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Enregistrement W3018411651 · doi:10.1111/acps.13177

Lifestyle behaviours during the COVID‐19 – time to connect

2020· letter· en· W3018411651 sur OpenAlex

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Notice bibliographique

RevueActa Psychiatrica Scandinavica · 2020
Typeletter
Langueen
DomainePsychology
ThématiqueCOVID-19 and Mental Health
Établissements canadiensMcMaster UniversitySt. Joseph’s Healthcare Hamilton
Organismes subventionnairesInstituto de Salud Carlos IIIFundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de JaneiroConselho Nacional de Desenvolvimento Científico e Tecnológico
Mots-clésLonelinessPsychological interventionSocial isolationMental healthIsolation (microbiology)AnxietyPsychiatryMedicinePublic healthPandemicDepression (economics)PsychologyDiseaseSocial distanceGerontologyCoronavirus disease 2019 (COVID-19)

Résumé

récupéré en direct d'OpenAlex

Loneliness and social isolation are associated with poor mental and physical health and may increase the likelihood of common mental disorders (depressive and anxiety disorders), substance use and cognitive decline (1, 2). At this moment, people around the globe have been urged to self-isolate and refrain from social interaction due to the COVID-19 pandemic. From public health and preventative care perspectives, there is a pressing need to provide individuals, communities and health agencies with information and interventions to maintain the healthiest possible lifestyle while in isolation. Healthy lifestyle (HL) behaviours have been consistently associated with reduced all-cause mortality, and increased lifespan and wellbeing (3). Unhealthy behaviours (poor-quality diet, lack of physical exercise, tobacco and alcohol use) are major contributors to the global burden of disease (4) and have also been associated with worse outcomes across psychiatric disorders (5). Moreover, it is increasingly acknowledged that unhealthy lifestyles may be a driving force in the epidemic of common mental disorders (6). Evidence suggests that the current pandemic-related, mandatory self-isolation may trigger depression and post-traumatic stress disorder (PTSD) (7) and that being a healthcare worker or having COVID-19 is risk factors for stress-related psychiatric disorders (8, 9). Given the lack of effective treatments for COVID-19, non-pharmacological interventions (NPIs) are mandatory to decrease disease transmission. NPIs include personal restrictions and physical-distancing policies, such as mass confinement and compulsory home isolation. NPIs may modify, for better or for worse, lifestyle behaviours. Increased adoption of unhealthy nutrition and sedentary behaviour, and decreased outdoor time and increased screen time are expected to occur. These behaviours may have unforeseen medium- and long-term consequences for mental and physical health (10). For instance, diminished physical activity resulting from home isolation may increase a wide range of negative cardio-metabolic and mental effects (11). Research has mostly focused on the psychological impact, rather than lifestyle issues under physical-distancing policies. Lifestyle behaviours including dietary changes, restricted physical activity and the effect of increased indoor and screen time remain an under-researched area (12). Of note, towards the end of the SARS epidemic, social support, mental health awareness and other lifestyles changes (exercise, more time for relaxation and restorative sleep) were all associated with decreased perceived stress and incidence of PTSD (13). The ongoing COVID-19 outbreak has led to an unprecedented public health crisis worldwide. From our perspective, several actions are required to minimize the transition to a social crisis with long-lasting consequences. It is time that such interventions start to include lifestyle guidelines with the aim to translate evidence into public health policies. This is crucial for the vulnerable groups, such as low-income families and children (14, 15), the elderly, socially isolated individuals and people with severe mental disorders (SMD). Regarding patients with SMD requiring admission, the field is recommending home hospitalizations to keep patients safe while avoiding formal hospital admissions (16). Regarding lifestyle guidelines, recent reviews have emphasized the role of maintaining a healthy nutritional status (17) and engaging in physical exercise at home (11) in the management of COVID-19 outbreak. Similar recommendations were made at the time of the influenza pandemic in 1918, when public health nurses adhered to precepts of good hygiene, nutrition, fresh air and rest (18). However, such lifestyle guidelines are not entirely evidence based. Indeed, they are basically the same guidance used during non-pandemic times. Observational data on how the general public and patients with psychiatric disorders actually deal with self-care, nutrition, physical activity or restorative sleep during confinement are lacking and represent a research gap. To address such gap, observational studies of lifestyle behaviours during the compulsory isolation are timely and clearly a necessary step for the design of rational and effective public policies. Such studies would provide the much-needed evidence to design interventions to prevent a new pandemic of psychiatric disorders and cardio-metabolic comorbidities as proposed by the COVID-19 Snapshot Monitoring (COSMO) initiative (19). Furthermore, data collection must be fast and provide useful and reliable information in real time to health authorities, media and citizens. Psychiatry and behavioural medicine may be particularly benefited from surveys and interventions carried out remotely to reach a large number of individuals in need. Large-scale surveys will require international networking to address changes in lifestyle behaviours and the expected consequences after the COVID-19 (9). We urge the field to embrace and extend eHealth and mobile health interventions, online monitoring surveys and big data technologies. Remote data collection using social networks, georeferencing and the available tools provided by data science is available, feasible and necessary in the context of this pandemic. Such tools provide the means of groups across the globe to connect and generate the real-time necessary data to inform policymakers. The authors received no financial support for the research, authorship and/or publication of this editorial. Dr. Balanzá-Martínez acknowledges the support from Instituto de Salud Carlos III (PI16/1770, PROBILIFE Study). Dr. De Boni acknowledges long-term funding from CNPq and FAPERJ. Dr. Balanzá-Martínez has been a consultant, advisor or Continuing Medical Education (CME) speaker over the last 3 years for the following companies: Angelini, Ferrer, Lundbeck, Nutrición Médica and Otsuka. The other authors declare no conflict of interest.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Intégrité de la recherche, Charge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesCharge utile insuffisante (le modèle a refusé de juger)
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,049
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0000,001
Études des sciences et des technologies0,0010,000
Communication savante0,0000,000
Science ouverte0,0020,000
Intégrité de la recherche0,0010,003
Charge utile insuffisante (le modèle a refusé de juger)0,0140,010

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,036
Tête enseignante GPT0,357
Écart entre enseignants0,321 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle