scholarly journals Changes in Eating Habits and Sedentary Behavior During the COVID-19 Pandemic in Adolescents With Chronic Conditions

2021 ◽  
Vol 9 ◽  
Author(s):  
Bruna Caruso Mazzolani ◽  
Fabiana Infante Smaira ◽  
Camilla Astley ◽  
Amanda Yuri Iraha ◽  
Ana Jessica Pinto ◽  
...  
Keyword(s):  
Sedentary Behavior ◽  
Screen Time ◽  
Chronic Conditions ◽  
Eating Habits ◽  
Multiple Chronic Conditions ◽  
Tertiary Referral ◽  
Tertiary Referral Hospital ◽  
School Closures ◽  
Specific Care ◽  
The Impact

Introduction: Among healthy adolescents, school closures and home confinement were shown to increase unhealthier eating habits and sedentary behavior. It remains unknown to which extent the pandemic has impacted the lifestyle of adolescents with chronic conditions. Thus, the aim of this study is to report on the impact of the COVID-19 outbreak on eating habits and sedentary behavior among adolescents with multiple chronic conditions (n = 347) from a tertiary, referral hospital vs. healthy peers.Methods: This observational study was conducted in São Paulo (Brazil) between July and October 2020, period in which a set of social distancing measures to contain the pandemic.Results: The main findings of this study were that adolescents with chronic conditions and health peers showed important changes in eating habits (e.g., more often cooking and eating in front of television than before quarantine). Also, 86.8% of adolescents with chronic conditions and 91.6% of healthy adolescents reported increasing screen time during pandemic. No major differences were observed between patients and controls.Conclusions: Adolescents with chronic conditions and healthy peers exposed to pandemic showed substantial changes in lifestyle, stressing the need for specific care to mitigate poor eating habits and excessive sedentary behavior for patients and healthy adolescents.

scholarly journals Changes in eating habits and sedentary behavior during the COVID-19 pandemic in adolescents with chronic conditions

2021 ◽  
Author(s):  
Bruna Caruso Mazzolani ◽  
Fabiana Infante Smaira ◽  
Camilla Astley ◽  
Amanda Yuri Iraha ◽  
Ana Jessica Pinto ◽  
...  
Keyword(s):  
Observational Study ◽  
Sedentary Behavior ◽  
Screen Time ◽  
Chronic Conditions ◽  
Eating Habits ◽  
Multiple Chronic Conditions ◽  
Tertiary Referral ◽  
Tertiary Referral Hospital ◽  
Specific Care ◽  
The Impact

Purpose: To report on the impact of the COVID-19 outbreak on eating habits and sedentary behavior among adolescents with multiple chronic conditions (n=347) from a tertiary, referral hospital vs. healthy peers. Methods: This observational study was conducted in Sao Paulo (Brazil) between July and October 2020, period in which a set of social distancing measures to contain the pandemic. Results: The main findings of this study were that adolescents with chronic conditions showed important changes in eating habits (e.g., less often consumption of convenience foods and more often eating in front of television than before quarantine). Also, 86.8% of adolescents with chronic conditions reported increasing screen time during pandemic. No major differences were observed between patients and controls. Conclusions: Adolescents with chronic conditions exposed to pandemic showed substantial changes in lifestyle, stressing the need for specific care to mitigate poor eating habits and excessive sedentary behavior in this group.

scholarly journals The Importance of Studying Health Behavior in Older Adults

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 827-827
Author(s):  
Jaime Hughes ◽  
Susan Hughes ◽  
Mina Raj ◽  
Janet Bettger
Keyword(s):  
Social Support ◽  
Older Adults ◽  
Behavior Change ◽  
Chronic Conditions ◽  
Population Level ◽  
Multiple Chronic Conditions ◽  
Prior Work ◽  
Time Changes ◽  
The Impact ◽  
Over Time

Abstract Behavior change is an inherent aspect of routine geriatric care. However, most research and clinical programs emphasis how to initiate behavior change with less emphasis placed on skills and strategies to maintain behaviors over time, including after an intervention has concluded. This presentation will provide an introduction to the symposium, including a review of prior work and our rationale for studying the critical yet overlooked construct of maintenance in older adults. Several key considerations in our work include the impact of multiple chronic conditions, declines in cognitive and functional capacity over time, changes in environmental context and/or social support, and sustainability of community and population-level programs and services.

scholarly journals Addressing Implementation Challenges to Digital Care Delivery for Adults With Multiple Chronic Conditions: Stakeholder Feedback in a Randomized Controlled Trial (Preprint)

2020 ◽  
Author(s):  
Kelly Williams ◽  
Sarah Markwardt ◽  
Shannon M Kearney ◽  
Jordan F Karp ◽  
Kevin L Kraemer ◽  
...  
Keyword(s):  
Chronic Conditions ◽  
Care Delivery ◽  
Text Message ◽  
Chronically Ill ◽  
High Tech ◽  
Multiple Chronic Conditions ◽  
Broadband Internet ◽  
Implementation Challenges ◽  
Before And After ◽  
The Impact

BACKGROUND Digital tools accessed via smartphones can promote chronic condition management, reduce disparities in health care and hospital readmissions, and improve quality of life. However, whether digital care strategies can be implemented successfully on a large scale with traditionally underserved populations remains uncertain. OBJECTIVE As part of a randomized trial comparing care delivery strategies for Medicaid and Medicare-Medicaid beneficiaries with multiple chronic conditions, our stakeholders identified implementation challenges, and we developed stakeholder-driven adaptions to improve a digitally delivered care management strategy (high-tech care). METHODS We used 4 mechanisms (study support log, Patient Partners Work Group log, case interview log, and implementation meeting minutes) to capture stakeholder feedback about technology-related challenges and solutions from 9 patient partners, 129 participants, and 32 care managers and used these data to develop and implement solutions. To assess the impact, we analyzed high-tech care exit surveys and intervention engagement outcomes (video visits and condition-specific text message check-ins sent at varying intervals) before and after each solution was implemented. RESULTS Challenges centered around 2 themes: difficulty using both smartphones and high-tech care components and difficulty using high-tech care components due to connectivity issues. To respond to the first theme’s challenges, we devised 3 solutions: tech visits (eg, in-person technology support visits), tech packet (eg, participant-facing technology user guide), and tailored condition-specific text message check-ins. During the first 20 months of implementation, 73 participants received at least one tech visit. We observed a 15% increase in video call completion for participants with data before and after the tech visit (n=25) and a 7% increase in check-in completion for participants with data before and after the tech visit (n=59). Of the 379 participants given a tech packet, 179 completed care during this timeframe and were eligible for an exit survey. Of the survey respondents, 76% (73/96) found the tech packet helpful and 64% (62/96) actively used it during care. To support condition-specific text message check-in completion, we allowed for adaption of day and/or time of the text message with 31 participants changing the time they received check-ins and change in standard biometric settings with 13 physicians requesting personalized settings for participants. To respond to the second theme’s challenges, tech visits or phone calls were made to demonstrate how to use a smartphone to connect or disconnect from the internet, to schedule video calls, or for condition-specific text message check-ins in a location with broadband/internet. CONCLUSIONS Having structured stakeholder feedback mechanisms is key to identify challenges and solutions to digital care engagement. Creating flexible and scalable solutions to technology-related challenges will increase equity in accessing digital care and support more effective engagement of chronically ill populations in the use of these digital care tools. CLINICALTRIAL ClinicalTrials.gov NCT03451630; https://clinicaltrials.gov/ct2/show/NCT03451630.

scholarly journals Changes in Physical Activity and Sedentary Behavior in Response to COVID-19 and Their Associations with Mental Health in 3052 US Adults

2020 ◽  
Vol 17 (18) ◽  
pp. 6469 ◽  
Author(s):  
Jacob Meyer ◽  
Cillian McDowell ◽  
Jeni Lansing ◽  
Cassandra Brower ◽  
Lee Smith ◽  
...  
Keyword(s):  
Public Health ◽  
Mental Health ◽  
Physical Activity ◽  
Sedentary Behavior ◽  
Screen Time ◽  
Social Connectedness ◽  
Anxiety Symptoms ◽  
Cross Sectional ◽  
Societal Changes ◽  
The Impact

The COVID-19 pandemic altered many facets of life. We aimed to evaluate the impact of COVID-19-related public health guidelines on physical activity (PA), sedentary behavior, mental health, and their interrelations. Cross-sectional data were collected from 3052 US adults 3–8 April 2020 (from all 50 states). Participants self-reported pre- and post-COVID-19 levels of moderate and vigorous PA, sitting, and screen time. Currently-followed public health guidelines, stress, loneliness, positive mental health (PMH), social connectedness, and depressive and anxiety symptoms were self-reported. Participants were grouped by meeting US PA guidelines, reporting ≥8 h/day of sitting, or ≥8 h/day of screen time, pre- and post-COVID-19. Overall, 62% of participants were female, with age ranging from 18–24 (16.6% of sample) to 75+ (9.3%). Self-reported PA was lower post-COVID among participants reporting being previously active (mean change: −32.3% [95% CI: −36.3%, −28.1%]) but largely unchanged among previously inactive participants (+2.3% [−3.5%, +8.1%]). No longer meeting PA guidelines and increased screen time were associated with worse depression, loneliness, stress, and PMH (p < 0.001). Self-isolation/quarantine was associated with higher depressive and anxiety symptoms compared to social distancing (p < 0.001). Maintaining and enhancing physical activity participation and limiting screen time increases during abrupt societal changes may mitigate the mental health consequences.

scholarly journals Comparison of EHR-based diagnosis documentation locations to a gold standard for risk stratification in patients with multiple chronic conditions

2017 ◽  
Vol 08 (03) ◽  
pp. 794-809 ◽  
Author(s):  
Shelby Martin ◽  
Jesse Wagner ◽  
Nicoleta Lupulescu-Mann ◽  
Katrina Ramsey ◽  
Aaron Cohen ◽  
...  
Keyword(s):  
Risk Stratification ◽  
Gold Standard ◽  
Chronic Conditions ◽  
Chart Review ◽  
Risk Scores ◽  
Multiple Chronic Conditions ◽  
Validity Testing ◽  
Combining Data ◽  
Diagnostic Statistics ◽  
The Impact

SummaryObjective: To measure variation among four different Electronic Health Record (EHR) system documentation locations versus ‘gold standard’ manual chart review for risk stratification in patients with multiple chronic illnesses.Methods: Adults seen in primary care with EHR evidence of at least one of 13 conditions were included. EHRs were manually reviewed to determine presence of active diagnoses, and risk scores were calculated using three different methodologies and five EHR documentation locations. Claims data were used to assess cost and utilization for the following year. Descriptive and diagnostic statistics were calculated for each EHR location. Criterion validity testing compared the gold standard verified diagnoses versus other EHR locations and risk scores in predicting future cost and utilization.Results: Nine hundred patients had 2,179 probable diagnoses. About 70% of the diagnoses from the EHR were verified by gold standard. For a subset of patients having baseline and prediction year data (n=750), modeling showed that the gold standard was the best predictor of outcomes on average for a subset of patients that had these data. However, combining all data sources together had nearly equivalent performance for prediction as the gold standard.Conclusions: EHR data locations were inaccurate 30% of the time, leading to improvement in overall modeling from a gold standard from chart review for individual diagnoses. However, the impact on identification of the highest risk patients was minor, and combining data from different EHR locations was equivalent to gold standard performance.Martin S, Wagner J, Lupulescu-Mann N et al. Comparison of EHR-based diagnosis documentation locations to a gold standard for risk stratification in patients with multiple chronic conditions . Appl Clin Inform 2017; 8: 794–809 https://doi.org/10.4338/ACI-2016-12-RA-0210

scholarly journals Clustering and Healthcare Costs With Multiple Chronic Conditions in a US Cross-Sectional Study

2020 ◽  
Author(s):  
cother hajat ◽  
yakir siegal ◽  
amalia adler-waxman
Keyword(s):  
Mental Health ◽  
Chronic Conditions ◽  
Healthcare Costs ◽  
Multiple Chronic Conditions ◽  
Cross Sectional ◽  
The Us ◽  
Health Related ◽  
Insured Patients ◽  
The Impact ◽  
Hiv Aids

Objective To investigate healthcare costs and contributors to costs for multiple chronic conditions (MCCs), common clusters of conditions and their impact on cost and utilisation. Methods This was a cross-sectional analysis of US financial claims data representative of the US population, including Medicare, Medicaid and Commercial insurance claims in 2015. Outcome measures included healthcare costs and contributors; ranking of clusters of conditions according to frequency, strength of association and unsupervised (k-means) analysis; the impact of clustering on costs and contributors to costs. Results Of 1,878,951 patients, 931,045(49.6%) had MCCs, 56.5% weighted to the US population. Mean age was 53.0 years(SD16.7); 393,121(42.20%) were male. Mean annual healthcare spending was $12,601, ranging from $4,385 (2 conditions) to $33,874 (11 conditions), with spending increasing by 22-fold for inpatient services, 6-fold for outpatient services, 4.5-fold for generic drugs and 4.2-fold for branded drugs. Cluster ranking using the 3 methodologies yielded similar results: highest ranked clusters included metabolic syndrome(12.2% of US insured patients), age related diseases(7.7%), renal failure(5.6%), respiratory disorders(4.5%), cardiovascular disease(CVD)(4.3%), cancers(4.1-4.3%), mental health-related clusters(1.0-1.5%) and HIV/AIDS(0.2%). Highest spending was in HIV/AIDS clusters ($48,293), mental health-related clusters ($38,952-$40,637), renal disease ($38,551) and CVD ($37,155); with 89.9% of spending on outpatient and inpatient care combined, and 10.1% on medication. Conclusion and Relevance Over 57% of insured patients in the US may have MCCs. MCC Clustering is frequent and is associated with healthcare utilisation. The findings favour health system redesign towards a multiple condition approach for clusters of chronic conditions, alongside other cost-containment measures for MCCs.

Sign in / Sign up

Export Citation Format

Share Document