ABSTRACT
Objectives/Introduction: Insomnia is the most prevalent sleep disorder worldwide and cognitive behavioural therapy is the front-line treatment. Digital health technologies have a role to play in screening and delivering interventions remotely and without the need for human intervention. The KANOPEE app, which provides a screening and behavioural intervention for insomnia symptoms through an interaction with a virtual agent, showed encouraging results in previous studies during and after the COVID-19 lockdown, but has not yet been evaluated in a controlled study. This study aims at comparing the benefits of KANOPEE, a smartphone application proposing repeated interactions with a virtual companion to screen and deliver personalized recommendations to deal with insomnia complaints;with another application proposing an electronic sleep diary and named "My Sleep Diary". The acceptance and potential benefits of these digital solutions are demonstrated in real-life settings (i.e., without soliciting human medical resources) and in the general population. Method(s): Subjects were included if they downloaded one of the apps between December 2020 and October 2021;and were of legal age. Both apps are available on downloading platforms in France and both groups were equivalent in terms of baseline characteristics. Primary outcome was Insomnia Severity Index (ISI) and secondary outcomes were Total Sleep Time (TST) and Sleep Efficiency (SE). Result(s): 535 users completed the 17-day intervention with KANOPEE and 489 users completed My Sleep Diary for 17 days. A differential effect was obtained for KANOPEE users compared to My Sleep Diary users regarding ISI score (interaction Time x Group: F [2,2014] = 16.9, p < 0.001) and TST (KANOPEE users gained 48 min of sleep after intervention, while My Sleep Diary users gained only 16 min of sleep). Patients with the most severe ISI score (>15) benefited the most from KANOPEE (interaction severity x Time: F [4,2014] = 26.3, p < 0.001). Conclusion(s): KANOPEE provides significantly greater benefits than an electronic sleep diary regarding reduction of insomnia complaints in a self-selected sample of the general population.
ABSTRACT
Background: Circadian system contributes to the regulation of inflammatory processes, but the role of circadian misalignment as a risk factor for contracting Covid-19 has up to now been poorly studied. The aim of this study was to explore the relationship between circadian misalignment (chronic disturbance of the circadian system) and the risk of Covid-19 infection in a population of subjects suspected of contact or infection with SARS-CoV-2. Method(s): Cross-sectional single-center study conducted during a period without lockdown in winter 2021. Recruitment took place in a Covid-19 outpatient testing center. Subjects between 18 and 45 years old were included whether they were symptomatic or not, healthcare workers or not, in contact with a Covid-19 case or not. To determine social jetlag, a proxy of circadian misalignment, they were asked about their usual sleep-wake behaviors. Usual sleep duration and sleep-wake timing were explored on workdays and free days. Social jetlag was defined as at least 2 h shift of circadian alignment (defined as the difference between mid-sleep on workdays and mid-sleep on free days, midsleep as the median between bedtime and rise time). Result(s): One thousand fourteen subjects were included (sampling rate: 10.8%, 39% men, mean age 28 +/- 8) with 56 subjects positive for Covid-19 (positivity rate: 5.5%). Usual mean sleep duration was equivalent in both groups (7 h47 versus 7 h49, p = 0.733). Social jetlag greater than 2 h comprised 33.3% of subjects in the Covid-19 group versus 20.6% in the control group (p = 0.026). After adjustment on age, gender, BMI and work schedules, subjects presenting with social jetlag greater than 2 h had a 2.07-fold higher likelihood to test positive than subjects who had identical sleep-wake timing on workdays and free days (OR = 2.07, 95%CI = [1.12e3.80], p = 0.024). Conclusion(s): Circadian misalignment not only is present in subjects infected by Covid-19 but could also be responsible for a higher likelihood of being infected. The chronobiological impact on the immune system or a higher likelihood of being exposed to social contacts during nocturnal activities could explain our findings, which need to be confirmed in a future large cohort study. Regular sleep-wake timing could ultimately become a target for preventing Covid-19 infection.
ABSTRACT
Introduction: Sleep disturbances are frequently reported in patients infected by Covid-19, but the role of sleep-wake behaviors as a risk factor to contract Covid-19 has up to now poorly been studied. The aim of this study was to explore the relationship between usual sleep-wake behaviors and the risk of Covid-19 infection in a population of subjects suspect of contact or infection with SARS-CoV-2. Methods: Cross-sectionnal monocentric study set during a nonconfined period in winter 2021. Recruitment took place in a Covid-19 ambulatory screening platform. Subjects between 18 and 45 years old were included whether they were symptomatic or not, healthcare workers or not, in contact with a Covid-19 case or not. They were asked about their usual sleep-wake behaviors. Usual sleep duration and sleep timing were explored during workdays and free days. Circadian misalignment was defined as at least 2 hours shift of circadian alignment (defined as the difference between mid-sleep during workdays and mid-sleep during free days, mid sleep as the middle between bedtime and getting up time). Results: One thousand eighteen subjects were included in our study (acceptance rate: 10.8%, 39% of men, mean age of 28± 8). Habitual mean sleep duration was equivalent in both groups (7h47 vs 7h49, p=0.733). Circadian misalignment greater than 2 hours concerned 33% of subjects in the Covid-19 group versus 20% of the control group (p=0.026). After adjustment on age, gender, BMI and work schedules, subjects presenting a circadian misalignment superior to 2 hours had 2.07 more chances to be tested positive than subjects which respected on identical sleep-wake timing between workdays and free days (OR=2.07, 95%CI= [1.12-3.80], p=0.024). Conclusion: Altered sleep not only is present in subjects infected by Covid-19 but could be responsible of a higher change to be infected. Chronobiological impact on immune system and higher chances to be exposed to social contacts could explain our findings which deserve to be confirmed through a future large cohort study. Ultimately regular sleep-wake pattern could constitute a privileged prevention target to fight Covid-19 infection.
ABSTRACT
The COVID-19 crisis has generated an increase of sleep problems in the general population. Digital technologies can help dealing with mental health repercussions of COVID-19 but their acceptance by the population need to be better understood. KANOPEE is a smartphone application providing interactions with a virtual companion to screen and deliver personalized advices to deal with sleep problems. In this study we tried to highlight the factors associated with acceptance of this app, among factors including user characteristics, perceived trustworthiness of the virtual companion and context of use. 3,479 users answered the acceptance questionnaires, with a very positive attitude towards the app. Results indicate that age, education, familiarity with technologies, trustworthiness of the virtual agent and length of interaction are significantly associated with acceptance of the app. To conclude, this study is one of the first to measure acceptance of a virtual companion providing support during the COVID-19 crisis, and provide avenues of research for design and evaluation of intelligent virtual agents for health. © 2021 ACM.
ABSTRACT
Introduction: Whether on the social, economic or scientific level, the digital sciences tend to change the conception of health. Computational Psychiatry, in the sense of a psychiatry based on “numbers” and information flow, has evolved rapidly. Methods: In this article, we propose the distinction between three fields of Computational Psychiatry. A first field corresponds to “Digital Psychiatry”, i.e. a field using digital, connected, tools in the main goal to collect digital data (especially important in this period of COVID-19). A second field corresponds to “Big Psychiatry”, or Big Data Psychiatry, which deals with large amounts of data, e.g. through recent methodologies in Machine learning or artificial intelligence. A third field corresponds to “Psychiatry Modeling”, which corresponds to the utilization of formal hypothesis (i.e. mathematical models) about brain and behavior (and their dysfunctions) in line with computational neurosciences. Results: The collection of digital data fits into methodologies of assessments and interventions in daily life, named Ecological Momentary Assessment. Of course, these digital data, which differ quantitatively and qualitatively from what psychiatry has been able to collect in its history, raise numerous epistemological and ethical questions. In the field of Big Psychiatry, most Machine learning techniques provide predictions rather than pathophysiological mechanisms, and these Machine learning techniques makes it possible to propose new delineations of disorders in a logic of stratified medicine. Lastly, resulting from studies in computational neurosciences, explanatory modeling of the brain (often called “Generative modeling”) proposes a number of theories to understand the functioning of the brain in psychiatric disorder (e.g. predictive coding, reinforcement learning, decision making theories, but also dynamical systems theories and graph and network theory). Discussion and conclusion: This field could offer a framework to characterize the origin of the psychiatric symptoms. Obviously, these three fields are highly mutually dependent, with for instance a data access provided by Digital Psychiatry (with Digital Tools), a data processing operated by Big Psychiatry (with Machine learning) and a formalization of hypotheses offered by Generative modeling of the brain from Psychiatry Modeling. This triple organization of Computational Psychiatry offers a robust framework for personalized and precision psychiatry, articulated around statistical and mathematical methodologies, focused on prediction and explanation and using qualitatively and quantitatively varied data. However, such a framework is necessarily geared to a common subject: the patient of the psychiatric clinic.