THE FRIENDLY CHATBOT: REVEALING WHY PEOPLE USE CHATBOTS THROUGH A STUDY OF USER EXPERIENCE OF CONVERSATIONAL AGENTS

Chatbots are becoming increasingly popular. However, little is known about the way chatbots should be designed. Whether the users should be informed or not beforehand that they are chatting with a chatbot is an open question. Similarly, questions related to the level of ‘humanistic’ tonality in interactions with chatbots are unanswered. In this paper, we present a controlled experiment in which 40 individuals participated. Their user experience was compared depending on whether they knew that they were chatting with chatbots before or afterwards. Two different versions of chatbots were tested (one with mechanical tonality and one with humanistic tonality). Our findings illustrate that: i) it is vital that the users enter the conversation knowing that they are chatting with a chatbot; ii) tonality matters, the way chatbots are designed is pivotal for the user experience, the ‘human-like’ and friendly chatbot was preferred over the mechanical, task-oriented chatbot

Hyperhidrosis in sleep disorders – A narrative review of mechanisms and clinical significance

Funding Information: Grant Fondecyt 1211443 Publisher Copyright: © 2022 European Sleep Research Society.Hyperhidrosis is characterized by excessive sweating beyond thermoregulatory needs that affects patients' quality of life. It results from an excessive stimulation of eccrine sweat glands in the skin by the sympathetic nervous system. Hyperhidrosis may be primary or secondary to an underlying cause. Nocturnal hyperhidrosis is associated with different sleep disorders, such as obstructive sleep apnea, insomnia, restless legs syndrome/periodic limb movement during sleep and narcolepsy. The major cause of the hyperhidrosis is sympathetic overactivity and, in the case of narcolepsy type 1, orexin deficiency may also contribute. In this narrative review, we will provide an outline of the possible mechanisms underlying sudomotor dysfunction and the resulting nocturnal hyperhidrosis in these different sleep disorders and explore its clinical relevance.Peer reviewe

Consumer sleep technology for the screening of obstructive sleep apnea and snoring : current status and a protocol for a systematic review and meta-analysis of diagnostic test accuracy

Funding Information: This work has received research funding from the European Union's Horizon 2020 research and innovation programme under grant agreement no. 965417. Timo Leppänen reports additional funding from NordForsk (NordSleep project 90458) via Business Finland (5133/31/2018), the Academy of Finland (323536), and the Research Committee of the Kuopio University Hospital Catchment Area for the State Research Funding (5041794). Erna Sif Arnardóttir and Anna Sigridur Islind report additional funding from NordForsk (NordSleep project 90458) via the Icelandic Research Fund. Funding Information: European Union's Horizon 2020 research and innovation program, Grant/Award Number: 965417; Academy of Finland, Grant/Award Number: 323536; Kuopio University Hospital Catchment Area for the State Research Funding, Grant/Award Number: 5041794; NordForsk, Grant/Award Number: 90458 Funding information Publisher Copyright: © 2023 The Authors. Journal of Sleep Research published by John Wiley & Sons Ltd on behalf of European Sleep Research Society.There are concerns about the validation and accuracy of currently available consumer sleep technology for sleep-disordered breathing. The present report provides a background review of existing consumer sleep technologies and discloses the methods and procedures for a systematic review and meta-analysis of diagnostic test accuracy of these devices and apps for the detection of obstructive sleep apnea and snoring in comparison with polysomnography. The search will be performed in four databases (PubMed, Scopus, Web of Science, and the Cochrane Library). Studies will be selected in two steps, first by an analysis of abstracts followed by full-text analysis, and two independent reviewers will perform both phases. Primary outcomes include apnea–hypopnea index, respiratory disturbance index, respiratory event index, oxygen desaturation index, and snoring duration for both index and reference tests, as well as the number of true positives, false positives, true negatives, and false negatives for each threshold, as well as for epoch-by-epoch and event-by-event results, which will be considered for the calculation of surrogate measures (including sensitivity, specificity, and accuracy). Diagnostic test accuracy meta-analyses will be performed using the Chu and Cole bivariate binomial model. Mean difference meta-analysis will be performed for continuous outcomes using the DerSimonian and Laird random-effects model. Analyses will be performed independently for each outcome. Subgroup and sensitivity analyses will evaluate the effects of the types (wearables, nearables, bed sensors, smartphone applications), technologies (e.g., oximeter, microphone, arterial tonometry, accelerometer), the role of manufacturers, and the representativeness of the samples.Peer reviewe

Automatic Detection of Electrodermal Activity Events during Sleep

Publisher Copyright: © 2023 by the authors.Currently, there is significant interest in developing algorithms for processing electrodermal activity (EDA) signals recorded during sleep. The interest is driven by the growing popularity and increased accuracy of wearable devices capable of recording EDA signals. If properly processed and analysed, they can be used for various purposes, such as identifying sleep stages and sleep-disordered breathing, while being minimally intrusive. Due to the tedious nature of manually scoring EDA sleep signals, the development of an algorithm to automate scoring is necessary. In this paper, we present a novel scoring algorithm for the detection of EDA events and EDA storms using signal processing techniques. We apply the algorithm to EDA recordings from two different and unrelated studies that have also been manually scored and evaluate its performances in terms of precision, recall, and (Formula presented.) score. We obtain (Formula presented.) scores of about 69% for EDA events and of about 56% for EDA storms. In comparison to the literature values for scoring agreement between experts, we observe a strong agreement between automatic and manual scoring of EDA events and a moderate agreement between automatic and manual scoring of EDA storms. EDA events and EDA storms detected with the algorithm can be further processed and used as training variables in machine learning algorithms to classify sleep health.Peer reviewe

Importance of getting enough sleep and daily activity data to assess variability : longitudinal observational study

Background: The gold standard measurement for recording sleep is polysomnography performed in a hospital environment for 1 night. This requires individuals to sleep with a device and several sensors attached to their face, scalp, and body, which is both cumbersome and expensive. Self-trackers, such as wearable sensors (eg, smartwatch) and nearable sensors (eg, sleep mattress), can measure a broad range of physiological parameters related to free-living sleep conditions; however, the optimal duration of such a self-tracker measurement is not known. For such free-living sleep studies with actigraphy, 3 to 14 days of data collection are typically used. Objective: The primary goal of this study is to investigate if 3 to 14 days of sleep data collection is sufficient while using self-trackers. The secondary goal is to investigate whether there is a relationship among sleep quality, physical activity, and heart rate. Specifically, we study whether individuals who exhibit similar activity can be clustered together and to what extent the sleep patterns of individuals in relation to seasonality vary. Methods: Data on sleep, physical activity, and heart rate were collected over 6 months from 54 individuals aged 52 to 86 years. The Withings Aura sleep mattress (nearable; Withings Inc) and Withings Steel HR smartwatch (wearable; Withings Inc) were used. At the individual level, we investigated the consistency of various physical activities and sleep metrics over different time spans to illustrate how sensor data from self-trackers can be used to illuminate trends. We used exploratory data analysis and unsupervised machine learning at both the cohort and individual levels. Results: Significant variability in standard metrics of sleep quality was found between different periods throughout the study. We showed specifically that to obtain more robust individual assessments of sleep and physical activity patterns through self-trackers, an evaluation period of >3 to 14 days is necessary. In addition, we found seasonal patterns in sleep data related to the changing of the clock for daylight saving time. Conclusions: We demonstrate that >2 months' worth of self-tracking data are needed to provide a representative summary of daily activity and sleep patterns. By doing so, we challenge the current standard of 3 to 14 days for sleep quality assessment and call for the rethinking of standards when collecting data for research purposes. Seasonal patterns and daylight saving time clock change are also important aspects that need to be taken into consideration when choosing a period for collecting data and designing studies on sleep. Furthermore, we suggest using self-trackers (wearable and nearable ones) to support longer-term evaluations of sleep and physical activity for research purposes and, possibly, clinical purposes in the future

Self-applied somnography : technical feasibility of electroencephalography and electro-oculography signal characteristics in sleep staging of suspected sleep-disordered adults

Funding Information: Financial support for this study was provided by the European Union's Horizon 2020 Research and Innovation Programme under grant agreement No 965417, by the Research Committee of the Kuopio University Hospital Catchment Area for the State Research Funding (Grants 5041807, 5041804, 5041803, 5041797, and 5041794), by the Finnish Cultural Foundation through Kainuu Regional Fund and Central fund, by Olvi Foundation, by the Finnish Anti‐Tuberculosis Association, by Tampere Tuberculosis Foundation, by the Research Foundation of the Pulmonary Diseases, by the NordForsk (NordSleep Project 90458) through the Business Finland (Grant 5133/31/2018), by the Kuopio University Hospital Research Foundation, and The Icelandic Centre for Research. Publisher Copyright: © 2023 The Authors. Journal of Sleep Research published by John Wiley & Sons Ltd on behalf of European Sleep Research Society.Sleep recordings are increasingly being conducted in patients’ homes where patients apply the sensors themselves according to instructions. However, certain sensor types such as cup electrodes used in conventional polysomnography are unfeasible for self-application. To overcome this, self-applied forehead montages with electroencephalography and electro-oculography sensors have been developed. We evaluated the technical feasibility of a self-applied electrode set from Nox Medical (Reykjavik, Iceland) through home sleep recordings of healthy and suspected sleep-disordered adults (n = 174) in the context of sleep staging. Subjects slept with a double setup of conventional type II polysomnography sensors and self-applied forehead sensors. We found that the self-applied electroencephalography and electro-oculography electrodes had acceptable impedance levels but were more prone to losing proper skin–electrode contact than the conventional cup electrodes. Moreover, the forehead electroencephalography signals recorded using the self-applied electrodes expressed lower amplitudes (difference 25.3%–43.9%, p < 0.001) and less absolute power (at 1–40 Hz, p < 0.001) than the polysomnography electroencephalography signals in all sleep stages. However, the signals recorded with the self-applied electroencephalography electrodes expressed more relative power (p < 0.001) at very low frequencies (0.3–1.0 Hz) in all sleep stages. The electro-oculography signals recorded with the self-applied electrodes expressed comparable characteristics with standard electro-oculography. In conclusion, the results support the technical feasibility of the self-applied electroencephalography and electro-oculography for sleep staging in home sleep recordings, after adjustment for amplitude differences, especially for scoring Stage N3 sleep.Peer reviewe

Generalizable Deep Learning-Based Sleep Staging Approach for Ambulatory Textile Electrode Headband Recordings

Publisher Copyright: © 2013 IEEE.Reliable, automated, and user-friendly solutions for the identification of sleep stages in home environment are needed in various clinical and scientific research settings. Previously we have shown that signals recorded with an easily applicable textile electrode headband (FocusBand, T 2 Green Pty Ltd) contain characteristics similar to the standard electrooculography (EOG, E1-M2). We hypothesize that the electroencephalographic (EEG) signals recorded using the textile electrode headband are similar enough with standard EOG in order to develop an automatic neural network-based sleep staging method that generalizes from diagnostic polysomnographic (PSG) data to ambulatory sleep recordings of textile electrode-based forehead EEG. Standard EOG signals together with manually annotated sleep stages from clinical PSG dataset (n = 876) were used to train, validate, and test a fully convolutional neural network (CNN). Furthermore, ambulatory sleep recordings including a standard set of gel-based electrodes and the textile electrode headband were conducted for 10 healthy volunteers at their homes to test the generalizability of the model. In the test set (n = 88) of the clinical dataset, the model's accuracy for 5-stage sleep stage classification was 80% (κ = 0.73) using only the single-channel EOG. The model generalized well for the headband-data, reaching 82% (κ = 0.75) overall sleep staging accuracy. In comparison, accuracy of the model was 87% (κ = 0.82) in home recordings using the standard EOG. In conclusion, the CNN model shows potential on automatic sleep staging of healthy individuals using a reusable electrode headband in a home environment.Peer reviewe

Deep learning-based algorithm accurately classifies sleep stages in preadolescent children with sleep-disordered breathing symptoms and age-matched controls

Funding Information: This study was funded by Nordforsk (NordSleep, no. 90458) via Business Finland (no. 5133/31/2018) and via the Icelandic Centre for Research, the Icelandic Research Fund (no. 174067), the Landspitali University Hospital Science Fund 2019 (no. 893831), the European Union’s Horizon 2020 Research and Innovation Programme (grant no. 965417), the National Health and Medical Research Council (NHMRC) of Australia (project nos. 2001729 and 2007001), the Academy of Finland (project no. 323536), the Research Committee of the Kuopio University Hospital Catchment Area for the State Research Funding (project nos. 5041794 and 5041803), and the Finnish Anti-Tuberculosis Association and the Research Foundation of the Pulmonary Diseases. The birth cohort study was funded by the European Commission: (a) under the 6th Framework Program (FOOD-CT-2005-514000) within the collaborative research initiative “EuroPrevall” and (b) under the 7th Framework Program (FP7-KBBE-2012-6; grant agreement no. 312147) within the collaborative project “iFAAM.” Additional funds were received by the Icelandic birth cohort center from Landspitali University Hospital Science Fund, and GlaxoSmithKline Iceland. The funders were not involved in the study design, collection, analysis, interpretation of data, the writing of this article or the decision to submit it for publication. Publisher Copyright: Copyright © 2023 Somaskandhan, Leppänen, Terrill, Sigurdardottir, Arnardottir, Ólafsdóttir, Serwatko, Sigurðardóttir, Clausen, Töyräs and Korkalainen.INTRODUCTION: Visual sleep scoring has several shortcomings, including inter-scorer inconsistency, which may adversely affect diagnostic decision-making. Although automatic sleep staging in adults has been extensively studied, it is uncertain whether such sophisticated algorithms generalize well to different pediatric age groups due to distinctive EEG characteristics. The preadolescent age group (10-13-year-olds) is relatively understudied, and thus, we aimed to develop an automatic deep learning-based sleep stage classifier specifically targeting this cohort. METHODS: A dataset (n = 115) containing polysomnographic recordings of Icelandic preadolescent children with sleep-disordered breathing (SDB) symptoms, and age and sex-matched controls was utilized. We developed a combined convolutional and long short-term memory neural network architecture relying on electroencephalography (F4-M1), electrooculography (E1-M2), and chin electromyography signals. Performance relative to human scoring was further evaluated by analyzing intra- and inter-rater agreements in a subset (n = 10) of data with repeat scoring from two manual scorers. RESULTS: The deep learning-based model achieved an overall cross-validated accuracy of 84.1% (Cohen's kappa κ = 0.78). There was no meaningful performance difference between SDB-symptomatic (n = 53) and control subgroups (n = 52) [83.9% (κ = 0.78) vs. 84.2% (κ = 0.78)]. The inter-rater reliability between manual scorers was 84.6% (κ = 0.78), and the automatic method reached similar agreements with scorers, 83.4% (κ = 0.76) and 82.7% (κ = 0.75). CONCLUSION: The developed algorithm achieved high classification accuracy and substantial agreements with two manual scorers; the performance metrics compared favorably with typical inter-rater reliability between manual scorers and performance reported in previous studies. These suggest that our algorithm may facilitate less labor-intensive and reliable automatic sleep scoring in preadolescent children.Peer reviewe

The European Insomnia Guideline : An update on the diagnosis and treatment of insomnia 2023

Publisher Copyright: © 2023 The Authors. Journal of Sleep Research published by John Wiley & Sons Ltd on behalf of European Sleep Research Society.Progress in the field of insomnia since 2017 necessitated this update of the European Insomnia Guideline. Recommendations for the diagnostic procedure for insomnia and its comorbidities are: clinical interview (encompassing sleep and medical history); the use of sleep questionnaires and diaries (and physical examination and additional measures where indicated) (A). Actigraphy is not recommended for the routine evaluation of insomnia (C), but may be useful for differential-diagnostic purposes (A). Polysomnography should be used to evaluate other sleep disorders if suspected (i.e. periodic limb movement disorder, sleep-related breathing disorders, etc.), treatment-resistant insomnia (A) and for other indications (B). Cognitive-behavioural therapy for insomnia is recommended as the first-line treatment for chronic insomnia in adults of any age (including patients with comorbidities), either applied in-person or digitally (A). When cognitive-behavioural therapy for insomnia is not sufficiently effective, a pharmacological intervention can be offered (A). Benzodiazepines (A), benzodiazepine receptor agonists (A), daridorexant (A) and low-dose sedating antidepressants (B) can be used for the short-term treatment of insomnia (≤ 4 weeks). Longer-term treatment with these substances may be initiated in some cases, considering advantages and disadvantages (B). Orexin receptor antagonists can be used for periods of up to 3 months or longer in some cases (A). Prolonged-release melatonin can be used for up to 3 months in patients ≥ 55 years (B). Antihistaminergic drugs, antipsychotics, fast-release melatonin, ramelteon and phytotherapeutics are not recommended for insomnia treatment (A). Light therapy and exercise interventions may be useful as adjunct therapies to cognitive-behavioural therapy for insomnia (B).Peer reviewe

Adverse effects of obstructive sleep apnea: Interindividual differences in symptoms and biomarkers

Introduction: Obstructive sleep apnea (OSA) is a common sleep disorder that is characterised by frequent cessation of breathing during sleep and excessive daytime sleepiness. Epidemiological studies show that at least 2-4% of the general population suffers from OSA and that the prevalence is much higher in obese subjects. OSA patients usually snore loudly but also have other more variable symptoms such as nocturnal sweating. It is important to recognise better the clinical symptoms of OSA as such knowledge is e.g. informative for the need of a sleep study in undiagnosed OSA patients. OSA is a known risk factor for cardiovascular disease (CVD) but some patients appear to be protected against the adverse consequences of OSA. It is important to understand this inter-individual difference better, e.g. by measuring inflammatory biomarkers as indicators of increased CVD risk. This information can be used for the development of personalized treatment of OSA and better prevention of its comorbidities. Objectives: To assess whether frequent nocturnal sweating is a symptom of OSA. To assess the relationship of OSA severity with levels of inflammatory biomarkers interleukin-6 (IL-6), C-reactive protein (CRP) and leptin in blood, independent of obesity. To evaluate the role of interindividual differences in both nocturnal sweating and levels of inflammatory biomarkers in blood. Methods: The relationship between OSA and reported nocturnal sweating was assessed by comparing 822 untreated OSA subjects in the Icelandic Sleep Apnea Cohort to 703 subjects in a general population cohort. The ISAC cohort was re-assessed two years after starting positive airway pressure (PAP) treatment (n=741). The OSA patients underwent a sleep study assessing OSA severity by four different indices; the apnea-hypopnea index (AHI), the oxygen desaturation index (ODI), hypoxia time (minutes with oxygen saturation <90%) and the minimum oxygen saturation (minSaO2) during the night. Magnetic resonance imaging of abdominal visceral and subcutaneous fat volume was performed. Objective nocturnal sweating was assessed by the electrodermal activity (EDA) index during sleep in a subset of 15 otherwise healthy OSA males while untreated and after 3 months of PAP treatment. Measurement of serum IL-6, CRP and leptin levels was performed cross-sectionally for the first 454 untreated OSA subjects in the ISAC cohort. Results: Frequent nocturnal sweating (≥3x a week) was reported by 31.1% of the OSA cohort vs. only 11.1% of the general population cohort (p<0.0001). This difference remained significant after adjustment for demographic factors. Nocturnal sweating was related to younger age, presence of CVD and hypertension, daytime sleepiness, and difficulties initiating and maintaining sleep but no gender differences were found. The prevalence of frequent nocturnal sweating decreased to 11.5% with PAP treatment (p<0.003). Also, in the 15 OSA patients with objective measures of sweating, the mean (±standard deviation) EDA index during sleep decreased from 132 (±22) events/hr. in untreated patients to 79 (±18) events/hr. on PAP treatment (p=0.04). Untreated patients with high EDA indices also had high systolic blood pressure in the evening and morning (p<0.01). A decrease in EDA index with treatment correlated with a decrease in systolic and diastolic blood pressure (p<0.05). In untreated ISAC participants (n=454), oxygen desaturation severity indices were significantly correlated with higher levels of IL-6, CRP and leptin, but AHI was not. When stratified by BMI category (BMI <30, BMI 30-35 and BMI ≥35 kg/m2), OSA severity was associated with IL-6 and CRP levels in obese participants only. However, no relationship was found between OSA severity and leptin levels, in any of the BMI groups. A multiple linear regression model confirmed an independent association between OSA severity and IL-6 levels as well as an interaction between OSA severity and BMI. The degree of obesity altered the relationship between OSA severity and IL-6 levels, so that no relationship was found in the nonobese but only in the obese subjects. A similar but weaker relationship was found between OSA severity and BMI on CRP levels for males and postmenopausal women. However, no relationship of OSA severity was found with leptin levels. Conclusions: Frequent nocturnal sweating is reported by a third of OSA patients. The prevalence of reported frequent nocturnal sweating was threefold higher in untreated OSA patients than in the general population and decreased to general population levels with full PAP treatment. Objective measurements also showed a decrease of nocturnal sweating with treatment. Frequent nocturnal sweating in OSA patients may be an indicator of increased CVD risk. OSA severity is an independent predictor of increased IL-6 and CRP levels and increased CVD risk but this association is only found in obese patients.This work was supported by NIH grant HL72067 for “A Family Linkage Study of Obstructive Sleep Apnea” and HL94307 for “Endophenotypes of Sleep Apnea and Role of Obesity”, the Eimskip Fund of the University of Iceland, the Landspitali University Hospital Research Fund, The Icelandic Research Fund and the Icelandic Graduate Research Fund