Stephanie E. Bonn

Physical Activity and Survival among Men Diagnosed with Prostate Cancer

Background: Few studies have investigated the association between post-diagnosis physical activity and mortality among men diagnosed with prostate cancer. The aim of this study was to investigate the effect of physical activity after a prostate cancer diagnosis on both overall and prostate cancer–specific mortality in a large cohort. Methods: Data from 4,623 men diagnosed with localized prostate cancer 1997–2002 and followed-up until 2012 were analyzed. HRs with 95% confidence intervals (CI) were estimated using Cox proportional hazards models to examine the association between post-diagnosis recreational MET-h/d, time spent walking/bicycling, performing household work or exercising, and time to overall and prostate cancer–specific death. All models were adjusted for potential confounders. Results: During the follow-up, 561 deaths of any cause and 194 deaths from prostate cancer occurred. Statistically significantly lower overall mortality rates were found among men engaged in ≥5 recreational MET-h/d (HR, 0.63; 95% CI, 0.52–0.77), walking/bicycling ≥20 min/d (HR, 0.70; 95% CI, 0.57–0.86), performing household work ≥1 h/d (HR, 0.71; 95% CI, 0.59–0.86), or exercising ≥1 h/wk (HR, 0.74; 95% CI, 0.61–0.90), compared with less active men within each activity type. For prostate cancer–specific mortality, statistically significantly lower mortality rates were seen among men walking/bicycling ≥20 min/d (HR, 0.61; 95% CI, 0.43–0.87) or exercising ≥1 h/wk (HR, 0.68; 95% CI, 0.48–0.94). Conclusions: Higher levels of physical activity were associated with reduced rates of overall and prostate cancer–specific mortality. Impact: Our study further strengthens previous results indicating beneficial effects of physical activity on survival among men with prostate cancer. Cancer Epidemiol Biomarkers Prev; 24(1); 57–64. ©2014 AACR.

Two New Meal- and Web-Based Interactive Food Frequency Questionnaires: Validation of Energy and Macronutrient Intake

Background Meal-Q and its shorter version, MiniMeal-Q, are 2 new Web-based food frequency questionnaires. Their meal-based and interactive format was designed to promote ease of use and to minimize answering time, desirable improvements in large epidemiological studies. Objective We evaluated the validity of energy and macronutrient intake assessed with Meal-Q and MiniMeal-Q as well as the reproducibility of Meal-Q. Methods Healthy volunteers aged 20-63 years recruited from Stockholm County filled out the 174-item Meal-Q. The questionnaire was compared to 7-day weighed food records (WFR; n=163), for energy and macronutrient intake, and to doubly labeled water (DLW; n=39), for total energy expenditure. In addition, the 126-item MiniMeal-Q was evaluated in a simulated validation using truncated Meal-Q data. We also assessed the answering time and ease of use of both questionnaires. Results Bland-Altman plots showed a varying bias within the intake range for all validity comparisons. Cross-classification of quartiles placed 70%-86% in the same/adjacent quartile with WFR and 77% with DLW. Deattenuated and energy-adjusted Pearson correlation coefficients with the WFR ranged from r=0.33-0.74 for macronutrients and was r=0.18 for energy. Correlations with DLW were r=0.42 for Meal-Q and r=0.38 for MiniMeal-Q. Intraclass correlations for Meal-Q ranged from r=0.57-0.90. Median answering time was 17 minutes for Meal-Q and 7 minutes for MiniMeal-Q, and participants rated both questionnaires as easy to use. Conclusions Meal-Q and MiniMeal-Q are easy to use and have short answering times. The ranking agreement is good for most of the nutrients for both questionnaires and Meal-Q shows fair reproducibility.

How Valid are Web-Based Self-Reports of Weight?

Background Many studies rely on self-reported anthropometric data. While paper-based self-reports have been the standard collection mode, the number of studies collecting self-reported data via the Web is increasing rapidly. Although numerous studies have shown good agreement between self-reported and measured weight using paper-based questionnaires, the validity of using the Web to inquire about weight is unknown. Objective The objective of this study was to validate Web-based self-reports of bodyweight compared to weight measured at the study center. Methods The validity of weight self-reported via the Web was assessed by comparing self-reports against measurements of weight in a convenience sample of 149 individuals (77.2% women, 115/149), aged 20-65 years. Study participants self-reported their weight via a Web-based questionnaire and thereafter had their weight measured in the research center. Results The Spearman correlation coefficient between self-reported and measured weight was 0.98 (P<.001). The mean difference between self-reported and measured weight was -1.2 (SD 2.6) kg. There was a statistically significant difference between self-reported and measured weight with the self-reported being lower (P<.001). Subjects with a body mass index (BMI) ≥25 kg/m2, and subjects ≥30 years of age, under-reported their weight statistically significantly more than subjects with a BMI <25 kg/m2, and subjects <30 years of age, respectively. Conclusions Our results show that self-reported weight via the Web can be a valid method of data collection.

Active-Q: Validation of the Web-Based Physical Activity Questionnaire Using Doubly Labeled Water

Background Increased use of the Internet provides new opportunities for collecting data in large studies. The aim of our new Web-based questionnaire, Active-Q, is to assess total physical activity and inactivity in adults. Active-Q assesses habitual activity during the past year via questions in four different domains: (1) daily occupation, (2) transportation to and from daily occupation, (3) leisure time activities, and (4) sporting activities. Objective The objective of our study is to validate Active-Q’s energy expenditure estimates using the doubly labeled water (DLW) method, and to assess the reproducibility of Active-Q by comparing the results of the questionnaire completed by the same group on two occasions. Methods The validity and reproducibility of Active-Q were assessed in a group of 37 individuals, aged 20 to 65 years. Active-Q was distributed via email to the participants. The total energy expenditure of the participants was assessed using DLW for 11 consecutive days. Results The median time to complete Active-Q was 6.1 minutes. The majority of participants (27/37, 73%) reported that the questionnaire was “easy” or “very easy” to answer. On average, Active-Q overestimated the total daily energy expenditure by 440 kJ compared with the DLW. The Spearman correlation between the two methods was r = 0.52 (P < .001). The intraclass correlation coefficient for total energy expenditure between the results of Active-Q completed on two occasions was 0.83 (95% CI 0.73-0.93). Conclusions Active-Q is a valid and reproducible method of assessing total energy expenditure. It is also a user-friendly method and suitable for Web-based data collection in large epidemiological studies.

Body mass index and mortality in men with prostate cancer.

Body Mass index (BMI) has been shown to affect risk and mortality of several cancers. Prostate cancer and obesity are major public health concerns for middle‐aged and older men. Previous studies of pre‐diagnostic BMI have found an increased risk of prostate cancer mortality in obese patients.

Relative Validity of Micronutrient and Fiber Intake Assessed With Two New Interactive Meal- and Web-Based Food Frequency Questionnaires

Background The meal- and Web-based food frequency questionnaires, Meal-Q and MiniMeal-Q, were developed for cost-efficient assessment of dietary intake in epidemiological studies. Objective The objective of this study was to evaluate the relative validity of micronutrient and fiber intake assessed with Meal-Q and MiniMeal-Q. The reproducibility of Meal-Q was also evaluated. Methods A total of 163 volunteer men and women aged between 20 and 63 years were recruited from Stockholm County, Sweden. Assessment of micronutrient and fiber intake with the 174-item Meal-Q was compared to a Web-based 7-day weighed food record (WFR). Two administered Meal-Q questionnaires were compared for reproducibility. The 126-item MiniMeal-Q, developed after the validation study, was evaluated in a simulated validation by using truncated Meal-Q data. Results The study population consisted of approximately 80% women (129/163) with a mean age of 33 years (SD 12) who were highly educated (130/163, 80% with >12 years of education) on average. Cross-classification of quartiles with the WFR placed 69% to 90% in the same/adjacent quartile for Meal-Q and 67% to 89% for MiniMeal-Q. Bland-Altman plots with the WFR and the questionnaires showed large variances and a trend of increasing underestimation with increasing intakes. Deattenuated and energy-adjusted Spearman rank correlations between the questionnaires and the WFR were in the range ρ=.25-.69, excluding sodium that was not statistically significant. Cross-classifications of quartiles of the 2 Meal-Q administrations placed 86% to 97% in the same/adjacent quartile. Intraclass correlation coefficients for energy-adjusted intakes were in the range of .50-.76. Conclusions With the exception of sodium, this validation study demonstrates Meal-Q and MiniMeal-Q to be useful methods for ranking micronutrient and fiber intake in epidemiological studies with Web-based data collection.

A New Mobile Phone-Based Tool for Assessing Energy and Certain Food Intakes in Young Children: A Validation Study

Background Childhood obesity is an increasing health problem globally. Obesity may be established already at pre-school age. Further research in this area requires accurate and easy-to-use methods for assessing the intake of energy and foods. Traditional methods have limited accuracy, and place large demands on the study participants and researchers. Mobile phones offer possibilities for methodological advancements in this area since they are readily available, enable instant digitalization of collected data, and also contain a camera to photograph pre- and post-meal food items. We have recently developed a new tool for assessing energy and food intake in children using mobile phones called the Tool for Energy Balance in Children (TECH). Objective The main aims of our study are to (1) compare energy intake by means of TECH with total energy expenditure (TEE) measured using a criterion method, the doubly labeled water (DLW) method, and (2) to compare intakes of fruits and berries, vegetables, juice, and sweetened beverages assessed by means of TECH with intakes obtained using a Web-based food frequency questionnaire (KidMeal-Q) in 3 year olds. Methods In this study, 30 Swedish 3 year olds were included. Energy intake using TECH was compared to TEE measured using the DLW method. Intakes of vegetables, fruits and berries, juice, as well as sweetened beverages were assessed using TECH and compared to the corresponding intakes assessed using KidMeal-Q. Wilcoxon matched pairs test, Spearman rank order correlations, and the Bland-Altman procedure were applied. Results The mean energy intake, assessed by TECH, was 5400 kJ/24h (SD 1500). This value was not significantly different (P=.23) from TEE (5070 kJ/24h, SD 600). However, the limits of agreement (2 standard deviations) in the Bland-Altman plot for energy intake estimated using TECH compared to TEE were wide (2990 kJ/24h), and TECH overestimated high and underestimated low energy intakes. The Bland-Altman plots for foods showed similar patterns. The mean intakes of vegetables, fruits and berries, juice, and sweetened beverages estimated using TECH were not significantly different from the corresponding intakes estimated using KidMeal-Q. Moderate but statistically significant correlations (ρ=.42-.46, P=.01-.02) between TECH and KidMeal-Q were observed for intakes of vegetables, fruits and berries, and juice, but not for sweetened beverages. Conclusion We found that one day of recordings using TECH was not able to accurately estimate intakes of energy or certain foods in 3 year old children.

Body mass index in relation to serum prostate-specific antigen levels and prostate cancer risk.

High Body mass index (BMI) has been directly associated with risk of aggressive or fatal prostate cancer. One possible explanation may be an effect of BMI on serum levels of prostate‐specific antigen (PSA). To study the association between BMI and serum PSA as well as prostate cancer risk, a large cohort of men without prostate cancer at baseline was followed prospectively for prostate cancer diagnoses until 2015. Serum PSA and BMI were assessed among 15,827 men at baseline in 2010–2012. During follow‐up, 735 men were diagnosed with prostate cancer with 282 (38.4%) classified as high‐grade cancers. Multivariable linear regression models and natural cubic linear regression splines were fitted for analyses of BMI and log‐PSA. For risk analysis, Cox proportional hazards regression models were used to estimate hazard ratios (HR) and 95% confidence intervals (CI) and natural cubic Cox regression splines producing standardized cancer‐free probabilities were fitted. Results showed that baseline Serum PSA decreased by 1.6% (95% CI: −2.1 to −1.1) with every one unit increase in BMI. Statistically significant decreases of 3.7, 11.7 and 32.3% were seen for increasing BMI‐categories of 25 < 30, 30 < 35 and ≥35 kg/m2, respectively, compared to the reference (18.5 < 25 kg/m2). No statistically significant associations were seen between BMI and prostate cancer risk although results were indicative of a positive association to incidence rates of high‐grade disease and an inverse association to incidence of low‐grade disease. However, findings regarding risk are limited by the short follow‐up time. In conclusion, BMI was inversely associated to PSA‐levels. BMI should be taken into consideration when referring men to a prostate biopsy based on serum PSA‐levels.

A validation study of the web-based physical activity questionnaire Active-Q against the GENEA Accelerometer

Background Valid physical activity assessment in epidemiological studies is essential to study associations with various health outcomes. Objective To validate the Web-based physical activity questionnaire Active-Q by comparing results of time spent at different physical activity levels with results from the GENEA accelerometer and to assess the reproducibility of Active-Q by comparing two admissions of the questionnaire. Methods A total of 148 men (aged 33 to 86 years) responded to Active-Q twice and wore the accelerometer during seven consecutive days on two occasions. Time spent on six different physical activity levels including sedentary, light (LPA), moderate (MPA), and vigorous (VPA) as well as additional combined categories of sedentary-to-light and moderate-to-vigorous (MVPA) physical activity was assessed. Validity of Active-Q was determined using Spearman correlation coefficients with 95% confidence intervals (CI) and the Bland-Altman method. Reproducibility was assessed using intraclass correlation coefficients (ICCs) comparing two admissions of the questionnaire. Results The validity correlation coefficients were statistically significant for time spent at all activity levels; sedentary (r=0.19, 95% CI: 0.04-0.34), LPA (r=0.15, 95% CI: 0.00-0.31), sedentary-to-light (r=0.35, 95% CI: 0.19-0.51), MPA (r=0.27, 95% CI: 0.12-0.42), VPA (r=0.54, 95% CI: 0.42-0.67), and MVPA (r=0.35, 95% CI: 0.21-0.48). The Bland-Altman plots showed a negative mean difference for time in LPA and positive mean differences for time spent in MPA, VPA and MVPA. The ICCs of test-retest reliability ranged between r=0.51-0.80 for the different activity levels in Active-Q. Conclusions More moderate and vigorous activities and less light activities were reported in Active-Q compared to accelerometer measurements. Active-Q shows comparable validity and reproducibility to other physical activity questionnaires used today.

Feasibility of a novel web-based physical activity questionnaire for young children

Accurate assessment of childrens physical activity is critical in determining associations between childhood physical activity and health later in life. We have developed a web-based questionnaire, KidActive-Q, to assess physical activity in early childhood. The feasibility and reproducibility of KidActive-Q were assessed in a sample of 20 children aged 2 to 6 years. The questionnaire was distributed to parents via e-mail on two occasions. The median time required to complete KidActive-Q was 2.5 minutes. All the parents reported that the questionnaire was easy or very easy to fill. The intraclass correlations for test-retest reliability were 0.60 (95% confidence interval: 0.31–0.88) for daily time spent outdoors, and 0.85 (95% confidence interval: 0.72–0.97) for daily time watching television. Our results suggest that KidActive-Q is a user-friendly tool for assessing physical activity in early childhood. To our knowledge, this is the first web-based questionnaire specifically developed for young children.