Validation of Total Water Intake from the Automated Self-Administered 24-h Recall, 4-d Food Records, and a Food Frequency Questionnaire Using Doubly Labeled Water.

BACKGROUND: Although prior evidence indicates that water intake is important for health, the ability to accurately measure community-dwelling intake is limited. Only a few studies have evaluated self-reported water intake against an objective recovery biomarker. OBJECTIVES: The aim was to compare preformed water intakes (all sources including food) by multiple Automated Self-Administered 24-h recalls (ASA24s), food frequency questionnaires (FFQs), and 4-d food records (4DFRs) against a recovery biomarker, doubly labeled water (DLW), to assess measurement error. METHODS: Over 1 y, 1082 women and men (50%), aged 50 to 74 y, were asked to complete 6 ASA24s, 2 FFQs, 2 unweighted 4DFRs, and an administration of DLW (n = 686). Geometric means of water intake by self-report tools were compared with DLW. Attenuation factors and correlation coefficients between self-reported and the recovery biomarker (DLW) were estimated. RESULTS: Mean water intakes by DLW were 2777 mL/d (interquartile range, 2350 to 3331) in women and 3243 mL/d (interquartile range, 2720 to 3838) in men. Compared with DLW, water intake was underestimated by 18% to 31% on ASA24s and 43% to 44% on 4DFRs. Estimated geometric means from FFQs differed from DLW by -1% to +13%. For a single ASA24, FFQ, and 4DFR, attenuation factors were 0.28, 0.27, and 0.32 and correlation coefficients were 0.46, 0.48, and 0.49, respectively. Repeated use of 6 ASA24s, 2 FFQs, and 2 4DFRs improved attenuation factors to 0.43, 0.32, and 0.39 and correlation coefficients to 0.58, 0.53, and 0.54, respectively. CONCLUSIONS: FFQs may better estimate population means for usual water intake compared with ASA24 and 4DFR. Similar attenuation factors and correlation coefficients across all self-report tools indicate that researchers have 3 feasible options if the goal is understanding intake-disease relationships. The findings are useful for planning future nutrition studies that set policy priorities for populations and to understand the health impact of water. This trial was registered at clinicaltrials.gov as NCT03268577.

Using a smartphone application to capture daily work activities: a longitudinal pilot study in a farming population.

OBJECTIVES: Smartphones are increasingly used to collect real-time information on time-varying exposures. We developed and deployed an application (app) to evaluate the feasibility of using smartphones to collect real-time information on intermittent agricultural activities and to characterize agricultural task variability in a longitudinal study of farmers. METHODS: We recruited 19 male farmers, aged 50-60 years, to report their farming activities on 24 randomly selected days over 6 months using the Life in a Day app. Eligibility criteria include personal use of an iOS or Android smartphone and >4 h of farming activities at least two days per week. We developed a study-specific database of 350 farming tasks that were provided in the app; 152 were linked to questions that were asked when the activity ended. We report eligibility, study compliance, number of activities, duration of activities by day and task, and responses to the follow-up questions. RESULTS: Of the 143 farmers we reached out to for this study, 16 were not reached by phone or refused to answer eligibility questions, 69 were ineligible (limited smartphone use and/or farming time), 58 met study criteria, and 19 agreed to participate. Refusals were mostly related to uneasiness with the app and/or time commitment (32 of 39). Participation declined gradually over time, with 11 farmers reporting activities through the 24-week study period. We obtained data on 279 days (median 554 min/day; median 18 days per farmer) and 1,321 activities (median 61 min/activity; median 3 activities per day per farmer). The activities were predominantly related to animals (36%), transportation (12%), and equipment (10%). Planting crops and yard work had the longest median durations; short-duration tasks included fueling trucks, collecting/storing eggs, and tree work. Time period-specific variability was observed; for example, crop-related activities were reported for an average of 204 min/day during planting but only 28 min/day during pre-planting and 110 min/day during the growing period. We obtained additional information for 485 (37%) activities; the most frequently asked questions were related to "feed animals" (231 activities) and "operate fuel-powered vehicle (transportation)" (120 activities). CONCLUSIONS: Our study demonstrated feasibility and good compliance in collecting longitudinal activity data over 6 months using smartphones in a relatively homogeneous population of farmers. We captured most of the farming day and observed substantial heterogeneity in activities, highlighting the need for individual activity data when characterizing exposure in farmers. We also identified several areas for improvement. In addition, future evaluations should include more diverse populations.

Measurement Error Affecting Web- and Paper-Based Dietary Assessment Instruments: Insights From the Multi-Cohort Eating and Activity Study for Understanding Reporting Error.

Few biomarker-based validation studies have examined error in online self-report dietary assessment instruments, and food records (FRs) have been considered less than food frequency questionnaires (FFQs) and 24-hour recalls (24HRs). We investigated measurement error in online and paper-based FFQs, online 24HRs, and paper-based FRs in 3 samples drawn primarily from 3 cohorts, comprising 1,393 women and 1,455 men aged 45-86 years. Data collection occurred from January 2011 to October 2013. Attenuation factors and correlation coefficients between reported and true usual intake for energy, protein, sodium, potassium, and respective densities were estimated using recovery biomarkers. Across studies, average attenuation factors for energy were 0.07, 0.07, and 0.19 for a single FFQ, 24HR, and FR, respectively. Correlation coefficients for energy were 0.24, 0.23, and 0.40, respectively. Excluding energy, the average attenuation factors across nutrients and studies were 0.22 for a single FFQ, 0.22 for a single 24HR, and 0.51 for a single FR. Corresponding correlation coefficients were 0.31, 0.34, and 0.53, respectively. For densities (nutrient expressed relative to energy), the average attenuation factors across studies were 0.37, 0.17, and 0.50, respectively. The findings support prior research suggesting different instruments have unique strengths that should be leveraged in epidemiologic research.

Examining the association between meal context and diet quality: an observational study of meal context in older adults.

BACKGROUND: Though a healthy diet is widely associated with reduced risks for chronic disease and mortality, older adults in the U.S. on average do not meet dietary recommendations. Given that few studies have examined the association between meal context on older adult diet quality, the aims of this study were (1) to compare the dietary quality of foods consumed in different meal contexts, as measured by the Healthy Eating Index 2015 (HEI-2015): meal location, the presence of others, and the use of electronic screens; and (2) to examine which components of the HEI-2015 drove differences in HEI-2015 total scores by meal context. METHODS: Interactive Diet and Activity Tracking in AARP study participants (50-74 years) completed the Automated Self-Administered 24-h Dietary Assessment tool (ASA24, version 2011) that included foods and beverages consumed and three meal contexts: "at home" versus "away from home," "alone" versus "with company," and "with screen time" versus "without screen time." A population ratio approach was used to estimate HEI-2015 total and component scores for all food items consumed by meal context. Mean HEI-2015 scores (range: 0-100) for the three meal context variables were compared using t-tests. Where there were significant differences in total scores, additional t-tests were used to explore which HEI-2015 components were the primary drivers. All tests were stratified by sex and adjusted for multiple comparisons. RESULTS: HEI-2015 scores were lower for meals consumed away vs. at home (mean difference (SE), males: - 8.23 (1.02); females: - 7.29 (0.93); both p < 0.0001) and for meals eaten with vs. without company (mean difference (SE), males: - 6.61 (1.06); females: - 7.34 (1.18); both p < 0.0001). There was no difference comparing with vs. without screen time. When HEI-2015 component scores were examined, fewer total fruits, whole grains, and dairy were consumed away from home or with company; more total vegetables and greens and beans, and less added sugars were consumed with company. CONCLUSIONS: Our findings suggest an association between the behavior cues of meal location and companions and dietary choices among older adults. Future studies can explore the individual and interactive effects of meal context on diet quality and subsequent health outcomes.

Performance and Feasibility of Recalls Completed Using the Automated Self-Administered 24-Hour Dietary Assessment Tool in Relation to Other Self-Report Tools and Biomarkers in the Interactive Diet and Activity Tracking in AARP (IDATA) Study.

BACKGROUND: Automated Self-Administered 24-Hour Dietary Assessment Tool (ASA24) is a self-administered web-based tool designed to collect detailed dietary data at low cost in observational studies. OBJECTIVE: The objectives of this study were to describe, overall and by demographic groups, the performance and feasibility of ASA24-2011 recalls and compare Healthy Eating Index-2015 (HEI-2015) total and component scores to 4-day food records (4DFRs) and food frequency questionnaires (FFQs). DESIGN: Over 12 months, participants completed up to 6 ASA24 recalls, 2 web-based FFQs, and 2 unweighed paper-and-pencil 4DFRs. Up to 3 attempts were made to obtain each ASA24 recall. Participants were administered doubly-labeled water to provide a measure of total energy expenditure and collected two 24-hour urine samples to assess concentrations of nitrogen, sodium, and potassium. PARTICIPANTS/SETTING: From January through September 2012, 1,110 adult members of AARP, 50 to 74 years of age, were recruited from the Pittsburgh, PA, area to participate in the Interactive Diet and Activity Tracking in AARP (IDATA) study. After excluding 33 participants who had not completed any dietary assessments, 531 men and 546 women remained. MAIN OUTCOME MEASURES: Response rates, nutrient intakes compared to recovery biomarkers across each ASA24 administration day, and HEI-2015 total and component scores were measured. STATISTICAL ANALYSES PERFORMED: Means, medians, standard deviations, interquartile ranges, and HEI-2015 total and component scores computed using a multivariate measurement error model are presented. RESULTS: Ninety-one percent of men and 86% of women completed 3 ASA24 recalls. Approximately three-quarters completed 5 or more, higher than the completion rates for 2 4DFRs and 2 FFQs. Approximately, three-quarters of men and 70% of women completed ASA24 on the first attempt; 1 in 5 completed it on the second. Completion rates varied slightly by age and body mass index. Median time to complete ASA24-2011 (current version: ASA24-2020) declined with subsequent recalls from 55 to 41 minutes in men and from 58 to 42 minutes in women and was lowest in those younger than 60 years. Mean nutrient intakes were similar across recalls. For each recording day, energy intakes estimated by ASA24 were lower than energy expenditure. Reported intakes for protein, potassium, and sodium were closer to recovery biomarkers for women, but not for men. Geometric means of reported intakes of these nutrients did not systematically vary across ASA24 administrations, but differences between reported intakes and biomarkers differed by nutrient. Of 100 possible points, HEI-2015 total scores were nearly identical for 4DFRs and ASA24 recalls and higher for FFQs (men: 61, 60, and 68; women: 64, 64, and 72, respectively). CONCLUSIONS: ASA24, a freely available dietary assessment tool for use in large-scale nutrition research, was found to be highly feasible. Similar to previously reported data for nutrient intakes, HEI-2015 total and component scores for ASA24 recalls were comparable to those for 4DFRs, but not FFQs. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT03268577 (http://www.clinicaltrials.gov).

Inverse association between changes in energetic cost of walking and vertical accelerations in non-metastatic breast cancer survivors.

PURPOSE: With accelerometry, the utility to detect changes in physical activity are predicated on the assumption that walking energetics and gait mechanics do not change. The present work examined associations between changes (∆) in walking energetics, exercise self-efficacy, and several accelerometer-derived metrics. METHODS: Secondary analyses were performed among a sub-sample (n = 29) of breast cancer survivors participating in a larger randomized trial. During 4 min of treadmill walking (0.89 m s-1, 0% grade), indirect calorimetry quantified steady-state energy expenditure (EE), wherein, participants were fitted with a heart rate monitor and hip-worn triaxial accelerometer. Exercise self-efficacy was measured using a 9-item questionnaire, while vector magnitude (VM) and individual planes (e.g., mediolateral, vertical, and anteroposterior) of the movement were extracted for data analyses. Evaluations were made at baseline and after 3 months. RESULTS: From baseline to 3 months, the energetic cost of walking (kcals min-1) significantly decreased by an average of - 5.1% (p = 0.001; d = 0.46). Conversely, VM significantly increased (p = 0.007; d = 0.53), exclusively due to greater vertical accelerations (acc) (+ 5.7 ± 7.8 acc; p = 0.001; d = 0.69). Changes in vertical accelerations were inversely and positively associated with ∆walking EE (r = - 0.37; p = 0.047) and ∆exercise self-efficacy (r = 0.39; p = 0.034), respectively. CONCLUSION: Hip-worn accelerometers do not appear well-suited to correctly detect changes in ease of walking as evidenced by reduced energetic cost. Further research should determine if a divergence between measured EE and vertical accelerations could contribute to erroneous inferences in free-living physical activity.

Associations of Sedentary Time with Energy Expenditure and Anthropometric Measures.

PURPOSE: To investigate associations between accelerometer-determined sedentary time (ST) in prolonged (≥30 min) and nonprolonged (<30 min) bouts with physical activity energy expenditure (PAEE) from doubly labeled water. Additionally, associations between ST and body mass index (BMI) and waist circumference were examined. METHODS: Data from 736 women and 655 men age 43 to 82 yr were analyzed. Participants wore the Actigraph GT3X for 7 d on two occasions approximately 6 months apart, and the average of the measurements was used. Physical activity energy expenditure was estimated by subtracting resting metabolic rate and the thermic effect of food from doubly labeled water estimates of total daily energy expenditure. Cross-sectional associations were analyzed using isotemporal substitution modeling. RESULTS: Reallocation of prolonged ST to nonprolonged was not associated with increased PAEE and only significantly associated with lower BMI (ß = -0.57 kg·m; 95% confidence interval, -0.94 to -0.20) and waist circumference (ß = -1.61 cm; 95% confidence interval, -2.61 to -0.60) in men. Replacing either type of ST with light or moderate-to-vigorous physical activity was significantly associated with higher PAEE, and lower BMI and waist circumference in both women and men. CONCLUSIONS: Limiting time spent sedentary as well as decreasing ST accumulated in prolonged bouts may have beneficial effects on BMI and waist circumference. Replacing any type of ST with activities of light or higher intensity may also have a substantial impact on PAEE.

Reporting accelerometer methods in physical activity intervention studies: a systematic review and recommendations for authors.

OBJECTIVE: This systematic review assessed the completeness of accelerometer reporting in physical activity (PA) intervention studies and assessed factors related to accelerometer reporting. DESIGN: The PubMed database was used to identify manuscripts for inclusion. Included studies were PA interventions that used accelerometers, were written in English and were conducted between 1 January 1998 and 31 July 2014. 195 manuscripts from PA interventions that used accelerometers to measure PA were included. Manuscript completeness was scored using 12 questions focused on 3 accelerometer reporting areas: accelerometer information, data processing and interpretation and protocol non-compliance. Variables, including publication year, journal focus and impact factor, and population studied were evaluated to assess trends in reporting completeness. RESULTS: The number of manuscripts using accelerometers to assess PA in interventions increased from 1 in 2002 to 29 in the first 7 months of 2014. Accelerometer reporting completeness correlated weakly with publication year (r=0.24, p<0.001). Correlations were greater when we assessed improvements over time in reporting data processing in manuscripts published in PA-focused journals (r=0.43, p=0.002) compared to manuscripts published in non-PA-focused journals (r=0.19, p=0.021). Only 7 of 195 (4%) manuscripts reported all components of accelerometer use, and only 132 (68%) reported more than half of the components. CONCLUSIONS: Accelerometer reporting of PA in intervention studies has been poor and improved only minimally over time. We provide recommendations to improve accelerometer reporting and include a template to standardise reports.

Physical Activity Assessment with the ActiGraph GT3X and Doubly Labeled Water.

PURPOSE: To compare the degree to which four accelerometer metrics-total activity counts per day (TAC per day), steps per day (steps per day), physical activity energy expenditure (PAEE) (kcal·kg·d), and moderate- to vigorous-intensity physical activity (MVPA) (min·d)-were correlated with PAEE measured by doubly labeled water (DLW). Additionally, accelerometer metrics based on vertical axis counts and triaxial counts were compared. METHODS: This analysis included 684 women and 611 men age 43 to 83 yr. Participants wore the Actigraph GT3X on the hip for 7 d twice during the study and the average of the two measurements was used. Each participant also completed one DLW measurement, with a subset having a repeat. PAEE was estimated by subtracting resting metabolic rate and the thermic effect of food from total daily energy expenditure estimated by DLW. Partial Spearman correlations were used to estimate associations between PAEE and each accelerometer metric. RESULTS: Correlations between the accelerometer metrics and DLW-determined PAEE were higher for triaxial counts than vertical axis counts. After adjusting for weight, age, accelerometer wear time, and fat free mass, the correlation between TAC per day based on triaxial counts and DLW-determined PAEE was 0.44 in women and 0.41 in men. Correlations for steps per day and accelerometer-estimated PAEE with DLW-determined PAEE were similar. After adjustment for within-person variation in DLW-determined PAEE, the correlations for TAC per day increased to 0.61 and 0.49, respectively. Correlations between MVPA and DLW-determined PAEE were lower, particularly for modified bouts of ≥10 min. CONCLUSIONS: Accelerometer measures that represent total activity volume, including TAC per day, steps per day, and PAEE, were more highly correlated with DLW-determined PAEE than MVPA using traditional thresholds and should be considered by researchers seeking to reduce accelerometer data to a single metric.

NCI Funding Trends and Priorities in Physical Activity and Energy Balance Research Among Cancer Survivors.

There is considerable evidence that a healthy lifestyle consisting of physical activity, healthy diet, and weight control is associated with reduced risk of morbidity and mortality after cancer. However, these behavioral interventions are not widely adopted in practice or community settings. Integrating heath behavior change interventions into standard survivorship care for the growing number of cancer survivors requires an understanding of the current state of the science and a coordinated scientific agenda for the future with focused attention in several priority areas. To facilitate this goal, this paper presents trends over the past decade of the National Cancer Institute (NCI) research portfolio, fiscal year 2004 to 2014, by funding mechanism, research focus, research design and methodology, primary study exposures and outcomes, and study team expertise and composition. These data inform a prioritized research agenda for the next decade focused on demonstrating value and feasibility and creating desire for health behavior change interventions at multiple levels including the survivor, clinician, and healthcare payer to facilitate the development and implementation of appropriately targeted, adaptive, effective, and sustainable programs for all survivors.

Ability of thigh-worn ActiGraph and activPAL monitors to classify posture and motion.

PURPOSE: This study compared sitting, standing, and stepping classifications from thigh-worn ActiGraph and activPAL monitors under laboratory and free-living conditions. METHODS: Adults wore both monitors on the right thigh while performing activities (six sitting, two standing, nine stepping, and one cycling) and writing on a whiteboard with intermittent stepping under laboratory conditions (n = 21) and under free-living conditions for 3 d (n = 18). Percent time correctly classified was calculated under laboratory conditions. Between-monitor agreement and weighted κ were calculated under free-living conditions. RESULTS: In the laboratory, both monitors correctly classified 100% of standing time and >95% of the time spent in four of six sitting postures. Both monitors demonstrated misclassification of laboratory stool sitting time (ActiGraph 14% vs. activPAL 95%). ActivPAL misclassified 14% of the time spent sitting with legs outstretched; ActiGraph was 100% accurate. Monitors were >95% accurate for stepping, although ActiGraph was less so for descending stairs (86%), ascending stairs (92%), and running at 2.91 m·s(-1) (93%). Monitors classified whiteboard writing differently (ActiGraph 83% standing/15% stepping vs. activPAL 98% standing/2% stepping). ActivPAL classified 93% of cycling time as stepping, whereas ActiGraph classified <1% of cycling time as stepping. During free-living wear, monitors had substantial agreement (86% observed; weighted κ = 0.77). Monitors classified similar amounts of time as sitting (ActiGraph 64% vs. activPAL 62%). There were differences in time recorded as standing (ActiGraph 21% vs. activPAL 27%) and stepping (ActiGraph 15% vs. activPAL 11%). CONCLUSIONS: Differences in data processing algorithms may have resulted in the observed disagreement in posture and activity classification between thigh-worn ActiGraph and activPAL. Despite between-monitor agreement in classifying sitting time under free-living conditions, ActiGraph appears to be more sensitive to free-living upright walking motions than activPAL.

Self-reported pediatric measures of physical activity, sedentary behavior, and strength impact for PROMIS: item development.

BACKGROUND: Children's activity level is commonly assessed in clinical research, but rigorous assessment tools for children are scarce. Our objectives were to improve pediatric activity self-report measures using qualitative methods to develop item pools that measure these concepts. METHODS: On the basis of the items generated from our conceptual framework development, we applied cognitive interviews and comprehensibility reviews to ensure children readily understood the items. RESULTS: Our methods resulted in 129 unique items-physical activities (80 items), sedentary behaviors (23 items), and strengthening activities (26 items)-that were comprehensible to children between the ages of 8 and 18 years. Comprehensibility review resulted in the deletion of 4 items. CONCLUSIONS: The resultant item pools reflect children's experiences and understanding of the concepts of physical activities, sedentary behaviors, and strengthening activities. The item pools will undergo calibration using item response theory to support computer-adaptive test administration of self- and proxy-reported outcomes.

Self-reported pediatric measures of physical activity, sedentary behavior, and strength impact for PROMIS: conceptual framework.

PURPOSE: Children's physical activity (PA) levels are commonly assessed in pediatric clinical research, but rigorous self-report assessment tools for children are scarce, and computer adaptive test implementations are rare. Our objective was to improve pediatric self-report measures of activity using semistructured interviews with experts and children for conceptualization of a child-informed framework. METHODS: Semistructured interviews were conducted to conceptualize PA, sedentary behaviors, and strengthening activities. We performed systematic literature reviews to identify item-level concepts used to assess these 3 domains. RESULTS: We developed conceptual frameworks for each domain using words and phrases identified by children as relevant. CONCLUSIONS: Semistructured interview methods provide valuable information of children's perspectives and the ways children recall previous activities. Conceptualized domains of PA are based on the literature and expert views, which also reflect children's experiences and understanding, providing a basis for pediatric self-report instruments.

Utility of computer-assisted approaches for population surveillance of physical activity.

BACKGROUND: Computer-assisted techniques may be a useful way to enhance physical activity surveillance and increase accuracy of reported behaviors. PURPOSE: Evaluate the reliability and validity of a physical activity (PA) self-report instrument administered by telephone and internet. METHODS: The telephone-administered Active Australia Survey was adapted into 2 forms for internet self-administration: survey questions only (internet-text) and with videos demonstrating intensity (internet-video). Data were collected from 158 adults (20-69 years, 61% female) assigned to telephone (telephone-interview) (n = 56), internet-text (n = 51), or internet-video (n = 51). Participants wore an accelerometer and completed a logbook for 7 days. Test-retest reliability was assessed using intraclass correlation coefficients (ICC). Convergent validity was assessed using Spearman correlations. RESULTS: Strong test-retest reliability was observed for PA variables in the internet-text (ICC = 0.69 to 0.88), internet-video (ICC = 0.66 to 0.79), and telephone-interview (ICC = 0.69 to 0.92) groups (P-values < 0.001). For total PA, correlations (ρ) between the survey and Actigraph+logbook were ρ = 0.47 for the internet-text group, ρ = 0.57 for the internet-video group, and ρ = 0.65 for the telephone-interview group. For vigorous-intensity activity, the correlations between the survey and Actigraph+logbook were 0.52 for internet-text, 0.57 for internet-video, and 0.65 for telephone-interview (P < .05). CONCLUSIONS: Internet-video of the survey had similar test-retest reliability and convergent validity when compared with the telephone-interview, and should continue to be developed.

Validation of a previous-day recall measure of active and sedentary behaviors.

PURPOSE: A previous-day recall (PDR) may be a less error-prone alternative to traditional questionnaire-based estimates of physical activity and sedentary behavior (e.g., past year), but the validity of the method is not established. We evaluated the validity of an interviewer administered PDR in adolescents (12-17 yr) and adults (18-71 yr). METHODS: In a 7-d study, participants completed three PDR, wore two activity monitors, and completed measures of social desirability and body mass index. PDR measures of active and sedentary time was contrasted against an accelerometer (ActiGraph) by comparing both to a valid reference measure (activPAL) using measurement error modeling and traditional validation approaches. RESULTS: Age- and sex-specific mixed models comparing PDR to activPAL indicated the following: 1) there was a strong linear relationship between measures for sedentary (regression slope, ß1 = 0.80-1.13) and active time (ß1 = 0.64-1.09), 2) person-specific bias was lower than random error, and 3) correlations were high (sedentary: r = 0.60-0.81; active: r = 0.52-0.80). Reporting errors were not associated with body mass index or social desirability. Models comparing ActiGraph to activPAL indicated the following: 1) there was a weaker linear relationship between measures for sedentary (ß1 = 0.63-0.73) and active time (ß1 = 0.61-0.72), (2) person-specific bias was slightly larger than random error, and (3) correlations were high (sedentary: r = 0.68-0.77; active: r = 0.57-0.79). CONCLUSIONS: Correlations between the PDR and the activPAL were high, systematic reporting errors were low, and the validity of the PDR was comparable with the ActiGraph. PDR may have value in studies of physical activity and health, particularly those interested in measuring the specific type, location, and purpose of activity-related behaviors.

Perceived neighborhood environment and physical activity in 11 countries: do associations differ by country?

BACKGROUND: Increasing empirical evidence supports associations between neighborhood environments and physical activity. However, since most studies were conducted in a single country, particularly western countries, the generalizability of associations in an international setting is not well understood. The current study examined whether associations between perceived attributes of neighborhood environments and physical activity differed by country. METHODS: Population representative samples from 11 countries on five continents were surveyed using comparable methodologies and measurement instruments. Neighborhood environment × country interactions were tested in logistic regression models with meeting physical activity recommendations as the outcome, adjusted for demographic characteristics. Country-specific associations were reported. RESULTS: Significant neighborhood environment attribute × country interactions implied some differences across countries in the association of each neighborhood attribute with meeting physical activity recommendations. Across the 11 countries, land-use mix and sidewalks had the most consistent associations with physical activity. Access to public transit, bicycle facilities, and low-cost recreation facilities had some associations with physical activity, but with less consistency across countries. There was little evidence supporting the associations of residential density and crime-related safety with physical activity in most countries. CONCLUSION: There is evidence of generalizability for the associations of land use mix, and presence of sidewalks with physical activity. Associations of other neighborhood characteristics with physical activity tended to differ by country. Future studies should include objective measures of neighborhood environments, compare psychometric properties of reports across countries, and use better specified models to further understand the similarities and differences in associations across countries.

Patterns of neighborhood environment attributes related to physical activity across 11 countries: a latent class analysis.

BACKGROUND: Neighborhood environment studies of physical activity (PA) have been mainly single-country focused. The International Prevalence Study (IPS) presented a rare opportunity to examine neighborhood features across countries. The purpose of this analysis was to: 1) detect international neighborhood typologies based on participants' response patterns to an environment survey and 2) to estimate associations between neighborhood environment patterns and PA. METHODS: A Latent Class Analysis (LCA) was conducted on pooled IPS adults (N=11,541) aged 18 to 64 years old (mean=37.5±12.8 yrs; 55.6% women) from 11 countries including Belgium, Brazil, Canada, Colombia, Hong Kong, Japan, Lithuania, New Zealand, Norway, Sweden, and the U.S. This subset used the Physical Activity Neighborhood Environment Survey (PANES) that briefly assessed 7 attributes within 10-15 minutes walk of participants' residences, including residential density, access to shops/services, recreational facilities, public transit facilities, presence of sidewalks and bike paths, and personal safety. LCA derived meaningful subgroups from participants' response patterns to PANES items, and participants were assigned to neighborhood types. The validated short-form International Physical Activity Questionnaire (IPAQ) measured likelihood of meeting the 150 minutes/week PA guideline. To validate derived classes, meeting the guideline either by walking or total PA was regressed on neighborhood types using a weighted generalized linear regression model, adjusting for gender, age and country. RESULTS: A 5-subgroup solution fitted the dataset and was interpretable. Neighborhood types were labeled, "Overall Activity Supportive (52% of sample)", "High Walkable and Unsafe with Few Recreation Facilities (16%)", "Safe with Active Transport Facilities (12%)", "Transit and Shops Dense with Few Amenities (15%)", and "Safe but Activity Unsupportive (5%)". Country representation differed by type (e.g., U.S. disproportionally represented "Safe but Activity Unsupportive"). Compared to the Safe but Activity Unsupportive, two types showed greater odds of meeting PA guideline for walking outcome (High Walkable and Unsafe with Few Recreation Facilities, OR=2.26 (95% CI 1.18-4.31); Overall Activity Supportive, OR=1.90 (95% CI 1.13-3.21). Significant but smaller odds ratios were also found for total PA. CONCLUSIONS: Meaningful neighborhood patterns generalized across countries and explained practical differences in PA. These observational results support WHO/UN recommendations for programs and policies targeted to improve features of the neighborhood environment for PA.

The association between television watching time and all-cause mortality after breast cancer.

PURPOSE: Sedentary time is a rapidly emerging independent risk factor for mortality in the general population, but its prognostic effect among cancer survivors is unknown. In a multiethnic, prospective cohort of breast cancer survivors, we hypothesized that television watching time would be independently associated with an increased risk of death from any cause. METHODS: The Health, Eating, Activity, and Lifestyle Study cohort included 687 women diagnosed with local or regional breast cancer. On average 30 (±4) months postdiagnosis, women completed self-report assessments on time spent sitting watching television/videos in a typical day in the previous year. Multivariate Cox proportional hazards models were used to estimate hazard ratios (HR) and 95 % confidence intervals (CI) for death from any cause (n = 89) during the 7 years of follow-up. RESULTS: Television time (top tertile vs. bottom tertile) was positively related to risk of death (HR, 1.94; 95 % CI, 1.02, 3.66, p trend = 0.024), but the association was attenuated and not statistically significant after adjustment for aerobic moderate-vigorous intensity physical activity (HR, 1.70; 95 % CI, 0.89, 3.22, p trend = 0.14) and all covariates (HR, 1.39; 95 % CI, 0.69, 2.82, p trend = 0.48). CONCLUSION: In this first published investigation on this topic, we did not observe a statistically significant multivariate-adjusted association between television watching time and risk of death among women diagnosed with breast cancer. IMPLICATIONS FOR CANCER SURVIVORS: These results begin an evidence base on this topic that can be built upon to inform lifestyle recommendations for this expanding, aging population.

Improving self-reports of active and sedentary behaviors in large epidemiologic studies.

Questionnaires that assess active and sedentary behaviors in large-scale epidemiologic studies are known to contain substantial errors. We present three options for improving measures of physical activity behaviors in large-scale epidemiologic studies, discuss the problems and prospects for each of these options, and highlight a new direction for measuring these behaviors in such studies.