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1.
Int J Environ Res Public Health ; 19(13)2022 06 25.
Article in English | MEDLINE | ID: covidwho-1934044

ABSTRACT

Low back pain (LBP) carries a high risk of chronicization and disability, greatly impacting the overall demand for care and costs, and its treatment is at risk of scarce adherence. This work introduces a new scenario based on the use of a mobile health tool, the Dress-KINESIS, to support the traditional rehabilitation approach. The tool proposes targeted self-manageable exercise plans for improving pain and disability, but it also monitors their efficacy. Since LBP prevention is the key strategy, the tool also collects real-patient syndromic information, shares valid educational messages and fosters self-determined motivation to exercise. Our analysis is based on a comparison of the performance of the traditional rehabilitation process for non-specific LBP patients and some different scenarios, designed by including the Dress-KINESIS's support in the original process. The results of the simulations show that the integrated approach leads to a better capacity for taking on patients while maintaining the same physiotherapists' effort and costs, and it decreases healthcare costs during the two years following LBP onset. These findings suggest that the healthcare system should shift the paradigm towards citizens' participation and the digital support, with the aim of improving its efficiency and citizens' quality of life.


Subject(s)
Low Back Pain , Physical Therapists , Telemedicine , Humans , Low Back Pain/rehabilitation , Public Health , Quality of Life
2.
Int J Environ Res Public Health ; 19(3)2022 Jan 24.
Article in English | MEDLINE | ID: covidwho-1649176

ABSTRACT

Digital technologies have been extensively employed in response to the SARS-CoV-2 pandemic worldwide. This study describes the methodology of the two-phase internet-based EPICOVID19 survey, and the characteristics of the adult volunteer respondents who lived in Italy during the first (April-May 2020) and the second wave (January-February 2021) of the epidemic. Validated scales and ad hoc questionnaires were used to collect socio-demographic, medical and behavioural characteristics, as well as information on COVID-19. Among those who provided email addresses during phase I (105,355), 41,473 participated in phase II (mean age 50.7 years ± 13.5 SD, 60.6% females). After a median follow-up of ten months, 52.8% had undergone nasopharyngeal swab (NPS) testing and 13.2% had a positive result. More than 40% had undergone serological test (ST) and 11.9% were positive. Out of the 2073 participants with at least one positive ST, 72.8% had only negative results from NPS or never performed it. These results indicate that a large fraction of individuals remained undiagnosed, possibly contributing to the spread of the virus in the community. Participatory online surveys offer a unique opportunity to collect relevant data at individual level from large samples during confinement.


Subject(s)
COVID-19 , Adult , Female , Humans , Internet , Italy/epidemiology , Male , Middle Aged , Pandemics , SARS-CoV-2 , Surveys and Questionnaires
3.
Maturitas ; 158: 61-69, 2022 04.
Article in English | MEDLINE | ID: covidwho-1549976

ABSTRACT

Objective To investigate sex- and gender-based differences linked to SARS-COV-2 infection and to explore the role of hormonal therapy (HT) in females. Study design Data from the self-administered, cross-sectional, web-based EPICOVID19 survey of 198,822 adults living in Italy who completed an online questionnaire during the first wave of the epidemic in Italy (April-May 2020) were analyzed. Main outcomes measures Multivariate binary logistic and multinomial regression models were respectively used to estimate the odds ratios (ORs) with 95% confidence intervals (CIs) for positive nasopharyngeal swab (NPS) test results and severe SARS-CoV-2 infection. Results The data from 6,873 participants (mean age 47.9 ± 14.1 years, 65.8% females) who had a known result from an NPS test were analyzed. According to the multivariate analysis, females had lower odds of a positive result from the NPS test (aOR 0.75, 95%CI 0.66-0.85) and of having a severe infection (aOR 0.46, 95%CI 0.37-0.57) than did their male counterparts. These differences were greater with decreasing age in both sexes. In addition, females aged ≥60 years receiving HT (N = 2,153, 47.6%) had a 46% lower probability of having a positive NPS test (aOR 0.54, 95%CI 0.36-0.80) than their same-aged peers who had never used HT; there were no differences in the younger age groups with respect to HT status. Conclusion Female sex was associated with an age-dependent lower risk of having a severe SARS-CoV-2 infection than their male counterparts. Age seemed to modify the relationship between HT status and infection: while the two were not related among younger participants, it was negative in the older ones. Future prospective studies are needed to elucidate the potential protective role sex hormones may play. Trial registration ClinicalTrials.gov NCT04471701.


Subject(s)
Age Factors , COVID-19 , Sex Factors , Adult , COVID-19/epidemiology , Cross-Sectional Studies , Female , Humans , Internet , Male , Middle Aged , SARS-CoV-2 , Surveys and Questionnaires
4.
Int J Environ Res Public Health ; 17(23)2020 11 26.
Article in English | MEDLINE | ID: covidwho-945825

ABSTRACT

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic management is limited by great uncertainty, for both health systems and citizens. Facing this information gap requires a paradigm shift from traditional approaches to healthcare to the participatory model of improving health. This work describes the design and function of the Doing Risk sElf-assessment and Social health Support for COVID (Dress-COV) system. It aims to establish a lasting link between the user and the tool; thus, enabling modeling of the data to assess individual risk of infection, or developing complications, to improve the individual's self-empowerment. The system uses bot technology of the Telegram application. The risk assessment includes the collection of user responses and the modeling of data by machine learning models, with increasing appropriateness based on the number of users who join the system. The main results reflect: (a) the individual's compliance with the tool; (b) the security and versatility of the architecture; (c) support and promotion of self-management of behavior to accommodate surveillance system delays; (d) the potential to support territorial health providers, e.g., the daily efforts of general practitioners (during this pandemic, as well as in their routine practices). These results are unique to Dress-COV and distinguish our system from classical surveillance applications.


Subject(s)
COVID-19 , Epidemiological Monitoring , Pandemics , Software , Adult , Databases, Factual , Female , Health Promotion , Humans , Italy , Machine Learning , Male , Middle Aged , Risk Assessment , Surveys and Questionnaires
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