Health Information Technology Commons^™

The Use Of Deception In Dementia-Care Robots: Should Robots Tell "White Lies" To Limit Emotional Distress?, Samuel R. Cox, Grace Cheong, Wei Tsang Ooi

ROSA Journal Articles and Publications

With projections of ageing populations and increasing rates of dementia, there is need for professional caregivers. Assistive robots have been proposed as a solution to this, as they can assist people both physically and socially. However, caregivers often need to use acts of deception (such as misdirection or white lies) in order to ensure necessary care is provided while limiting negative impacts on the cared-for such as emotional distress or loss of dignity. We discuss such use of deception, and contextualise their use within robotics.

Potential And Pitfalls Of Mobile Mental Health Apps In Traditional Treatment: An Umbrella Review, Jerica Koh, Germaine Y. Q. Tng, Andree Hartanto

Research Collection School of Social Sciences

While the rapid growth of mobile mental health applications has offered an avenue of support unbridled by physical distance, time, and cost, the digitalization of traditional interventions has also triggered doubts surrounding their effectiveness and safety. Given the need for a more comprehensive and up-to-date understanding of mobile mental health apps in traditional treatment, this umbrella review provides a holistic summary of their key potential and pitfalls. A total of 36 reviews published between 2014 and 2022—including systematic reviews, meta-analyses, scoping reviews, and literature reviews—were identified from the Cochrane library, Medline (via PubMed Central), and Scopus databases. The majority of …

Identifying App-Based Meditation Habits And The Associated Mental Health Benefits: Longitudinal Observational Study, Chad Stecher, Vincent Berardi, Ryan Fowers, Jaclyn Christ, Yunro Chung, Jennifer Huberty

Psychology Faculty Articles and Research

Background: Behavioral habits are often initiated by contextual cues that occur at approximately the same time each day; so, it may be possible to identify a reflexive habit based on the temporal similarity of repeated daily behavior. Mobile health tools provide the detailed, longitudinal data necessary for constructing such an indicator of reflexive habits, which can improve our understanding of habit formation and help design more effective mobile health interventions for promoting healthier habits.

Objective: This study aims to use behavioral data from a commercial mindfulness meditation mobile phone app to construct an indicator of reflexive meditation habits …

Detecting Receptivity For Mhealth Interventions In The Natural Environment, Varun Mishra, Florian Künzler, Jan-Niklas Kramer, Elgar Fleisch, Tobias Kowatsch, David Kotz

Dartmouth Scholarship

Just-In-Time Adaptive Intervention (JITAI) is an emerging technique with great potential to support health behavior by providing the right type and amount of support at the right time. A crucial aspect of JITAIs is properly timing the delivery of interventions, to ensure that a user is receptive and ready to process and use the support provided. Some prior works have explored the association of context and some user-specific traits on receptivity, and have built post-study machine-learning models to detect receptivity. For effective intervention delivery, however, a JITAI system needs to make in-the-moment decisions about a user's receptivity. To this end, …

When Do Drivers Interact With In-Vehicle Well-Being Interventions? An Exploratory Analysis Of A Longitudinal Study On Public Roads, Kevin Koch, Varun Mishra, Shu Liu, Thomas Berger, Elgar Fleisch, David Kotz, Felix Wortmann

Dartmouth Scholarship

Recent developments of novel in-vehicle interventions show the potential to transform the otherwise routine and mundane task of commuting into opportunities to improve the drivers' health and well-being. Prior research has explored the effectiveness of various in-vehicle interventions and has identified moments in which drivers could be interruptible to interventions. All the previous studies, however, were conducted in either simulated or constrained real-world driving scenarios on a pre-determined route. In this paper, we take a step forward and evaluate when drivers interact with in-vehicle interventions in unconstrained free-living conditions.

To this end, we conducted a two-month longitudinal study with 10 …

Identifying Depression In The National Health And Nutrition Examination Survey Data Using A Deep Learning Algorithm, Jihoon Oh, Kyongsik Yun, Uri Maoz, Tae-Suk Kim, Jeong-Ho Chae

Psychology Faculty Articles and Research

Background

As depression is the leading cause of disability worldwide, large-scale surveys have been conducted to establish the occurrence and risk factors of depression. However, accurately estimating epidemiological factors leading up to depression has remained challenging. Deep-learning algorithms can be applied to assess the factors leading up to prevalence and clinical manifestations of depression.

Methods

Customized deep-neural-network and machine-learning classifiers were assessed using survey data from 19,725 participants from the NHANES database (from 1999 through 2014) and 4949 from the South Korea NHANES (K-NHANES) database in 2014.

Results

A deep-learning algorithm showed area under the receiver operating characteristic curve (AUCs) …

Spatio-Temporal Distribution Of Negative Emotions In New York City After A Natural Disaster As Seen In Social Media, Oliver Gruebner, Sarah R. Lowe, Martin Sykora, Ketan Shankardass, Sv Subramanian, Sandro Galea

Department of Psychology Faculty Scholarship and Creative Works

Disasters have substantial consequences for population mental health. We used Twitter to (1) extract negative emotions indicating discomfort in New York City (NYC) before, during, and after Superstorm Sandy in 2012. We further aimed to (2) identify whether pre- or peri-disaster discomfort were associated with peri- or post-disaster discomfort, respectively, and to (3) assess geographic variation in discomfort across NYC census tracts over time. Our sample consisted of 1,018,140 geo-located tweets that were analyzed with an advanced sentiment analysis called ”Extracting the Meaning Of Terse Information in a Visualization of Emotion” (EMOTIVE). We calculated discomfort rates for 2137 NYC census …

Openness, Neuroticism, Conscientiousness, And Family Health And Aging Concerns Interact In The Prediction Of Health-Related Internet Searches In A Representative U.S. Sample, Tim Bogg, Phuong T. Vo

Psychology Faculty Research Publications

Recent estimates suggest 60 % of the U.S. adult population uses the Internet to find health-related information. The goal of the present study was to model health-related Internet searches as a function of an interdependent system of personality adaptation in the context of recent health and aging-related concerns. Assessments of background factors, Big Five personality traits, past-month health and aging-related concerns, and the frequency of past-month health-related Internet searches (via Google, Yahoo, AOL, Bing, or some other search engine) were obtained from a representative U.S. sample (N = 1,015). Controlling for background factors, regression analyses showed more frequent health-related …