340 research outputs found
GLOBEM Dataset: Multi-Year Datasets for Longitudinal Human Behavior Modeling Generalization
Recent research has demonstrated the capability of behavior signals captured
by smartphones and wearables for longitudinal behavior modeling. However, there
is a lack of a comprehensive public dataset that serves as an open testbed for
fair comparison among algorithms. Moreover, prior studies mainly evaluate
algorithms using data from a single population within a short period, without
measuring the cross-dataset generalizability of these algorithms. We present
the first multi-year passive sensing datasets, containing over 700 user-years
and 497 unique users' data collected from mobile and wearable sensors, together
with a wide range of well-being metrics. Our datasets can support multiple
cross-dataset evaluations of behavior modeling algorithms' generalizability
across different users and years. As a starting point, we provide the benchmark
results of 18 algorithms on the task of depression detection. Our results
indicate that both prior depression detection algorithms and domain
generalization techniques show potential but need further research to achieve
adequate cross-dataset generalizability. We envision our multi-year datasets
can support the ML community in developing generalizable longitudinal behavior
modeling algorithms.Comment: Thirty-sixth Conference on Neural Information Processing Systems
Datasets and Benchmarks Trac
MindShift: Leveraging Large Language Models for Mental-States-Based Problematic Smartphone Use Intervention
Problematic smartphone use negatively affects physical and mental health.
Despite the wide range of prior research, existing persuasive techniques are
not flexible enough to provide dynamic persuasion content based on users'
physical contexts and mental states. We first conduct a Wizard-of-Oz study
(N=12) and an interview study (N=10) to summarize the mental states behind
problematic smartphone use: boredom, stress, and inertia. This informs our
design of four persuasion strategies: understanding, comforting, evoking, and
scaffolding habits. We leverage large language models (LLMs) to enable the
automatic and dynamic generation of effective persuasion content. We develop
MindShift, a novel LLM-powered problematic smartphone use intervention
technique. MindShift takes users' in-the-moment physical contexts, mental
states, app usage behaviors, users' goals & habits as input, and generates
high-quality and flexible persuasive content with appropriate persuasion
strategies. We conduct a 5-week field experiment (N=25) to compare MindShift
with baseline techniques. The results show that MindShift significantly
improves intervention acceptance rates by 17.8-22.5% and reduces smartphone use
frequency by 12.1-14.4%. Moreover, users have a significant drop in smartphone
addiction scale scores and a rise in self-efficacy. Our study sheds light on
the potential of leveraging LLMs for context-aware persuasion in other behavior
change domains
Mood ratings and digital biomarkers from smartphone and wearable data differentiates and predicts depression status : A longitudinal data analysis
Depression is a prevalent mental disorder. Current clinical and self-reported assessment methods of depression are laborious and incur recall bias. Their sporadic nature often misses severity fluctuations. Previous research highlights the potential of in-situ quantification of human behaviour using mobile sensors to augment traditional methods of depression management. In this paper, we study whether self-reported mood scores and passive smartphone and wearable sensor data could be used to classify people as depressed or non-depressed. In a longitudinal study, our participants provided daily mood (valence and arousal) scores and collected data using their smartphones and Oura Rings. We computed daily aggregations of mood, sleep, physical activity, phone usage, and GPS mobility from raw data to study the differences between the depressed and non-depressed groups and created population-level Machine Learning classification models of depression. We found statistically significant differences in GPS mobility, phone usage, sleep, physical activity and mood between depressed and non-depressed groups. An XGBoost model with daily aggregations of mood and sensor data as predictors classified participants with an accuracy of 81.43% and an Area Under the Curve of 82.31%. A Support Vector Machine using only sensor-based predictors had an accuracy of 77.06% and an Area Under the Curve of 74.25%. Our results suggest that digital biomarkers are promising in differentiating people with and without depression symptoms. This study contributes to the body of evidence supporting the role of unobtrusive mobile sensor data in understanding depression and its potential to augment depression diagnosis and monitoring. (c) 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CCPeer reviewe
A Framework for Designing Fair Ubiquitous Computing Systems
Over the past few decades, ubiquitous sensors and systems have been an
integral part of humans' everyday life. They augment human capabilities and
provide personalized experiences across diverse contexts such as healthcare,
education, and transportation. However, the widespread adoption of ubiquitous
computing has also brought forth concerns regarding fairness and equitable
treatment. As these systems can make automated decisions that impact
individuals, it is essential to ensure that they do not perpetuate biases or
discriminate against specific groups. While fairness in ubiquitous computing
has been an acknowledged concern since the 1990s, it remains understudied
within the field. To bridge this gap, we propose a framework that incorporates
fairness considerations into system design, including prioritizing stakeholder
perspectives, inclusive data collection, fairness-aware algorithms, appropriate
evaluation criteria, enhancing human engagement while addressing privacy
concerns, and interactive improvement and regular monitoring. Our framework
aims to guide the development of fair and unbiased ubiquitous computing
systems, ensuring equal treatment and positive societal impact.Comment: 8 pages, 1 figure, published in 2023 ACM International Joint
Conference on Pervasive and Ubiquitous Computing & the 2023 ACM International
Symposium on Wearable Computin
Wearable artificial intelligence for anxiety and depression: A scoping review
Background:
Anxiety and depression are the most common mental disorders worldwide. Owing to the lack of psychiatrists around the world, the incorporation of AI and wearable devices (wearable artificial intelligence (AI)) have been exploited to provide mental health services.
Objective:
The current review aimed to explore the features of wearable AI used for anxiety and depression to identify application areas and open research issues.
Methods:
We searched 8 electronic databases (MEDLINE, PsycINFO, EMBASE, CINAHL, IEEE Xplore, ACM Digital Library, Scopus, and Google Scholar). Then, we checked studies that cited the included studies, and screened studies that were cited by the included studies. Study selection and data extraction were carried out by two reviewers independently. The extracted data were aggregated and summarized using the narrative synthesis.
Results:
Of the 1203 citations identified, 69 studies were included in this review. About two thirds of the studies used wearable AI for depression while the remaining studies used it for anxiety. The most frequent application of wearable AI was diagnosing anxiety and depression while no studies used it for treatment purposes. The majority of studies targeted individuals between the ages of 18 and 65. The most common wearable devices used in the studies were Actiwatch AW4. The wrist-worn devices were most common in the studies. The most commonly used data for model development were physical activity data, sleep data, and heart rate data. The most frequently used dataset from open sources was Depresjon. The most commonly used algorithms were Random Forest (RF) and Support Vector Machine (SVM).
Conclusions:
Wearable AI can offer great promise in providing mental health services related to anxiety and depression. Wearable AI can be used by individuals as a pre-screening assessment of anxiety and depression. Further reviews are needed to statistically synthesize studies’ results related to the performance and effectiveness of wearable AI. Given its potential, tech companies should invest more in wearable AI for treatment purposes for anxiety and depression
Digital health tools for the passive monitoring of depression: a systematic review of methods
The use of digital tools to measure physiological and behavioural variables of potential relevance to mental health is a growing field sitting at the intersection between computer science, engineering, and clinical science. We summarised the literature on remote measuring technologies, mapping methodological challenges and threats to reproducibility, and identified leading digital signals for depression. Medical and computer science databases were searched between January 2007 and November 2019. Published studies linking depression and objective behavioural data obtained from smartphone and wearable device sensors in adults with unipolar depression and healthy subjects were included. A descriptive approach was taken to synthesise study methodologies. We included 51 studies and found threats to reproducibility and transparency arising from failure to provide comprehensive descriptions of recruitment strategies, sample information, feature construction and the determination and handling of missing data. The literature is characterised by small sample sizes, short follow-up duration and great variability in the quality of reporting, limiting the interpretability of pooled results. Bivariate analyses show consistency in statistically significant associations between depression and digital features from sleep, physical activity, location, and phone use data. Machine learning models found the predictive value of aggregated features. Given the pitfalls in the combined literature, these results should be taken purely as a starting point for hypothesis generation. Since this research is ultimately aimed at informing clinical practice, we recommend improvements in reporting standards including consideration of generalisability and reproducibility, such as wider diversity of samples, thorough reporting methodology and the reporting of potential bias in studies with numerous features
Talk2Care: Facilitating Asynchronous Patient-Provider Communication with Large-Language-Model
Despite the plethora of telehealth applications to assist home-based older
adults and healthcare providers, basic messaging and phone calls are still the
most common communication methods, which suffer from limited availability,
information loss, and process inefficiencies. One promising solution to
facilitate patient-provider communication is to leverage large language models
(LLMs) with their powerful natural conversation and summarization capability.
However, there is a limited understanding of LLMs' role during the
communication. We first conducted two interview studies with both older adults
(N=10) and healthcare providers (N=9) to understand their needs and
opportunities for LLMs in patient-provider asynchronous communication. Based on
the insights, we built an LLM-powered communication system, Talk2Care, and
designed interactive components for both groups: (1) For older adults, we
leveraged the convenience and accessibility of voice assistants (VAs) and built
an LLM-powered VA interface for effective information collection. (2) For
health providers, we built an LLM-based dashboard to summarize and present
important health information based on older adults' conversations with the VA.
We further conducted two user studies with older adults and providers to
evaluate the usability of the system. The results showed that Talk2Care could
facilitate the communication process, enrich the health information collected
from older adults, and considerably save providers' efforts and time. We
envision our work as an initial exploration of LLMs' capability in the
intersection of healthcare and interpersonal communication.Comment: Under submission to CHI202
Modelos de Aprendizaje Automático basados en CRISP-DM para el Análisis de los niveles de Depresión en los estudiantes de la Escuela Politécnica Nacional
El presente proyecto analiza las variables de depresión que puede tener un estudiante universitario de la Escuela Politécnica Nacional (EPN) mediante modelos de aprendizaje automático (ML). Participaron un total de 302 estudiantes de distintas carreras quienes completaron de manera voluntaria y anónima una encuesta en línea constituida por el Inventario de Depresión de Beck II (BDI-II). Las 19 preguntas de la encuesta están relacionadas al estilo de vida promedio de un estudiante de la EPN y fueron revisadas y avaladas sobre su relación con trastornos depresivos por una profesional en el campo de la psicología. Se utilizó la metodología CRISP-DM para las fases del proyecto que consistieron en el análisis de la situación actual, planteamiento de objetivos, recolección, análisis y preparación de datos, construcción de modelos de ML para predecir la severidad de depresión con base en las métricas de BDI-II y evaluación de modelos. Se obtuvo un modelo con 0.59 de exactitud y se verificó que las variables de género, edad y relaciones interpersonales son las más significativas al determinar la severidad de depresión
Modelos de Aprendizaje Automático basados en CRISP-DM para el Análisis de los niveles de Depresión en los estudiantes de la Escuela Politécnica Nacional
This project analyzes the depression rates among students from Escuela Politécnica Nacional (EPN). A total of 302 students from different EPN careers, voluntarily and anonymously completed an online survey of the Beck Depression Inventory-II (BDI-II). In addition, they were asked to answer 19 questions related to the lifestyle of an EPN student; These questions were reviewed and endorsed about their possible relationship with depressive disorders by a professional in the field of psychology. The CRISP-DM methodology was used for the project phases, which involved the analysis of the current situation, objectives setting, data collection, data preparation, and construction of ML models that allows predicting the degree of depression based on the BDI-II metrics and evaluation of the models. The model obtained has 0.59 accuracy score and shows that variables of gender, age and relationships are significant to determine severity depression.El presente proyecto analiza las variables de depresión que puede tener un estudiante universitario de la Escuela Politécnica Nacional (EPN) mediante modelos de aprendizaje automático (ML). Participaron un total de 302 estudiantes de distintas carreras quienes completaron de manera voluntaria y anónima una encuesta en línea constituida por el Inventario de Depresión de Beck II (BDI-II). Las 19 preguntas de la encuesta están relacionadas al estilo de vida promedio de un estudiante de la EPN y fueron revisadas y avaladas sobre su relación con trastornos depresivos por una profesional en el campo de la psicología. Se utilizó la metodología CRISP-DM para las fases del proyecto que consistieron en el análisis de la situación actual, planteamiento de objetivos, recolección, análisis y preparación de datos, construcción de modelos de ML para predecir la severidad de depresión con base en las métricas de BDI-II y evaluación de modelos. Se obtuvo un modelo con 0.59 de exactitud y se verificó que las variables de género, edad y relaciones interpersonales son las más significativas al determinar la severidad de depresión
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