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| http://dx.doi.org/10.1002/wps.20805 | DOI Listing |
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The role of impulsivity and emotional dysregulation in smartphone overdependence explored through network analysis.
Sci Rep
January 2025
Department of Psychology, Jeonbuk National University, 567 Baekje-daero, Deokjin-gu, Jeonju-si, Jeonbuk, 54896, Republic of Korea.
Smartphone overdependence is a maladaptive behavior characterized by excessive and uncontrollable smartphone use despite negative consequences. Impulsivity and emotional dysregulation, which are multidimensional constructs with each factor exerting a different effect, have been found to influence the development and persistence of smartphone overdependence. This study utilized network analysis to investigate the intricate relationships between the factors of impulsivity, emotional dysregulation, and smartphone overdependence.
View Article and Find Full Text PDFThe COVID-19 pandemic and increased demands for neurologists have inspired the creation of remote, digitalized tests of neurological functions. This study investigates two tests from the Neurological Functional Tests Suite (NeuFun-TS) smartphone application, the "Postural Sway" and "Pronator Drift" tests. These tests capture different domains of postural control and motoric dysfunction in healthy volunteers (n=13) and people with neurological disorders (n=68 relapsing-remitting multiple sclerosis [MS]; n=21 secondary progressive MS; n=23 primary progressive MS; n=13 other inflammatory neurological diseases; n=21 non-inflammatory neurological diseases; n=4 clinically isolated syndrome; n=1 radiologically isolated syndrome).
View Article and Find Full Text PDFUtility of Consumer-Grade Wearable Devices for Inferring Physical and Mental Health Outcomes in Severe Mental Illness: Systematic Review.
JMIR Ment Health
January 2025
School for Health Sciences, University of Manchester, Manchester, United Kingdom.
Background: Digital wearable devices, worn on or close to the body, have potential for passively detecting mental and physical health symptoms among people with severe mental illness (SMI); however, the roles of consumer-grade devices are not well understood.
Objective: This study aims to examine the utility of data from consumer-grade, digital, wearable devices (including smartphones or wrist-worn devices) for remotely monitoring or predicting changes in mental or physical health among adults with schizophrenia or bipolar disorder. Studies were included that passively collected physiological data (including sleep duration, heart rate, sleep and wake patterns, or physical activity) for at least 3 days.
Longitudinal trajectories of digital upper limb biomarkers for multiple sclerosis.
Eur J Neurol
January 2025
Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Victoria, Australia.
Background: Upper limb dysfunction is a common debilitating feature of relapsing-remitting multiple sclerosis (RRMS). We aimed to examine the longitudinal trajectory of the iPad®-based Manual Dexterity Test (MDT) and predictors of change over time.
Methods: We prospectively enrolled RRMS patients (limited to Expanded Disability Status Scale (EDSS) < 4).
AI-based medication adherence prediction in patients with schizophrenia and attenuated psychotic disorders.
Schizophr Res
December 2024
Boehringer Ingelheim Pharmaceuticals Inc., 900 Ridgebury Road, Ridgefield, CT 06877, USA. Electronic address:
Objective: The capacity of machine-learning algorithms to predict medication adherence was assessed using data from AiCure, a computer vision-assisted smartphone application, which records the medication ingestion event.
Methods: Patients treated with BI 409306 were recruited from two Phase II randomized, placebo-controlled trials in schizophrenia (NCT03351244) and attenuated psychotic disorders (NCT03230097). A machine-learning model was optimized to predict overall trial adherence using AiCure data collected over three monitoring periods (7/10/14 days), adherence cut-offs (0.
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