Sedentary behavior includes all waking day behaviors in a sitting/lying posture expending ≤1.5 times the resting energy demand (35). It has been suggested that sedentary behavior is a highly prevalent, independent chronic disease risk factor (26,39). Most of the evidence for health effects has been based on leisure screen behavior (i.e., TV viewing) or total sitting/sedentary time derived from single self-report measures or objective monitoring (39). Although objective methods enhance precision of health risk estimates for total sedentary time, they are still expensive, difficult to administer to large free-living populations, and do not provide information on the domains in which sitting is accumulated (e.g., for transportation or at work). Determinants of adults’ sitting time differ by domain (29), and some domain-specific sedentary behaviors (e.g., TV viewing) may have stronger associations with health outcomes (36). Large-scale observational studies therefore need reliable and valid sedentary behavior questionnaires to examine differential health effects and to enable identification of determinants of domain-specific sedentary behaviors (29). Few questionnaires characterize sedentary behavior across all common domains of daily life in the general adult population (19). Instead, they tend to focus on a specific domain (e.g., leisure time (9), workplace sitting (7,10,28)) or a specific population (e.g., working force (6) or elderly (15)). Two recent multidomain sedentary behavior questionnaires did not cover all common domains (e.g., sitting for meals), which may affect total sitting time estimates. More importantly, they require participants to recall sitting on a usual week/weekend day and exhibit relatively weak validity compared with accelerometry (25,31). Given their low intensity and habitual nature, sedentary behaviors are difficult to recall. Questionnaires therefore benefit from a short, recent recall frame, such as the last 7 d, which allows for recollection of specific rather than usual behaviors (27). A last 7-d recall frame also captures more intraindividual variability in sedentary behavior than recall frames that are even shorter, such as the past day (11). Only one questionnaire has so far incorporated sedentary time patterns (10), which have been shown to have independent health associations (20). Finally, self-report questionnaires have higher feasibility for implementation in large studies compared with equally valid interviewer-administered ones. To date, sedentary behavior questionnaires have mostly been validated in specific populations (e.g., overweight adults (22,31), breast cancer patients (11), and middle-age women (25)), which limit generalizability of their psychometric properties to general adult populations. Furthermore, questionnaires have been compared predominantly with a waist- or hip-mounted accelerometer (6,7,10,12,15,25,31), which have important limitations. Removal of these monitors during sleep and water-based activities results in misclassification between sedentary and nonwear time (40), which may be overcome by waterproof monitors that allow for a 24-h·d−1 wear protocol. Second, because these accelerometers do not discriminate between postures, they misclassify standing as sedentary time. Thigh-worn monitors that measure thigh acceleration and position with respect to gravity have been shown to accurately measure posture-based sitting time (17,21,23,32). This method has, however, rarely been used as criterion when validating sedentary behavior questionnaires (11,22). Finally, domain-specific criterion measures, derived from simultaneously collected objective monitor and log data, are also underused. No studies have so far annotated thigh accelerometry-based sedentary time with domain-specific log data, despite the merit of such a criterion when validating domain-specific questionnaires (9,19). We examined the test–retest reliability, criterion validity, and absolute agreement of a self-report, domain-specific, last 7-d sedentary behavior questionnaire (SIT-Q-7d), which assesses volume and patterns of sedentary time, in a sample of the Flemish and English general adult population. Criterion measures allowing for a 24-h wear protocol were used, providing 1) total and domain-specific sedentary time and patterns, derived from a combined criterion of thigh accelerometry and a domain log, and 2) total sedentary time derived from individually calibrated combined HR and movement sensing. METHODS Sedentary Behavior Questionnaire: SIT-Q-7d The SIT-Q-7d is a self-administered questionnaire that quantifies time spent sedentary in the last 7 d as well as sleeping/napping time. Sedentary time (“sitting or lying down”) is assessed across five different domains covering adults’ daily life activities. These include, in this order, 1) meals (sum of breakfast, lunch, and dinner), 2) transportation (sum of to and from occupation, as part of occupation and getting about apart from occupation), 3) occupation (i.e., work, study, and volunteering, sum of two main occupations), 4) leisure screen time (sum of watching TV/DVDs/videos, using computer apart from work, and playing sedentary computer games), and 5) time spent sedentary in other activities (sum of reading, household tasks, caring for children, grandchildren, elderly, or disabled relatives, hobbies, socializing, listening to music, and other activities). The questionnaire enables calculation of domain-specific and total sedentary time. The first page of the SIT-Q-7d provides general instructions for correct completion of the questionnaire, which also emphasize the importance of entering each period of sitting only once, to avoid double counting of sitting time. Subsequently, these instructions on mutual exclusivity of reported sitting time are repeated throughout the questionnaire, tailored to each domain specifically (e.g., section 2—Meals: “DO NOT INCLUDE time spent eating while watching TV. This is part of section 5 (i.e., screen time and other activities)”). Questions on sleeping/napping, meals, nonoccupational transportation, screen time, and other sedentary activities are queried for weekdays and weekend days separately to account for week and weekend differences. The number of interruptions in sedentary time (standing up or walking somewhere) in occupational and TV viewing sedentary time is also assessed. The SIT-Q-7d was based on the SIT-Q, a domain-specific measure of habitual sedentary behaviors with a reference frame of the past year. The SIT-Q was developed through a three-stage process of expert review, cognitive interviewing, and pilot testing and demonstrates acceptable measurement properties for use in epidemiological studies (24). The SIT-Q-7d was developed by changing the reference frame from the past year (SIT-Q) to the last 7 d (SIT-Q-7d). Given the shorter reference frame, an introductory question determines whether the amount of sitting during the last 7 d was similar to a typical week on a five-point Likert scale. Furthermore, due to shortening the reference frame, participants are restricted to reporting on two main occupations instead of three. For ease of completion, coding, and analysis, the response format was changed to closed categorical rather than open-ended questions. Sections on sitting during meals, transportation, and leisure were broken down (e.g., transportation to and from occupation, as part of occupation, and getting about apart from occupation) to lower cognitive demands on participants in terms of presummarizing sitting durations (27). A food-frequency section on snacking while watching TV was added, because this may be an important mediator of the health effects of TV viewing (18). To maximize clarity of questions, appropriateness of response categories, question order, and presentation, both the Dutch and English versions were pilot tested before the reliability and validity study. The questionnaire and recommended processing codes are available in http://www.mrc-epid.cam.ac.uk/research/resources/. Participants and Design Flemish sample Dutch-speaking Flemish adults, ages 20–60 yr, were recruited while visiting a variety of theoretical and practical adult education classes in an adult education school located in a suburb of Ghent, Belgium. A total of 62 adults (40% men) agreed to participate. They self-reported their sex, date of birth, height, weight, and education level on the day of recruitment (day 0). They were instructed how to continuously wear a thigh accelerometer (activPAL3™; PAL Technologies, Glasgow, UK) and simultaneously complete a simple domain log for 7 d while maintaining their normal activities, starting the same evening until their evening class the following week (day 7). On day 7, participants handed in both thigh accelerometer and domain log, before they received the SIT-Q-7d questionnaire for the first time (test), to avoid reference to the log when completing the test questionnaire. The “last 7-d” reference frame of the questionnaire coincided with the 7-d administration of the combined criterion measure (thigh accelerometer and domain log). The test questionnaire was completed during the recess break. During a final visit, 2 wk later (day 21), participants received the retest questionnaire, which was again completed during recess. Participants who were unable to complete their retest questionnaire during the break were instructed to complete it at home the same evening and send it back in a prestamped envelope. Those with a test–retest interval longer than 8 wk were excluded from the analysis. The study was approved by the Ghent University Ethics Committee (reference number EC/2011/1236), and all participants provided written informed consent. English sample Participants were recruited from the randomized controlled Diabetes Risk Communication Trial (DRCT) (16). In short, Cambridgeshire residents born between 1950 and 1975 and registered with participating general practices were eligible to participate if they were able to walk unaided and were not pregnant, lactating, or previously diagnosed with diabetes, a terminal illness with a prognosis 0.6) compared with estimates of sitting during transportation, meals, and other sedentary time (19,25). Scheers et al. (34) recently annotated sedentary time derived from the SenseWear Armband (≤1.8 MET) with electronic activity diary information to determine validity of domain-sitting indices derived from the Flemish Physical Activity Computerized Questionnaire (FPACQ). This questionnaire showed similar correlations for domain-specific sitting indices that were common between the FPACQ and SIT-Q-7d instruments (FPACQ: screen time, r = 0.57; motorized transport, r = 0.58; eating time, r = 0.26). Relatively high validity was found for the SIT-Q-7d for TV viewing, nonoccupational computer use, and total screen time for both weekdays and weekend days (all ≥0.57). Few studies have so far examined domain-specific sitting by weekdays and weekend days. Compared with a log, Marshall et al. (25) found relatively similar results for weekday TV viewing and computer use, but could not replicate validity for the weekend. Although weekend days generally tend to be less structured in terms of time use, potentially resulting in lower validity results (6,12), this argument may be less applicable to screen time compared with other behaviors such as meal time or other leisure sitting. Screen time may still mainly be an evening activity and was relatively similar in duration between the week and the weekend. The SIT-Q-7d generally overestimated total and some domain-specific sedentary time indices compared with the log-annotated thigh accelerometry criterion. Previous studies using total thigh accelerometry-based sitting time as a criterion measure have shown both under- and overestimations for composite total sitting time (11,22). The SIT-Q-7d aimed to be comprehensive in terms of daily sedentary behaviors, also including domains (e.g., meals) that were not included in questionnaires showing underestimation of total sedentary time (15). Furthermore, sedentary behaviors have a much higher likelihood to cooccur (e.g., sitting while working and listening to music) compared with higher intensity activities. Double reporting of such composite behaviors may have caused overestimation, despite repeated questionnaire instructions on mutual exclusivity of reported time spent in different domains. Administration by interview instead of self-report could diminish this error and explain discrepancies with other measures such as the PAST questionnaire (11). In addition, brevity of wording in the domain-specific log compared with the SIT-Q-7d may have influenced comparison between the questionnaire and the criterion measure as well. Similar to the majority of other questionnaires (11,19,22), the 95% limits of agreement were wide for all measures examined, making the SIT-Q-7d less suitable for estimations of sedentary time at the individual level and for capturing change in sedentary time in intervention studies. Test–retest reliability was fair to good for total and fair to excellent for domain-specific sedentary time in both samples, except for sitting during meals, which was poor in the Flemish but excellent in the English sample. The difference in reliability for sitting during meals between both samples may be explained by several factors, including differences in regularity of meal patterns (due to differences in sociocultural determinants), which would make associated behaviors easier to recall reliably, and differences in the cultural importance assigned to meal circumstances, which would differentially influence memory allocation and thus recall bias (14), although we have no firm evidence to support these hypotheses. Although variation in study populations, test–retest intervals, and reference time frames hamper direct comparison between studies, reliability coefficients derived in this study were broadly comparable with those found in previous studies (11,15,19,25). A consistent pattern in both samples and across the literature is that those indicators with poor reliability tended to be specific leisure sedentary behaviors, such as sitting for household tasks, hobbies, socializing, and listening to music, which are typically performed on a more sporadic and/or less prolonged basis (15,19,33). To examine whether behavior change between both administrations, due to the last 7-d reference frame and the relatively long test–retest interval in some participants, would have negatively influenced the reliability results, a sensitivity analysis only including those who reported a similar test and retest week was performed. ICC was comparable, suggesting that the effect of behavior change on our results was minimal. In the English sample, reliability was generally somewhat stronger among men than women. Few studies have so far examined sex differences due to smaller study samples and results are mixed and difficult to interpret due to potential bias in the convenience samples used, including sex differences in education level and weight status (6,25). Women may have less structured lives due to higher prevalence of household and child care activities, which may compromise ability to report sedentary time across the day in a reliable manner. More women in our study did not work outside the home compared with men (15% vs 9%, respectively). Further large-scale studies should aim to examine this potential sex pattern. Our study showed very poor psychometric properties, as well as substantial missing data, for the items determining the number of breaks in occupational sitting and while watching TV, indicating the difficulty of recalling this irregular behavior in a reliable and accurate manner. One previous study has determined the validity of a self-report measure of breaks during occupational sitting, showing modest validity (rho = 0.26) compared with breaks derived from hip-mounted accelerometry. Comparability between studies is hampered due to differences in administration method (self-report vs interviewer administered), criterion, and study population. Clark et al. (10) evaluated this item in full-time employees with predominantly sedentary jobs, who likely have less varied patterns of occupational sitting and breaks in occupational sitting. Further research in heterogeneous populations is needed to establish whether breaks in sedentary time could be assessed reliably and validly by questionnaire. In addition, studies implementing comprehensive sedentary behavior questionnaires such as the SIT-Q-7d should aim to take into account the different measurement properties of the individual sedentary behavior items when interpreting results, for example, relating to associations with health outcomes. This study confirms reliability and relative validity of a questionnaire specifically designed to estimate total and domain-specific sedentary time, in the general adult population ages 20–60 yr. Unlike most previous validation studies, which used homogenous study populations, we were able to use population-based samples that were rather balanced in terms of sex, age, education level (which was less balanced in the Flemish sample compared with the English), and BMI, supporting the use of the SIT-Q-7d in general adult population studies in this age range. Furthermore, the questionnaire is self-administered rather than by interview, increasing its feasibility for implementation in large-scale observational studies. Self-report, noncomputerized questionnaires may however be associated with relatively more missing data, as was also the case in our study. Another unique strength of the study is the use of a combined thigh-accelerometer and domain log criterion, which allowed for detailed relative and absolute validity analysis by domain and weekdays and weekend days with a strong criterion (17,21,23,32). The overlap between the questionnaire recall frame and criterion administration time frame in both substudies eliminates the possibility that lack of agreement is caused by fluctuations in behavior. Sensitivity analysis in those who provided two valid weekend days of criterion data (94% of the Flemish sample and 63% of the English sample) instead of only one showed very similar validity results (data not shown). In addition, both criterion monitors allowed for a 24-h wearing protocol, reducing misclassification of nonwear and sedentary time (40), which may be particularly relevant for sedentary behaviors in the evening (25). Finally, the large population-based sample of English adults enabled stratified reliability and validity analysis by sex, and sensitivity analysis in those with a comparable test and retest week. The following limitations should also be considered. The validity of branched equation model estimates of the 1.5 MET threshold, or the 0.075 m·s−2 threshold for trunk acceleration, is yet to be fully evaluated. For example, Scheers et al. (34) used a threshold of 1.8 MET on the basis of estimates from another combined sensor. In addition, the keeping of a daily domain log, as was employed in the relatively small Flemish study sample, may have resulted in priming of memory, hence better recall for the test administration of the SIT-Q-7d. This effect is however likely to be minimal as the log predominantly aimed to time-stamp domains, irrespective of whether this time was spent sedentary or active, and the log design did not prompt participants to estimate durations. Other sources of error associated with the domain log may include its 15-min time resolution, potential delayed completion of the log (e.g., at the end of the day instead of contemporaneously), and potential nonsynchronization between participant’s watch or other time-keeping device and the thigh accelerometer. The test–retest interval was not standard for all participants, which might have increased the difference in actual behavior between both reference weeks, thus potentially reducing reliability estimates. However, sensitivity analysis indicated this effect to be minimal. Finally, our study did not assess responsiveness to behavior change of the SIT-Q-7d, which would need to be established before using this questionnaire to track longitudinal shifts of sitting at a population level. In conclusion, the SIT-Q-7d is a comprehensive questionnaire providing total and domain-specific sedentary behavior estimates with acceptable reliability and relative validity in the general adult population ages 20–60 yr. It is less reliable and valid for assessing breaks in sedentary time. These psychometric properties, together with the self-administration method, support the usefulness of this questionnaire for population surveillance purposes, as well as observational studies aiming to examine determinants of overall and domain-specific sitting, and associations with health outcomes. Future studies are warranted to examine psychometric properties of the SIT-Q-7d in other populations, such as the elderly, as well as the instrument’s ability to detect change in sedentary behavior.
