Overall Summary
Overview
This study investigates whether individuals who concentrate their weekly exercise into 1-2 days, termed the 'Weekend Warrior' (WW) pattern, experience similar protection against neurodegenerative diseases (NDDs) like dementia and Parkinsonism as those who exercise regularly throughout the week. Using accelerometer data from the UK Biobank, participants were categorized based on their moderate-to-vigorous physical activity (MVPA) into WW, regular, and inactive groups. The study found that both WW and regular exercisers had significantly lower risks of developing NDDs compared to inactive individuals, suggesting that achieving the recommended amount of MVPA, regardless of its distribution, is crucial in reducing the risk of these diseases.
Key Findings
- Total MVPA's Role in NDD Prevention: Both the 'Weekend Warrior' and regular exercise patterns result in a significantly lower risk of dementia and Parkinsonism compared to inactivity. This suggests that the total weekly amount of moderate-to-vigorous physical activity is more critical than how it is distributed throughout the week.
- Comparable Risk Reduction: The risk reduction for dementia and Parkinsonism was similar between WW and regularly active individuals, highlighting the potential flexibility in exercise routines for reducing NDD risk.
- Demographic Patterns: Weekend Warriors were generally younger, male, and had higher socioeconomic status, which could influence the observed associations and suggests demographic factors may play a role in activity pattern adoption.
- Sensitivity to WW Definitions: The findings remained robust across different definitions of the WW pattern and various MVPA thresholds, reinforcing the consistency of the protective association against NDDs.
- Limitations of Short-term Data: The use of a single week of accelerometer data may not reflect habitual activity patterns, suggesting the need for caution in interpreting these results and considering longer-term data in future studies.
Strengths
- Comprehensive Data Handling: The study utilizes data from the UK Biobank, providing a large sample size with detailed accelerometer-based activity measurements, enhancing the study's reliability and relevance.
- Detailed Methodology: The precise description of data processing and inclusion/exclusion criteria strengthens the study’s methodological rigor and reproducibility.
- Robust Statistical Analysis: The use of Cox proportional hazards models with adjustments for a wide range of covariates ensures a thorough analysis of time-to-event data, controlling for confounders effectively.
- Broad Contextual Relevance: The study effectively places its findings in the context of existing research on physical activity patterns and health outcomes, enhancing the understanding of potential benefits across different health domains.
Areas for Improvement
- Expand on Machine Learning Details: Providing specific details about the machine learning models used for activity classification, including their performance metrics, would enhance transparency and allow better evaluation of the data processing accuracy.
- Longitudinal Activity Data: Addressing the limitation of single-week accelerometer data by utilizing longer-term activity monitoring in future research could provide a more accurate reflection of habitual physical activity patterns.
- Standardize WW Definitions: Developing and advocating for a standardized definition of the 'Weekend Warrior' pattern would improve comparability across studies and help clarify the relationship between exercise concentration and health outcomes.
Significant Elements
Figure
Description: Fig. 1 details the participant selection process, showing exclusions due to data quality and missing covariates, ultimately leading to the final sample size.
Relevance: This flow diagram is crucial for understanding the data cleaning process and ensuring transparency in how the study's analytical cohort was derived.
Table
Description: Table 1 presents baseline characteristics of participants across different activity patterns and MVPA thresholds.
Relevance: This table is essential for assessing group comparability and identifying potential confounders that might affect the study's findings.
Conclusion
The findings of this study underscore the potential flexibility in achieving health benefits through physical activity, as both 'Weekend Warrior' and regular exercise patterns are associated with reduced risks of neurodegenerative diseases. This suggests that the total volume of moderate-to-vigorous physical activity is more critical than its distribution across the week. However, the study highlights areas for future research, such as the need for longer-term activity data and standardized definitions for exercise patterns. These findings offer practical insights for individuals who may struggle to maintain consistent daily exercise routines, indicating that concentrated exercise efforts can still confer significant health benefits.
Section Analysis
Abstract
Overview
This study investigated whether the "weekend warrior" (WW) approach to physical activity, where individuals concentrate their exercise into 1-2 days, offers similar protection against neurodegenerative diseases (NDDs) like dementia and Parkinsonism as regular exercise. Using accelerometer data from the UK Biobank, a large-scale health study, they categorized participants into WW, regular, and inactive groups based on their moderate to vigorous physical activity (MVPA). They found that both WW and regular exercisers had a significantly lower risk of developing NDDs compared to inactive individuals. This suggests that achieving the recommended amount of MVPA, regardless of its distribution throughout the week, may be key to lowering the risk of NDDs.
Key Aspects
- Study Design and Data Collection: The study uses data from the UK Biobank, a large-scale biomedical database and research resource, containing in-depth genetic and health information from half a million UK participants. Participants wore accelerometers, devices that measure movement and acceleration, for a week to track their physical activity. The researchers used this data to categorize participants into "weekend warrior" (concentrated activity in 1-2 days), regular, and inactive patterns based on minutes of moderate to vigorous physical activity (MVPA).
- Outcome Measures and Statistical Analysis: The primary outcomes were all-cause dementia and Parkinsonism, neurodegenerative diseases affecting cognitive function and movement, respectively. The study used Cox proportional hazards models, a statistical method to analyze the time it takes for an event (like developing a disease) to occur, considering various factors like age, sex, and pre-existing health conditions. Hazard ratios (HR) were calculated, representing the relative risk of developing the outcome in one group compared to another. For example, an HR of 0.68 suggests a 32% lower risk.
- Key Findings: The study found that both "weekend warrior" and regular activity patterns were associated with a lower risk of dementia and Parkinsonism compared to inactivity. This held true even when the definition of "weekend warrior" was made stricter (75% of activity in 1-2 days). The risk reduction was similar between the "weekend warrior" and regular groups, suggesting that total MVPA may be more important than its distribution throughout the week.
Strengths
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Clear research question and context
The abstract clearly states the research question, which is whether the "weekend warrior" (WW) physical activity pattern is associated with a lower risk of neurodegenerative diseases (NDDs). The context, provided by current physical activity guidelines and the uncertainty surrounding the WW pattern's benefits for NDDs, is well-articulated.
"While guidelines recommend 150 min of moderate to vigorous physical activity (MVPA) weekly to enhance health, it remains unclear whether concentrating these activities into 1–2 days of the week, “weekend warrior” (WW) pattern, has the same benefit for neurodegenerative diseases (NDDs)." (Page 1)
Suggestions for Improvement
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Clarify data processing and analysis
While the abstract mentions using accelerometer data, it lacks specifics about the data processing and analysis methods. A concise description of how the accelerometer data was used to categorize participants into different physical activity patterns would strengthen the abstract.
Implementation: Include a brief explanation of how the accelerometer data was processed and used to define the WW, regular, and inactive patterns. For example, mention the use of machine learning models or specific thresholds used to categorize participants.
"This prospective study was conducted using accelerometer-based physical activity data" (Page 1)
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Strengthen the concluding statement
The abstract could be improved by explicitly stating the study's main conclusion in a more direct and impactful way. While the final sentence alludes to the association, a stronger concluding statement would enhance clarity and leave a lasting impression on the reader.
Implementation: Revise the final sentence to explicitly state the main finding. For example, "This study found that concentrating recommended physical activity into 1-2 days a week, a pattern known as 'weekend warrior,' is associated with a lower risk of developing neurodegenerative diseases, similar to the benefits observed with regular physical activity."
"Concentrating recommended physical activities into 1–2 days per week is associated with a lower incidence of NDDs." (Page 1)
Introduction
Overview
This introduction sets the stage for investigating the relationship between the "Weekend Warrior" (WW) physical activity pattern and the risk of neurodegenerative diseases (NDDs) like dementia and Parkinsonism. It begins by acknowledging the established benefits of regular moderate-to-vigorous physical activity (MVPA) for cardiovascular health, as recommended by guidelines. However, it points out that many people struggle to meet these guidelines consistently throughout the week, leading to the adoption of the WW pattern, where the recommended MVPA is concentrated into 1-2 days. The introduction then highlights the existing evidence supporting the cardiovascular benefits of the WW pattern but notes the lack of clarity regarding its impact on NDDs. This gap in knowledge forms the rationale for the study, which aims to investigate the potential association between the WW pattern and the incidence of dementia and Parkinsonism using data from the UK Biobank. The study hypothesizes that engaging in MVPA predominantly over 1-2 days a week may be associated with a decreased risk of these NDDs.
Key Aspects
- Weekend Warrior Pattern: The "Weekend Warrior" (WW) physical activity pattern involves completing the majority of one's weekly moderate-to-vigorous physical activity (MVPA) in one or two sessions. MVPA refers to activities like brisk walking, jogging, or cycling that elevate heart rate and breathing. This pattern contrasts with regular exercise, where MVPA is distributed throughout the week.
- Neurodegenerative Diseases: Neurodegenerative diseases (NDDs), such as Parkinson's disease and dementia, are conditions characterized by progressive damage to nerve cells in the brain, leading to cognitive and motor impairments. The study investigates the potential protective effect of the WW pattern against these diseases.
- UK Biobank Dataset: The UK Biobank is a large-scale biomedical database and research resource containing genetic and health information from half a million UK participants. This study utilizes accelerometer data from the UK Biobank to objectively measure physical activity levels and categorize participants into different activity patterns.
Strengths
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Context and rationale
The introduction effectively establishes the context of the research by presenting the existing physical activity guidelines and highlighting the uncertainty surrounding the WW pattern's impact on NDDs. It logically connects the known benefits of MVPA for cardiovascular health with the potential implications for NDDs, given the established link between cardiovascular health and NDD development.
"Studies have highlighted that MVPA is particularly beneficial in reducing the risk of neurodegenerative diseases (NDDs), like Parkinsonism and dementia [10,11]. Cardiovascular diseases and its traditional risk factors are known to be strongly linked with the development of NDDs [12]." (Page 2)
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Clear research question
The introduction clearly states the research question, which is to investigate the association between the WW pattern and the incidence of dementia and Parkinsonism. This clarity helps focus the reader's attention and sets the stage for the subsequent sections of the paper.
"We investigated the potential association between the WW pattern under the recommended physical activity and the incidence of dementia and Parkinsonism." (Page 2)
Suggestions for Improvement
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Precise WW pattern definition
While the introduction provides a good overview of the WW pattern and its potential benefits, it would be helpful to include a more precise definition of the WW pattern as it will be used in this study. This would enhance the clarity and reproducibility of the research.
Implementation: Incorporate a clear definition of the WW pattern, specifying the criteria used to classify participants as WW exercisers. For example, explicitly state the percentage of weekly MVPA that needs to be performed within 1-2 days to qualify as a WW pattern.
"However, the specific association between the WW pattern and NDDs remains unclear." (Page 2)
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UK Biobank description
The introduction mentions the use of the UK Biobank dataset. Providing a brief overview of the key features of this dataset relevant to the study, such as the sample size, age range, and data collection methods, would strengthen the introduction.
Implementation: Include a concise summary of the UK Biobank dataset, highlighting its relevance to the study. For example, mention the number of participants, the types of data collected (accelerometer, health records, etc.), and the period of data collection.
"Using the UK Biobank dataset, we utilized accelerometer data from over 100,000 participants." (Page 2)
Materials and Methods
Overview
This section details the study's design, data collection, processing, and analysis methods. Using data from the UK Biobank, a large-scale biomedical database and research resource, the study investigated the association between the "Weekend Warrior" (WW) physical activity pattern and the incidence of neurodegenerative diseases (NDDs) like dementia and Parkinsonism. Accelerometer data was used to objectively measure physical activity, classifying participants into WW, regular, and inactive groups based on the concentration and total amount of moderate-to-vigorous physical activity (MVPA). The study employed Cox proportional hazard models to analyze the relationship between these activity patterns and NDDs, adjusting for various demographic, socioeconomic, lifestyle, and clinical covariates. Several sensitivity analyses were conducted to assess the robustness of the findings under different assumptions and definitions.
Key Aspects
- Study Population and Data Collection: The UK Biobank study recruited participants aged 40-69 years between 2006 and 2010 from various regions across the UK. Participants provided detailed information through questionnaires, physical examinations, and biological samples. A subset of participants (103,662) also wore an Axivity AX3 triaxial accelerometer for a week between 2013 and 2015, capturing acceleration data at 100 Hz. This accelerometer data was used to objectively measure physical activity levels.
- Physical Activity Measurement and Pattern Definition: Participants' physical activity was classified into moderate-to-vigorous physical activity (MVPA), light physical activity, sedentary behavior, and sleep using machine learning models trained on data from wearable cameras and time-use diaries. The total weekly MVPA minutes were calculated for each participant. The "Weekend Warrior" (WW) pattern was defined as concentrating more than 50% or 75% of total weekly MVPA minutes over 1-2 days. Participants not meeting the weekly MVPA threshold (150 or 300 minutes) were classified as inactive, while those meeting the threshold but not fitting the WW pattern were classified as regular.
- Outcome Ascertainment and Covariates: The study outcomes were all-cause dementia and Parkinsonism, identified using ICD-9 and ICD-10 codes from hospital records and death registries. Follow-up time was calculated from the last day of accelerometer use to the first outcome event, death, loss to follow-up, or the end of the study period. Several covariates were considered, including age, sex, ethnicity, socioeconomic status (Townsend Deprivation Index), education, employment, smoking, alcohol consumption, diet quality, and prevalent hypertension, diabetes, and cardiovascular disease.
- Statistical Analysis: Cox proportional hazard models were used to analyze the association between physical activity patterns and outcomes, adjusting for the covariates. The proportional hazards assumption was assessed using Schoenfeld residuals. Sensitivity analyses included using different MVPA thresholds (quartiles), different WW definitions (50% or 75% MVPA concentration, weekend activity), excluding early follow-up data (to address reverse causality), incorporating sedentary time, handling missing data with multiple imputation, and analyzing subgroups with pre-existing conditions.
Strengths
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Detailed accelerometer data processing
The detailed description of the accelerometer data processing, including the device used (Axivity AX3), data capture frequency (100 Hz), gravity calibration, aggregation into 5-second epochs, and the definition of non-wearing time, demonstrates a high level of methodological rigor. This detailed explanation enhances the reproducibility of the study.
"The device captured acceleration data at 100 Hz within a ±8 g range over seven days. Signals were gravity-calibrated and aggregated into 5-s epochs as mean vector magnitudes." (Page 2)
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Well-defined inclusion/exclusion criteria
Clearly defining the inclusion and exclusion criteria for the study population, such as requiring at least 7 full days of wear time, having wear data in each 1-hour period, and specifying calibration error tolerances, strengthens the study's internal validity and reduces potential biases.
"We excluded individuals: (1) who with less than 7 full days of wear time, (2) those without wear data in each 1-h period of the 24-h cycle, (3) those with calibration errors exceeding a tolerance of 10 mg or with abnormally high acceleration values (>100 mg), (4) those who have missing covariates, (5) those have been followed up for less than two years." (Page 2)
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Comprehensive covariate inclusion
The comprehensive list of covariates, including demographic, socioeconomic, lifestyle factors, and clinical history, demonstrates a thorough approach to controlling for potential confounding variables and strengthens the validity of the study's findings.
"In this study, baseline age was determined by combining the participants' date of birth with the date they stopped wearing the accelerometer. Participants self-reported their ethnicity, choosing from White, Black, Asian, or other categories. The Townsend Deprivation Index was used to assess the impact of socioeconomic factors on health outcomes." (Page 3)
Suggestions for Improvement
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Elaborate on machine learning models
While the paper mentions using machine learning models to classify activity levels, it lacks detail about the specific models used, their performance metrics (e.g., accuracy, precision, recall), and the rationale for choosing these models. Providing more information would enhance transparency and allow for better evaluation of the methods.
Implementation: Specify the type of machine learning models employed (e.g., random forest, support vector machine), report their performance metrics on the training and validation sets, and justify the selection of these specific models based on their suitability for the task and data.
"Routine behaviors in wrist-worn accelerometer data were classified into MVPA, light physical activity, sedentary behavior, and sleep. They were estimated using machine learning models that were trained using wearable cameras and time-use diaries among 152 individuals in free-living conditions [15]." (Page 2)
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Explore alternative WW thresholds
The justification for using a 50% and 75% threshold for defining the WW pattern is based on a single prior study. Exploring a range of thresholds and evaluating their impact on the results would strengthen the analysis and provide a more nuanced understanding of the WW pattern's association with NDDs.
Implementation: Conduct a sensitivity analysis using a range of percentage thresholds (e.g., 40%, 60%, 80%) for defining the WW pattern. Compare the hazard ratios and confidence intervals across these different thresholds to assess the robustness of the findings and identify the optimal threshold for defining the WW pattern.
"Participants who reached the MVPA threshold and concentrated more than 50% or 75% of their total weekly MVPA exercise minutes over 1–2 days were defined as WW pattern [7], while those achieving the threshold but not WW were classified as regular pattern (eTable 1)." (Page 3)
Results
Overview
This section presents the findings of the study, which investigated the association between the "weekend warrior" (WW) physical activity pattern and the risk of developing dementia and Parkinsonism. The study analyzed data from 92,784 participants in the UK Biobank. Participants were categorized into WW, regular, and inactive groups based on their weekly moderate-to-vigorous physical activity (MVPA). The results showed that both WW and regular exercisers had a significantly lower risk of developing both dementia and Parkinsonism compared to inactive individuals. Several sensitivity analyses were conducted to test the robustness of the findings under different MVPA thresholds and WW definitions, and the results largely remained consistent, supporting the protective effect of the WW pattern against neurodegenerative diseases.
Key Aspects
- Participant Characteristics: Out of 92,784 participants, when using the 150-minute MVPA per week threshold, 43.34% were weekend warriors, 22.69% exercised regularly, and 33.97% were inactive. With the 300-minute threshold, the groups shifted to 21.16% weekend warriors, 17.58% regular, and 61.25% inactive. Weekend warriors tended to be younger, more often male, have higher socioeconomic status, and better overall health compared to the inactive group.
- Primary Outcome Analysis: The study found that Weekend Warriors had a significantly lower risk of dementia and Parkinsonism compared to inactive individuals. For dementia, the risk reduction was 32% with the 150-minute threshold and 35% with the 300-minute threshold. For Parkinsonism, the risk reduction was 53% and 42%, respectively. Similar trends were observed for regular exercisers.
- Sensitivity Analyses: Various sensitivity analyses were performed, including using different MVPA thresholds (based on quartiles), stricter WW definitions (75% of activity in 1-2 days), and weekend-only activity. These analyses generally supported the main findings, showing a consistent association between the WW pattern and reduced risk of NDDs.
Strengths
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Clear presentation of key findings
The Results section effectively presents the key findings of the study, clearly demonstrating the association between the WW pattern and a reduced risk of both dementia and Parkinsonism. The use of hazard ratios (HR) and 95% confidence intervals (CI) provides a quantifiable measure of this association, allowing for a clear interpretation of the results.
"After adjusting for sociodemographic, lifestyle covariates, and comorbidities, the WW pattern, when achieving 50% of the guideline-recommended activity ((cid:3)150 min or (cid:3)300 min) over 1–2 days, was associated with a lower risk of dementia ((cid:3)150 min: HR 0.68, 95% CI 0.56–0.84; (cid:3)300 min: HR 0.65, 95% CI 0.50–0.85) and Parkinsonism ((cid:3)150 min: HR 0.47, 95% CI 0.35–0.63; (cid:3)300 min: HR 0.58, 95% CI 0.41–0.82)" (Page 4)
Suggestions for Improvement
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Include survival curves
While the Results section presents the main findings, it lacks a clear visual representation of the survival curves for the different activity patterns. Including Kaplan-Meier survival curves would enhance the reader's understanding of the time-to-event data and provide a visual representation of the risk reduction over time.
Implementation: Generate Kaplan-Meier survival curves for each outcome (dementia and Parkinsonism) stratified by the three activity patterns (WW, regular, and inactive). Include these figures in the Results section and refer to them in the text when discussing the time-to-event analysis.
"After a median observation duration of 8.85 years, incidences of dementia and Parkinsonism emerged" (Page 4)
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Integrate sensitivity analyses
The Results section mentions several sensitivity analyses, but the results are presented in supplementary tables. Integrating the key findings of these sensitivity analyses into the main Results section would improve the flow and comprehensiveness of the results presentation.
Implementation: Incorporate the key findings of the sensitivity analyses into the main Results section. Briefly describe the different sensitivity analyses performed (e.g., different MVPA thresholds, different WW definitions) and report the corresponding HRs and 95% CIs. Discuss how these sensitivity analyses support the robustness of the main findings.
"Supportive and sensitivity analyses" (Page 4)
Non-Text Elements
Fig. 1. Flow diagram of participants.
First Reference in Text
A total of 92,784 participants were finally enrolled in this study (mean [SD] age, 61.88 [7.87] years; 40,474 [43.62%] men; 52, 310 [56.38%] women) (Fig. 1).
Description
- Overall structure: The diagram starts with the initial pool of participants who wore the accelerometer (N=103,662). It then depicts the exclusions made at various stages, including those with insufficient accelerometer data quality, missing covariates, and those diagnosed with dementia or Parkinsonism prior to or within two years of enrollment. The final sample sizes for the analysis of all-cause dementia (N=92,144) and all-cause Parkinsonism (N=92,048) are clearly indicated at the end of the flow.
- Visual representation of participant selection: The flow diagram visually represents the process of participant selection for the study. It shows the initial number of participants and how this number decreases at each stage due to specific exclusion criteria. Each step in the process is represented by a box, with arrows connecting the boxes to show the flow of participants.
- Exclusion criteria: Specific exclusion criteria are listed in the diagram, including insufficient accelerometer data quality (e.g., monitor not sufficiently calibrated, unrealistically high acceleration values), missing covariates (age, sex, ethnicity, etc.), and prior diagnosis of dementia or Parkinsonism.
- Number of participants excluded at each stage: The diagram clearly indicates the number of participants excluded at each stage. For example, it shows that 9,187 participants were excluded due to invalid wear time, 1,630 due to missing covariates, and 640 and 736 due to prior or early diagnosis of dementia and Parkinsonism, respectively.
Scientific Validity
- Transparency and reproducibility: The flow diagram clearly outlines the participant selection process, which is crucial for the transparency and reproducibility of the study. The explicit statement of exclusion criteria and the number of participants excluded at each step allows readers to understand how the final sample was derived and assess potential biases introduced by the selection process.
- Justification of exclusion criteria: The exclusion of participants with insufficient accelerometer data quality is a valid step to ensure the reliability of the physical activity measurements. Similarly, excluding individuals with missing covariates helps to avoid biases related to these variables. Excluding participants with prior or early diagnosis of the outcome is essential to assess the association between physical activity patterns and incident NDDs, rather than the influence of pre-existing conditions.
- Missing data handling: While the diagram mentions 'Missing covariates,' it would strengthen the scientific rigor to specify the exact covariates that were missing. This would enhance transparency and allow for a more thorough assessment of potential biases due to missing data.
- Data cleaning and preparation: The flow diagram provides a clear visual representation of the data cleaning and preparation steps, which are essential for ensuring the validity of the subsequent analyses. This contributes to the overall rigor of the study.
Communication
- Clarity and organization: The flow diagram is generally clear and easy to follow, effectively communicating the process of participant selection and the resulting sample sizes for the study. The use of clear labels and distinct categories makes it easy to understand the reasons for exclusion at each stage. The visual representation helps to quickly grasp the overall participant selection process.
- Completeness of information: The diagram could be improved by including the specific reasons for missing covariates. While the diagram mentions 'Missing covariates,' it doesn't detail what these covariates are. Providing this information directly in the diagram or in the caption would enhance transparency and allow readers to fully understand the reasons for exclusion.
- Visual representation: The diagram effectively uses visual cues, such as different shapes and colors, to distinguish between different stages of the participant selection process. This enhances readability and makes it easier to follow the flow of information.
Fig. 2. Associations Between WW pattern (≥150 or ≥300 min/wk with ≥50% over 1-2...
Full Caption
Fig. 2. Associations Between WW pattern (≥150 or ≥300 min/wk with ≥50% over 1-2 days) and Incidence of Dementia and Parkinsonism over 1-2 Days, Adjusted for Multiple Variables.
First Reference in Text
After adjusting for sociodemographic, lifestyle covariates, and comorbidities, the WW pattern, when achieving 50% of the guideline-recommended activity (≥150 min or ≥300 min) over 1-2 days, was associated with a lower risk of dementia (≥150 min: HR 0.68, 95% CI 0.56-0.84; ≥300 min: HR 0.65, 95% CI 0.50-0.85) and Parkinsonism (≥150 min: HR 0.47, 95% CI 0.35-0.63; ≥300 min: HR 0.58, 95% CI 0.41-0.82) (Fig. 2A and B).
Description
- Overall purpose: Figure 2 presents the results of a Cox proportional hazards model analysis, examining the association between different physical activity patterns (Weekend Warrior, Regular, Inactive) and the incidence of two neurodegenerative diseases: dementia and Parkinsonism. The analysis is stratified by two different weekly Moderate-to-Vigorous Physical Activity (MVPA) thresholds: ≥150 minutes and ≥300 minutes. A 'Weekend Warrior' is defined as someone who achieves at least 50% of their weekly MVPA in 1-2 days.
- Structure and content: The figure includes two panels (A and B), one for each MVPA threshold (≥150 min/week and ≥300 min/week, respectively). Each panel contains two forest plots, one for dementia and one for Parkinsonism. Each forest plot displays the hazard ratio (HR) and 95% confidence interval (CI) for the Regular and WW groups compared to the Inactive group (reference group).
- Interpretation of forest plots: A forest plot is a graphical representation of the results of a meta-analysis or, in this case, a Cox proportional hazards model. Each horizontal line represents a comparison between two groups (e.g., Regular vs. Inactive). The square on the line represents the point estimate of the hazard ratio (HR), and the horizontal line extending from the square represents the 95% confidence interval (CI). An HR less than 1 indicates a lower risk in the exposed group compared to the reference group.
Scientific Validity
- Appropriate statistical analysis: The use of Cox proportional hazards models is appropriate for analyzing time-to-event data, such as the incidence of dementia and Parkinsonism. Adjusting for relevant covariates is crucial to control for potential confounding and isolate the effect of physical activity patterns.
- Clear definition of variables: The study clearly defines the exposure variable (Weekend Warrior physical activity pattern) and the outcome variables (dementia and Parkinsonism). The use of pre-defined MVPA thresholds and the 50% criterion for defining the WW pattern adds clarity and rigor to the analysis.
- Addressing reverse causality: The study acknowledges the potential for reverse causality, which is a common concern in observational studies. While excluding individuals with early diagnoses is a good step, it doesn't completely eliminate the possibility that undiagnosed or preclinical disease may influence physical activity levels. This limitation should be discussed further.
- Presentation of results: The figure presents the results with appropriate measures of effect (hazard ratios) and uncertainty (95% confidence intervals). This allows readers to assess the statistical significance and magnitude of the associations.
Communication
- Use of appropriate visualization: The figure uses forest plots, a standard way to visually represent the results of a Cox proportional hazards model. The inclusion of hazard ratios and confidence intervals allows for a clear comparison of the risk between different physical activity patterns. The layout is generally clear, with separate panels for different MVPA thresholds.
- Transparency of adjusted variables: While the caption mentions adjustment for multiple variables, listing these variables directly in the figure or caption would improve transparency. Knowing the specific covariates included in the model is important for interpreting the results.
- Clarity of main message: The figure effectively communicates the main finding that the WW pattern is associated with a lower risk of both dementia and Parkinsonism compared to the inactive group. This message is clearly conveyed by the hazard ratios and confidence intervals presented in the forest plots.
- Accessibility of visual elements: The use of color to differentiate between outcome events (dementia and Parkinsonism) is helpful. However, consider using different shapes or patterns for the data points in addition to color to enhance accessibility for readers with color vision deficiencies.
Fig. 3. Associations Between WW pattern (≥150 or ≥300 min/wk with ≥75% over 1-2...
Full Caption
Fig. 3. Associations Between WW pattern (≥150 or ≥300 min/wk with ≥75% over 1-2 days) and Incidence of Dementia and Parkinsonism Over 1-2 Days, Adjusted for Multiple Variables.
First Reference in Text
In multivariable-adjusted models, the WW pattern persistently showed a lower risk for dementia (≥150 min: HR 0.61, 95% CI 0.41-0.91; ≥300 min: HR 0.34, 95% CI 0.14-0.82) and Parkinsonism (≥150 min: HR 0.22, 95% CI 0.10-0.47; ≥300 min: HR 0.20, 95% CI 0.05-0.80) (Fig. 3A and B).
Description
- Overall purpose and comparison with Figure 2: This figure, similar in structure to Figure 2, explores the relationship between the 'Weekend Warrior' (WW) physical activity pattern and the risk of dementia and Parkinsonism. However, it uses a more stringent definition of the WW pattern: individuals who complete at least 75% of their total weekly Moderate-to-Vigorous Physical Activity (MVPA) within 1-2 days. The analysis is again stratified by two weekly MVPA thresholds (≥150 minutes and ≥300 minutes).
- Structure and content: The figure comprises two panels (A and B), corresponding to the two MVPA thresholds. Each panel presents two forest plots, one for dementia and one for Parkinsonism. The forest plots display hazard ratios (HR) and 95% confidence intervals (CI) for the Regular and WW groups compared to the Inactive group, which serves as the reference.
- Interpretation of hazard ratios and confidence intervals: The hazard ratio (HR) quantifies the relative risk of the outcome in the exposed group (Regular or WW) compared to the reference group (Inactive). An HR less than 1 suggests a reduced risk, while an HR greater than 1 indicates an increased risk. The 95% confidence interval (CI) provides a range of plausible values for the true HR.
Scientific Validity
- Dose-response analysis: The use of a more stringent definition for the WW pattern (75% of MVPA in 1-2 days) allows for investigating the dose-response relationship between the concentration of physical activity and the risk of NDDs. This adds valuable information to the findings presented in Figure 2.
- Methodological rigor: The consistent use of Cox proportional hazards models and adjustment for multiple variables ensures methodological consistency and helps to control for confounding factors.
- Statistical power and precision: The small number of events in some groups, particularly for the WW pattern at the 300 min/week threshold, may limit the statistical power and precision of the estimates. This should be acknowledged and discussed as a potential limitation.
- Causality and confounding: While the results suggest a strong protective effect of the WW pattern, especially for Parkinsonism, it's important to consider the potential for residual confounding and the limitations of observational studies in establishing causal relationships.
Communication
- Visual consistency and clarity: The consistent use of forest plots maintains visual clarity and allows for direct comparison with Figure 2. The clear labeling of axes, groups, and effect estimates (HR and 95% CI) facilitates easy interpretation of the results.
- Effective communication of key findings: The figure effectively highlights the key finding that increasing the proportion of MVPA performed in a WW pattern to 75% strengthens the observed associations, particularly for Parkinsonism. This is clearly demonstrated by the smaller hazard ratios and narrower confidence intervals.
- Transparency of adjusted variables: Similar to Figure 2, explicitly listing the adjusted covariates in the figure or caption would enhance transparency and allow readers to fully understand the model.
Table 1. Characteristics of participants by the different thresholds of MVPA...
Full Caption
Table 1. Characteristics of participants by the different thresholds of MVPA per week.
First Reference in Text
The baseline characteristics of all participants stratified by different patterns were shown in Table 1.
Description
- Overall purpose: Table 1 presents the baseline characteristics of the study participants, stratified by their physical activity patterns (Inactive, Regular, and Weekend Warrior - WW) and two different weekly Moderate-to-Vigorous Physical Activity (MVPA) thresholds: ≥150 minutes and ≥300 minutes. The WW pattern is defined as achieving at least 50% of the weekly MVPA in one or two days.
- Structure and content: The table is organized into several sections: demographic and socioeconomic characteristics (age, sex, ethnicity, education, employment), lifestyle characteristics (diet, smoking, alcohol consumption), and clinical, medication, and medical history (blood pressure, BMI, prevalence of hypertension, diabetes, and cardiovascular disease).
- Data presentation format: For continuous variables, the table presents the median and interquartile range (IQR). The IQR represents the range between the 25th and 75th percentiles of the data distribution and is a measure of statistical dispersion. For categorical variables, the table shows the number (n) and percentage (%) of participants in each category.
- Definition of MVPA: MVPA refers to physical activity that is performed at an intensity equal to or greater than walking briskly. It is a key component of physical activity guidelines and is associated with various health benefits.
Scientific Validity
- Importance of baseline characteristics: Presenting baseline characteristics is crucial in observational studies to assess the comparability of different groups (Inactive, Regular, WW) and identify potential confounding factors. This information is essential for interpreting the results of the subsequent analyses.
- Relevance of variables: The choice of variables presented in the table is generally appropriate, covering key demographic, socioeconomic, lifestyle, and clinical factors that could influence the relationship between physical activity and NDD risk.
- Appropriate descriptive statistics: The use of median and IQR for continuous variables is appropriate given the likely skewed distribution of some of these variables (e.g., MVPA, sedentary time). Presenting both raw numbers and percentages for categorical variables enhances transparency.
- Statistical significance testing: The table could be improved by including a measure of statistical significance for the differences in baseline characteristics between groups (e.g., p-values from chi-squared tests or ANOVA). This would help readers assess the statistical significance of the observed differences.
Communication
- Clarity and organization: The table is well-organized, presenting the characteristics for each group (Inactive, Regular, WW) side-by-side, which facilitates comparison. The use of clear headings and labels makes it easy to understand the information presented.
- Data presentation: Providing the number (N) for each group is helpful for understanding the sample size and distribution across different physical activity patterns. Including both percentages and raw numbers for categorical variables enhances transparency and allows readers to fully grasp the data distribution.
- Completeness of information: While the table presents a comprehensive set of baseline characteristics, consider adding information on other relevant factors, such as physical activity levels prior to the study period, if available. This could provide additional context for interpreting the results.
Table 2. Associations between physical activity pattern and incident of outcome...
Full Caption
Table 2. Associations between physical activity pattern and incident of outcome across varying WW definition.
First Reference in Text
In models adjusted for multiple variables and stratified by MVPA quartiles, the WW pattern, characterized by exceeding MVPA percentiles of 25th, 50th, and 75th and completing 50% or 75% of the total exercise within 1-2 days, consistently demonstrated a lower risk for both dementia (≥115.2 min: HR 0.66, 95% CI 0.54-0.81; ≥230.4 min: HR 0.72, 95% CI 0.58-0.90; ≥403.2 min: HR 0.67, 95% CI 0.47-0.95) and Parkinsonism (≥115.2 min: HR 0.48, 95% CI 0.37-0.64; ≥230.4 min: HR 0.58, 95% CI 0.43-0.79; ≥403.2 min: HR 0.55, 95% CI 0.34-0.87) (Table 2).
Description
- Overall purpose: Table 2 presents the results of Cox proportional hazards models examining the association between different Weekend Warrior (WW) physical activity patterns and the incidence of dementia and Parkinsonism. The table explores various definitions of the WW pattern, based on different MVPA thresholds (25th, 50th, and 75th percentiles) and the proportion of MVPA completed within 1-2 days (50% or 75%).
- Structure and content: The table is organized by outcome (all-cause dementia and all-cause Parkinsonism) and then by WW definition. Each row represents a different analysis, with the WW definition specified in the first column. The remaining columns present the number of events, the number of participants, and the hazard ratio (HR) with its 95% confidence interval (CI) for each group (Inactive, Regular, WW).
- MVPA thresholds and percentiles: MVPA refers to moderate-to-vigorous physical activity, which is activity performed at an intensity equal to or greater than brisk walking. The different percentile thresholds represent different levels of MVPA, with higher percentiles indicating greater amounts of MVPA.
- Interpretation of hazard ratio and confidence interval: The hazard ratio (HR) is a measure of the relative risk of the outcome in the exposed group (e.g., WW) compared to the reference group (Inactive). An HR less than 1 indicates a lower risk in the exposed group. The 95% confidence interval (CI) provides a range of plausible values for the true HR.
Scientific Validity
- Sensitivity analysis with varying WW definitions: Exploring different definitions of the WW pattern is a strength of this analysis, as it allows for assessing the robustness of the findings across different thresholds and criteria. This helps to address the lack of a standardized definition of the WW pattern.
- Use of MVPA quartiles: The use of MVPA quartiles as thresholds provides a data-driven approach to defining different levels of physical activity. This is a more robust approach compared to using arbitrary thresholds.
- Methodological consistency: The consistent use of Cox proportional hazards models and adjustment for multiple variables ensures methodological rigor and helps to control for confounding factors.
- Statistical power: The small number of events in some groups, especially for the more stringent WW definitions, may limit the statistical power of these analyses. This should be acknowledged and discussed as a potential limitation.
Communication
- Clarity and organization: The table is clearly structured, presenting the results for different WW definitions and MVPA thresholds in a systematic way. The use of consistent labels and abbreviations makes it easy to follow the different analyses.
- Data presentation: Presenting the number of events and participants for each analysis is essential for transparency and allows readers to assess the underlying data. Including both hazard ratios and confidence intervals provides a clear measure of effect and uncertainty.
- Comparison with regular exercisers: While the table focuses on the WW pattern, consider adding a separate column for the regular exercisers group for each analysis. This would allow for a direct comparison between the two active groups and provide a more complete picture of the relationship between physical activity and NDD risk.
Discussion
Overview
This Discussion section summarizes the study's main finding: concentrating the recommended amount of moderate-to-vigorous physical activity (MVPA) into 1-2 days a week (the Weekend Warrior or WW pattern) is associated with a lower risk of dementia and Parkinsonism, similar to the benefit seen with regular exercise. The authors connect this finding to existing research showing similar benefits of the WW pattern for other health outcomes, like cardiovascular health. They also discuss the study's limitations, such as relying on a single week of accelerometer data and the lack of a standardized WW definition, and suggest directions for future research, including longer-term activity monitoring and the development of a standardized WW definition.
Key Aspects
- WW Pattern and NDD Risk: The study's primary finding is that individuals who concentrate their moderate-to-vigorous physical activity (MVPA) into 1-2 days per week (Weekend Warrior or WW pattern) have a reduced risk of developing neurodegenerative diseases (NDDs), specifically dementia and Parkinsonism. This risk reduction is comparable to that observed in individuals who distribute their MVPA more regularly throughout the week. This suggests that the total amount of MVPA, rather than its distribution, is the key factor in reducing NDD risk.
- Consistency with Previous Research: The study's findings are consistent with previous research demonstrating the benefits of the WW pattern for various health outcomes, including cardiovascular health and mortality. This supports the idea that the WW pattern is a viable alternative for individuals who find it difficult to adhere to traditional exercise guidelines recommending daily or more frequent MVPA.
- Study Limitations: The study acknowledges several limitations, including the use of only one week of accelerometer data to assess physical activity patterns, the lack of a standardized definition for the WW pattern, and the potential for residual confounding. These limitations are important to consider when interpreting the results and highlight areas for future research.
Strengths
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Clear summary of findings
The Discussion effectively summarizes the key findings of the study, reiterating the main conclusion that concentrating exercise into 1-2 days (WW pattern) is associated with a lower risk of NDDs, similar to regular exercise. This reinforces the main message and provides a clear takeaway for the reader.
"The main findings indicated that amongst individuals who met the guideline-prescribed weekly MVPA durations (either the standard 150 min/week or the extended 300 min/week), concentrating 50% of their exercise over 1–2 days or 1–2 consecutive days, associated with lower risk of NDDs." (Page 6)
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Effective connection to existing literature
The Discussion effectively connects the study's findings to existing literature on the WW pattern and its health benefits. It highlights the consistency of the current results with previous research showing similar benefits for cardiovascular health and mortality, strengthening the overall argument and placing the study within a broader context.
"Presently, relevant studies have shown that WW patterns are associated with a variety of health benefits such as reduced risk of cardiovascular disease, metabolic syndrome, and death [5,7,24,25]." (Page 6)
Suggestions for Improvement
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Discuss limitations of single-week accelerometer data
The Discussion mentions the limitations of using a single week of accelerometer data but doesn't fully explore the potential impact of this limitation on the findings. A more in-depth discussion of how this might have affected the categorization of participants into different activity patterns and the subsequent risk estimates would strengthen the limitations section.
Implementation: Expand the discussion of the single-week accelerometer data limitation. Discuss the possibility of misclassifying participants due to short-term variations in activity levels and how this misclassification could bias the results. Consider suggesting future research using longer periods of accelerometer data to address this limitation.
"The reliability of interpreting physical activity patterns from a single week of data collection is questionable, as this timeframe may not accurately reflect habitual PA behaviors." (Page 6)
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Discuss implications of varying WW definitions
The Discussion briefly mentions the lack of a standardized WW definition but doesn't fully explore the implications of using different definitions. A more detailed discussion of how different WW definitions might influence the results and the need for a standardized definition for future research would enhance the Discussion.
Implementation: Expand the discussion of the lack of a standardized WW definition. Explain how different criteria for classifying WW exercisers (e.g., percentage of MVPA, number of days) could lead to varying results. Discuss the challenges in comparing findings across studies using different definitions and advocate for the development of a standardized definition to improve the comparability and generalizability of future research.
"There is no standardised definition of WW pattern, and multiply of the above studies used exercise questionnaires." (Page 6)