117,592 research outputs found
Body-image dissatisfaction is strongly associated with chronic dysphoria.
No full textIndividual depressive symptoms may contribute to the risk of chronic depression. This study aimed to explore which symptoms predict chronic dysphoria, a hallmark of depression
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Three decades of internet- And computer-based interventions for the treatment of depression: Protocol for a systematic review and meta-analysis
Get PDFBackground: Depression is one of the leading causes of disability worldwide. Internet- and computer-based interventions (IBIs) have been shown to provide effective, scalable forms of treatment. More than 100 controlled trials and a growing number of meta-analyses published over the past 30 years have demonstrated the efficacy of IBIs in reducing symptoms in the short and long term. Despite the large body of research, no comprehensive review or meta-analysis has been conducted to date that evaluates how the effectiveness of IBIs has evolved over time. Objective: This systematic review and meta-analysis aims to evaluate whether there has been a change in the effectiveness of IBIs on the treatment of depression over the past 30 years and to identify potential variables moderating the effect size. Methods: A sensitive search strategy will be executed across the Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, EMBASE, and PsycINFO. Data extraction and evaluation will be conducted by two independent researchers. Risk of bias will be assessed. A multilevel meta-regression model will be used to analyze the data and estimate effect size. Results: The search was completed in mid-2019. We expect the results to be submitted for publication in early 2020. Conclusions: The year 2020 will mark 30 years since the first paper was published on the use of IBIs for the treatment of depression. Despite the large and rapidly growing body of research in the field, evaluations of effectiveness to date are missing the temporal dimension. This review will address that gap and provide valuable analysis of how the effectiveness of interventions has evolved over the past three decades; which participant-, intervention-, and study-related variables moderate changes in effectiveness; and where research in the field may benefit from increased focus
Characterization of recombinant human growth differentiation factor-9 signaling in ovarian granulosa cells
No full textCopyright © 2007 Elsevier Ireland Ltd All rights reserved.David G. Mottershead, Minna M. Pulkki, Pranuthi Muggalla, Arja Pasternack, Minna Tolonen, Samu Myllymaa, Olexandr Korchynskyi, Yoshihiro Nishi, Toshihiko Yanase, Stan Lun, Jennifer L. Juengel, Mika Laitinen and Olli Ritvoshttp://www.elsevier.com/wps/find/journaldescription.cws_home/506028/description#descriptio
Square Dancing with the Stars to Enhance Dynamic Hirschman Linkages?
No full textIn this Presidential Address, the author takes the reader on a reconnaissance of his life and time as a regional scientist. He points out scenery he found scintillating along the way, hoping that some may pick up the banner and chew on a few of the ideas for a while. He suggests a revisit to Albert O. Hirschman’s notion of key sectors and more empirical analysis related to Marcus Berliant’s and Masahisa Fujita’s notion of knowledge creation and transfer.Presidential Address, San Antonio, Texas, March 29, 2014 (53rd Meetings of the Southern Regional Science Association
Psychometric Properties of the Adult Self-Report: Data from over 11,000 American Adults
Get PDFThe first purpose of this study was to examine the factor structure of the Adult Self-Report (ASR) via traditional confirmatory factor analysis (CFA) and contemporary exploratory structural equation modeling (ESEM). The second purpose was to examine the measurement invariance of the ASR subscales across age groups. We used baseline data from the Adolescent Brain Cognitive Development study. ASR data from 11,773 participants were used to conduct the CFA and ESEM analyses and data from 11,678 participants were used to conduct measurement invariance testing. Fit indices supported both the CFA and ESEM solutions, with the ESEM solution yielding better fit indices. However, several items in the ESEM solution did not sufficiently load on their intended factors and/or cross-loaded on unintended factors. Results from the measurement invariance analysis suggested that the ASR subscales are robust and fully invariant across subgroups of adults formed on the basis of age (18–35 years vs. 36–59 years). Future research should seek to both CFA and ESEM to provide a more comprehensive assessment of the ASR.</p
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Letter from unknown writer to Jesse L. Boyce
No full textLetter to Jesse L. Boyce from unknown author (possibly Jack) about the investigation into the powder magazine located in the Grand Canyon. Some personal news is included in the letter such as the writer's marriage to the daughter of C.A. Taylor, former Supervisor of Cochise County
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
Mielenterveyden häiriöiden arvioiminen digitaalisilla biomarkkereilla
No full textAbstractMental disorders such as depression and anxiety significantly contribute to the global disease burden. The World Health Organization estimates that mental disorders affect one in eight people globally. Mental disorders lead to adverse health outcomes and have a direct cost impact on society. Despite the availability of effective therapy and medication, a key challenge in diagnosing, monitoring and treating mental disorders is the inadequacy of assessment methods.This article-based doctoral thesis develops and investigates the feasibility of tools leveraging smartphones and wearables, statistics and machine learning technology to augment the traditional methods of mental disorder care. We developed a smartphone-based application for passive and active data collection leveraging embedded smartphone sensors and a data analysis and behaviour modelling pipeline for quantifying digital biomarkers from smartphone data, predictive analysis, and monitoring mental disorder symptoms.We found statistically significant differences in digital biomarkers and moods of people with and without symptoms of depression. We found a statistically significant relationship between digital biomarkers, mood, and symptoms of depression and anxiety. We show that digital biomarkers and mood can predict symptoms of depression, and that it is feasible to passively monitor fluctuations in mental disorder symptoms to inform clinical decisions.The key findings in this thesis show the feasibility of augmenting the current mental disorder care methods with evidence-based and continuous assessment of symptoms in general and clinical populations in everyday life. The tools developed in this thesis could be tailored for various mental disorders such as schizophrenia, post-traumatic stress disorder, bipolar disorder, and anomalous human behaviours such as sleep disorders, sedentary behaviours and problematic smartphone use. Collaborating with public health policymakers and clinicians, we see the potential to impact mental disorder care with just-in-time clinical interventions based on automated early detection of mental disorders and flagging deterioration of mental disorder symptoms.Original papersOriginal papers are not included in the electronic version of the dissertation.Opoku Asare, K., Visuri, A., Vega, J., & Ferreira, D. (2022). Me in the wild: An exploratory study using smartphones to detect the onset of depression. In X. Gao, A. Jamalipour, & L. Guo (Eds.), Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering: Vol 440. Wireless Mobile Communication and Healthcare (pp. 121–145). https://doi.org/10.1007/978-3-031-06368-8_9Self-archived versionOpoku Asare, K., Terhorst, Y., Vega, J., Peltonen, E., Lagerspetz, E., & Ferreira, D. (2021). Predicting depression from smartphone behavioral markers using machine learning methods, hyperparameter optimization, and feature importance analysis: Exploratory study. JMIR mHealth and uHealth, 9(7), e26540. https://doi.org/10.2196/26540Self-archived versionMoshe, I., Terhorst, Y., Opoku Asare, K., Sander, L. B., Ferreira, D., Baumeister, H., Mohr, D. C., & Pulkki-Råback, L. (2021). Predicting symptoms of depression and anxiety using smartphone and wearable data. Frontiers in Psychiatry, 12, 625247. https://doi.org/10.3389/fpsyt.2021.625247Self-archived versionOpoku Asare, K., Moshe, I., Terhorst, Y., Vega, J., Hosio, S., Baumeister, H., Pulkki-Råback, L., & Ferreira, D. (2022). Mood ratings and digital biomarkers from smartphone and wearable data differentiates and predicts depression status: A longitudinal data analysis. Pervasive and Mobile Computing, 83, 101621. https://doi.org/10.1016/j.pmcj.2022.101621Self-archived versionTiivistelmäMielenterveyden häiriöt, kuten masennus ja ahdistuneisuus, vaikuttavat merkittävästi maailman sairausrasitteeseen. Maailman terveysjärjestö arvioi, että mielenterveyden häiriöistä kärsii maailmanlaajuisesti yksi kahdeksasta ihmisestä. Mielenterveyden häiriöt johtavat haitallisiin terveysvaikutuksiin, ja niillä on suora taloudellinen vaikutus yhteiskuntaan. Tehokkaiden hoitojen ja lääkkeiden saatavuudesta huolimatta mielenterveyden häiriöiden diagnosoinnissa, seurannassa ja hoidossa keskeisenä haasteena on arviointimenetelmien riittämättömyys.Tämä artikkeliperustainen väitöskirja kehittää ja tutkii älypuhelinten ja älyrannekkeiden, tilastotieteen ja koneoppimisteknologian hyödyntämisen mahdollisuuksia perinteisten mielenterveyden häiriöiden hoitomenetelmien täydentämisessä. Kehitimme älypuhelinsovelluksen, joka kerää tietoa passiivisesti ja aktiivisesti puhelimen sisäisten sensorien avulla. Sovellus analysoi keräämänsä tiedot käyttäytymismallinnusta hyödyntäen. Kehitetyn sovelluksen avulla pystyttiin määrittämään älypuhelinten keräämästä datasta digitaalisia biomarkkereita, suorittamaan ennakoivaa analyysiä ja seuraamaan mielenterveyden häiriöiden oireita.Havaitsimme digitaalisissa biomarkkereissa ja ihmisten mielialoissa tilastollisesti merkittäviä eroja vertailtaessa ajanjaksoja, jolloin henkilöt kokivat tai eivät kokeneet masennuksen oireita. Löysimme myös tilastollisesti merkittävän yhteyden digitaalisten biomarkkereiden, mielialojen sekä masennuksen ja ahdistuksen oireiden välillä. Osoitamme, että digitaaliset biomarkkerit ja mieliala voivat ennustaa masennuksen oireita, ja mielenterveysoireiden vaihtelun passiivinen seuranta on toteutettavissa kliinisten päätösten tueksi.Tämän väitöskirjan keskeiset tulokset osoittavat, että nykyisiä mielenterveyden häiriöiden hoitomenetelmiä voidaan täydentää näyttöön perustuvalla ja jatkuvalla arkielämän oireiden arvioinnilla yleisessä ja kliinisessä väestössä. Tässä väitöskirjassa kehitetyt työkalut voidaan räätälöidä eri mielenterveyden häiriöihin, kuten skitsofreniaan, traumaperäiseen stressihäiriöön, kaksisuuntaiseen mielialahäiriöön sekä poikkeuksellisiin käyttäytymismalleihin, kuten unihäiriöihin, istumatyön aiheuttamiin käyttäytymismuutoksiin ja ongelmalliseen älypuhelimen käyttöön. Yhteistyössä kansanterveyspäättäjien ja kliinikoiden kanssa näemme potentiaalia vaikuttaa mielenterveyden häiriöiden hoitoon mahdollistamalla juuri oikea-aikaiset kliiniset toimenpiteet mielenterveyden häiriöiden varhaisen havaitsemisen ja mielenterveysoireiden pahentumisen automaattisen havaitsemisen avulla.OsajulkaisutOsajulkaisut eivät sisälly väitöskirjan elektroniseen versioon.Opoku Asare, K., Visuri, A., Vega, J., & Ferreira, D. (2022). Me in the wild: An exploratory study using smartphones to detect the onset of depression. In X. Gao, A. Jamalipour, & L. Guo (Eds.), Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering: Vol 440. Wireless Mobile Communication and Healthcare (pp. 121–145). https://doi.org/10.1007/978-3-031-06368-8_9Rinnakkaistallennettu versioOpoku Asare, K., Terhorst, Y., Vega, J., Peltonen, E., Lagerspetz, E., & Ferreira, D. (2021). Predicting depression from smartphone behavioral markers using machine learning methods, hyperparameter optimization, and feature importance analysis: Exploratory study. JMIR mHealth and uHealth, 9(7), e26540. https://doi.org/10.2196/26540Rinnakkaistallennettu versioMoshe, I., Terhorst, Y., Opoku Asare, K., Sander, L. B., Ferreira, D., Baumeister, H., Mohr, D. C., & Pulkki-Råback, L. (2021). Predicting symptoms of depression and anxiety using smartphone and wearable data. Frontiers in Psychiatry, 12, 625247. https://doi.org/10.3389/fpsyt.2021.625247Rinnakkaistallennettu versioOpoku Asare, K., Moshe, I., Terhorst, Y., Vega, J., Hosio, S., Baumeister, H., Pulkki-Råback, L., & Ferreira, D. (2022). Mood ratings and digital biomarkers from smartphone and wearable data differentiates and predicts depression status: A longitudinal data analysis. Pervasive and Mobile Computing, 83, 101621. https://doi.org/10.1016/j.pmcj.2022.101621Rinnakkaistallennettu versioAcademic dissertation to be presented with the assent of the Doctoral Programme Committee of Information Technology and Electrical Engineering of the University of Oulu for public defence in the OP auditorium (L10), Linnanmaa, on 1 September 2023, at 3 p.m.Abstract
Mental disorders such as depression and anxiety significantly contribute to the global disease burden. The World Health Organization estimates that mental disorders affect one in eight people globally. Mental disorders lead to adverse health outcomes and have a direct cost impact on society. Despite the availability of effective therapy and medication, a key challenge in diagnosing, monitoring and treating mental disorders is the inadequacy of assessment methods.
This article-based doctoral thesis develops and investigates the feasibility of tools leveraging smartphones and wearables, statistics and machine learning technology to augment the traditional methods of mental disorder care. We developed a smartphone-based application for passive and active data collection leveraging embedded smartphone sensors and a data analysis and behaviour modelling pipeline for quantifying digital biomarkers from smartphone data, predictive analysis, and monitoring mental disorder symptoms.
We found statistically significant differences in digital biomarkers and moods of people with and without symptoms of depression. We found a statistically significant relationship between digital biomarkers, mood, and symptoms of depression and anxiety. We show that digital biomarkers and mood can predict symptoms of depression, and that it is feasible to passively monitor fluctuations in mental disorder symptoms to inform clinical decisions.
The key findings in this thesis show the feasibility of augmenting the current mental disorder care methods with evidence-based and continuous assessment of symptoms in general and clinical populations in everyday life. The tools developed in this thesis could be tailored for various mental disorders such as schizophrenia, post-traumatic stress disorder, bipolar disorder, and anomalous human behaviours such as sleep disorders, sedentary behaviours and problematic smartphone use. Collaborating with public health policymakers and clinicians, we see the potential to impact mental disorder care with just-in-time clinical interventions based on automated early detection of mental disorders and flagging deterioration of mental disorder symptoms.Tiivistelmä
Mielenterveyden häiriöt, kuten masennus ja ahdistuneisuus, vaikuttavat merkittävästi maailman sairausrasitteeseen. Maailman terveysjärjestö arvioi, että mielenterveyden häiriöistä kärsii maailmanlaajuisesti yksi kahdeksasta ihmisestä. Mielenterveyden häiriöt johtavat haitallisiin terveysvaikutuksiin, ja niillä on suora taloudellinen vaikutus yhteiskuntaan. Tehokkaiden hoitojen ja lääkkeiden saatavuudesta huolimatta mielenterveyden häiriöiden diagnosoinnissa, seurannassa ja hoidossa keskeisenä haasteena on arviointimenetelmien riittämättömyys.
Tämä artikkeliperustainen väitöskirja kehittää ja tutkii älypuhelinten ja älyrannekkeiden, tilastotieteen ja koneoppimisteknologian hyödyntämisen mahdollisuuksia perinteisten mielenterveyden häiriöiden hoitomenetelmien täydentämisessä. Kehitimme älypuhelinsovelluksen, joka kerää tietoa passiivisesti ja aktiivisesti puhelimen sisäisten sensorien avulla. Sovellus analysoi keräämänsä tiedot käyttäytymismallinnusta hyödyntäen. Kehitetyn sovelluksen avulla pystyttiin määrittämään älypuhelinten keräämästä datasta digitaalisia biomarkkereita, suorittamaan ennakoivaa analyysiä ja seuraamaan mielenterveyden häiriöiden oireita.
Havaitsimme digitaalisissa biomarkkereissa ja ihmisten mielialoissa tilastollisesti merkittäviä eroja vertailtaessa ajanjaksoja, jolloin henkilöt kokivat tai eivät kokeneet masennuksen oireita. Löysimme myös tilastollisesti merkittävän yhteyden digitaalisten biomarkkereiden, mielialojen sekä masennuksen ja ahdistuksen oireiden välillä. Osoitamme, että digitaaliset biomarkkerit ja mieliala voivat ennustaa masennuksen oireita, ja mielenterveysoireiden vaihtelun passiivinen seuranta on toteutettavissa kliinisten päätösten tueksi.
Tämän väitöskirjan keskeiset tulokset osoittavat, että nykyisiä mielenterveyden häiriöiden hoitomenetelmiä voidaan täydentää näyttöön perustuvalla ja jatkuvalla arkielämän oireiden arvioinnilla yleisessä ja kliinisessä väestössä. Tässä väitöskirjassa kehitetyt työkalut voidaan räätälöidä eri mielenterveyden häiriöihin, kuten skitsofreniaan, traumaperäiseen stressihäiriöön, kaksisuuntaiseen mielialahäiriöön sekä poikkeuksellisiin käyttäytymismalleihin, kuten unihäiriöihin, istumatyön aiheuttamiin käyttäytymismuutoksiin ja ongelmalliseen älypuhelimen käyttöön. Yhteistyössä kansanterveyspäättäjien ja kliinikoiden kanssa näemme potentiaalia vaikuttaa mielenterveyden häiriöiden hoitoon mahdollistamalla juuri oikea-aikaiset kliiniset toimenpiteet mielenterveyden häiriöiden varhaisen havaitsemisen ja mielenterveysoireiden pahentumisen automaattisen havaitsemisen avulla
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