Engagement, Acceptability, and Effectiveness of the Self-Care and Coach-Supported Versions of the Vira Digital Behavior Change Platform Among Young Adults at Risk for Depression and Obesity: Pilot Randomized Controlled Trial.

BACKGROUND: Adolescence and early adulthood are pivotal stages for the onset of mental health disorders and the development of health behaviors. Digital behavioral activation interventions, with or without coaching support, hold promise for addressing risk factors for both mental and physical health problems by offering scalable approaches to expand access to evidence-based mental health support. OBJECTIVE: This 2-arm pilot randomized controlled trial evaluated 2 versions of a digital behavioral health product, Vira (Ksana Health Inc), for their feasibility, acceptability, and preliminary effectiveness in improving mental health in young adults with depressive symptoms and obesity risk factors. METHODS: A total of 73 participants recruited throughout the United States were randomly assigned to use Vira either as a self-guided product (Vira Self-Care) or with support from a health coach (Vira+Coaching) for 12 weeks. The Vira smartphone app used passive sensing of behavioral data related to mental health and obesity risk factors (ie, activity, sleep, mobility, and language patterns) and offered users personalized insights into patterns of behavior associated with their daily mood. Participants completed self-reported outcome measures at baseline and follow-up (12 weeks). All study procedures were completed via digital communications. RESULTS: Both versions of Vira showed strong user engagement, acceptability, and evidence of effectiveness in improving mental health and stress. However, users receiving coaching exhibited more sustained engagement with the platform and reported greater reductions in depression (Cohen d=0.45, 95% CI 0.10-0.82) and anxiety (Cohen d=0.50, 95% CI 0.13-0.86) compared to self-care users. Both interventions also resulted in reduced stress (Vira+Coaching: Cohen d=-1.05, 95% CI -1.57 to --0.50; Vira Self-Care: Cohen d=-0.78, 95% CI -1.33 to -0.23) and were perceived as useful and easy to use. Coached users also reported reductions in sleep-related impairment (Cohen d=-0.51, 95% CI -1.00 to -0.01). Moreover, participants increased their motivation for and confidence in making behavioral changes, with greater improvements in confidence among coached users. CONCLUSIONS: An app-based intervention using passive mobile sensing to track behavior and deliver personalized insights into behavior-mood associations demonstrated feasibility, acceptability, and preliminary effectiveness for reducing depressive symptoms and other mental health problems in young adults. Future directions include (1) optimizing the interventions, (2) conducting a fully powered trial that includes an active control condition, and (3) testing mediators and moderators of outcome effects. TRIAL REGISTRATION: ClinicalTrials.gov NCT05638516; https://clinicaltrials.gov/study/NCT05638516.

Mobile sensing-based depression severity assessment in participants with heterogeneous mental health conditions.

Mobile sensing-based depression severity assessment could complement the subjective questionnaires-based assessment currently used in practice. However, previous studies on mobile sensing for depression severity assessment were conducted on homogeneous mental health condition participants; evaluation of possible generalization across heterogeneous groups has been limited. Similarly, previous studies have not investigated the potential of free-living audio data for depression severity assessment. Audio recordings from free-living could provide rich sociability features to characterize depressive states. We conducted a study with 11 healthy individuals, 13 individuals with major depressive disorder, and eight individuals with schizoaffective disorders. Communication logs and location data from the participants' smartphones and continuous audio recordings of free-living from a wearable audioband were obtained over a week for each participant. The depression severity prediction model trained using communication log and location data features had a root mean squared error (rmse) of 6.80. Audio-based sociability features further reduced the rmse to 6.07 (normalized rmse of 0.22). Audio-based sociability features also improved the F1 score in the five-class depression category classification model from 0.34 to 0.46. Thus, free-living audio-based sociability features complement the commonly used mobile sensing features to improve depression severity assessment. The prediction results obtained with mobile sensing-based features are better than the rmse of 9.83 (normalized rmse of 0.36) and the F1 score of 0.25 obtained with a baseline model. Additionally, the predicted depression severity had a significant correlation with reported depression severity (correlation coefficient of 0.76, p < 0.001). Thus, our work shows that mobile sensing could model depression severity across participants with heterogeneous mental health conditions, potentially offering a screening tool for depressive symptoms monitoring in the broader population.

A template and tutorial for preregistering studies using passive smartphone measures.

Passive smartphone measures hold significant potential and are increasingly employed in psychological and biomedical research to capture an individual's behavior. These measures involve the near-continuous and unobtrusive collection of data from smartphones without requiring active input from participants. For example, GPS sensors are used to determine the (social) context of a person, and accelerometers to measure movement. However, utilizing passive smartphone measures presents methodological challenges during data collection and analysis. Researchers must make multiple decisions when working with such measures, which can result in different conclusions. Unfortunately, the transparency of these decision-making processes is often lacking. The implementation of open science practices is only beginning to emerge in digital phenotyping studies and varies widely across studies. Well-intentioned researchers may fail to report on some decisions due to the variety of choices that must be made. To address this issue and enhance reproducibility in digital phenotyping studies, we propose the adoption of preregistration as a way forward. Although there have been some attempts to preregister digital phenotyping studies, a template for registering such studies is currently missing. This could be problematic due to the high level of complexity that requires a well-structured template. Therefore, our objective was to develop a preregistration template that is easy to use and understandable for researchers. Additionally, we explain this template and provide resources to assist researchers in making informed decisions regarding data collection, cleaning, and analysis. Overall, we aim to make researchers' choices explicit, enhance transparency, and elevate the standards for studies utilizing passive smartphone measures.

Identifying Person-Specific Drivers of Depression in Adolescents: Protocol for a Smartphone-Based Ecological Momentary Assessment and Passive Sensing Study.

BACKGROUND: Adolescence is marked by an increasing risk of depression and is an optimal window for prevention and early intervention. Personalizing interventions may be one way to maximize therapeutic benefit, especially given the marked heterogeneity in depressive presentations. However, empirical evidence that can guide personalized intervention for youth is lacking. Identifying person-specific symptom drivers during adolescence could improve outcomes by accounting for both developmental and individual differences. OBJECTIVE: This study leverages adolescents' everyday smartphone use to investigate person-specific drivers of depression and validate smartphone-based mobile sensing data against established ambulatory methods. We describe the methods of this study and provide an update on its status. After data collection is completed, we will address three specific aims: (1) identify idiographic drivers of dynamic variability in depressive symptoms, (2) test the validity of mobile sensing against ecological momentary assessment (EMA) and actigraphy for identifying these drivers, and (3) explore adolescent baseline characteristics as predictors of these drivers. METHODS: A total of 50 adolescents with elevated symptoms of depression will participate in 28 days of (1) smartphone-based EMA assessing depressive symptoms, processes, affect, and sleep; (2) mobile sensing of mobility, physical activity, sleep, natural language use in typed interpersonal communication, screen-on time, and call frequency and duration using the Effortless Assessment of Risk States smartphone app; and (3) wrist actigraphy of physical activity and sleep. Adolescents and caregivers will complete developmental and clinical measures at baseline, as well as user feedback interviews at follow-up. Idiographic, within-subject networks of EMA symptoms will be modeled to identify each adolescent's person-specific drivers of depression. Correlations among EMA, mobile sensor, and actigraph measures of sleep, physical, and social activity will be used to assess the validity of mobile sensing for identifying person-specific drivers. Data-driven analyses of mobile sensor variables predicting core depressive symptoms (self-reported mood and anhedonia) will also be used to assess the validity of mobile sensing for identifying drivers. Finally, between-subject baseline characteristics will be explored as predictors of person-specific drivers. RESULTS: As of October 2023, 84 families were screened as eligible, of whom 70% (n=59) provided informed consent and 46% (n=39) met all inclusion criteria after completing baseline assessment. Of the 39 included families, 85% (n=33) completed the 28-day smartphone and actigraph data collection period and follow-up study visit. CONCLUSIONS: This study leverages depressed adolescents' everyday smartphone use to identify person-specific drivers of adolescent depression and to assess the validity of mobile sensing for identifying these drivers. The findings are expected to offer novel insights into the structure and dynamics of depressive symptomatology during a sensitive period of development and to inform future development of a scalable, low-burden smartphone-based tool that can guide personalized treatment decisions for depressed adolescents. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/43931.

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.

Identifying Links Between Productivity and Biobehavioral Rhythms Modeled From Multimodal Sensor Streams: Exploratory Quantitative Study.

BACKGROUND: Biobehavioral rhythms are biological, behavioral, and psychosocial processes with repeating cycles. Abnormal rhythms have been linked to various health issues, such as sleep disorders, obesity, and depression. OBJECTIVE: This study aims to identify links between productivity and biobehavioral rhythms modeled from passively collected mobile data streams. METHODS: In this study, we used a multimodal mobile sensing data set consisting of data collected from smartphones and Fitbits worn by 188 college students over a continuous period of 16 weeks. The participants reported their self-evaluated daily productivity score (ranging from 0 to 4) during weeks 1, 6, and 15. To analyze the data, we modeled cyclic human behavior patterns based on multimodal mobile sensing data gathered during weeks 1, 6, 15, and the adjacent weeks. Our methodology resulted in the creation of a rhythm model for each sensor feature. Additionally, we developed a correlation-based approach to identify connections between rhythm stability and high or low productivity levels. RESULTS: Differences exist in the biobehavioral rhythms of high- and low-productivity students, with those demonstrating greater rhythm stability also exhibiting higher productivity levels. Notably, a negative correlation (C=-0.16) was observed between productivity and the SE of the phase for the 24-hour period during week 1, with a higher SE indicative of lower rhythm stability. CONCLUSIONS: Modeling biobehavioral rhythms has the potential to quantify and forecast productivity. The findings have implications for building novel cyber-human systems that align with human beings' biobehavioral rhythms to improve health, well-being, and work performance.

The predictive value of supervised machine learning models for insomnia symptoms through smartphone usage behavior.

Introduction: Digital phenotyping can be an innovative and unobtrusive way to improve the detection of insomnia. This study explores the correlations between smartphone usage features (SUF) and insomnia symptoms and their predictive value for detecting insomnia symptoms. Methods: In an observational study of a German convenience sample, the Insomnia Severity Index (ISI) and smartphone usage data (e.g., time the screen was active, longest time the screen was inactive in the night) for the previous 7 days were obtained. SUF (e.g., min, mean) were calculated from the smartphone usage data. Correlation analyses between the ISI and SUF were conducted. For the specification of the machine learning models (ML), 80 % of the data was allocated to training, 20 % to testing, and five-fold cross-validation was used. Six algorithms (support vector machine, XGBoost, Random Forest, k-Nearest-Neighbor, Naive Bayes, and Logistic Regressions) were specified to predict ISI scores ≥15. Results: 752 participants (51.1 % female, mean ISI = 10.23, mean age = 41.92) were included in the analyses. Small correlations between some of the SUF and insomnia symptoms were found. In the ML models, sensitivity was low, ranging from 0.05 to 0.27 in the testing subsample. Random Forest and Naive Bayes were the best-performing algorithms. Yet, their AUCs (0.57, 0.58 respectively) in the testing subsample indicated a low discrimination capacity. Conclusions: Given the small magnitude of the correlations and low discrimination capacity of the ML models, SUFs, as measured in this study, do not appear to be sufficient for detecting insomnia symptoms. Further research is necessary to explore whether examining intra-individual variations and subpopulations or employing alternative smartphone sensors yields more promising outcomes.

Loneliness in the Daily Lives of People With Mood and Psychotic Disorders.

BACKGROUND AND HYPOTHESIS: Loneliness, the subjective experience of feeling alone, is associated with physical and psychological impairments. While there is an extensive literature linking loneliness to psychopathology, limited work has examined loneliness in daily life in those with serious mental illness. We hypothesized that trait and momentary loneliness would be transdiagnostic and relate to symptoms and measures of daily functioning. STUDY DESIGN: The current study utilized ecological momentary assessment and passive sensing to examine loneliness in those with schizophrenia (N = 59), bipolar disorder (N = 61), unipolar depression (N = 60), remitted unipolar depression (N = 51), and nonclinical comparisons (N = 82) to examine relationships of both trait and momentary loneliness to symptoms and social functioning in daily life. STUDY RESULTS: Findings suggest that both trait and momentary loneliness are higher in those with psychopathology (F(4,284) = 28.00, P < .001, ηp2 = 0.27), and that loneliness significantly relates to social functioning beyond negative symptoms and depression (ß = -0.44, t = 6.40, P < .001). Furthermore, passive sensing measures showed that greater movement (ß = -0.56, t = -3.29, P = .02) and phone calls (ß = -0.22, t = 12.79, P = .04), but not text messaging, were specifically related to decreased loneliness in daily life. Individuals higher in trait loneliness show stronger relationships between momentary loneliness and social context and emotions in everyday life. CONCLUSIONS: These findings provide further evidence pointing to the importance of loneliness transdiagnostically and its strong relation to social functioning. Furthermore, we show that passive sensing technology can be used to measure behaviors related to loneliness in daily life that may point to potential treatment implications or early detection markers of loneliness.

Investigation of daily patterns for smartphone keystroke dynamics based on loneliness and social isolation.

This study examined the relationship between loneliness levels and daily patterns of mobile keystroke dynamics in healthy individuals. Sixty-six young healthy Koreans participated in the experiment. Over five weeks, the participants used a custom Android keyboard. We divided the participants into four groups based on their level of loneliness (no loneliness, moderate loneliness, severe loneliness, and very severe loneliness). The very severe loneliness group demonstrated significantly higher typing counts during sleep time than the other three groups (one-way ANOVA, F = 3.75, p < 0.05). In addition, the average cosine similarity value of weekday and weekend typing patterns in the very severe loneliness group was higher than that in the no loneliness group (Welch's t-test, t = 2.27, p < 0.05). This meant that the no loneliness group's weekday and weekend typing patterns varied, whereas the very severe loneliness group's weekday and weekend typing patterns did not. Our results indicated that individuals with very high levels of loneliness tended to use mobile keyboards during late-night hours and did not significantly change their smartphone usage behavior between weekdays and weekends. These findings suggest that mobile keystroke dynamics have the potential to be used for the early detection of loneliness and the development of targeted interventions.

Diversity, equity, and inclusivity in observational ambulatory assessment: Recommendations from two decades of Electronically Activated Recorder (EAR) research.

Ambient audio sampling methods such as the Electronically Activated Recorder (EAR) have become increasingly prominent in clinical and social sciences research. These methods record snippets of naturalistically assessed audio from participants' daily lives, enabling novel observational research about the daily social interactions, identities, environments, behaviors, and speech of populations of interest. In practice, these scientific opportunities are equaled by methodological challenges: researchers' own cultural backgrounds and identities can easily and unknowingly permeate the collection, coding, analysis, and interpretation of social data from daily life. Ambient audio sampling poses unique and significant challenges to cultural humility, diversity, equity, and inclusivity (DEI) in scientific research that require systematized attention. Motivated by this observation, an international consortium of 21 researchers who have used ambient audio sampling methodologies created a workgroup with the aim of improving upon existing published guidelines. We pooled formally and informally documented challenges pertaining to DEI in ambient audio sampling from our collective experience on 40+ studies (most of which used the EAR app) in clinical and healthy populations ranging from children to older adults. This article presents our resultant recommendations and argues for the incorporation of community-engaged research methods in observational ambulatory assessment designs looking forward. We provide concrete recommendations across each stage typical of an ambient audio sampling study (recruiting and enrolling participants, developing coding systems, training coders, handling multi-linguistic participants, data analysis and interpretation, and dissemination of results) as well as guiding questions that can be used to adapt these recommendations to project-specific constraints and needs.

Digital Phenotyping for Monitoring Mental Disorders: Systematic Review.

BACKGROUND: The COVID-19 pandemic has increased the impact and spread of mental illness and made health services difficult to access; therefore, there is a need for remote, pervasive forms of mental health monitoring. Digital phenotyping is a new approach that uses measures extracted from spontaneous interactions with smartphones (eg, screen touches or movements) or other digital devices as markers of mental status. OBJECTIVE: This review aimed to evaluate the feasibility of using digital phenotyping for predicting relapse or exacerbation of symptoms in patients with mental disorders through a systematic review of the scientific literature. METHODS: Our research was carried out using 2 bibliographic databases (PubMed and Scopus) by searching articles published up to January 2023. By following the PRISMA (Preferred Reporting Items for Systematic Review and Meta-Analysis) guidelines, we started from an initial pool of 1150 scientific papers and screened and extracted a final sample of 29 papers, including studies concerning clinical populations in the field of mental health, which were aimed at predicting relapse or exacerbation of symptoms. The systematic review has been registered on the web registry Open Science Framework. RESULTS: We divided the results into 4 groups according to mental disorder: schizophrenia (9/29, 31%), mood disorders (15/29, 52%), anxiety disorders (4/29, 14%), and substance use disorder (1/29, 3%). The results for the first 3 groups showed that several features (ie, mobility, location, phone use, call log, heart rate, sleep, head movements, facial and vocal characteristics, sociability, social rhythms, conversations, number of steps, screen on or screen off status, SMS text message logs, peripheral skin temperature, electrodermal activity, light exposure, and physical activity), extracted from data collected via the smartphone and wearable wristbands, can be used to create digital phenotypes that could support gold-standard assessment and could be used to predict relapse or symptom exacerbations. CONCLUSIONS: Thus, as the data were consistent for almost all the mental disorders considered (mood disorders, anxiety disorders, and schizophrenia), the feasibility of this approach was confirmed. In the future, a new model of health care management using digital devices should be integrated with the digital phenotyping approach and tailored mobile interventions (managing crises during relapse or exacerbation).

Mobile sensing to advance tumor modeling in cancer patients: A conceptual framework.

As mobile and wearable devices continue to grow in popularity, there is strong yet unrealized potential to harness people's mobile sensing data to improve our understanding of their cellular and biologically-based diseases. Breakthrough technical innovations in tumor modeling, such as the three dimensional tumor microenvironment system (TMES), allow researchers to study the behavior of tumor cells in a controlled environment that closely mimics the human body. Although patients' health behaviors are known to impact their tumor growth through circulating hormones (cortisol, melatonin), capturing this process is a challenge to rendering realistic tumor models in the TMES or similar tumor modeling systems. The goal of this paper is to propose a conceptual framework that unifies researchers from digital health, data science, oncology, and cellular signaling, in a common cause to improve cancer patients' treatment outcomes through mobile sensing. In support of our framework, existing studies indicate that it is feasible to use people's mobile sensing data to approximate their underlying hormone levels. Further, it was found that when cortisol is cycled through the TMES based on actual patients' cortisol levels, there is a significant increase in pancreatic tumor cell growth compared to when cortisol levels are at normal healthy levels. Taken together, findings from these studies indicate that continuous monitoring of people's hormone levels through mobile sensing may improve experimentation in the TMES, by informing how hormones should be introduced. We hope our framework inspires digital health researchers in the psychosocial sciences to consider how their expertise can be applied to advancing outcomes across levels of inquiry, from behavioral to cellular.

m-Path: an easy-to-use and highly tailorable platform for ecological momentary assessment and intervention in behavioral research and clinical practice.

In this paper, we present m-Path (www.m-Path.io), an online platform that provides an easy-to-use and highly tailorable framework for implementing smartphone-based ecological momentary assessment (EMA) and intervention (EMI) in both research and clinical practice in the context of blended care. Because real-time monitoring and intervention in people's everyday lives have unparalleled benefits compared to traditional data collection techniques (e.g., retrospective surveys or lab-based experiments), EMA and EMI have become popular in recent years. Although a surge in the use of these methods has led to a myriad of EMA and EMI applications, many existing platforms only focus on a single aspect of daily life data collection (e.g., assessment vs. intervention, active self-report vs. passive mobile sensing, research-dedicated vs. clinically-oriented tools). With m-Path, we aim to integrate all of these facets into a single platform, as it is exactly this all-in-one approach that fosters the clinical utility of accumulated scientific knowledge. To this end, we offer a comprehensive platform to set up complex and highly adjustable EMA and EMI designs with advanced functionalities, using an intuitive point-and click web interface that is accessible for researchers and clinicians with limited programming skills. We discuss the strengths of daily life data collection and intervention in general and m-Path in particular. We describe the regular workflow to set up an EMA or EMI design within the m-Path framework, and summarize both the basic functionalities and more advanced features of our software.

Effects of chest movements while sitting on Navon task performance and stress levels.

Background: This study explored physical activity during remote work, most of which takes place while sitting in front of a computer. The purpose of Experiment 1 was to develop a classification for body motion by creating a neural net that can distinguish among several kinds of chest movement. Experiment 2 examined the effects of chest movements on stress and performance on the Navon test to validate the model developed in Experiment 1. Method and results: The procedures for this study were as follows.Experiment 1: Creation of the body movement classification model and preliminary experiment for Experiment 2.Data from five participants were used to construct a machine-learning categorization model. The other three participants participated in a pilot study for Experiment 2.Experiment 2: Model validation and confirmation of stress measurement validity.We recruited 34 new participants to test the validity of the model developed in Experiment 1. We asked 10 of the 34 participants to retake the stress measurement since the results of the stress assessment were unreliable.Using LSTM models, we classified six categories of chest movement in Experiment 1: walking, standing up and sitting down, sitting still, rotating, swinging, and rocking. The LSTM models yielded an accuracy rate of 83.8%. Experiment 2 tested the LSTM model and found that Navon task performance correlated with swinging chest movement. Due to the limited reliability of the stress measurement results, we were unable to draw a conclusion regarding the effects of body movements on stress. In terms of cognitive performance, swinging of the chest reduced RT and increased accuracy on the Navon task (ß = .015 [-.003,.054], R2 = .31). Conclusions: LSTM classification successfully distinguished subtle movements of the chest; however, only swinging was related to cognitive performance. Chest movements reduced the reaction time, improving cognitive performance. However, the stress measurements were not stable; thus, we were unable to draw a clear conclusion about the relationship between body movement and stress. The results indicated that swinging of the chest improved reaction times in the Navon task, while sitting still was not related to cognitive performance or stress. The present article discusses how to collect sensor data and analyze it using machine-learning methods as well as the future applicability of measuring physical activity during remote work.

Never miss a beep: Using mobile sensing to investigate (non-)compliance in experience sampling studies.

Given the increasing number of studies in various disciplines using experience sampling methods, it is important to examine compliance biases because related patterns of missing data could affect the validity of research findings. In the present study, a sample of 592 participants and more than 25,000 observations were used to examine whether participants responded to each specific questionnaire within an experience sampling framework. More than 400 variables from the three categories of person, behavior, and context, collected multi-methodologically via traditional surveys, experience sampling, and mobile sensing, served as predictors. When comparing different linear (logistic and elastic net regression) and non-linear (random forest) machine learning models, we found indication for compliance bias: response behavior was successfully predicted. Follow-up analyses revealed that study-related past behavior, such as previous average experience sampling questionnaire response rate, was most informative for predicting compliance, followed by physical context variables, such as being at home or at work. Based on our findings, we discuss implications for the design of experience sampling studies in applied research and future directions in methodological research addressing experience sampling methodology and missing data.

Continuous Assessment of Function and Disability via Mobile Sensing: Real-World Data-Driven Feasibility Study.

BACKGROUND: Functional limitations are associated with poor clinical outcomes, higher mortality, and disability rates, especially in older adults. Continuous assessment of patients' functionality is important for clinical practice; however, traditional questionnaire-based assessment methods are very time-consuming and infrequently used. Mobile sensing offers a great range of sources that can assess function and disability daily. OBJECTIVE: This work aims to prove the feasibility of an interpretable machine learning pipeline for predicting function and disability based on the World Health Organization Disability Assessment Schedule (WHODAS) 2.0 outcomes of clinical outpatients, using passively collected digital biomarkers. METHODS: One-month-long behavioral time-series data consisting of physical and digital activity descriptor variables were summarized using statistical measures (minimum, maximum, mean, median, SD, and IQR), creating 64 features that were used for prediction. We then applied a sequential feature selection to each WHODAS 2.0 domain (cognition, mobility, self-care, getting along, life activities, and participation) in order to find the most descriptive features for each domain. Finally, we predicted the WHODAS 2.0 functional domain scores using linear regression using the best feature subsets. We reported the mean absolute errors and the mean absolute percentage errors over 4 folds as goodness-of-fit statistics to evaluate the model and allow for between-domain performance comparison. RESULTS: Our machine learning-based models for predicting patients' WHODAS functionality scores per domain achieved an average (across the 6 domains) mean absolute percentage error of 19.5%, varying between 14.86% (self-care domain) and 27.21% (life activities domain). We found that 5-19 features were sufficient for each domain, and the most relevant being the distance traveled, time spent at home, time spent walking, exercise time, and vehicle time. CONCLUSIONS: Our findings show the feasibility of using machine learning-based methods to assess functional health solely from passively sensed mobile data. The feature selection step provides a set of interpretable features for each domain, ensuring better explainability to the models' decisions-an important aspect in clinical practice.

Nonstationary relationships among individuals' concurrent exposures to noise, air pollution and greenspace: A mobility-based study using GPS and mobile sensing data.

Individuals are often exposed to multiple environmental factors simultaneously. Understanding their joint effects is essential for developing effective public health policies. However, there has been a lack of research examining individuals' concurrent exposures to multiple environmental factors during people's daily mobility. To address this gap, this study investigated the relationships between and geographic patterns of individual exposures to air pollution (PM2.5), noise and greenspace using individual-level real-time GPS and mobile sensing data collected in outdoor environments. The findings indicate that the relationships between individual exposures to air pollution, noise and greenspace vary across different value ranges of exposures. The study also reveals that people's concurrent exposures to multiple environmental factors exhibit spatial nonstationary and strong clustering patterns. These results highlight the importance of considering spatial nonstationary and spatial heterogeneity of environmental exposures in understanding the relationships between multiple exposures in environmental health research.

Multimodal, Idiographic Ambulatory Sensing Will Transform our Understanding of Emotion.

Emotions are inherently complex - situated inside the brain while being influenced by conditions inside the body and outside in the world - resulting in substantial variation in experience. Most studies, however, are not designed to sufficiently sample this variation. In this paper, we discuss what could be discovered if emotion were systematically studied within persons 'in the wild', using biologically-triggered experience sampling: a multimodal and deeply idiographic approach to ambulatory sensing that links body and mind across contexts and over time. We outline the rationale for this approach, discuss challenges to its implementation and widespread adoption, and set out opportunities for innovation afforded by emerging technologies. Implementing these innovations will enrich method and theory at the frontier of affective science, propelling the contextually situated study of emotion into the future.

LEAN: Real-Time Analysis of Resistance Training Using Wearable Computing.

The use of fitness apps to track physical exercise has been proven to promote weight loss and increase physical activity. The most popular forms of exercise are cardiovascular training and resistance training. The overwhelming majority of cardio tracking apps automatically track and analyse outdoor activity with relative ease. In contrast, nearly all commercially available resistance tracking apps only record trivial data, such as the exercise weight and repetition number via manual user input, a level of functionality not far from that of a pen and paper. This paper presents LEAN, a resistance training app and exercise analysis (EA) system for both the iPhone and Apple Watch. The app provides form analysis using machine learning, automatic repetition counting in real time, and other important but seldom studied exercise metrics, such as range of motion on a per-repetition level and average repetition time. All features are implemented using lightweight inference methods that enable real-time feedback on resource-constrained devices. The performance evaluation includes a user survey and benchmarking of all data science features using both ground-truth data from complementary modalities and comparisons with commercial apps.

Perceiving noise in daily life: How real-time sound characteristics affect personal momentary noise annoyance in various activity microenvironments and times of day.

Annoyance is a major health burden induced by environmental noise. However, our understanding of the health impacts of noise is seriously undermined by the fixed contextual unit and limited sound characteristics (e.g., the sound level only) used in noise exposure assessments as well as the stationarity assumption made for exposure-response relationships. To address these limitations, we analyze the complex and dynamic relationships between personal momentary noise annoyance and real-time noise exposure in various activity microenvironments and times of day, taking into account individual mobility, multiple sound characteristics and nonstationary relationships. Using real-time mobile sensing, we collected individual data of momentary noise annoyance, real-time noise exposure as well as daily activities and travels in Hong Kong. A new sound characteristic, namely sound increment, is defined to capture the sudden increase in sound level over time and is used along with the sound level to achieve a multi-faceted assessment of personal real-time noise exposure at the moment of annoyance responses. Further, the complex noise exposure-annoyance relationships are learned using logistic regression and random forest models while controlling the effects of daily activity microenvironments, individual sociodemographic attributes and temporal contexts. The results indicate that the effects of the real-time sound level and sound increment on personal momentary noise annoyance are nonlinear, despite the overall significant and positive impacts, and different sound characteristics can produce a joint effect on annoyance. We also find that the daily activity microenvironments and individual sociodemographic attributes can affect noise annoyance and its relationship with different sound characteristics to varying degrees. Due to the temporal changes in daily activities and travels, the noise exposure-annoyance relationships can also vary over different times of the day. These findings can inform both local governments and residents with scientific evidence to promote the creation of acoustically comfortable living environments.