tour, resulting in enhancing the touristic experience in the area. Using a common smartphone device, as well as common wearable devices (i.e., smartwatches), the OPTORER service will provide an end-to-end solution by developing the algorithms and end-user applications, together with an orchestration platform responsible for managing, operating, and executing the service that produces and presents to the end user results derived from solving dynamically complex optimization problems.

Decimeter-Level Accuracy for Smartphone Real-Time Kinematic Positioning Implementing a Robust Kalman Filter Approach and Inertial Navigation System Infusion in Complex Urban Environments.

New smartphones provide real-time access to GNSS pseudorange, Doppler, or carrier-phase measurement data at 1 Hz. Simultaneously, they can receive corrections broadcast by GNSS reference stations to perform real-time kinematic (RTK) positioning. This study aims at the real-time positioning capabilities of smartphones using raw GNSS measurements as a conventional method and proposes an improvement to the positioning through the integration of Inertial Navigation System (INS) measurements. A U-Blox GNSS receiver, model ZED-F9R, was used as a benchmark for comparison. We propose an enhanced ambiguity resolution algorithm that integrates the traditional LAMBDA method with an adaptive thresholding mechanism based on real-time quality metrics. The RTK/INS fusion method integrates RTK and INS measurements using an extended Kalman filter (EKF), where the state vector x includes the position, velocity, orientation, and their respective biases. The innovation here is the inclusion of a real-time weighting scheme that adjusts the contribution of the RTK and INS measurements based on their current estimated accuracy. Also, we use the tightly coupled (TC) RTK/INS fusion framework. By leveraging INS data, the system can maintain accurate positioning even when the GNSS data are unreliable, allowing for the detection and exclusion of abnormal GNSS measurements. However, in complex urban areas such as Qazvin City in Iran, the fusion method achieved positioning accuracies of approximately 0.380 m and 0.415 m for the Xiaomi Mi 8 and Samsung Galaxy S21 Ultra smartphones, respectively. The subsequent detailed analysis across different urban streets emphasized the significance of choosing the right positioning method based on the environmental conditions. In most cases, RTK positioning outperformed Single-Point Positioning (SPP), offering decimeter-level precision, while the fusion method bridged the gap between the two, showcasing improved stability accuracy. The comparative performance between the Samsung Galaxy S21 Ultra and Xiaomi Mi 8 revealed minor differences, likely attributed to variations in the hardware design and software algorithms. The fusion method emerged as a valuable alternative when the RTK signals were unavailable or impractical. This demonstrates the potential of integrating RTK and INS measurements for enhanced real-time smartphone positioning, particularly in challenging urban environments.

SmartVR Pointer: Using Smartphones and Gaze Orientation for Selection and Navigation in Virtual Reality.

Some of the barriers preventing virtual reality (VR) from being widely adopted are the cost and unfamiliarity of VR systems. Here, we propose that in many cases, the specialized controllers shipped with most VR head-mounted displays can be replaced by a regular smartphone, cutting the cost of the system, and allowing users to interact in VR using a device they are already familiar with. To achieve this, we developed SmartVR Pointer, an approach that uses smartphones to replace the specialized controllers for two essential operations in VR: selection and navigation by teleporting. In SmartVR Pointer, a camera mounted on the head-mounted display (HMD) is tilted downwards so that it points to where the user will naturally be holding their phone in front of them. SmartVR Pointer supports three selection modalities: tracker based, gaze based, and combined/hybrid. In the tracker-based SmartVR Pointer selection, we use image-based tracking to track a QR code displayed on the phone screen and then map the phone's position to a pointer shown within the field of view of the camera in the virtual environment. In the gaze-based selection modality, the user controls the pointer using their gaze and taps on the phone for selection. The combined technique is a hybrid between gaze-based interaction in VR and tracker-based Augmented Reality. It allows the user to control a VR pointer that looks and behaves like a mouse pointer by moving their smartphone to select objects within the virtual environment, and to interact with the selected objects using the smartphone's touch screen. The touchscreen is used for selection and dragging. The SmartVR Pointer is simple and requires no calibration and no complex hardware assembly or disassembly. We demonstrate successful interactive applications of SmartVR Pointer in a VR environment with a demo where the user navigates in the virtual environment using teleportation points on the floor and then solves a Tetris-style key-and-lock challenge.

Smartphone Prospects in Bridge Structural Health Monitoring, a Literature Review.

Bridges are critical components of transportation networks, and their conditions have effects on societal well-being, the economy, and the environment. Automation needs in inspections and maintenance have made structural health monitoring (SHM) systems a key research pillar to assess bridge safety/health. The last decade brought a boom in innovative bridge SHM applications with the rise in next-generation smart and mobile technologies. A key advancement within this direction is smartphones with their sensory usage as SHM devices. This focused review reports recent advances in bridge SHM backed by smartphone sensor technologies and provides case studies on bridge SHM applications. The review includes model-based and data-driven SHM prospects utilizing smartphones as the sensing and acquisition portal and conveys three distinct messages in terms of the technological domain and level of mobility: (i) vibration-based dynamic identification and damage-detection approaches; (ii) deformation and condition monitoring empowered by computer vision-based measurement capabilities; (iii) drive-by or pedestrianized bridge monitoring approaches, and miscellaneous SHM applications with unconventional/emerging technological features and new research domains. The review is intended to bring together bridge engineering, SHM, and sensor technology audiences with decade-long multidisciplinary experience observed within the smartphone-based SHM theme and presents exemplary cases referring to a variety of levels of mobility.

Early detection of diseases causing dementia using digital navigation and gait measures: A systematic review of evidence.

Wearable digital technologies capable of measuring everyday behaviors could improve the early detection of dementia-causing diseases. We conducted two systematic reviews following Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines to establish the evidence base for measuring navigation and gait, two everyday behaviors affected early in AD and non-AD disorders and not adequately measured in current practice. PubMed and Web of Science databases were searched for studies on asymptomatic and early-stage symptomatic individuals at risk of dementia, with the Newcastle-Ottawa Scale used to assess bias and evaluate methodological quality. Of 316 navigation and 2086 gait records identified, 27 and 83, respectively, were included in the final sample. We highlight several measures that may identify at-risk individuals, whose quantifiability with different devices mitigates the risk of future technological obsolescence. Beyond navigation and gait, this review also provides the framework for evaluating the evidence base for future digital measures of behaviors considered for early disease detection.

The Digital Navigator: Standardizing Human Technology Support in App-Integrated Clinical Care.

Background: Mental health apps offer scalable care, yet clinical adoption is hindered by low user engagement and integration challenges into clinic workflows. Human support staff called digital navigators, trained in mental health technology, could enhance care access and patient adherence and remove workflow burdens from clinicians. While the potential of this role is clear, training staff to become digital navigators and assessing their impact are primary challenges. Methods: We present a detailed manual/framework for implementation of the Digital Navigator within a short-term, cognitive-behavioral therapy-focused hybrid clinic. We analyze patient engagement, satisfaction, and digital phenotyping data quality outcomes. Data from 83 patients, for the period spanning September 2022 to September 2023, included Digital Navigator satisfaction, correlated with demographics, mindLAMP app satisfaction, engagement, and passive data quality. Additionally, average passive data across 33 clinic patients from November 2023 to January 2024 were assessed for missingness. Results: Digital Navigator satisfaction averaged 18.8/20. Satisfaction was not influenced by sex, race, gender, or education. Average passive data quality across 33 clinic patients was 0.82 at the time this article was written. Digital Navigator satisfaction scores had significant positive correlation with both clinic app engagement and perception of that app. Conclusions: Results demonstrate preliminary support and patient endorsement for the Digital Navigator role and positive outcomes around digital engagement and digital phenotyping data quality. Through sharing training resources and standardizing the role, we aim to enable clinicians and researchers to adapt and utilize the Digital Navigator for their own needs.

Accuracy of Smartphone-Based Three-Dimensional Facial Scanning System: A Systematic Review.

BACKGROUND: Recently, the integration of 3D face scanning into smartphones has raised vast interest in plastic surgery. With the release of smartphones featuring 3D face scanning technology, users now can capture detailed 3D models of their faces using their smartphones. However, trueness and precision of this system is less well established. METHODS: PubMed, Cochrane Library, Embase, ScienceDirect, Scopus, and Web of Science databases were searched for studies evaluating 3D scanning of smartphone devices and conventional 3D imaging systems from January 1, 2017, to June 1, 2023. A qualitative systematic review was conducted by two review authors after independently selecting studies, extracting data, and assessing the risk of bias of included studies. RESULTS: A total of 11 studies were included, all focusing on the accuracy of smartphone 3D facial scanning. The results show that although smartphones perform poorly on deep and irregular surfaces, they are accurate enough for clinical applications and have the advantage of being economical and portable. CONCLUSIONS: Smartphone-based 3D facial scanning has been basically validated for clinical application, showing broad clinical application prospects in plastic surgery. LEVEL OF EVIDENCE II: This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors   www.springer.com/00266 .

Feasibility and Acceptability Evaluation of a Digital Therapeutic Program for Improving Cancer Prevention: A Quasi-experimental Pre-post Interventional Pilot Study.

Previous studies have proved that healthy behaviors hinder the onset and progression of tumors. Digital therapeutics (DTx), playing a pivotal role in facilitating behavioral adjustments through educational interventions, lifestyle support, and symptom monitoring, contribute to the goal of tumor prevention. We aim to optimize the evaluation of the feasibility and acceptability of DTx for cancer prevention. This involves assessing AITI's daily activity rates and user feedback, and comparing changes in behavioral habits and differences in SF-36 before and after the intervention. In a 4-week trial with 57 participants engaging actively, we found both the average daily activity rate and 4-week retention rate at 35 (61.4%). The USE Questionnaire scores (validity, ease of use, acquisition, and satisfaction) ranged from 68.06 to 83.10, indicating AITI's user-friendliness and acceptability. Furthermore, positive habit changes were noted among participants in exercise and diet (p < 0.0001), suggesting the effectiveness of the DTx approach in modifying behavioral habits related to physical activity and nutrition. This pilot study underscores the potential of DTx in advancing cancer prevention. However, larger and longer studies are needed to comprehensively assess its impact.