The rise of consumer health wearables: promises and barriers

Will consumer wearable technology ever be adopted or accepted by the medical community? Patients and practitioners regularly use digital technology (e.g., thermometers and glucose monitors) to identify and discuss symptoms. In addition, a third of general practitioners in the United Kingdom report that patients arrive with suggestions for treatment based on online search results. However, consumer health wearables are predicted to become the next “Dr Google.” One in six (15%) consumers in the United States currently uses wearable technology, including smartwatches or fitness bands. While 19 million fitness devices are likely to be sold this year, that number is predicted to grow to 110 million in 2018. As the line between consumer health wearables and medical devices begins to blur, it is now possible for a single wearable device to monitor a range of medical risk factors. Potentially, these devices could give patients direct access to personal analytics that can contribute to their health, facilitate preventive care, and aid in the management of ongoing illness. However, how this new wearable technology might best serve medicine remains unclea

COVID-19 what have we learned? The rise of social machines and connected devices in pandemic management following the concepts of predictive, preventive and personalised medicine

A comprehensive bibliographic review with R statistical methods of the COVID pandemic in PubMed literature and Web of Science Core Collection, supported with Google Scholar search. In addition, a case study review of emerging new approaches in different regions, using medical literature, academic literature, news articles and other reliable data sources. Public responses of mistrust about privacy data misuse differ across countries, depending on the chosen public communication strategy

The epigenetic of the panic disorder

From this review, I discussed the epigenetics of panic disorder .epigenetic is the changes in the heritable phenotype without any change from the DNA sequence. The roles of different types of epigenetics (DNA methylation and chromatin remodeling) have been controversial from one study to another. The researcher focused on the roles of the candidate genes in the development and progress of panic disorder .there different candidate genes located the different chromosomes such as 1q, 2p, 2q, 3, 7, 9, 11, 12q13, 12q23, and 15 that may be used as markers give for the future promise to diagnosis panic disorder in early life span. Recently, no studies have demonstrated gene therapy's roles in treating panic disorder, and the basic contribution of interventions such as exposure, cognitive therapy, relaxation training, and breathing retraining has not yet been identified. Differences between these patients that assess the efficacy of exposure-based cognitive-behavioral therapy and related neuroplastic changes can be demonstrated by altered protection signal processing and anterior cingulate cortex-amygdala couplin

The Double-edged Sword: A Mixed Methods Study of the Interplay between Bipolar Disorder and Technology Use

Human behavior is increasingly reflected or acted out through technology. This is of particular salience when it comes to changes in behavior associated with serious mental illnesses including schizophrenia and bipolar disorder. Early detection is crucial for these conditions but presently very challenging to achieve. Potentially, characteristics of these conditions\u27 traits and symptoms, at both idiosyncratic and collective levels, may be detectable through technology use patterns. In bipolar disorder specifically, initial evidence associates changes in mood with changes in technology-mediated communication patterns. However much less is known about how people with bipolar disorder use technology more generally in their lives, how they view their technology use in relation to their illness, and, perhaps most crucially, the causal relationship (if any exists) between their technology use and their disease. To address these uncertainties, we conducted a survey of people with bipolar disorder (N = 84). Our results indicate that technology use varies markedly with changes in mood and that technology use broadly may have potential as an early warning signal of mood episodes. We also find that technology for many of these participants is a double-edged sword: acting as both a culprit that can trigger or exacerbate symptoms as well as a support mechanism for recovery. These findings have implications for the design of both early warning systems and technology-mediated interventions

Tunteiden Havaitseminen Arkielämässä Koneoppimisen ja Puettavien Laitteiden Avulla

Tavoitteet. Tämän tutkimuksen tavoitteena on arvioida tunteiden havaitsemisen mahdollisuutta arkielämässä puettavien laitteiden ja koneoppimismallien avulla. Tunnetiloilla on tärkeä rooli päätöksenteossa, havaitsemisessa ja käyttäytymisessä, mikä tekee objektiivisesta tunnetilojen havaitsemisesta arvokkaan tavoitteen, sekä mahdollisten sovellusten että tunnetiloja koskevan ymmärryksen syventämisen kannalta. Tunnetiloihin usein liittyy mitattavissa olevia fysiologisia ja käyttäymisen muutoksia, mikä mahdollistaa koneoppimismallien kouluttamisen muutoksia aiheuttaneen tunnetilan havaitsemiseksi. Suurin osa tunteiden havaitsemiseen liittyvästä tutkimuksesta on toteutettu laboratorio-olosuhteissa käyttämällä tunteita herättäviä ärsykkeitä tai tehtäviä, mikä herättää kysymyksen siitä että yleistyvätkö näissä olosuhteissa saadut tulokset arkielämään. Vaikka puettavien laitteiden ja kännykkäkyselyiden kehittyminen on helpottanut aiheen tutkimista arkielämässä, tutkimusta tässä ympäristössä on vielä niukasti. Tässä tutkimuksessa itseraportoituja tunnetiloja ennustetaan koneoppimismallien avulla arkielämässä havaittavissa olevien tunnetilojen selvittämiseksi. Lisäksi tutkimuksessa käytetään mallintulkintamenetelmiä mallien hyödyntämien yhteyksien tunnistamiseksi. Metodit. Aineisto tätä tutkielmaa varten on peräisin tutkimuksesta joka suoritettiin osana Helsingin Yliopiston ja VTT:n Sisu at Work projektia, missä 82:ta tietotyöläistä neljästä suomalaisesta organisaatiosta tutkittiin kolmen viikon ajan. Osallistujilla oli jakson aikana käytettävissään mittalaitteet jotka mittasivat fotoplethysmografiaa (PPG), ihon sähkönjohtavuutta (EDA) ja kiihtyvyysanturi (ACC) signaaleita, lisäksi heille esitettiin kysymyksiä koetuista tunnetiloista kolmesti päivässä puhelinsovelluksen avulla. Signaalinkäsittelymenetelmiä sovellettiin signaaleissa esiintyvien liikeartefaktien ja muiden ongelmien korjaamiseksi. Sykettä (HR) ja sykevälinvaihtelua (HRV) kuvaavia piirteitä irroitettiin PPG signaalista, fysiologista aktivaatiota kuvaavia piirteitä EDA signaalista, sekä liikettä kuvaavia piirteitä ACC signaalista. Seuraavaksi koneoppimismalleja koulutettiin ennustamaan raportoituja tunnetiloja irroitetujen piirteiden avulla. Mallien suoriutumista vertailtiin suhteessa odotusarvoihin havaittavissa olevien tunnetilojen määrittämiseksi. Lisäksi permutaatiotärkeyttä sekä Shapley additive explanations (SHAP) arvoja hyödynnettiin malleille tärkeiden yhteyksien selvittämiseksi. Tulokset ja johtopäätökset. Mallit tunnetiloille virkeä, keskittynyt ja innostunut paransivat suoriutumistaan yli odotusarvon, joista mallit tunnetilalle virkeä paransivat suoriutumista tilastollisesti merkitsevästi. Permutaatiotärkeys korosti liike- ja HRV-piirteiden merkitystä, kun SHAP arvojen tarkastelu nosti esiin matalan liikkeen, matalan EDA:n, sekä korkean HRV:n merkityksen mallien ennusteille. Nämä tulokset ovat lupaavia korkean aktivaation positiivisten tunnetilojen havaitsemiselle arkielämässä, sekä nostavat esiin mahdollisia yhteyksiä jatkotutkimusta varten.Objectives. This study aims to evaluate feasibility of affect detection in daily life using wearable devices and machine learning models. Affective states play an important role in decision making, perception and behaviour, making objective detection of affective states a desirable goal both for potential applications and as a way to gain insight into affective phenomena. Affective states have been found to have measurable physiological and behavioral changes, which allows training of machine learning models for detecting the underlying affects. Majority of affect detection studies have been conducted in laboratory conditions using affect elicitation stimuli or tasks, raising the question whether results from these studies will generalize to daily life. Although development of wearable devices and mobile surveys have facilitated evaluation in the context of daily life, research here remains sparse. In this study, self-reported affective states are predicted using machine learning models to identify which affective states can be detected in daily life. Additionally, model interpretation methods will be used to identify which relationships the models found important for their predictions. Methods. Data for this thesis came from a study conducted as a part of Sisu at Work project between University of Helsinki and VTT, where 82 knowledge workers from four Finnish organizations were studied for a period of three weeks. During this period, the participants were queried by mobile surveys about their affective states thrice a day, while they also used wearable devices to record photoplethysmography (PPG), electrodermal activity (EDA) and accelerometry (ACC) signals. A signal processing pipeline was implemented to deal with movement artefacts and other issues with the data. Features describing heart rate (HR) and heart rate variation (HRV) were extraced from PPG, physiological activation from EDA and movement from ACC signals. Models were then fitted to predict the reported affective states using the extracted features. Model performance was compared against a baseline to identify which affects could be reliably detected, while permutation importance and Shapley additive explanations (SHAP) values were used to identify important relationships established by the models. Results and conclusions. Models for affective state vigor showed improvements over baseline with statistical significance, while improvements were also noted for affects focused and enthusiastic. Permutation importance highlighted the significance of movement and HRV features, while examination of SHAP values indicated that low movement, low EDA and high HRV impacted model predictions the most. These results indicate potential for detecting high activation affective states in daily life and propose potential relationships for future research